system

Abstract

The system according to this embodiment aims to support home learning and stimulate children's motivation to learn. [Solution] The system according to this embodiment comprises an assistance unit, a management unit, a motivation-generating unit, and a identification unit. The assistance unit helps the child with their homework. The management unit manages the progress of the homework assisted by the assistance unit. The motivation-generating unit stimulates learning motivation based on the progress managed by the management unit. The identification unit identifies areas of weakness based on the motivation stimulated by the motivation-generating unit.

Description

Technical Field

[0004] ,

[0006] , , , , ,

[0005] , , , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the support for home learning is insufficient, it is difficult for guardians to manage the learning progress of children when they are busy, and children are likely to lose their learning motivation.

[0005] The system according to the embodiment aims to support home learning and stimulate children's learning motivation.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an assistance unit, a management unit, a motivation-generating unit, and a identification unit. The assistance unit helps children with their homework. The management unit manages the progress of the homework assisted by the assistance unit. The motivation-generating unit stimulates learning motivation based on the progress managed by the management unit. The identification unit identifies areas of difficulty based on the motivation generated by the motivation-generating unit. [Effects of the Invention]

[0007] The system according to this embodiment can support home learning and stimulate children's motivation to learn. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An autonomous robot agent system according to an embodiment of the present invention is a system that supports children's home learning. This autonomous robot agent system assists children with their homework, manages their individual learning progress, and stimulates their motivation to learn. Even when parents are not present when children are studying at home, the autonomous robot agent system provides learning guidance tailored to each child, reducing the burden on parents. For example, the autonomous robot agent system assists children with their homework. For instance, if a child asks a question about something they don't understand in their homework, the autonomous robot agent system will provide an explanation. The autonomous robot agent system also manages the child's learning progress, allowing parents to check the progress on their smartphones or tablets. This makes it easier for parents to understand their child's learning situation. Next, the autonomous robot agent system provides learning guidance that incorporates game elements to stimulate children's motivation to learn. For example, the autonomous robot agent system engages in casual conversation with children to encourage them to study. It also increases children's motivation to learn by awarding points, levels, badges, etc., for daily progress. Furthermore, the autonomous robot agent system automatically detects areas where children struggle and provides additional explanations and tasks in those areas. For example, an autonomous robot agent system can analyze a child's learning data and identify areas where the child struggles. It can then provide additional explanations and, if necessary, assign extra tasks. This makes it easier for the child to overcome their weaknesses. This allows the autonomous robot agent system to reduce the burden on parents and increase children's motivation to learn. Parents can more easily monitor their child's learning progress and feel more confident entrusting their child's education to the system. Furthermore, children can experience the joy of learning through learning with the autonomous robot agent system. This enables the autonomous robot agent system to assist with children's homework and support the creation and implementation of learning curricula.

[0029] The autonomous robot agent system according to this embodiment comprises an assistance unit, a management unit, a motivation-generating unit, and a identification unit. The assistance unit assists children with their homework. For example, if a child asks a question about something they don't understand in their homework, the assistance unit provides an explanation. The assistance unit can also provide hints when a child is solving a homework problem. The assistance unit can also display the progress so that the child can check their homework progress. The management unit manages the progress of the homework assisted by the assistance unit. For example, the management unit can record the progress of the child's homework and notify the parent or guardian of the progress. For example, the management unit can analyze the progress of the child's homework and adjust the learning plan based on the progress. For example, the management unit can visualize the progress of the child's homework and display the progress in graphs or charts. The motivation-generating unit stimulates learning motivation based on the progress managed by the management unit. The motivation-generating unit can, for example, give points, levels, badges, etc., to increase a child's motivation to learn. The motivation-generating unit can also, for example, provide learning guidance that incorporates game elements to increase a child's motivation to learn. The motivation-generating unit can, for example, engage in casual conversation to encourage a child to study in order to increase their motivation to learn. The identification unit identifies areas of weakness based on the motivation generated by the motivation-generating unit. The identification unit can, for example, analyze a child's learning data to identify areas of weakness. The identification unit can, for example, provide additional explanations about areas of weakness based on a child's learning data. The identification unit can, for example, provide additional tasks about areas of weakness based on a child's learning data. As a result, the autonomous robot agent system according to this embodiment can help children with their homework, manage their progress, generate motivation to learn, and identify areas of weakness.

[0030] The Helper Service assists children with their homework. For example, if a child asks a question about something they don't understand in their homework, the Helper Service will provide an explanation. Specifically, the Helper Service uses natural language processing technology to understand the child's question and generate an appropriate answer. For example, if it's a math problem, the Helper Service will analyze the problem and explain the solution steps in order. The Helper Service can also provide hints when the child is working on their homework. Hints are provided in stages according to the difficulty of the problem and the child's level of understanding, supporting the child in arriving at the answer on their own. Furthermore, the Helper Service can display progress so that the child can check their homework progress. The progress display visually shows the degree of completion of homework and the correct answer rate for each problem, making it easy for the child to understand their learning progress. Through these functions, the Helper Service helps children to work on their homework efficiently and deepen their understanding of what they are learning. In addition, the Helper Service can flexibly adapt to the child's learning style and pace. For example, if a child has difficulty with a particular subject or problem, the support department can provide special assistance in that subject or problem to promote the child's understanding. Furthermore, the support department can accumulate the child's learning history and use it as a reference when creating future learning plans based on past learning content and achievements. In this way, the support department can comprehensively support the child's learning and provide an effective learning environment.

[0031] The management department manages the progress of homework assisted by the assistance department. For example, the management department can record the progress of children's homework and notify parents of the progress. Specifically, the management department collects detailed data such as which problems the child solved, how much time was spent on them, and the accuracy rate, and reports this to parents regularly. Reports are made via email or a dedicated app, allowing parents to understand their child's learning progress in real time. The management department can also analyze the progress of children's homework and adjust learning plans based on that progress. For example, if progress is slow in a particular subject or problem, the management department can adjust the study time for that subject or problem to increase, ensuring that the child is learning in a balanced way. Furthermore, the management department can visualize the progress and display it in graphs and charts. This allows children to understand their learning progress at a glance and maintain their motivation. Through these functions, the management department can efficiently manage children's learning and strengthen collaboration with parents. In addition, the management department can accumulate children's learning data over the long term and perform trend analysis and performance evaluation based on past data. This allows for continuous monitoring of a child's learning progress and growth, and enables the provision of appropriate support as needed.

[0032] The Motivation Enhancement Unit stimulates learning motivation based on progress managed by the Management Unit. For example, the Motivation Enhancement Unit can award points, levels, and badges to increase a child's motivation to learn. Specifically, children earn points each time they complete homework, and when they accumulate a certain number of points, they level up or are awarded badges. This allows children to learn in a game-like manner and maintain their motivation. The Motivation Enhancement Unit can also provide learning instruction that incorporates game elements. For example, it can provide an environment where children can learn while having fun through quiz-style questions or time-attack challenges. Furthermore, the Motivation Enhancement Unit can engage in casual conversation to encourage studying. For example, if a child has lost interest in learning, the Motivation Enhancement Unit can provide topics based on the child's interests and concerns, naturally guiding them back to learning. This allows children to continue learning without stress. Through these functions, the Motivation Enhancement Unit can enhance a child's motivation to learn and support their continued learning. In addition, the Motivation Enhancement Unit can provide individually optimized motivation strategies based on the child's learning history and progress. For example, for children who have difficulty with a particular subject or problem, special rewards can be set for that subject or problem to stimulate their motivation to learn. Furthermore, the motivation-enhancing unit can collect feedback from children and continuously evaluate and improve the effectiveness of motivation strategies. This allows the motivation-enhancing unit to provide effective support to increase children's motivation to learn and maximize learning outcomes.

[0033] The Specialist Unit identifies areas of weakness based on the motivation elicited by the Motivation Generation Unit. For example, the Specialist Unit can analyze a child's learning data to identify areas of weakness. Specifically, it analyzes data such as which problems a child spends the most time on and which problems they make the most mistakes on to identify areas of weakness. Based on this data, the Specialist Unit can also provide additional explanations about areas of weakness. For example, it can provide more detailed explanations and examples for problems the child struggles with to help deepen their understanding. The Specialist Unit can also assign additional tasks in areas of weakness. This allows the child to focus on and overcome their weaknesses. Through these functions, the Specialist Unit can improve the quality of a child's learning and support effective learning. Furthermore, the Specialist Unit can accumulate children's learning data over the long term and perform trend analysis and performance evaluation based on past data. This allows for continuous monitoring of the child's learning progress and growth, and provides appropriate support as needed. In addition, the Specialist Unit can utilize AI technology to analyze children's learning patterns and tendencies and provide individually optimized learning plans. This allows the specific unit to comprehensively support children's learning and provide an effective learning environment.

[0034] The management unit includes a monitoring unit that allows parents to check progress on their smartphones or tablets. For example, the management unit allows parents to check the progress of their children's homework on their smartphones or tablets. The management unit can also, for example, allow parents to check their children's learning progress in real time on their smartphones or tablets. The management unit can also, for example, allow parents to view their children's learning progress in graphs and charts on their smartphones or tablets. This allows parents to check their children's learning progress on their smartphones or tablets.

[0035] The motivation-enhancing unit includes a game unit that provides game elements. For example, to increase a child's motivation to learn, the motivation-enhancing unit can award points, levels, badges, etc. For example, to increase a child's motivation to learn, the motivation-enhancing unit can also provide learning guidance that incorporates game elements. For example, to increase a child's motivation to learn, the motivation-enhancing unit can engage in casual conversation and encourage studying. In this way, by incorporating game elements, it is possible to increase a child's motivation to learn.

[0036] The identification unit includes an explanation unit that identifies areas of weakness and provides additional explanations and assignments for those areas. For example, the identification unit analyzes a child's learning data to identify areas where the child struggles. The identification unit can also provide additional explanations for areas where the child struggles based on the child's learning data. For example, the identification unit can also provide additional assignments for areas where the child struggles based on the child's learning data. In this way, by identifying areas of weakness and providing additional explanations and assignments, it is possible to support the child's learning.

[0037] The assistance team selects the most appropriate method of assistance when helping with homework, by referring to the child's past learning history. For example, the assistance team might re-present a problem the child previously struggled with and carefully explain how to solve it. Alternatively, they might present a problem in a subject the child excels at and then provide additional application problems in that subject. The assistance team can also create a balanced set of problems based on the child's past learning history and provide assistance accordingly. This allows them to select the most appropriate method of assistance by referring to the child's past learning history.

[0038] The tutoring service customizes the assistance provided with homework based on the child's current learning situation. For example, the tutoring service may present problems related to the unit the child is currently working on and explain how to solve them. The tutoring service can also, depending on the child's current learning situation, present review problems to check their understanding. For example, based on the child's current learning situation, the tutoring service can also present preparatory problems to help the child move on to the next step. This allows for effective support by customizing the assistance according to the child's current learning situation.

[0039] The assistance team adapts their methods of helping with homework according to each child's learning style. For example, for children with a visual learning style, the assistance team uses diagrams and illustrations to explain things. For children with an auditory learning style, the assistance team can provide explanations verbally. For children with an experiential learning style, the assistance team can assign problems that require hands-on work. By adapting their methods to each child's learning style, they can provide effective support.

[0040] The assistance team selects the content of their homework assistance based on the child's interests and concerns. For example, they might ask the child a question related to a topic the child is interested in and explain how to solve it. Alternatively, they might ask an application problem in a field the child is interested in and delve deeper into that field. The assistance team can also provide relevant learning materials and assist based on the child's interests and concerns. This allows for effective support by selecting assistance based on the child's interests and concerns.

[0041] The management department selects the optimal management method by referring to the child's past learning data when monitoring progress. For example, the management department creates an optimal learning schedule based on the child's past learning data. The management department can also adjust the progress management method by referring to the child's past learning data. For example, the management department can analyze the child's past learning data and visualize the learning progress. This allows the management department to select the optimal management method by referring to past learning data.

[0042] The management department customizes the content of progress management based on the child's learning objectives. For example, the management department adjusts the content of progress management based on the child's learning objectives. For example, the management department can also provide specific steps to help the child achieve their learning objectives. For example, the management department can change the method of progress management according to the child's learning objectives. This allows for effective management by customizing the content of management based on learning objectives.

[0043] The management department improves its management methods by incorporating parental feedback during progress monitoring. For example, the management department adjusts its progress monitoring methods based on feedback from parents. For example, the management department can also change the learning schedule to reflect parents' opinions. For example, the management department can improve the content of progress monitoring based on parental feedback. In this way, management methods can be improved by incorporating parental feedback.

[0044] The management department adapts its progress management methods to each child's learning environment. For example, if a child is studying at home, the management department provides a progress management method suitable for home learning. If a child is studying at a cram school, the management department can also provide a progress management method tailored to the cram school's curriculum. If a child is studying online, the management department can also provide a progress management method suitable for online learning. By adapting management methods to each learning environment, effective management becomes possible.

[0045] The motivation-enhancing unit selects the optimal method for stimulating motivation by referring to the child's past learning motivation data. For example, the motivation-enhancing unit provides an optimal learning environment based on the child's past learning motivation data. The motivation-enhancing unit can also adjust the method for increasing learning motivation by referring to the child's past learning motivation data. For example, the motivation-enhancing unit can analyze the child's past learning motivation data and suggest specific methods for stimulating learning motivation. In this way, the optimal method for stimulating motivation can be selected by referring to past learning motivation data.

[0046] The motivation-stimulating unit customizes the content of the stimuli based on the child's learning goals when stimulating motivation. For example, the motivation-stimulating unit provides specific tasks to stimulate learning motivation based on the child's learning goals. The motivation-stimulating unit can also adjust the methods used to enhance learning motivation to help the child achieve their learning goals. For example, the motivation-stimulating unit can present specific steps to stimulate learning motivation according to the child's learning goals. This allows for effective support by customizing the content of the stimuli based on learning goals.

[0047] The motivation-stimulating unit adapts its method of stimulating motivation according to the child's learning style. For example, for children with a visual learning style, the unit uses diagrams and illustrations to explain. For children with an auditory learning style, the unit can also provide explanations using audio. For children with an experiential learning style, the unit can present problems that require hands-on work. By adapting the stimulation method according to the learning style, effective support becomes possible.

[0048] The motivation stimulation unit selects content for stimulation based on the child's interests and concerns. For example, the unit may present questions related to a theme the child is interested in and explain how to solve those questions. The unit can also present application problems in a field the child is interested in and delve deeper into that field. For example, the unit can provide relevant learning materials based on the child's interests and concerns to stimulate motivation. This allows for effective support by selecting content for stimulation based on interests and concerns.

[0049] The identification unit, when identifying areas of weakness, selects the optimal identification method by referring to the child's past learning data. For example, the identification unit presents questions based on the child's past learning data to identify areas of weakness. The identification unit can also adjust the method for identifying areas of weakness by referring to the child's past learning data. For example, the identification unit can analyze the child's past learning data and present specific methods for identifying areas of weakness. This allows the optimal identification method to be selected by referring to past learning data.

[0050] The identification unit customizes specific content based on the child's learning objectives when identifying areas of weakness. For example, the identification unit provides specific tasks for identifying areas of weakness based on the child's learning objectives. The identification unit can also adjust the method by which it identifies areas of weakness in order to help the child achieve their learning objectives. For example, the identification unit can present specific steps for identifying areas of weakness depending on the child's learning objectives. This allows for effective support by customizing specific content based on learning objectives.

[0051] The identification unit adapts its identification method to each child's learning style when identifying areas of difficulty. For example, for children with a visual learning style, the unit uses diagrams and illustrations to identify areas of difficulty. For children with an auditory learning style, the unit can identify areas of difficulty by providing audio explanations. For children with an experiential learning style, the unit can identify areas of difficulty by presenting problems that require hands-on work. By adapting the identification method to each child's learning style, effective support becomes possible.

[0052] The support team selects specific content based on the child's interests when identifying areas of difficulty. For example, the support team might present problems related to a topic the child is interested in and explain how to solve them. Alternatively, the support team could present application problems in a field the child is interested in and delve deeper into that field. The support team could also provide relevant learning materials based on the child's interests to identify areas of difficulty. This allows for effective support by selecting specific content based on the child's interests.

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

[0054] The autonomous robot agent system can adjust its teaching methods according to the child's learning environment. For example, if the child is studying at home, the robot agent can provide teaching methods suitable for home learning. If the child is studying at a cram school, the robot agent can also provide teaching methods that match the cram school's curriculum. Furthermore, if the child is studying online, the robot agent can also provide teaching methods suitable for online learning. This allows for effective learning support by adjusting teaching methods according to the learning environment.

[0055] The autonomous robot agent system can adapt its teaching methods to a child's learning style. For example, for children with a visual learning style, the robot agent can use diagrams and illustrations to explain things. For children with an auditory learning style, the robot agent can provide explanations verbally. Furthermore, for children with an experiential learning style, the robot agent can present problems that require hands-on work to solve. This allows for effective learning support by adapting teaching methods to different learning styles.

[0056] An autonomous robot agent system can select learning content based on a child's interests. For example, it can present problems related to a topic the child is interested in and explain how to solve them. It can also present applied problems in a field the child is interested in and have the robot agent delve deeper into that field. Furthermore, based on the child's interests, the robot agent can provide relevant learning materials to support their learning. This enables effective learning support by selecting instructional content based on interests.

[0057] An autonomous robot agent system can select the optimal learning method by referring to a child's past learning data. For example, it can re-present problems that the child previously struggled with and carefully explain how to solve them. It can also present problems in areas where the child excels and then add additional application problems in those areas. Furthermore, based on the child's past learning data, the robot agent can create a balanced set of problems and provide learning guidance. In this way, the system can select the optimal learning method by referring to past learning data.

[0058] The autonomous robot agent system can customize the content of learning instruction based on a child's current learning situation. For example, it can present problems related to the unit the child is currently working on and explain how to solve them. Depending on the child's current learning situation, the robot agent can also present review problems to check their understanding. Furthermore, based on the child's current learning situation, the robot agent can present preparatory problems for moving on to the next step. This allows for effective learning support by customizing the instruction content according to the child's current learning situation.

[0059] An autonomous robotic agent system can customize the content of learning instruction based on a child's learning goals. For example, based on the child's learning goals, the robotic agent can provide specific tasks to stimulate learning motivation. The robotic agent can also adjust how it enhances learning motivation in order to help the child achieve their learning goals. Furthermore, depending on the child's learning goals, the robotic agent can present specific steps to stimulate learning motivation. This allows for effective learning support by customizing instruction content based on learning goals.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The helper team assists children with their homework. For example, if a child asks a question about something they don't understand in their homework, the helper team will explain it. They can also provide hints when the child is solving homework problems and display progress updates to track their progress. Step 2: The management department manages the progress of homework assisted by the assistance department. For example, they can record the progress of children's homework and notify parents. They can also analyze the progress, adjust the learning plan, and display the progress in graphs and charts. Step 3: The motivation-generating unit stimulates learning motivation based on progress managed by the management unit. For example, it can provide points, levels, badges, etc., or offer learning guidance that incorporates game elements. It can also engage in casual conversation to encourage studying. Step 4: The identification unit identifies areas of weakness based on the motivation elicited by the motivation extraction unit. For example, it can analyze the child's learning data to identify areas of weakness. It can also provide additional explanations or assign additional tasks related to those areas of weakness.

[0062] (Example of form 2) An autonomous robot agent system according to an embodiment of the present invention is a system that supports children's home learning. This autonomous robot agent system assists children with their homework, manages their individual learning progress, and stimulates their motivation to learn. Even when parents are not present when children are studying at home, the autonomous robot agent system provides learning guidance tailored to each child, reducing the burden on parents. For example, the autonomous robot agent system assists children with their homework. For instance, if a child asks a question about something they don't understand in their homework, the autonomous robot agent system will provide an explanation. The autonomous robot agent system also manages the child's learning progress, allowing parents to check the progress on their smartphones or tablets. This makes it easier for parents to understand their child's learning situation. Next, the autonomous robot agent system provides learning guidance that incorporates game elements to stimulate children's motivation to learn. For example, the autonomous robot agent system engages in casual conversation with children to encourage them to study. It also increases children's motivation to learn by awarding points, levels, badges, etc., for daily progress. Furthermore, the autonomous robot agent system automatically detects areas where children struggle and provides additional explanations and tasks in those areas. For example, an autonomous robot agent system can analyze a child's learning data and identify areas where the child struggles. It can then provide additional explanations and, if necessary, assign extra tasks. This makes it easier for the child to overcome their weaknesses. This allows the autonomous robot agent system to reduce the burden on parents and increase children's motivation to learn. Parents can more easily monitor their child's learning progress and feel more confident entrusting their child's education to the system. Furthermore, children can experience the joy of learning through learning with the autonomous robot agent system. This enables the autonomous robot agent system to assist with children's homework and support the creation and implementation of learning curricula.

[0063] The autonomous robot agent system according to this embodiment comprises an assistance unit, a management unit, a motivation-generating unit, and a identification unit. The assistance unit assists children with their homework. For example, if a child asks a question about something they don't understand in their homework, the assistance unit provides an explanation. The assistance unit can also provide hints when a child is solving a homework problem. The assistance unit can also display the progress so that the child can check their homework progress. The management unit manages the progress of the homework assisted by the assistance unit. For example, the management unit can record the progress of the child's homework and notify the parent or guardian of the progress. For example, the management unit can analyze the progress of the child's homework and adjust the learning plan based on the progress. For example, the management unit can visualize the progress of the child's homework and display the progress in graphs or charts. The motivation-generating unit stimulates learning motivation based on the progress managed by the management unit. The motivation-generating unit can, for example, give points, levels, badges, etc., to increase a child's motivation to learn. The motivation-generating unit can also, for example, provide learning guidance that incorporates game elements to increase a child's motivation to learn. The motivation-generating unit can, for example, engage in casual conversation to encourage a child to study in order to increase their motivation to learn. The identification unit identifies areas of weakness based on the motivation generated by the motivation-generating unit. The identification unit can, for example, analyze a child's learning data to identify areas of weakness. The identification unit can, for example, provide additional explanations about areas of weakness based on a child's learning data. The identification unit can, for example, provide additional tasks about areas of weakness based on a child's learning data. As a result, the autonomous robot agent system according to this embodiment can help children with their homework, manage their progress, generate motivation to learn, and identify areas of weakness.

[0064] The Helper Service assists children with their homework. For example, if a child asks a question about something they don't understand in their homework, the Helper Service will provide an explanation. Specifically, the Helper Service uses natural language processing technology to understand the child's question and generate an appropriate answer. For example, if it's a math problem, the Helper Service will analyze the problem and explain the solution steps in order. The Helper Service can also provide hints when the child is working on their homework. Hints are provided in stages according to the difficulty of the problem and the child's level of understanding, supporting the child in arriving at the answer on their own. Furthermore, the Helper Service can display progress so that the child can check their homework progress. The progress display visually shows the degree of completion of homework and the correct answer rate for each problem, making it easy for the child to understand their learning progress. Through these functions, the Helper Service helps children to work on their homework efficiently and deepen their understanding of what they are learning. In addition, the Helper Service can flexibly adapt to the child's learning style and pace. For example, if a child has difficulty with a particular subject or problem, the support department can provide special assistance in that subject or problem to promote the child's understanding. Furthermore, the support department can accumulate the child's learning history and use it as a reference when creating future learning plans based on past learning content and achievements. In this way, the support department can comprehensively support the child's learning and provide an effective learning environment.

[0065] The management department manages the progress of homework assisted by the assistance department. For example, the management department can record the progress of children's homework and notify parents of the progress. Specifically, the management department collects detailed data such as which problems the child solved, how much time was spent on them, and the accuracy rate, and reports this to parents regularly. Reports are made via email or a dedicated app, allowing parents to understand their child's learning progress in real time. The management department can also analyze the progress of children's homework and adjust learning plans based on that progress. For example, if progress is slow in a particular subject or problem, the management department can adjust the study time for that subject or problem to increase, ensuring that the child is learning in a balanced way. Furthermore, the management department can visualize the progress and display it in graphs and charts. This allows children to understand their learning progress at a glance and maintain their motivation. Through these functions, the management department can efficiently manage children's learning and strengthen collaboration with parents. In addition, the management department can accumulate children's learning data over the long term and perform trend analysis and performance evaluation based on past data. This allows for continuous monitoring of a child's learning progress and growth, and enables the provision of appropriate support as needed.

[0066] The Motivation Enhancement Unit stimulates learning motivation based on progress managed by the Management Unit. For example, the Motivation Enhancement Unit can award points, levels, and badges to increase a child's motivation to learn. Specifically, children earn points each time they complete homework, and when they accumulate a certain number of points, they level up or are awarded badges. This allows children to learn in a game-like manner and maintain their motivation. The Motivation Enhancement Unit can also provide learning instruction that incorporates game elements. For example, it can provide an environment where children can learn while having fun through quiz-style questions or time-attack challenges. Furthermore, the Motivation Enhancement Unit can engage in casual conversation to encourage studying. For example, if a child has lost interest in learning, the Motivation Enhancement Unit can provide topics based on the child's interests and concerns, naturally guiding them back to learning. This allows children to continue learning without stress. Through these functions, the Motivation Enhancement Unit can enhance a child's motivation to learn and support their continued learning. In addition, the Motivation Enhancement Unit can provide individually optimized motivation strategies based on the child's learning history and progress. For example, for children who have difficulty with a particular subject or problem, special rewards can be set for that subject or problem to stimulate their motivation to learn. Furthermore, the motivation-enhancing unit can collect feedback from children and continuously evaluate and improve the effectiveness of motivation strategies. This allows the motivation-enhancing unit to provide effective support to increase children's motivation to learn and maximize learning outcomes.

[0067] The Specialist Unit identifies areas of weakness based on the motivation elicited by the Motivation Generation Unit. For example, the Specialist Unit can analyze a child's learning data to identify areas of weakness. Specifically, it analyzes data such as which problems a child spends the most time on and which problems they make the most mistakes on to identify areas of weakness. Based on this data, the Specialist Unit can also provide additional explanations about areas of weakness. For example, it can provide more detailed explanations and examples for problems the child struggles with to help deepen their understanding. The Specialist Unit can also assign additional tasks in areas of weakness. This allows the child to focus on and overcome their weaknesses. Through these functions, the Specialist Unit can improve the quality of a child's learning and support effective learning. Furthermore, the Specialist Unit can accumulate children's learning data over the long term and perform trend analysis and performance evaluation based on past data. This allows for continuous monitoring of the child's learning progress and growth, and provides appropriate support as needed. In addition, the Specialist Unit can utilize AI technology to analyze children's learning patterns and tendencies and provide individually optimized learning plans. This allows the specific unit to comprehensively support children's learning and provide an effective learning environment.

[0068] The management unit includes a monitoring unit that allows parents to check progress on their smartphones or tablets. For example, the management unit allows parents to check the progress of their children's homework on their smartphones or tablets. The management unit can also, for example, allow parents to check their children's learning progress in real time on their smartphones or tablets. The management unit can also, for example, allow parents to view their children's learning progress in graphs and charts on their smartphones or tablets. This allows parents to check their children's learning progress on their smartphones or tablets.

[0069] The motivation-enhancing unit includes a game unit that provides game elements. For example, to increase a child's motivation to learn, the motivation-enhancing unit can award points, levels, badges, etc. For example, to increase a child's motivation to learn, the motivation-enhancing unit can also provide learning guidance that incorporates game elements. For example, to increase a child's motivation to learn, the motivation-enhancing unit can engage in casual conversation and encourage studying. In this way, by incorporating game elements, it is possible to increase a child's motivation to learn.

[0070] The identification unit includes an explanation unit that identifies areas of weakness and provides additional explanations and assignments for those areas. For example, the identification unit analyzes a child's learning data to identify areas where the child struggles. The identification unit can also provide additional explanations for areas where the child struggles based on the child's learning data. For example, the identification unit can also provide additional assignments for areas where the child struggles based on the child's learning data. In this way, by identifying areas of weakness and providing additional explanations and assignments, it is possible to support the child's learning.

[0071] The helper system estimates the child's emotions and adjusts its homework assistance methods based on those estimates. For example, if the child is stressed, the helper system will adjust the approach to start with easier problems. If the child is relaxed, the helper system may adjust the approach to challenge them with more difficult problems. If the child is losing focus, the helper system may suggest a short break before resuming assistance. This allows for more effective support by adjusting homework assistance methods according to the child's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0072] The assistance team selects the most appropriate method of assistance when helping with homework, by referring to the child's past learning history. For example, the assistance team might re-present a problem the child previously struggled with and carefully explain how to solve it. Alternatively, they might present a problem in a subject the child excels at and then provide additional application problems in that subject. The assistance team can also create a balanced set of problems based on the child's past learning history and provide assistance accordingly. This allows them to select the most appropriate method of assistance by referring to the child's past learning history.

[0073] The tutoring service customizes the assistance provided with homework based on the child's current learning situation. For example, the tutoring service may present problems related to the unit the child is currently working on and explain how to solve them. The tutoring service can also, depending on the child's current learning situation, present review problems to check their understanding. For example, based on the child's current learning situation, the tutoring service can also present preparatory problems to help the child move on to the next step. This allows for effective support by customizing the assistance according to the child's current learning situation.

[0074] The assistance system estimates the child's emotions and adjusts the timing of assistance based on the estimated emotions. For example, if the child is tired, the assistance system may delay the start of assistance and suggest a break. If the child is concentrating, the assistance system may start assisting immediately to maintain their concentration. If the child is excited, the assistance system may adjust the timing of assistance to provide assistance in a calmer state. This allows for effective support by adjusting the timing of assistance according to the child's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0075] The assistance team adapts their methods of helping with homework according to each child's learning style. For example, for children with a visual learning style, the assistance team uses diagrams and illustrations to explain things. For children with an auditory learning style, the assistance team can provide explanations verbally. For children with an experiential learning style, the assistance team can assign problems that require hands-on work. By adapting their methods to each child's learning style, they can provide effective support.

[0076] The assistance team selects the content of their homework assistance based on the child's interests and concerns. For example, they might ask the child a question related to a topic the child is interested in and explain how to solve it. Alternatively, they might ask an application problem in a field the child is interested in and delve deeper into that field. The assistance team can also provide relevant learning materials and assist based on the child's interests and concerns. This allows for effective support by selecting assistance based on the child's interests and concerns.

[0077] The management department estimates the child's emotions and adjusts the progress management method based on the estimated emotions. For example, if the child is stressed, the management department may reduce the frequency of progress checks and provide a relaxing environment. For example, if the child is relaxed, the management department may increase the frequency of progress checks and speed up the learning pace. For example, if the child is not concentrating, the management department may simplify the progress management method and perform short checks. This allows for effective management by adjusting the progress management method according to the child's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] The management department selects the optimal management method by referring to the child's past learning data when monitoring progress. For example, the management department creates an optimal learning schedule based on the child's past learning data. The management department can also adjust the progress management method by referring to the child's past learning data. For example, the management department can analyze the child's past learning data and visualize the learning progress. This allows the management department to select the optimal management method by referring to past learning data.

[0079] The management department customizes the content of progress management based on the child's learning objectives. For example, the management department adjusts the content of progress management based on the child's learning objectives. For example, the management department can also provide specific steps to help the child achieve their learning objectives. For example, the management department can change the method of progress management according to the child's learning objectives. This allows for effective management by customizing the content of management based on learning objectives.

[0080] The management department estimates the child's emotions and adjusts the frequency of progress checks based on the estimated emotions. For example, if the child is tired, the management department reduces the frequency of progress checks and suggests a break. For example, if the child is focused, the management department can increase the frequency of progress checks and speed up the learning pace. For example, if the child is excited, the management department can adjust the frequency of progress checks to conduct them in a calmer state. This allows for effective management by adjusting the frequency of progress checks according to the child's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The management department improves its management methods by incorporating parental feedback during progress monitoring. For example, the management department adjusts its progress monitoring methods based on feedback from parents. For example, the management department can also change the learning schedule to reflect parents' opinions. For example, the management department can improve the content of progress monitoring based on parental feedback. In this way, management methods can be improved by incorporating parental feedback.

[0082] The management department adapts its progress management methods to each child's learning environment. For example, if a child is studying at home, the management department provides a progress management method suitable for home learning. If a child is studying at a cram school, the management department can also provide a progress management method tailored to the cram school's curriculum. If a child is studying online, the management department can also provide a progress management method suitable for online learning. By adapting management methods to each learning environment, effective management becomes possible.

[0083] The motivation-enhancing unit estimates the child's emotions and adjusts the motivation-enhancing method based on the estimated emotions. For example, if the child is stressed, the motivation-enhancing unit provides a relaxing environment to stimulate learning motivation. For example, if the child is relaxed, the motivation-enhancing unit can also provide challenging tasks to increase learning motivation. For example, if the child is lacking concentration, the motivation-enhancing unit can suggest a short break and then resume motivation-enhancing. This allows for effective support by adjusting the motivation-enhancing method according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The motivation-enhancing unit selects the optimal method for stimulating motivation by referring to the child's past learning motivation data. For example, the motivation-enhancing unit provides an optimal learning environment based on the child's past learning motivation data. The motivation-enhancing unit can also adjust the method for increasing learning motivation by referring to the child's past learning motivation data. For example, the motivation-enhancing unit can analyze the child's past learning motivation data and suggest specific methods for stimulating learning motivation. In this way, the optimal method for stimulating motivation can be selected by referring to past learning motivation data.

[0085] The motivation-stimulating unit customizes the content of the stimuli based on the child's learning goals when stimulating motivation. For example, the motivation-stimulating unit provides specific tasks to stimulate learning motivation based on the child's learning goals. The motivation-stimulating unit can also adjust the methods used to enhance learning motivation to help the child achieve their learning goals. For example, the motivation-stimulating unit can present specific steps to stimulate learning motivation according to the child's learning goals. This allows for effective support by customizing the content of the stimuli based on learning goals.

[0086] The motivation-stimulating unit estimates the child's emotions and adjusts the timing of motivation stimulation based on the estimated emotions. For example, if the child is tired, the motivation-stimulating unit will delay the timing of motivation stimulation and suggest a break. For example, if the child is concentrating, the motivation-stimulating unit can start stimulation immediately and maintain concentration. For example, if the child is excited, the motivation-stimulating unit can adjust the timing of stimulation to perform it in a calm state. This allows for effective support by adjusting the timing of motivation stimulation according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The motivation-stimulating unit adapts its method of stimulating motivation according to the child's learning style. For example, for children with a visual learning style, the unit uses diagrams and illustrations to explain. For children with an auditory learning style, the unit can also provide explanations using audio. For children with an experiential learning style, the unit can present problems that require hands-on work. By adapting the stimulation method according to the learning style, effective support becomes possible.

[0088] The motivation stimulation unit selects content for stimulation based on the child's interests and concerns. For example, the unit may present questions related to a theme the child is interested in and explain how to solve those questions. The unit can also present application problems in a field the child is interested in and delve deeper into that field. For example, the unit can provide relevant learning materials based on the child's interests and concerns to stimulate motivation. This allows for effective support by selecting content for stimulation based on interests and concerns.

[0089] The identification unit estimates the child's emotions and adjusts the method of identifying areas of difficulty based on the estimated emotions. For example, if the child is stressed, the identification unit will start with easy problems to identify areas of difficulty. If the child is relaxed, the identification unit can also present more difficult problems to identify areas of difficulty. If the child is not concentrating, the identification unit can suggest a short break and then resume identifying areas of difficulty. This allows for effective support by adjusting the method of identifying areas of difficulty according to the child's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The identification unit, when identifying areas of weakness, selects the optimal identification method by referring to the child's past learning data. For example, the identification unit presents questions based on the child's past learning data to identify areas of weakness. The identification unit can also adjust the method for identifying areas of weakness by referring to the child's past learning data. For example, the identification unit can analyze the child's past learning data and present specific methods for identifying areas of weakness. This allows the optimal identification method to be selected by referring to past learning data.

[0091] The identification unit customizes specific content based on the child's learning objectives when identifying areas of weakness. For example, the identification unit provides specific tasks for identifying areas of weakness based on the child's learning objectives. The identification unit can also adjust the method by which it identifies areas of weakness in order to help the child achieve their learning objectives. For example, the identification unit can present specific steps for identifying areas of weakness depending on the child's learning objectives. This allows for effective support by customizing specific content based on learning objectives.

[0092] The identification unit estimates the child's emotions and adjusts the timing of identifying areas of difficulty based on the estimated emotions. For example, if the child is tired, the identification unit will delay identifying areas of difficulty and suggest a break. If the child is concentrating, the identification unit can immediately begin identifying areas of difficulty to maintain concentration. If the child is excited, the identification unit can adjust the timing of identifying areas of difficulty to ensure a calm state. This allows for effective support by adjusting the timing of identifying areas of difficulty according to the child's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The identification unit adapts its identification method to each child's learning style when identifying areas of difficulty. For example, for children with a visual learning style, the unit uses diagrams and illustrations to identify areas of difficulty. For children with an auditory learning style, the unit can identify areas of difficulty by providing audio explanations. For children with an experiential learning style, the unit can identify areas of difficulty by presenting problems that require hands-on work. By adapting the identification method to each child's learning style, effective support becomes possible.

[0094] The support team selects specific content based on the child's interests when identifying areas of difficulty. For example, the support team might present problems related to a topic the child is interested in and explain how to solve them. Alternatively, the support team could present application problems in a field the child is interested in and delve deeper into that field. The support team could also provide relevant learning materials based on the child's interests to identify areas of difficulty. This allows for effective support by selecting specific content based on the child's interests.

[0095] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0096] The autonomous robot agent system can adjust its teaching methods according to the child's learning environment. For example, if the child is studying at home, the robot agent can provide teaching methods suitable for home learning. If the child is studying at a cram school, the robot agent can also provide teaching methods that match the cram school's curriculum. Furthermore, if the child is studying online, the robot agent can also provide teaching methods suitable for online learning. This allows for effective learning support by adjusting teaching methods according to the learning environment.

[0097] The autonomous robot agent system can adapt its teaching methods to a child's learning style. For example, for children with a visual learning style, the robot agent can use diagrams and illustrations to explain things. For children with an auditory learning style, the robot agent can provide explanations verbally. Furthermore, for children with an experiential learning style, the robot agent can present problems that require hands-on work to solve. This allows for effective learning support by adapting teaching methods to different learning styles.

[0098] An autonomous robot agent system can select learning content based on a child's interests. For example, it can present problems related to a topic the child is interested in and explain how to solve them. It can also present applied problems in a field the child is interested in and have the robot agent delve deeper into that field. Furthermore, based on the child's interests, the robot agent can provide relevant learning materials to support their learning. This enables effective learning support by selecting instructional content based on interests.

[0099] The autonomous robot agent system can estimate a child's emotions and adjust its learning instruction methods based on those estimates. For example, if a child is stressed, the robot agent can provide a relaxing environment and conduct learning instruction. If the child is relaxed, the robot agent can provide challenging tasks to increase their motivation to learn. Furthermore, if a child is losing focus, the robot agent can suggest a short break and then resume instruction. This allows for effective learning support by adjusting instruction methods according to the child's emotions.

[0100] The autonomous robot agent system can estimate a child's emotions and adjust the timing of instruction based on those emotions. For example, if a child is tired, the robot agent can delay the start of instruction and suggest a break. If a child is focused, the robot agent can start instruction immediately and maintain their concentration. Furthermore, if a child is agitated, the robot agent can adjust the timing of instruction to provide guidance in a calmer environment. This allows for effective learning support by adjusting the timing of instruction according to the child's emotions.

[0101] An autonomous robot agent system can select the optimal learning method by referring to a child's past learning data. For example, it can re-present problems that the child previously struggled with and carefully explain how to solve them. It can also present problems in areas where the child excels and then add additional application problems in those areas. Furthermore, based on the child's past learning data, the robot agent can create a balanced set of problems and provide learning guidance. In this way, the system can select the optimal learning method by referring to past learning data.

[0102] The autonomous robot agent system can customize the content of learning instruction based on a child's current learning situation. For example, it can present problems related to the unit the child is currently working on and explain how to solve them. Depending on the child's current learning situation, the robot agent can also present review problems to check their understanding. Furthermore, based on the child's current learning situation, the robot agent can present preparatory problems for moving on to the next step. This allows for effective learning support by customizing the instruction content according to the child's current learning situation.

[0103] An autonomous robot agent system can estimate a child's emotions and select learning instruction content based on those emotions. For example, it can present problems related to topics the child is interested in and explain how to solve them. It can also present applied problems in areas the child is interested in and have the robot agent delve deeper into those areas. Furthermore, based on the child's interests, the robot agent can provide relevant learning materials to support learning. This enables effective learning support by selecting instruction content based on emotions.

[0104] The autonomous robot agent system can estimate a child's emotions and adjust its learning instruction methods based on those estimates. For example, if a child is stressed, the robot agent can provide a relaxing environment and conduct learning instruction. If the child is relaxed, the robot agent can provide challenging tasks to increase their motivation to learn. Furthermore, if a child is losing focus, the robot agent can suggest a short break and then resume instruction. This allows for effective learning support by adjusting instruction methods according to the child's emotions.

[0105] An autonomous robotic agent system can customize the content of learning instruction based on a child's learning goals. For example, based on the child's learning goals, the robotic agent can provide specific tasks to stimulate learning motivation. The robotic agent can also adjust how it enhances learning motivation in order to help the child achieve their learning goals. Furthermore, depending on the child's learning goals, the robotic agent can present specific steps to stimulate learning motivation. This allows for effective learning support by customizing instruction content based on learning goals.

[0106] The following briefly describes the processing flow for example form 2.

[0107] Step 1: The helper team assists children with their homework. For example, if a child asks a question about something they don't understand in their homework, the helper team will explain it. They can also provide hints when the child is solving homework problems and display progress updates to track their progress. Step 2: The management department manages the progress of homework assisted by the assistance department. For example, they can record the progress of children's homework and notify parents. They can also analyze the progress, adjust the learning plan, and display the progress in graphs and charts. Step 3: The motivation-generating unit stimulates learning motivation based on progress managed by the management unit. For example, it can provide points, levels, badges, etc., or offer learning guidance that incorporates game elements. It can also engage in casual conversation to encourage studying. Step 4: The identification unit identifies areas of weakness based on the motivation elicited by the motivation extraction unit. For example, it can analyze the child's learning data to identify areas of weakness. It can also provide additional explanations or assign additional tasks related to those areas of weakness.

[0108] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0109] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0110] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0111] Each of the multiple elements described above, including the assistance unit, management unit, motivation-generating unit, and identification unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the assistance unit is implemented by the control unit 46A of the smart device 14 and helps the child with their homework. The management unit is implemented by the identification processing unit 290 of the data processing device 12 and manages the progress of the homework. The motivation-generating unit is implemented by the control unit 46A of the smart device 14 and stimulates the child's motivation to learn. The identification unit is implemented by the identification processing unit 290 of the data processing device 12 and identifies areas of difficulty. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0112] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0113] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0114] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0115] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0116] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0118] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0119] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0120] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0121] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0122] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0123] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0124] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0126] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0127] Each of the multiple elements described above, including the assistance unit, management unit, motivation-generating unit, and identification unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the assistance unit is implemented by the control unit 46A of the smart glasses 214 and helps the child with their homework. The management unit is implemented, for example, by the identification processing unit 290 of the data processing device 12 and manages the progress of the homework. The motivation-generating unit is implemented, for example, by the control unit 46A of the smart glasses 214 and stimulates the child's motivation to learn. The identification unit is implemented, for example, by the identification processing unit 290 of the data processing device 12 and identifies areas of difficulty. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0128] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0129] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0130] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0131] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0132] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0134] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0135] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0136] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0137] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0138] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0140] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0142] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] Each of the multiple elements described above, including the assistance unit, management unit, motivation-generating unit, and identification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the assistance unit is implemented by the control unit 46A of the headset terminal 314 and assists the child with their homework. The management unit is implemented by the identification processing unit 290 of the data processing unit 12 and manages the progress of the homework. The motivation-generating unit is implemented by the control unit 46A of the headset terminal 314 and stimulates the child's motivation to learn. The identification unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies areas of difficulty. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0144] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0145] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0146] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0147] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0148] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0150] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0151] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0152] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0153] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0154] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0155] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0156] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0157] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0158] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0159] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0160] Each of the multiple elements described above, including the assistance unit, management unit, motivation-generating unit, and identification unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the assistance unit is implemented by the control unit 46A of the robot 414 and helps the child with their homework. The management unit is implemented by the identification processing unit 290 of the data processing unit 12 and manages the progress of the homework. The motivation-generating unit is implemented by the control unit 46A of the robot 414 and stimulates the child's motivation to learn. The identification unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies areas of difficulty. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0161] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0162] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0163] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0164] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0165] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0166] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0167] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0168] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0169] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0170] 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.

[0171] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0172] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0173] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0174] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0175] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0176] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0177] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0178] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0179] (Note 1) A club that helps children with their homework, A management department manages the progress of homework assisted by the aforementioned assistance department, A motivation-generating unit that elicits learning motivation based on the progress managed by the aforementioned management unit, The system includes a specification unit that identifies areas of weakness based on the motivation extracted by the motivation extraction unit. A system characterized by the following features. (Note 2) The aforementioned management department, It includes a monitoring section that allows parents to check the progress on their smartphones or tablets. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned motivation extraction unit is, It features a game section that provides gameplay elements. The system described in Appendix 1, characterized by the features described herein. (Note 4) The specified part is, It includes a section that identifies areas of weakness and provides additional explanations and assignments for those areas. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned assistance unit is, It estimates the child's emotions and adjusts how it helps with homework based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned assistance unit is, When helping with homework, refer to the child's past learning history to select the most appropriate method of assistance. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned assistance unit is, When helping with homework, customize the assistance based on the child's current learning situation. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned assistance unit is, The system estimates the child's emotions and adjusts the timing of assistance based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned assistance unit is, When helping with homework, adjust your approach based on your child's learning style. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned assistance unit is, When helping with homework, select tasks based on the child's interests and concerns. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned management department, Estimate the child's emotions and adjust the progress management method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned management department, When managing progress, refer to the child's past learning data to select the most suitable management method. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned management department, When managing progress, customize the management content based on the child's learning goals. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned management department, The system estimates the child's emotions and adjusts the frequency of progress checks based on those estimates. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned management department, When managing progress, we improve management methods by incorporating feedback from parents. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned management department, When managing progress, change the management method according to the child's learning environment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned motivation extraction unit is, We estimate the child's emotions and adjust the motivation-enhancing methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned motivation extraction unit is, When trying to motivate a child, refer to their past learning motivation data to select the most effective method. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned motivation extraction unit is, When stimulating motivation, customize the content of the stimuli based on the child's learning goals. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned motivation extraction unit is, We estimate the child's emotions and adjust the timing of motivation-enhancing activities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned motivation extraction unit is, When trying to motivate a child, change the method of motivation according to the child's learning style. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned motivation extraction unit is, When trying to motivate a child, select the content of the prompts based on the child's interests and concerns. The system described in Appendix 1, characterized by the features described herein. (Note 23) The specified part is, We estimate the child's emotions and adjust the method for identifying areas of difficulty based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The specified part is, When identifying areas of weakness, refer to the child's past learning data to select the most suitable identification method. The system described in Appendix 1, characterized by the features described herein. (Note 25) The specified part is, When identifying areas of weakness, customize specific content based on the child's learning goals. The system described in Appendix 1, characterized by the features described herein. (Note 26) The specified part is, It estimates the child's emotions and adjusts the timing of specific areas of difficulty based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The specified part is, When identifying areas of weakness, change the identification method according to the child's learning style. The system described in Appendix 1, characterized by the features described herein. (Note 28) The specified part is, When identifying areas of difficulty, select specific content based on the child's interests and concerns. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0180] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A club that helps children with their homework, A management department manages the progress of homework assisted by the aforementioned assistance department, A motivation-generating unit that elicits learning motivation based on the progress managed by the aforementioned management unit, The system includes a specification unit that identifies areas of weakness based on the motivation extracted by the motivation extraction unit. A system characterized by the following features.

2. The aforementioned management department, It includes a monitoring section that allows parents to check the progress on their smartphones or tablets. The system according to feature 1.

3. The aforementioned motivation extraction unit is, It features a game section that provides gameplay elements. The system according to feature 1.

4. The specified part is, It includes a section that identifies areas of weakness and provides additional explanations and assignments for those areas. The system according to feature 1.

5. The aforementioned assistance unit is, It estimates the child's emotions and adjusts how it helps with homework based on those estimated emotions. The system according to feature 1.

6. The aforementioned assistance unit is, When helping with homework, refer to the child's past learning history to select the most appropriate method of assistance. The system according to feature 1.

7. The aforementioned assistance unit is, When helping with homework, customize the assistance based on the child's current learning situation. The system according to feature 1.

8. The aforementioned assistance unit is, The system estimates the child's emotions and adjusts the timing of assistance based on those emotions. The system according to feature 1.

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