system

Abstract

The system according to this embodiment aims to promote intellectual exploration while making use of children's instinctive curiosity. [Solution] The system according to this embodiment comprises an analysis unit, a motivation enhancement unit, and a feedback unit. The analysis unit analyzes content that children are interested in. The motivation enhancement unit enhances the motivation of children to take action spontaneously based on prompts generated by the analysis unit. The feedback unit provides feedback based on data of children who have taken action as a result of the motivation enhancement unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is mainstream to restrict children's use of the Internet and SNS, and there is room for improvement in promoting intellectual exploration while making the most of children's natural curiosity.

[0005] The system according to the embodiment aims to promote intellectual exploration while making the most of children's natural curiosity.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a motivation enhancement unit, and a feedback unit. The analysis unit analyzes content that children are interested in. The motivation enhancement unit enhances the child's motivation to take action spontaneously based on prompts generated by the analysis unit. The feedback unit provides feedback based on data of the child's actions taken by the motivation enhancement unit. [Effects of the Invention]

[0007] The system according to this embodiment can promote intellectual exploration while making use of children's instinctive curiosity. [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, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The intelligent experience platform according to an embodiment of the present invention is a system that does not restrict children's use of the internet or social media, but rather utilizes data on browsing and input content to provide a new way to enrich intellectual exploration while leveraging children's instinctive curiosity. This system analyzes content that children are interested in and generates prompts to encourage the next action. Next, it reinforces the motivation for children to take action spontaneously based on the generated prompts. Furthermore, it visualizes how skills improve based on data of the child's actual actions and provides feedback. This provides children with an environment in which they can proactively explore their interests and intelligently utilize digital devices. For example, when analyzing content that children are interested in, the AI ​​collects data such as websites the child has viewed, social media posts, and search history, and analyzes it. For example, if a child is interested in soccer, the AI ​​analyzes soccer-related content and generates prompts to encourage the next action. This allows the child to take concrete actions to deepen their interest. Next, it reinforces the motivation for children to take action spontaneously based on the generated prompts. For example, by showing the possibility of improving skills by actually playing soccer and the probability of becoming a professional, children can intuitively understand their own growth and gain motivation to take proactive action. Furthermore, the system visualizes how skills improve based on data from children's actual actions and provides feedback. For example, it analyzes videos and behavioral data of children playing soccer to visualize what kind of practice methods will improve their skills. This allows children to concretely understand their own progress and increases their motivation to take further action. This system provides children with an environment in which they can proactively explore their interests and wisely utilize digital devices. This makes it possible to support healthy development without relying on devices. In this way, the intellectual experience platform can enrich intellectual exploration by allowing children to take spontaneous action and receive feedback.

[0029] The intelligent experience platform according to this embodiment comprises an analysis unit, a motivation enhancement unit, and a feedback unit. The analysis unit analyzes content that children are interested in. The analysis unit collects data such as websites viewed by children, social media posts, and search history, and the AI ​​analyzes this data. For example, if a child is interested in soccer, the analysis unit analyzes soccer-related content and generates prompts to encourage the next action. The motivation enhancement unit enhances the child's motivation to take action spontaneously based on the generated prompts. For example, the motivation enhancement unit shows the child the possibility of improving their skills by actually playing soccer and the probability of becoming a professional, allowing the child to intuitively understand their own growth and gain the motivation to take proactive action. The feedback unit provides feedback based on the data of the child who has taken action based on the motivation enhancement unit. For example, the feedback unit analyzes videos and behavioral data of the child playing soccer and visualizes what kind of practice methods will improve their skills. This allows the child to concretely grasp their own growth and further increase their motivation to take action. Thus, the intelligent experience platform according to this embodiment can enrich children's intellectual exploration by allowing them to take action spontaneously and receive feedback.

[0030] The analytics department analyzes content that children are interested in. Specifically, it collects data such as websites children visit, social media posts they make, and their search history, and AI analyzes this data. The AI ​​uses natural language processing technology and machine learning algorithms to identify children's interests and concerns. For example, if a child is interested in soccer, the AI ​​extracts soccer-related keywords and content and generates prompts based on this to encourage the next action. These prompts indicate what the child should do next and include specific suggestions such as "watch a soccer match," "practice soccer," or "read a book about soccer." This allows the analytics department to deeply understand children's interests and provide specific guidance to encourage their next actions. Furthermore, the analytics department can track changes in children's interests in real time and update prompts as needed. This ensures that children are constantly receiving new stimuli and can maintain their interest.

[0031] The Motivation Enhancement Department strengthens children's motivation to take spontaneous action based on generated prompts. Specifically, by showing children the potential for skill improvement through actual soccer play and the probability of becoming a professional, children can intuitively understand their own growth and gain motivation to take proactive action. For example, the Motivation Enhancement Department provides tools to visualize the results of practice when children practice soccer. This allows children to concretely see the improvement of their skills and increases their motivation to practice further. The Motivation Enhancement Department also provides feedback that celebrates the goals and achievements that children have achieved. For example, it shows practice results in graphs and charts so that the degree of achievement can be visually confirmed. Furthermore, the Motivation Enhancement Department suggests new goals and challenges to keep children interested. This allows children to constantly strive towards new goals and maintain their motivation. The Motivation Enhancement Department supports children in continuing to take spontaneous action by providing individualized feedback tailored to their interests and concerns.

[0032] The Feedback Department provides feedback based on data from children who have taken action as a result of the Motivation Enhancement Department. Specifically, it analyzes videos and behavioral data of children playing soccer to visualize what kind of practice methods will improve their skills. The Feedback Department uses AI to perform video analysis and evaluate the children's movements and techniques during play. For example, it analyzes shooting form and dribbling techniques and specifically points out areas for improvement. It also analyzes the frequency and duration of children's practice based on behavioral data and proposes an optimal practice schedule. This allows children to concretely understand their own progress and increases their motivation to take further action. Furthermore, the Feedback Department tracks children's growth over the long term and reports on their progress regularly. This allows children to continuously check their own progress and maintain their motivation. The Feedback Department supports children in continuing to take spontaneous action by providing individualized feedback tailored to their interests and concerns. As a result, the intellectual experience platform according to this embodiment can enrich children's intellectual exploration by allowing them to take spontaneous action and receive feedback.

[0033] The data collection unit collects data such as websites visited by children, social media posts, and search history. For example, the data collection unit collects URLs and viewing times of websites visited by children, content and number of likes on social media posts, and keywords from search history. By collecting this data, the data collection unit provides data to identify content that interests children. This allows the data collection unit to collect data to identify content that interests children. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the URLs of websites visited by children into an AI, which can then analyze the content to identify interests.

[0034] The analysis unit analyzes the data collected by the collection unit to identify content that children are interested in. For example, the analysis unit analyzes collected website URLs, social media posts, and search history keywords using text mining techniques. For example, the analysis unit extracts frequently occurring keywords from the collected data and uses them to identify content that children are interested in. The analysis unit can also use data mining techniques to analyze patterns in the collected data and identify content that children are interested in. For example, the analysis unit finds specific patterns in the collected data and uses them to identify content that children are interested in. This allows the analysis unit to generate prompts to encourage the next action by identifying content that children are interested in. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the collected data into an AI, which can then analyze the data to identify interests.

[0035] The motivation enhancement unit helps children take spontaneous action based on generated prompts. For example, the motivation enhancement unit presents generated prompts to children and encourages them to take action based on those prompts. For example, by showing children the possibility of improving their skills by actually playing soccer or the probability of becoming a professional, the motivation enhancement unit helps children intuitively understand their own growth and gain the motivation to take proactive action. The motivation enhancement unit can also set specific goals for children to take action and encourage them to act towards those goals. For example, the motivation enhancement unit sets a goal for children to practice soccer every day and encourages them to act towards that goal. In this way, the motivation enhancement unit can enhance the motivation of children to take spontaneous action. Some or all of the above processes in the motivation enhancement unit may be performed using AI, for example, or not using AI. For example, the motivation enhancement unit can input generated prompts into AI and encourage the AI ​​to take action based on those prompts.

[0036] The feedback unit analyzes data from the child's actual actions and visualizes how their skills improve. For example, the feedback unit analyzes video and behavioral data of a child playing soccer and visualizes what kind of practice methods would improve their skills. For example, the feedback unit uses video analysis technology to analyze the child's movements and visually shows areas for improvement. The feedback unit can also show the degree of skill improvement in graphs and charts based on behavioral data. For example, the feedback unit collects data on the child's practice time and practice content and visualizes the degree of skill improvement based on this data. This allows the feedback unit to help the child concretely understand their own progress and increase their motivation to take further action. Some or all of the above processing in the feedback unit may be performed using AI, or not. For example, the feedback unit can input video data of the child's play into an AI, which can then analyze the data and visualize the degree of skill improvement.

[0037] The analysis department improves the accuracy of its analysis by considering the child's past interests and concerns. For example, the analysis department analyzes relevant new content based on content the child has frequently viewed in the past. For example, the analysis department analyzes the highlights of the latest soccer matches based on soccer-related content the child has viewed in the past. The analysis department can also analyze content that the child might be interested in based on keywords the child has searched for in the past. For example, the analysis department analyzes relevant training videos based on the keyword "soccer training methods" the child has searched for in the past. The analysis department can also analyze relevant content based on online events the child has participated in in the past. For example, the analysis department analyzes information on relevant seminars based on online soccer seminars the child has participated in in the past. This improves the accuracy of the analysis by considering the child's past interests and concerns. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input the child's past interest data into an AI, which can then analyze that data to improve the accuracy of the analysis.

[0038] The analysis department applies different analysis algorithms depending on the child's age and grade level during analysis. For example, when analyzing content for preschoolers, the analysis department applies an algorithm that emphasizes visually appealing elements. For instance, when analyzing educational apps for preschoolers, the analysis department emphasizes colorful illustrations and animations. The analysis department can also apply an algorithm that emphasizes learning effectiveness when analyzing content for elementary school students. For example, when analyzing math materials for elementary school students, the analysis department emphasizes the difficulty level of the problems and the clarity of the explanations. The analysis department can also apply an algorithm that provides in-depth knowledge when analyzing content for middle and high school students. For example, when analyzing science documentaries for middle and high school students, the analysis department emphasizes specialized content and detailed experiments. This allows the analysis department to provide appropriate content according to the child's age and grade level. Some or all of the above processing in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input the child's age data into an AI, which can then analyze the data and apply an appropriate analysis algorithm.

[0039] The analysis department prioritizes analyzing highly relevant content, taking into account the child's geographical location. For example, it might prioritize analyzing event information in the child's local area. For instance, it might prioritize information about soccer events held in the child's area. The analysis department can also prioritize analyzing content related to the curriculum of the school the child attends. For example, it might prioritize analyzing relevant learning content based on the curriculum of the school the child attends. The analysis department can also prioritize analyzing content related to places the child frequently visits. For example, it might prioritize analyzing content related to libraries or parks that the child frequently visits. This allows the analysis department to provide highly relevant content based on the child's geographical location. Some or all of the above processing in the analysis department may be performed using AI, or not. For example, the analysis department can input the child's geographical location data into an AI, which can then analyze the data and prioritize the analysis of highly relevant content.

[0040] The analysis department analyzes the child's social media activity during the analysis and identifies relevant content. For example, the analysis department identifies relevant content based on the content of posts from accounts the child follows. For example, the analysis department identifies relevant soccer training videos based on the content of posts from soccer players the child follows. The analysis department can also identify relevant content based on the activities of groups the child participates in. For example, the analysis department identifies relevant match highlight videos based on the activities of soccer groups the child participates in. The analysis department can also identify relevant content based on the content of posts the child shares. For example, the analysis department identifies relevant match analysis videos based on soccer match results shared by the child. This allows the analysis department to provide relevant content based on the child's social media activity. Some or all of the above processing in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input the child's social media data into an AI, which can then analyze the data to identify relevant content.

[0041] The Motivation Enhancement Department selects the optimal enhancement method by referring to the child's past behavioral history when enhancing motivation. For example, the Motivation Enhancement Department provides similar success experiences based on the child's past successes. For example, based on the child's past experience of scoring a goal in a soccer match, the Motivation Enhancement Department sets a goal of scoring in the next match as well. The Motivation Enhancement Department can also suggest methods to overcome failures based on the child's past failures. For example, based on the child's past experience of making a mistake in a soccer match, the Motivation Enhancement Department suggests practice methods to overcome that mistake. The Motivation Enhancement Department can also suggest new related activities based on activities the child has shown interest in in the past. For example, based on the child's past experience of showing interest in soccer, the Motivation Enhancement Department suggests new related training methods. In this way, the Motivation Enhancement Department can provide the optimal enhancement method based on the child's past behavioral history. Some or all of the above processing in the Motivation Enhancement Department may be performed using AI, for example, or not. For example, the Motivation Enhancement Department can input the child's past behavioral data into an AI, which can then analyze the data and select the optimal enhancement method.

[0042] The Motivation Enhancement Unit customizes the means of motivation enhancement based on the child's current living situation. For example, if the child is in the middle of school test periods, the Motivation Enhancement Unit provides learning-related enhancement means. For example, the Motivation Enhancement Unit sets goals for the child to study for tests efficiently. The Motivation Enhancement Unit can also provide enhancement means that utilize free time if the child is on summer vacation. For example, the Motivation Enhancement Unit sets goals for the child to acquire new skills during summer vacation. The Motivation Enhancement Unit can also provide enhancement means within reasonable limits if the child is unwell. For example, the Motivation Enhancement Unit suggests relaxation methods to help the child recover. In this way, the Motivation Enhancement Unit can provide enhancement means that are appropriate to the child's current living situation. Some or all of the above processes in the Motivation Enhancement Unit may be performed using AI, for example, or not using AI. For example, the Motivation Enhancement Unit can input data on the child's living situation into AI, and the AI ​​can analyze that data to customize the enhancement means.

[0043] The Motivation Enhancement Department selects the optimal motivation enhancement method when considering the child's geographical location. For example, the Motivation Enhancement Department provides enhancement methods that encourage participation based on event information in the child's area. For example, the Motivation Enhancement Department might encourage the child to participate in a soccer event held in their area. The Motivation Enhancement Department can also provide enhancement methods related to the curriculum of the school the child attends. For example, the Motivation Enhancement Department might encourage the child to participate in practice with the school's soccer team. The Motivation Enhancement Department can also provide enhancement methods related to places the child frequently visits. For example, the Motivation Enhancement Department might suggest soccer practice at a park the child frequently visits. This allows the Motivation Enhancement Department to provide the optimal enhancement method based on the child's geographical location. Some or all of the above processing in the Motivation Enhancement Department may be performed using AI, or not. For example, the Motivation Enhancement Department can input the child's geographical location data into an AI, which can then analyze the data to select the optimal enhancement method.

[0044] The Motivation Enhancement Department analyzes a child's social media activity and proposes enhancement measures during motivation enhancement. For example, the Motivation Enhancement Department proposes relevant enhancement measures based on the activity content of accounts the child follows. For example, the Motivation Enhancement Department proposes similar training based on the training methods of soccer players the child follows. The Motivation Enhancement Department can also propose relevant enhancement measures based on the activity content of groups the child participates in. For example, the Motivation Enhancement Department proposes similar training based on the training content of the soccer group the child participates in. The Motivation Enhancement Department can also propose relevant enhancement measures based on the content of posts the child shares. For example, the Motivation Enhancement Department proposes methods for analyzing soccer matches based on the results of soccer matches the child shares. In this way, the Motivation Enhancement Department can provide enhancement measures based on the child's social media activity. Some or all of the above processing in the Motivation Enhancement Department may be performed using AI, for example, or not using AI. For example, the Motivation Enhancement Department can input the child's social media data into an AI, which can then analyze the data and propose enhancement measures.

[0045] The feedback unit selects the optimal feedback method by referring to the child's past behavioral data during the feedback process. For example, the feedback unit provides feedback on similar successes based on the child's past successes. For instance, it might emphasize a child's past success in scoring a goal in a soccer match. The feedback unit can also provide feedback on how to overcome failures based on the child's past failures. For example, it might offer advice on how to overcome mistakes based on a child's past mistake in a soccer match. Furthermore, the feedback unit can provide feedback on new related activities based on activities the child has shown interest in in the past. For example, it might suggest new related training methods based on a child's past interest in soccer. This allows the feedback unit to provide the optimal feedback method based on the child's past behavioral data. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the child's past behavioral data into an AI, which can then analyze the data to select the optimal feedback method.

[0046] The feedback unit customizes the feedback method based on the child's current living situation when providing feedback. For example, if the child is in the middle of school test periods, the feedback unit provides learning-related feedback. For example, the feedback unit provides advice on how the child can study for tests efficiently. The feedback unit can also provide feedback that makes use of free time if the child is on summer vacation. For example, the feedback unit provides advice on how the child can acquire new skills during summer vacation. The feedback unit can also provide feedback within reasonable limits if the child is unwell. For example, the feedback unit suggests ways for the child to relax and recover. In this way, the feedback unit can provide feedback methods that are appropriate to the child's current living situation. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input data on the child's living situation into AI, and the AI ​​can analyze that data to customize the feedback method.

[0047] The feedback unit selects the most appropriate feedback method when providing feedback, taking into account the child's geographical location. For example, the feedback unit provides feedback that encourages participation based on event information in the child's area. For example, the feedback unit encourages the child to participate in a soccer event held in their area. The feedback unit can also provide feedback related to the curriculum of the school the child attends. For example, the feedback unit encourages the child to participate in practice with the school's soccer team. The feedback unit can also provide feedback related to places the child frequently visits. For example, the feedback unit suggests soccer practice at a park the child often visits. This allows the feedback unit to provide the most appropriate feedback method based on the child's geographical location. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not. For example, the feedback unit can input the child's geographical location data into an AI, which can then analyze the data to select the most appropriate feedback method.

[0048] The feedback unit analyzes the child's social media activity and proposes feedback methods during the feedback process. For example, the feedback unit proposes relevant feedback methods based on the activity of accounts the child follows. For example, the feedback unit proposes similar training methods based on the training methods of soccer players the child follows. The feedback unit can also propose relevant feedback methods based on the activity of groups the child participates in. For example, the feedback unit proposes similar training methods based on the training content of the soccer group the child participates in. The feedback unit can also propose relevant feedback methods based on the content of posts the child shares. For example, the feedback unit proposes methods for analyzing a soccer match based on the results of a soccer match the child shared. In this way, the feedback unit can provide feedback methods based on the child's social media activity. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input the child's social media data into an AI, which can then analyze the data and propose feedback methods.

[0049] The data collection unit analyzes the child's past browsing history and selects the optimal collection method. For example, the collection unit prioritizes collecting relevant data based on content the child has frequently viewed in the past. For instance, it collects relevant new soccer content based on soccer-related websites the child has frequently visited in the past. The collection unit can also collect data that the child might be interested in based on keywords the child has searched for in the past. For example, it collects relevant training data based on the keyword "soccer training methods" the child has searched for in the past. The collection unit can also collect relevant data based on online events the child has participated in in the past. For example, it collects relevant seminar data based on online soccer seminars the child has participated in in the past. This allows the collection unit to provide the optimal collection method based on the child's past browsing history. Some or all of the above processing in the collection unit may be performed using AI, for example, or not. For example, the collection unit can input the child's past browsing data into an AI, which can then analyze the data and select the optimal collection method.

[0050] The data collection unit filters the data based on the child's current living situation and areas of interest. For example, if the child is in the middle of school exams, the data collection unit prioritizes collecting learning-related data. For example, it collects learning data to help the child study for tests efficiently. The data collection unit can also prioritize collecting data on how the child utilizes their free time if the child is on summer vacation. For example, it collects data on how the child acquires new skills during summer vacation. The data collection unit can also prioritize collecting health-related data if the child is unwell. For example, it collects health data to help the child recover. This allows the data collection unit to provide data filtering tailored to the child's current living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the child's living situation data into an AI, which can then analyze and filter the data.

[0051] The data collection unit prioritizes collecting highly relevant data, taking into account the child's geographical location. For example, the unit prioritizes collecting event information in the area where the child lives. For instance, it prioritizes collecting information about soccer events held in the area where the child lives. The unit can also prioritize collecting data related to the curriculum of the school the child attends. For example, it prioritizes collecting practice data for the soccer team at the school the child attends. The unit can also prioritize collecting data related to places the child frequently visits. For example, it prioritizes collecting soccer practice data at parks the child frequently visits. This allows the data collection unit to provide highly relevant data based on the child's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the child's geographical location data into an AI, which can then analyze the data and prioritize collecting highly relevant data.

[0052] The data collection unit analyzes the child's social media activity and collects relevant data when collecting data. For example, the data collection unit collects relevant data based on the content of posts from accounts the child follows. For example, the data collection unit collects relevant soccer training data based on the content of posts from soccer players the child follows. The data collection unit can also collect relevant data based on the activities of groups the child participates in. For example, the data collection unit collects relevant training data based on the training content of the soccer group the child participates in. The data collection unit can also collect relevant data based on the content of posts the child shares. For example, the data collection unit collects relevant match analysis data based on soccer match results shared by the child. In this way, the data collection unit can provide relevant data based on the child's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the child's social media data into AI, which can then analyze the data and collect relevant data.

[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 analytics department can adjust its analysis methods to consider children's learning styles when analyzing content that interests them. For example, children with a visual learning style can have image and video content analyzed preferentially. Children with an auditory learning style can have audio content such as podcasts and audiobooks analyzed preferentially. Furthermore, children with an experiential learning style can have experiential content such as interactive games and experimental videos analyzed preferentially. This allows the analytics department to provide optimal content tailored to each child's learning style.

[0055] The analytics department can adjust its content analysis methods to take into account the child's learning progress. For example, if the child is a beginner-level learner, it can prioritize analyzing basic content, such as content explaining basic soccer rules and techniques. If the child is an intermediate-level learner, it can prioritize analyzing more advanced content, such as content on tactics and teamwork. Furthermore, if the child is an advanced-level learner, it can prioritize analyzing more specialized content, such as content on professional players' playing styles and training methods. This allows the analytics department to provide optimal content tailored to the child's learning progress.

[0056] The Motivation Enhancement Department can adjust its motivation enhancement methods to suit each child's learning style. For example, children with a visual learning style can be motivated by providing visual feedback. For instance, the results of soccer practice can be visualized using graphs and charts. Children with an auditory learning style can be motivated by providing audio feedback. For example, the results of practice can be explained verbally. Furthermore, children with an experiential learning style can be motivated by providing interactive feedback. For example, game-based feedback can be provided. In this way, the Motivation Enhancement Department can provide the optimal motivation enhancement method tailored to each child's learning style.

[0057] The feedback system can adjust the content of the feedback based on the child's learning progress. For example, if the child is a beginner-level learner, it can provide basic feedback, such as feedback on fundamental techniques and rules. If the child is an intermediate-level learner, it can provide more advanced feedback, such as feedback on tactics and teamwork. Furthermore, if the child is an advanced-level learner, it can provide specialized feedback, such as feedback on the playing style and training methods of professional players. This allows the feedback system to provide optimal feedback tailored to the child's learning progress.

[0058] The data collection unit can adjust its data collection methods to suit each child's learning style. For example, for children with a visual learning style, it can prioritize collecting image and video data, such as video data of soccer matches and practices. Similarly, for children with an auditory learning style, it can prioritize collecting audio data, such as commentary and analysis audio from soccer matches. Furthermore, for children with an experiential learning style, it can prioritize collecting interactive data, such as data on the use of soccer practice apps. This allows the data collection unit to provide the optimal data collection method tailored to each child's learning style.

[0059] The analytics department can analyze children's social media activity and identify relevant content. For example, it can identify relevant content based on the posts of accounts children follow. For instance, it can identify relevant soccer training videos based on posts by soccer players children follow. It can also identify relevant content based on the activities of groups children participate in. For example, it can identify relevant match highlight videos based on the activities of soccer groups children participate in. Furthermore, it can identify relevant content based on posts children share. For example, it can identify relevant match analysis videos based on soccer match results children share. This allows the analytics department to provide relevant content based on children's social media activity.

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

[0061] Step 1: The analysis department analyzes content that children are interested in. Specifically, it collects data such as websites children visit, social media posts they make, and their search history, and the AI ​​analyzes this data. For example, if a child is interested in soccer, the AI ​​analyzes soccer-related content and generates prompts to encourage the next action. Step 2: The motivation enhancement unit reinforces the child's motivation to take action spontaneously based on the generated prompts. For example, by showing the child the possibility of improving their skills by actually playing soccer or the probability of becoming a professional, the child can intuitively understand their own growth and gain the motivation to take proactive action. Step 3: The Feedback Department provides feedback based on data from the children who have taken action as a result of the Motivation Enhancement Department. For example, they analyze videos and behavioral data of the children playing soccer to visualize what kind of practice methods would improve their skills. This allows the children to concretely understand their own progress and further motivates them to take action.

[0062] (Example of form 2) The intelligent experience platform according to an embodiment of the present invention is a system that does not restrict children's use of the internet or social media, but rather utilizes data on browsing and input content to provide a new way to enrich intellectual exploration while leveraging children's instinctive curiosity. This system analyzes content that children are interested in and generates prompts to encourage the next action. Next, it reinforces the motivation for children to take action spontaneously based on the generated prompts. Furthermore, it visualizes how skills improve based on data of the child's actual actions and provides feedback. This provides children with an environment in which they can proactively explore their interests and intelligently utilize digital devices. For example, when analyzing content that children are interested in, the AI ​​collects data such as websites the child has viewed, social media posts, and search history, and analyzes it. For example, if a child is interested in soccer, the AI ​​analyzes soccer-related content and generates prompts to encourage the next action. This allows the child to take concrete actions to deepen their interest. Next, it reinforces the motivation for children to take action spontaneously based on the generated prompts. For example, by showing the possibility of improving skills by actually playing soccer and the probability of becoming a professional, children can intuitively understand their own growth and gain motivation to take proactive action. Furthermore, the system visualizes how skills improve based on data from children's actual actions and provides feedback. For example, it analyzes videos and behavioral data of children playing soccer to visualize what kind of practice methods will improve their skills. This allows children to concretely understand their own progress and increases their motivation to take further action. This system provides children with an environment in which they can proactively explore their interests and wisely utilize digital devices. This makes it possible to support healthy development without relying on devices. In this way, the intellectual experience platform can enrich intellectual exploration by allowing children to take spontaneous action and receive feedback.

[0063] The intelligent experience platform according to this embodiment comprises an analysis unit, a motivation enhancement unit, and a feedback unit. The analysis unit analyzes content that children are interested in. The analysis unit collects data such as websites viewed by children, social media posts, and search history, and the AI ​​analyzes this data. For example, if a child is interested in soccer, the analysis unit analyzes soccer-related content and generates prompts to encourage the next action. The motivation enhancement unit enhances the child's motivation to take action spontaneously based on the generated prompts. For example, the motivation enhancement unit shows the child the possibility of improving their skills by actually playing soccer and the probability of becoming a professional, allowing the child to intuitively understand their own growth and gain the motivation to take proactive action. The feedback unit provides feedback based on the data of the child who has taken action based on the motivation enhancement unit. For example, the feedback unit analyzes videos and behavioral data of the child playing soccer and visualizes what kind of practice methods will improve their skills. This allows the child to concretely grasp their own growth and further increase their motivation to take action. Thus, the intelligent experience platform according to this embodiment can enrich children's intellectual exploration by allowing them to take action spontaneously and receive feedback.

[0064] The analytics department analyzes content that children are interested in. Specifically, it collects data such as websites children visit, social media posts they make, and their search history, and AI analyzes this data. The AI ​​uses natural language processing technology and machine learning algorithms to identify children's interests and concerns. For example, if a child is interested in soccer, the AI ​​extracts soccer-related keywords and content and generates prompts based on this to encourage the next action. These prompts indicate what the child should do next and include specific suggestions such as "watch a soccer match," "practice soccer," or "read a book about soccer." This allows the analytics department to deeply understand children's interests and provide specific guidance to encourage their next actions. Furthermore, the analytics department can track changes in children's interests in real time and update prompts as needed. This ensures that children are constantly receiving new stimuli and can maintain their interest.

[0065] The Motivation Enhancement Department strengthens children's motivation to take spontaneous action based on generated prompts. Specifically, by showing children the potential for skill improvement through actual soccer play and the probability of becoming a professional, children can intuitively understand their own growth and gain motivation to take proactive action. For example, the Motivation Enhancement Department provides tools to visualize the results of practice when children practice soccer. This allows children to concretely see the improvement of their skills and increases their motivation to practice further. The Motivation Enhancement Department also provides feedback that celebrates the goals and achievements that children have achieved. For example, it shows practice results in graphs and charts so that the degree of achievement can be visually confirmed. Furthermore, the Motivation Enhancement Department suggests new goals and challenges to keep children interested. This allows children to constantly strive towards new goals and maintain their motivation. The Motivation Enhancement Department supports children in continuing to take spontaneous action by providing individualized feedback tailored to their interests and concerns.

[0066] The Feedback Department provides feedback based on data from children who have taken action as a result of the Motivation Enhancement Department. Specifically, it analyzes videos and behavioral data of children playing soccer to visualize what kind of practice methods will improve their skills. The Feedback Department uses AI to perform video analysis and evaluate the children's movements and techniques during play. For example, it analyzes shooting form and dribbling techniques and specifically points out areas for improvement. It also analyzes the frequency and duration of children's practice based on behavioral data and proposes an optimal practice schedule. This allows children to concretely understand their own progress and increases their motivation to take further action. Furthermore, the Feedback Department tracks children's growth over the long term and reports on their progress regularly. This allows children to continuously check their own progress and maintain their motivation. The Feedback Department supports children in continuing to take spontaneous action by providing individualized feedback tailored to their interests and concerns. As a result, the intellectual experience platform according to this embodiment can enrich children's intellectual exploration by allowing them to take spontaneous action and receive feedback.

[0067] The data collection unit collects data such as websites visited by children, social media posts, and search history. For example, the data collection unit collects URLs and viewing times of websites visited by children, content and number of likes on social media posts, and keywords from search history. By collecting this data, the data collection unit provides data to identify content that interests children. This allows the data collection unit to collect data to identify content that interests children. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the URLs of websites visited by children into an AI, which can then analyze the content to identify interests.

[0068] The analysis unit analyzes the data collected by the collection unit to identify content that children are interested in. For example, the analysis unit analyzes collected website URLs, social media posts, and search history keywords using text mining techniques. For example, the analysis unit extracts frequently occurring keywords from the collected data and uses them to identify content that children are interested in. The analysis unit can also use data mining techniques to analyze patterns in the collected data and identify content that children are interested in. For example, the analysis unit finds specific patterns in the collected data and uses them to identify content that children are interested in. This allows the analysis unit to generate prompts to encourage the next action by identifying content that children are interested in. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the collected data into an AI, which can then analyze the data to identify interests.

[0069] The motivation enhancement unit helps children take spontaneous action based on generated prompts. For example, the motivation enhancement unit presents generated prompts to children and encourages them to take action based on those prompts. For example, by showing children the possibility of improving their skills by actually playing soccer or the probability of becoming a professional, the motivation enhancement unit helps children intuitively understand their own growth and gain the motivation to take proactive action. The motivation enhancement unit can also set specific goals for children to take action and encourage them to act towards those goals. For example, the motivation enhancement unit sets a goal for children to practice soccer every day and encourages them to act towards that goal. In this way, the motivation enhancement unit can enhance the motivation of children to take spontaneous action. Some or all of the above processes in the motivation enhancement unit may be performed using AI, for example, or not using AI. For example, the motivation enhancement unit can input generated prompts into AI and encourage the AI ​​to take action based on those prompts.

[0070] The feedback unit analyzes data from the child's actual actions and visualizes how their skills improve. For example, the feedback unit analyzes video and behavioral data of a child playing soccer and visualizes what kind of practice methods would improve their skills. For example, the feedback unit uses video analysis technology to analyze the child's movements and visually shows areas for improvement. The feedback unit can also show the degree of skill improvement in graphs and charts based on behavioral data. For example, the feedback unit collects data on the child's practice time and practice content and visualizes the degree of skill improvement based on this data. This allows the feedback unit to help the child concretely understand their own progress and increase their motivation to take further action. Some or all of the above processing in the feedback unit may be performed using AI, or not. For example, the feedback unit can input video data of the child's play into an AI, which can then analyze the data and visualize the degree of skill improvement.

[0071] The analysis unit estimates the child's emotions and adjusts the content analysis method based on the estimated emotions. For example, if the child is excited, the analysis unit prioritizes analyzing more stimulating content. For example, when the child is excited, the analysis unit prioritizes analyzing action games or sports content. The analysis unit can also prioritize analyzing content suitable for learning if the child is calm. For example, when the child is calm, the analysis unit prioritizes analyzing educational videos or documentaries. The analysis unit can also prioritize analyzing relaxing content if the child is tired. For example, when the child is tired, the analysis unit prioritizes analyzing relaxation music or nature scenes. In this way, the analysis unit can provide a content analysis method that is tailored 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. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis department can input children's emotional data into an AI, which can then analyze that data and adjust the content analysis method accordingly.

[0072] The analysis department improves the accuracy of its analysis by considering the child's past interests and concerns. For example, the analysis department analyzes relevant new content based on content the child has frequently viewed in the past. For example, the analysis department analyzes the highlights of the latest soccer matches based on soccer-related content the child has viewed in the past. The analysis department can also analyze content that the child might be interested in based on keywords the child has searched for in the past. For example, the analysis department analyzes relevant training videos based on the keyword "soccer training methods" the child has searched for in the past. The analysis department can also analyze relevant content based on online events the child has participated in in the past. For example, the analysis department analyzes information on relevant seminars based on online soccer seminars the child has participated in in the past. This improves the accuracy of the analysis by considering the child's past interests and concerns. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input the child's past interest data into an AI, which can then analyze that data to improve the accuracy of the analysis.

[0073] The analysis department applies different analysis algorithms depending on the child's age and grade level during analysis. For example, when analyzing content for preschoolers, the analysis department applies an algorithm that emphasizes visually appealing elements. For instance, when analyzing educational apps for preschoolers, the analysis department emphasizes colorful illustrations and animations. The analysis department can also apply an algorithm that emphasizes learning effectiveness when analyzing content for elementary school students. For example, when analyzing math materials for elementary school students, the analysis department emphasizes the difficulty level of the problems and the clarity of the explanations. The analysis department can also apply an algorithm that provides in-depth knowledge when analyzing content for middle and high school students. For example, when analyzing science documentaries for middle and high school students, the analysis department emphasizes specialized content and detailed experiments. This allows the analysis department to provide appropriate content according to the child's age and grade level. Some or all of the above processing in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input the child's age data into an AI, which can then analyze the data and apply an appropriate analysis algorithm.

[0074] The analysis unit estimates the child's emotions and adjusts the display method of the analysis results based on the estimated emotions. For example, if the child is excited, the analysis unit provides a colorful and dynamic display method. For example, when the child is excited, the analysis unit provides a display method that includes animation and interactive elements. The analysis unit can also provide a simple and highly visible display method if the child is calm. For example, when the child is calm, the analysis unit provides a simple design and static display method. The analysis unit can also provide a display method with eye-friendly colors if the child is tired. For example, when the child is tired, the analysis unit provides a display method that uses muted colors and soft lighting. In this way, the analysis unit can provide a display method that is appropriate for the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis department can input children's emotional data into an AI, which can then analyze the data and adjust how it is displayed.

[0075] The analysis department prioritizes analyzing highly relevant content, taking into account the child's geographical location. For example, it might prioritize analyzing event information in the child's local area. For instance, it might prioritize information about soccer events held in the child's area. The analysis department can also prioritize analyzing content related to the curriculum of the school the child attends. For example, it might prioritize analyzing relevant learning content based on the curriculum of the school the child attends. The analysis department can also prioritize analyzing content related to places the child frequently visits. For example, it might prioritize analyzing content related to libraries or parks that the child frequently visits. This allows the analysis department to provide highly relevant content based on the child's geographical location. Some or all of the above processing in the analysis department may be performed using AI, or not. For example, the analysis department can input the child's geographical location data into an AI, which can then analyze the data and prioritize the analysis of highly relevant content.

[0076] The analysis department analyzes the child's social media activity during the analysis and identifies relevant content. For example, the analysis department identifies relevant content based on the content of posts from accounts the child follows. For example, the analysis department identifies relevant soccer training videos based on the content of posts from soccer players the child follows. The analysis department can also identify relevant content based on the activities of groups the child participates in. For example, the analysis department identifies relevant match highlight videos based on the activities of soccer groups the child participates in. The analysis department can also identify relevant content based on the content of posts the child shares. For example, the analysis department identifies relevant match analysis videos based on soccer match results shared by the child. This allows the analysis department to provide relevant content based on the child's social media activity. Some or all of the above processing in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input the child's social media data into an AI, which can then analyze the data to identify relevant content.

[0077] The Motivation Enhancement Unit estimates a child's emotions and adjusts its motivation enhancement methods based on those estimates. For example, if a child is excited, the Motivation Enhancement Unit can set challenging goals to increase their motivation. For instance, when a child is excited, it might set a goal of scoring a goal in a soccer match. The Motivation Enhancement Unit can also maintain motivation by setting gradual goals when a child is calm. For example, when a child is calm, it might set a goal of practicing soccer every day. Furthermore, if a child is tired, the Motivation Enhancement Unit can restore their motivation by setting short-term goals. For example, when a child is tired, it might set a goal of a short period of refreshment. This allows the Motivation Enhancement Unit to provide motivation enhancement methods tailored 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. Some or all of the above-described processes in the motivation enhancement unit may be performed using AI, for example, or without AI. For example, the motivation enhancement unit can input child emotional data into AI, which can then analyze the data and adjust the motivation enhancement method.

[0078] The Motivation Enhancement Department selects the optimal enhancement method by referring to the child's past behavioral history when enhancing motivation. For example, the Motivation Enhancement Department provides similar success experiences based on the child's past successes. For example, based on the child's past experience of scoring a goal in a soccer match, the Motivation Enhancement Department sets a goal of scoring in the next match as well. The Motivation Enhancement Department can also suggest methods to overcome failures based on the child's past failures. For example, based on the child's past experience of making a mistake in a soccer match, the Motivation Enhancement Department suggests practice methods to overcome that mistake. The Motivation Enhancement Department can also suggest new related activities based on activities the child has shown interest in in the past. For example, based on the child's past experience of showing interest in soccer, the Motivation Enhancement Department suggests new related training methods. In this way, the Motivation Enhancement Department can provide the optimal enhancement method based on the child's past behavioral history. Some or all of the above processing in the Motivation Enhancement Department may be performed using AI, for example, or not. For example, the Motivation Enhancement Department can input the child's past behavioral data into an AI, which can then analyze the data and select the optimal enhancement method.

[0079] The Motivation Enhancement Unit customizes the means of motivation enhancement based on the child's current living situation. For example, if the child is in the middle of school test periods, the Motivation Enhancement Unit provides learning-related enhancement means. For example, the Motivation Enhancement Unit sets goals for the child to study for tests efficiently. The Motivation Enhancement Unit can also provide enhancement means that utilize free time if the child is on summer vacation. For example, the Motivation Enhancement Unit sets goals for the child to acquire new skills during summer vacation. The Motivation Enhancement Unit can also provide enhancement means within reasonable limits if the child is unwell. For example, the Motivation Enhancement Unit suggests relaxation methods to help the child recover. In this way, the Motivation Enhancement Unit can provide enhancement means that are appropriate to the child's current living situation. Some or all of the above processes in the Motivation Enhancement Unit may be performed using AI, for example, or not using AI. For example, the Motivation Enhancement Unit can input data on the child's living situation into AI, and the AI ​​can analyze that data to customize the enhancement means.

[0080] The Motivation Enhancement Unit estimates a child's emotions and determines the priority of motivation enhancement based on the estimated emotions. For example, if a child is excited, the Motivation Enhancement Unit will motivate them to take immediate action. For instance, if a child is excited, the Motivation Enhancement Unit will encourage them to immediately participate in a soccer match. The Motivation Enhancement Unit can also motivate a child to take planned action if they are calm. For example, if a child is calm, the Motivation Enhancement Unit will encourage them to plan their soccer practice. Furthermore, if a child is tired, the Motivation Enhancement Unit can motivate them to prioritize rest before taking action. For example, if a child is tired, the Motivation Enhancement Unit will encourage them to rest first, and then practice soccer. In this way, the Motivation Enhancement Unit can provide a priority for motivation enhancement that is tailored 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. Some or all of the above-described processes in the motivation enhancement unit may be performed using AI, for example, or without AI. For example, the motivation enhancement unit can input children's emotional data into AI, which can then analyze the data to determine the priority of motivation enhancement.

[0081] The Motivation Enhancement Department selects the optimal motivation enhancement method when considering the child's geographical location. For example, the Motivation Enhancement Department provides enhancement methods that encourage participation based on event information in the child's area. For example, the Motivation Enhancement Department might encourage the child to participate in a soccer event held in their area. The Motivation Enhancement Department can also provide enhancement methods related to the curriculum of the school the child attends. For example, the Motivation Enhancement Department might encourage the child to participate in practice with the school's soccer team. The Motivation Enhancement Department can also provide enhancement methods related to places the child frequently visits. For example, the Motivation Enhancement Department might suggest soccer practice at a park the child frequently visits. This allows the Motivation Enhancement Department to provide the optimal enhancement method based on the child's geographical location. Some or all of the above processing in the Motivation Enhancement Department may be performed using AI, or not. For example, the Motivation Enhancement Department can input the child's geographical location data into an AI, which can then analyze the data to select the optimal enhancement method.

[0082] The Motivation Enhancement Department analyzes a child's social media activity and proposes enhancement measures during motivation enhancement. For example, the Motivation Enhancement Department proposes relevant enhancement measures based on the activity content of accounts the child follows. For example, the Motivation Enhancement Department proposes similar training based on the training methods of soccer players the child follows. The Motivation Enhancement Department can also propose relevant enhancement measures based on the activity content of groups the child participates in. For example, the Motivation Enhancement Department proposes similar training based on the training content of the soccer group the child participates in. The Motivation Enhancement Department can also propose relevant enhancement measures based on the content of posts the child shares. For example, the Motivation Enhancement Department proposes methods for analyzing soccer matches based on the results of soccer matches the child shares. In this way, the Motivation Enhancement Department can provide enhancement measures based on the child's social media activity. Some or all of the above processing in the Motivation Enhancement Department may be performed using AI, for example, or not using AI. For example, the Motivation Enhancement Department can input the child's social media data into an AI, which can then analyze the data and propose enhancement measures.

[0083] The feedback unit estimates the child's emotions and adjusts the feedback method based on the estimated emotions. For example, if the child is excited, the feedback unit emphasizes positive feedback. For example, if the child is excited, the feedback unit praises good play in a soccer match. The feedback unit can also provide detailed feedback if the child is calm. For example, if the child is calm, the feedback unit provides technical advice on soccer. The feedback unit can also provide concise and easy-to-understand feedback if the child is tired. For example, if the child is tired, the feedback unit conveys the main points with a short comment. In this way, the feedback unit can provide feedback methods that are appropriate to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the child's emotional data into an AI, which then analyzes the data and adjusts the feedback method accordingly.

[0084] The feedback unit selects the optimal feedback method by referring to the child's past behavioral data during the feedback process. For example, the feedback unit provides feedback on similar successes based on the child's past successes. For instance, it might emphasize a child's past success in scoring a goal in a soccer match. The feedback unit can also provide feedback on how to overcome failures based on the child's past failures. For example, it might offer advice on how to overcome mistakes based on a child's past mistake in a soccer match. Furthermore, the feedback unit can provide feedback on new related activities based on activities the child has shown interest in in the past. For example, it might suggest new related training methods based on a child's past interest in soccer. This allows the feedback unit to provide the optimal feedback method based on the child's past behavioral data. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the child's past behavioral data into an AI, which can then analyze the data to select the optimal feedback method.

[0085] The feedback unit customizes the feedback method based on the child's current living situation when providing feedback. For example, if the child is in the middle of school test periods, the feedback unit provides learning-related feedback. For example, the feedback unit provides advice on how the child can study for tests efficiently. The feedback unit can also provide feedback that makes use of free time if the child is on summer vacation. For example, the feedback unit provides advice on how the child can acquire new skills during summer vacation. The feedback unit can also provide feedback within reasonable limits if the child is unwell. For example, the feedback unit suggests ways for the child to relax and recover. In this way, the feedback unit can provide feedback methods that are appropriate to the child's current living situation. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input data on the child's living situation into AI, and the AI ​​can analyze that data to customize the feedback method.

[0086] The feedback unit estimates the child's emotions and determines the priority of feedback based on the estimated emotions. For example, if the child is excited, the feedback unit provides immediate feedback. For example, if the child is excited, the feedback unit provides immediate feedback after a soccer match. The feedback unit can also provide planned feedback if the child is calm. For example, if the child is calm, the feedback unit provides planned feedback after soccer practice. The feedback unit can also prioritize rest if the child is tired, and then provide feedback afterward. For example, if the child is tired, the feedback unit first encourages rest and then provides feedback. This allows the feedback unit to provide feedback priorities according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input child emotional data into an AI, which then analyzes that data to determine the priority of the feedback.

[0087] The feedback unit selects the most appropriate feedback method when providing feedback, taking into account the child's geographical location. For example, the feedback unit provides feedback that encourages participation based on event information in the child's area. For example, the feedback unit encourages the child to participate in a soccer event held in their area. The feedback unit can also provide feedback related to the curriculum of the school the child attends. For example, the feedback unit encourages the child to participate in practice with the school's soccer team. The feedback unit can also provide feedback related to places the child frequently visits. For example, the feedback unit suggests soccer practice at a park the child often visits. This allows the feedback unit to provide the most appropriate feedback method based on the child's geographical location. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not. For example, the feedback unit can input the child's geographical location data into an AI, which can then analyze the data to select the most appropriate feedback method.

[0088] The feedback unit analyzes the child's social media activity and proposes feedback methods during the feedback process. For example, the feedback unit proposes relevant feedback methods based on the activity of accounts the child follows. For example, the feedback unit proposes similar training methods based on the training methods of soccer players the child follows. The feedback unit can also propose relevant feedback methods based on the activity of groups the child participates in. For example, the feedback unit proposes similar training methods based on the training content of the soccer group the child participates in. The feedback unit can also propose relevant feedback methods based on the content of posts the child shares. For example, the feedback unit proposes methods for analyzing a soccer match based on the results of a soccer match the child shared. In this way, the feedback unit can provide feedback methods based on the child's social media activity. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input the child's social media data into an AI, which can then analyze the data and propose feedback methods.

[0089] The data collection unit estimates the child's emotions and adjusts the timing of data collection based on the estimated emotions. For example, if the child is excited, the data collection unit collects data in real time. For example, if the child is excited, the data collection unit collects data in real time during a soccer match. The data collection unit can also collect data periodically if the child is calm. For example, if the child is calm, the data collection unit collects data periodically during soccer practice. The data collection unit can also collect data after the child has rested if the child is tired. For example, if the child is tired, the data collection unit collects data after the child has rested. This allows the data collection unit to provide data collection timing that corresponds to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input children's emotional data into an AI, which can then analyze the data and adjust the timing of data collection.

[0090] The data collection unit analyzes the child's past browsing history and selects the optimal collection method. For example, the collection unit prioritizes collecting relevant data based on content the child has frequently viewed in the past. For instance, it collects relevant new soccer content based on soccer-related websites the child has frequently visited in the past. The collection unit can also collect data that the child might be interested in based on keywords the child has searched for in the past. For example, it collects relevant training data based on the keyword "soccer training methods" the child has searched for in the past. The collection unit can also collect relevant data based on online events the child has participated in in the past. For example, it collects relevant seminar data based on online soccer seminars the child has participated in in the past. This allows the collection unit to provide the optimal collection method based on the child's past browsing history. Some or all of the above processing in the collection unit may be performed using AI, for example, or not. For example, the collection unit can input the child's past browsing data into an AI, which can then analyze the data and select the optimal collection method.

[0091] The data collection unit filters the data based on the child's current living situation and areas of interest. For example, if the child is in the middle of school exams, the data collection unit prioritizes collecting learning-related data. For example, it collects learning data to help the child study for tests efficiently. The data collection unit can also prioritize collecting data on how the child utilizes their free time if the child is on summer vacation. For example, it collects data on how the child acquires new skills during summer vacation. The data collection unit can also prioritize collecting health-related data if the child is unwell. For example, it collects health data to help the child recover. This allows the data collection unit to provide data filtering tailored to the child's current living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the child's living situation data into an AI, which can then analyze and filter the data.

[0092] The data collection unit estimates the child's emotions and determines the priority of data to collect based on the estimated emotions. For example, if the child is excited, the data collection unit prioritizes data that needs to be collected immediately. For example, if the child is excited, the data collection unit prioritizes collecting data from a soccer match. The data collection unit can also prioritize data that needs to be collected systematically if the child is calm. For example, if the child is calm, the data collection unit systematically collects data from soccer practice. The data collection unit can also prioritize data that needs to be collected after rest if the child is tired. For example, if the child is tired, the data collection unit collects data from soccer practice after rest. This allows the data collection unit to provide data prioritization according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input children's emotional data into an AI, which then analyzes that data to determine the priority of the data to collect.

[0093] The data collection unit prioritizes collecting highly relevant data, taking into account the child's geographical location. For example, the unit prioritizes collecting event information in the area where the child lives. For instance, it prioritizes collecting information about soccer events held in the area where the child lives. The unit can also prioritize collecting data related to the curriculum of the school the child attends. For example, it prioritizes collecting practice data for the soccer team at the school the child attends. The unit can also prioritize collecting data related to places the child frequently visits. For example, it prioritizes collecting soccer practice data at parks the child frequently visits. This allows the data collection unit to provide highly relevant data based on the child's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the child's geographical location data into an AI, which can then analyze the data and prioritize collecting highly relevant data.

[0094] The data collection unit analyzes the child's social media activity and collects relevant data when collecting data. For example, the data collection unit collects relevant data based on the content of posts from accounts the child follows. For example, the data collection unit collects relevant soccer training data based on the content of posts from soccer players the child follows. The data collection unit can also collect relevant data based on the activities of groups the child participates in. For example, the data collection unit collects relevant training data based on the training content of the soccer group the child participates in. The data collection unit can also collect relevant data based on the content of posts the child shares. For example, the data collection unit collects relevant match analysis data based on soccer match results shared by the child. In this way, the data collection unit can provide relevant data based on the child's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the child's social media data into AI, which can then analyze the data and collect relevant data.

[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 analytics department can adjust its analysis methods to consider children's learning styles when analyzing content that interests them. For example, children with a visual learning style can have image and video content analyzed preferentially. Children with an auditory learning style can have audio content such as podcasts and audiobooks analyzed preferentially. Furthermore, children with an experiential learning style can have experiential content such as interactive games and experimental videos analyzed preferentially. This allows the analytics department to provide optimal content tailored to each child's learning style.

[0097] The Motivation Enhancement Department can estimate a child's emotions and adjust the reward system based on those estimates. For example, if a child is excited, immediate rewards can be provided to increase their motivation. For instance, praise or a badge could be given immediately after scoring a goal in a soccer match. If a child is calm, rewards for long-term goals can be set. For example, a large reward could be given upon achieving a certain period of practice. Furthermore, if a child is tired, rewards that encourage rest can be provided. For example, a goal aimed at short-term refreshment could be set, and a reward could be given for achieving it. In this way, the Motivation Enhancement Department can provide a reward system that is tailored to the child's emotions.

[0098] The feedback system can estimate a child's emotions and adjust the content of the feedback based on those estimates. For example, if a child is excited, emphasizing positive feedback can encourage further action. For instance, praising good play in a soccer match. If a child is calm, providing detailed feedback can support skill improvement. For example, offering technical advice on soccer. Furthermore, if a child is tired, providing concise and easy-to-understand feedback can help them understand. For example, conveying the main points with short comments. In this way, the feedback system can provide feedback tailored to the child's emotions.

[0099] The data collection unit can estimate a child's emotions and adjust the data collection method based on the estimated emotions. For example, if a child is excited, real-time data collection can provide immediate feedback. For instance, real-time data collection during a soccer match. If a child is calm, regular data collection can capture long-term trends. For example, regular data collection during soccer practice. Furthermore, if a child is tired, data collection can be done within reasonable limits by collecting data after rest. For example, soccer practice data can be collected after rest. In this way, the data collection unit can provide data collection methods that are tailored to the child's emotions.

[0100] The analytics department can estimate a child's emotions and adjust the content analysis method based on those estimates. For example, if a child is excited, it can prioritize analyzing more stimulating content, such as action games or sports. If a child is calm, it can prioritize analyzing content suitable for learning, such as educational videos or documentaries. Furthermore, if a child is tired, it can prioritize analyzing relaxing content, such as relaxation music or nature scenes. In this way, the analytics department can provide content analysis methods tailored to the child's emotions.

[0101] The analytics department can adjust its content analysis methods to take into account the child's learning progress. For example, if the child is a beginner-level learner, it can prioritize analyzing basic content, such as content explaining basic soccer rules and techniques. If the child is an intermediate-level learner, it can prioritize analyzing more advanced content, such as content on tactics and teamwork. Furthermore, if the child is an advanced-level learner, it can prioritize analyzing more specialized content, such as content on professional players' playing styles and training methods. This allows the analytics department to provide optimal content tailored to the child's learning progress.

[0102] The Motivation Enhancement Department can adjust its motivation enhancement methods to suit each child's learning style. For example, children with a visual learning style can be motivated by providing visual feedback. For instance, the results of soccer practice can be visualized using graphs and charts. Children with an auditory learning style can be motivated by providing audio feedback. For example, the results of practice can be explained verbally. Furthermore, children with an experiential learning style can be motivated by providing interactive feedback. For example, game-based feedback can be provided. In this way, the Motivation Enhancement Department can provide the optimal motivation enhancement method tailored to each child's learning style.

[0103] The feedback system can adjust the content of the feedback based on the child's learning progress. For example, if the child is a beginner-level learner, it can provide basic feedback, such as feedback on fundamental techniques and rules. If the child is an intermediate-level learner, it can provide more advanced feedback, such as feedback on tactics and teamwork. Furthermore, if the child is an advanced-level learner, it can provide specialized feedback, such as feedback on the playing style and training methods of professional players. This allows the feedback system to provide optimal feedback tailored to the child's learning progress.

[0104] The data collection unit can adjust its data collection methods to suit each child's learning style. For example, for children with a visual learning style, it can prioritize collecting image and video data, such as video data of soccer matches and practices. Similarly, for children with an auditory learning style, it can prioritize collecting audio data, such as commentary and analysis audio from soccer matches. Furthermore, for children with an experiential learning style, it can prioritize collecting interactive data, such as data on the use of soccer practice apps. This allows the data collection unit to provide the optimal data collection method tailored to each child's learning style.

[0105] The analytics department can analyze children's social media activity and identify relevant content. For example, it can identify relevant content based on the posts of accounts children follow. For instance, it can identify relevant soccer training videos based on posts by soccer players children follow. It can also identify relevant content based on the activities of groups children participate in. For example, it can identify relevant match highlight videos based on the activities of soccer groups children participate in. Furthermore, it can identify relevant content based on posts children share. For example, it can identify relevant match analysis videos based on soccer match results children share. This allows the analytics department to provide relevant content based on children's social media activity.

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

[0107] Step 1: The analysis department analyzes content that children are interested in. Specifically, it collects data such as websites children visit, social media posts they make, and their search history, and the AI ​​analyzes this data. For example, if a child is interested in soccer, the AI ​​analyzes soccer-related content and generates prompts to encourage the next action. Step 2: The motivation enhancement unit reinforces the child's motivation to take action spontaneously based on the generated prompts. For example, by showing the child the possibility of improving their skills by actually playing soccer or the probability of becoming a professional, the child can intuitively understand their own growth and gain the motivation to take proactive action. Step 3: The Feedback Department provides feedback based on data from the children who have taken action as a result of the Motivation Enhancement Department. For example, they analyze videos and behavioral data of the children playing soccer to visualize what kind of practice methods would improve their skills. This allows the children to concretely understand their own progress and further motivates them to take action.

[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 analysis unit, motivation enhancement unit, feedback unit, and data collection unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart device 14, which collects data such as websites viewed by the child, SNS posts, and search history, and the AI ​​analyzes it. The motivation enhancement unit is implemented by the identification processing unit 290 of the data processing unit 12, which enhances the child's motivation to take action spontaneously based on the generated prompts. The feedback unit is implemented by the control unit 46A of the smart device 14, which provides feedback based on data of the child's actual actions. The data collection unit collects data such as websites viewed by the child, SNS posts, and search history using the camera 42 and communication I / F 44 of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

[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 analysis unit, motivation enhancement unit, feedback unit, and data collection unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart glasses 214, which collects data such as websites viewed by the child, SNS posts, and search history, and the AI ​​analyzes it. The motivation enhancement unit is implemented by the identification processing unit 290 of the data processing unit 12, which enhances the child's motivation to take action spontaneously based on the generated prompts. The feedback unit is implemented by the control unit 46A of the smart glasses 214, which provides feedback based on data of the child's actual actions. The data collection unit collects data such as websites viewed by the child, SNS posts, and search history using the camera 42 and communication I / F 44 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

[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 analysis unit, motivation enhancement unit, feedback unit, and data collection unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the headset terminal 314, which collects data such as websites viewed by the child, SNS posts, and search history, and the AI ​​analyzes it. The motivation enhancement unit is implemented by the identification processing unit 290 of the data processing unit 12, which enhances the child's motivation to take action spontaneously based on the generated prompts. The feedback unit is implemented by the control unit 46A of the headset terminal 314, which provides feedback based on data of the child's actual actions. The data collection unit collects data such as websites viewed by the child, SNS posts, and search history using the camera 42 and communication I / F 44 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

[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 analysis unit, motivation enhancement unit, feedback unit, and data collection unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the robot 414, which collects data such as websites viewed by the child, SNS posts, and search history, and the AI ​​analyzes it. The motivation enhancement unit is implemented by the identification processing unit 290 of the data processing unit 12, which enhances the child's motivation to take spontaneous action based on the generated prompts. The feedback unit is implemented by the control unit 46A of the robot 414, which provides feedback based on data of the child's actual actions. The data collection unit collects data such as websites viewed by the child, SNS posts, and search history using the camera 42 and communication I / F 44 of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[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) The analytics department analyzes content that children are interested in, A motivation enhancement unit that strengthens the motivation of children to take action spontaneously based on prompts generated by the analysis unit, The system includes a feedback unit that provides feedback based on data of children who have taken action as a result of the aforementioned motivation enhancement unit. A system characterized by the following features. (Note 2) It includes a data collection unit that collects data such as websites visited by children, social media posts, and search history. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is The data collection unit analyzes the collected data to identify content that children are interested in. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned motivation enhancement unit is To encourage children to take spontaneous action based on generated prompts. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned feedback unit is By analyzing data from children's actual actions, we can visualize how their skills improve. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit is We estimate children's emotions and adjust the content analysis method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit is When conducting analysis, consider the child's past interests and concerns to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit is During the analysis, different analytical algorithms are applied depending on the child's age and grade level. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit is The system estimates the child's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit is During analysis, the system prioritizes analyzing highly relevant content by considering the child's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is During the analysis, we analyze children's social media activity and identify relevant content. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned motivation enhancement unit is We estimate the child's emotions and adjust motivation-enhancing methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned motivation enhancement unit is When strengthening motivation, refer to the child's past behavioral history to select the most appropriate reinforcement method. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned motivation enhancement unit is When strengthening motivation, customize the reinforcement methods based on the child's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned motivation enhancement unit is The system estimates the child's emotions and determines priorities for motivational enhancement based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned motivation enhancement unit is When enhancing motivation, consider the child's geographical location to select the most appropriate method of reinforcement. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned motivation enhancement unit is When strengthening motivation, we analyze children's social media activity and propose methods for reinforcement. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned feedback unit is Estimate the child's emotions and adjust the feedback method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned feedback unit is When providing feedback, refer to the child's past behavioral data to select the most appropriate feedback method. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned feedback unit is When providing feedback, customize the feedback method based on the child's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned feedback unit is The system estimates the child's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned feedback unit is When providing feedback, the most appropriate feedback method will be selected, taking into account the child's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned feedback unit is During feedback sessions, we analyze children's social media activity and suggest feedback methods. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned collection unit is We estimate the child's emotions and adjust the timing of data collection based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 25) The aforementioned collection unit is During data collection, the child's past browsing history is analyzed to select the most suitable collection method. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned collection unit is When collecting data, filtering is performed based on the child's current living situation and areas of interest. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned collection unit is We estimate the child's emotions and prioritize the data to collect based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned collection unit is When collecting data, the system prioritizes collecting highly relevant data by considering the child's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned collection unit is When collecting data, analyze children's social media activity and collect relevant data. The system described in Appendix 2, 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. The analysis department analyzes content that children are interested in, A motivation enhancement unit that strengthens the motivation of children to take action spontaneously based on prompts generated by the analysis unit, The system includes a feedback unit that provides feedback based on data of children who have taken action as a result of the aforementioned motivation enhancement unit. A system characterized by the following features.

2. It includes a data collection unit that collects data such as websites visited by children, social media posts, and search history. The system according to feature 1.

3. The aforementioned motivation enhancement unit is To encourage children to take spontaneous action based on generated prompts. The system according to feature 1.

4. The aforementioned feedback unit is By analyzing data from children's actual actions, we can visualize how their skills improve. The system according to feature 1.

5. The aforementioned analysis unit is We estimate children's emotions and adjust the content analysis method based on the estimated emotions. The system according to feature 1.

6. The aforementioned analysis unit is When conducting analysis, consider the child's past interests and concerns to improve the accuracy of the analysis. The system according to feature 1.

7. The aforementioned analysis unit is During the analysis, different analytical algorithms are applied depending on the child's age and grade level. The system according to feature 1.

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