system

Abstract

The system according to the embodiment aims to support reskilling for middle-aged and senior citizens and increase their motivation to learn. [Solution] A system according to an embodiment includes a visualization unit, a training unit, a proposal unit, a generation unit, a dialogue unit, a story generation unit, and a community unit. The visualization unit visualizes a user as a character. The training unit develops the character visualized by the visualization unit. The proposal unit proposes a study method for the genre in which the user wishes to improve their skills. The generation unit generates unique learning materials for the user based on the study method proposed by the proposal unit. The dialogue unit engages in dialogue with the user based on the learning materials generated by the generation unit. The story generation unit generates a game story or character evolution based on information obtained by the dialogue unit. The community unit creates a community that can be shared with other users.

Description

[Technical Field]

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

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

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

[0004] With existing technologies, there is room for improvement in supporting reskilling for middle-aged and senior people in terms of maintaining their motivation to learn and providing effective learning methods.

[0005] The system according to the embodiment aims to support reskilling for middle-aged and senior citizens and increase their motivation to learn. [Means for solving the problem]

[0006] The system according to the embodiment includes a visualization unit, a training unit, a proposal unit, a generation unit, a dialogue unit, a story generation unit, and a community unit. The visualization unit visualizes a user as a character. The training unit develops the character visualized by the visualization unit. The proposal unit proposes a study method for the genre in which the user wishes to improve their skills. The generation unit generates learning materials unique to the user based on the study method proposed by the proposal unit. The dialogue unit engages in dialogue with the user based on the learning materials generated by the generation unit. The story generation unit generates a game story or character evolution based on information obtained by the dialogue unit. The community unit creates a community that can be shared with other users. [Effects of the Invention]

[0007] The system according to the embodiment can support reskilling for middle-aged and senior citizens and increase their motivation to learn. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A reskilling support system according to an embodiment of the present invention visualizes a user as a character and provides an interface for developing that character. This system proposes learning methods for the genre in which the user wishes to improve their skills and introduces a function for generating user-specific learning materials. Furthermore, the system understands the user's interests and goals through dialogue with the user and generates a game story and character evolution with a role-playing game feel. Furthermore, a function is added for creating a community that can be shared with other users and for users with the same goals and interests to collaborate on quests. For example, the reskilling support system allows users to customize their own characters and develop them by acquiring skills and experience. Next, AI understands the user's interests and goals and suggests optimal learning methods based on that understanding. Furthermore, through dialogue with the user, the user clarifies their goals and interests, and the game story progresses based on that understanding. Finally, the user can acquire practical skills by working on projects with other users. This allows the reskilling support system to provide a multifunctional interface for supporting user learning and promoting growth. For example, the system allows users to customize their own characters and develop them by acquiring skills and experience. Furthermore, AI understands the user's interests and goals and suggests optimal learning methods based on that understanding. Furthermore, through dialogue with other users, players clarify their own goals and interests, and the game story progresses based on these. Finally, by working on projects together with other users, players can acquire practical skills.

[0029] A reskilling support system according to an embodiment includes a visualization unit, a training unit, a proposal unit, a generation unit, a dialogue unit, a story generation unit, and a community unit. The visualization unit visualizes a user as a character. For example, a user can customize their own character and select the type of avatar and the visualization accuracy. The visualization unit can also customize the character based on the user's occupation or hobbies. The training unit develops the character visualized by the visualization unit. For example, when a user acquires a new skill, that skill is reflected in the character, causing the character to evolve. The training unit can also analyze the user's past activity history and optimize the character's growth process. The proposal unit suggests a learning method for the genre in which the user wants to improve their skills. For example, AI can understand the user's interests and goals and suggest an optimal learning method based on that. The proposal unit can also adjust the use of suggested technical terms depending on the user's level of expertise. The generation unit generates unique learning materials for the user based on the learning methods suggested by the proposal unit. For example, AI can analyze the user's past learning history and generate optimal learning materials. The generation unit can also estimate the user's emotions and adjust the content of the generated learning materials based on the estimated user emotions. The dialogue unit engages in dialogue with the user based on the learning materials generated by the generation unit. For example, the AI ​​can clarify the user's goals and interests through dialogue with the user and customize the dialogue content based on that. The dialogue unit can also estimate the user's emotions and adjust the way the dialogue progresses based on the estimated user emotions. The story generation unit generates a game story and character evolution based on the information obtained by the dialogue unit. For example, the AI ​​can estimate the user's emotions and adjust the way the story progresses based on the estimated user emotions. The story generation unit can also analyze the user's past story history and generate an optimal story. The community unit creates a community that can be shared with other users. For example, it provides a chat function and forum for users to communicate with each other. The community unit also has a quest unit for users to collaborate on quests.As a result, the reskilling support system according to the embodiment can provide a multifunctional interface for supporting the user's learning and promoting their growth.

[0030] The suggestion unit may include an interest understanding unit that understands the user's interests. The interest understanding unit understands the user's interests. For example, the user's interests are collected using a questionnaire. The interest understanding unit can also analyze the user's behavioral history to understand the interests. For example, the interests are identified based on data on content the user has previously viewed or events the user has attended. The interest understanding unit can also estimate the user's emotions and adjust the method of understanding the interests based on the estimated user emotions. For example, if the user is relaxed, detailed interest understanding is performed. In this way, the user's interests can be understood and a more appropriate learning method can be suggested.

[0031] The generation unit may include a data protection unit that protects the user's data. The data protection unit protects the user's data. For example, the data protection unit protects the user's data using encryption technology. The data protection unit may also perform access control to prevent unauthorized access to the user's data. For example, different access rights may be set for each user, restricting the viewing and editing of data. The data protection unit may also estimate the user's emotions and adjust the data protection method based on the estimated user's emotions. For example, if the user is relaxed, a detailed data protection method may be provided. This protects the user's data and ensures privacy.

[0032] The story generation unit can generate a game story. The story generation unit generates a game story. For example, AI can grasp the user's interests and desired direction and generate a story based on that. The story generation unit can also estimate the user's emotions and adjust the way the story progresses based on the estimated user's emotions. For example, if the user is relaxed, the story progresses at a leisurely pace. The story generation unit can also analyze the user's past story history and generate an optimal story. For example, the optimal story is generated based on the user's past story history. In this way, generating a game story makes learning more enjoyable for the user.

[0033] The community unit may include a communication unit for users to communicate with each other. The communication unit provides a function for users to communicate with each other. For example, users can communicate with each other in real time using a chat function. The communication unit may also provide a forum for users to share information with each other. For example, a user may post a question or opinion, and other users may respond or comment on it. The communication unit may also estimate a user's emotions and adjust the method of communication based on the estimated user's emotions. For example, if a user is relaxed, the communication may be conducted at a leisurely pace. This allows users to communicate with each other, thereby increasing their motivation to learn.

[0034] The community unit may include a quest unit for collaboratively completing quests. The quest unit provides a function for users to collaboratively complete quests. For example, a quest for achieving a specific goal can be set, and users can cooperate to progress through the quest. The quest unit can also estimate the user's emotions and adjust the way the quest progresses based on the estimated user's emotions. For example, if the user is relaxed, the quest can progress at a leisurely pace. The quest unit can also analyze the user's past quest history and provide the most appropriate quest. For example, the quest can be provided based on the user's past quest history. This allows users to collaborate on quests and advance their learning.

[0035] The visualization unit can analyze the user's past activity history and optimally adjust the character's growth process. The visualization unit analyzes the user's past activity history and optimally adjusts the character's growth process. For example, the visualization unit reflects the character's growth based on skills the user has learned in the past. The visualization unit can also optimize the timing for improving the character's skills from the user's past activity history. The visualization unit can also analyze the user's past activity history and suggest skills necessary for the character's growth. In this way, the character's growth is optimized by analyzing the user's past activity history. The analysis of the activity history is performed, for example, using log data collection and analysis methods.

[0036] The visualization unit can provide customizable options based on the user's occupation and hobbies when visualizing the character. The visualization unit provides customizable options based on the user's occupation and hobbies when visualizing the character. For example, the character's clothing and tools can be customized according to the user's occupation. The character's background and environment can also be customized based on the user's hobbies. The character can also be equipped with items related to the user's occupation and hobbies. This provides a more personalized experience by customizing the character based on the user's occupation and hobbies. The specific content and criteria of the customizable options need to be clarified, for example, the types of options and the customization method.

[0037] The visualization unit can select an appropriate display means according to the user's input method when visualizing a character. The visualization unit selects an appropriate display means according to the user's input method (voice, text, image, etc.) when visualizing a character. For example, when a user uses voice input, the character's movements can be displayed in accordance with the voice. Also, when a user uses text input, the character's movements can be displayed in accordance with the text. Also, when a user uses image input, the character's movements can be displayed in accordance with the image. This allows for more intuitive operation by selecting the optimal display means according to the user's input method. When selecting a display means according to the input method, it is necessary to clarify the specific method and criteria, such as voice input, text input, image input, etc.

[0038] During training, the training unit can analyze the user's past learning history and provide an appropriate training plan. During training, the training unit can analyze the user's past learning history and provide an appropriate training plan. For example, the training unit can propose an optimal training plan based on the skills the user has learned in the past. It can also propose an effective training method based on the user's past learning history. It can also analyze the user's past learning history and customize the training plan. In this way, the optimal training plan can be provided by analyzing the user's past learning history. The analysis of the learning history is performed, for example, by collecting and analyzing log data.

[0039] The training unit can customize the training content based on the user's current skill level during training. The training unit customizes the training content based on the user's current skill level during training. For example, the training content is adjusted according to the user's current skill level. It can also provide training content of an appropriate level of difficulty based on the user's skill level. It can also evaluate the user's skill level and propose an optimal training plan. This allows for more effective learning by customizing the training content based on the user's current skill level. The method of skill level evaluation and customization needs to be clarified, for example, by clarifying the skill evaluation criteria and customization method.

[0040] The training department can improve the training method by reflecting user feedback during training. The training department can improve the training method by reflecting user feedback during training. For example, the training method can be adjusted based on user feedback. The training content can also be improved by reflecting user feedback. The training method can also be proposed by analyzing user feedback. In this way, by reflecting user feedback, the training method can be improved and more effective learning can be achieved. The process for collecting and improving feedback needs to be clarified, for example, the method for collecting feedback and the process for improvement.

[0041] The suggestion unit can adjust the level of detail of the suggestion based on the user's interests and desired direction when making a suggestion. The suggestion unit adjusts the level of detail of the suggestion based on the user's interests and desired direction when making a suggestion. For example, the suggestion unit makes detailed suggestions based on the user's interests. The suggestion unit can also adjust the level of detail of the suggestion based on the user's desired direction. The suggestion unit can also understand the user's interests and desired direction and make optimal suggestions. By adjusting the level of detail of the suggestion based on the user's interests and desired direction, more appropriate suggestions can be made. The interests and desired direction can be understood, for example, by using a questionnaire or an analysis of behavioral history.

[0042] The suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. ... For example, the suggestion unit improves the accuracy of the suggestion based on the user's past suggestion results. The suggestion unit can also analyze the user's past suggestion results and make an optimal suggestion. The suggestion content can also be improved by referring to the user's past suggestion results. In this way, the suggestion accuracy is improved by referring to the user's past suggestion results. The process for evaluating the suggestion results and improving the accuracy needs to be clarified, for example, the method for evaluating the suggestion results and the process for improving the accuracy.

[0043] The suggestion unit can adjust the use of technical terms in the proposal according to the user's level of expertise when making a proposal. The suggestion unit adjusts the use of technical terms in the proposal according to the user's level of expertise when making a proposal. For example, technical terms are used according to the user's level of expertise. The suggestion unit can also adjust the content of the proposal based on the user's level of expertise. The suggestion unit can also evaluate the user's level of expertise and make optimal suggestions. In this way, by adjusting the use of technical terms in the proposal according to the user's level of expertise, it is possible to make suggestions that are easier to understand. The method for evaluating the level of expertise and selecting terms needs to be clarified, for example, by clarifying the evaluation criteria for expertise and the method for selecting terms.

[0044] The generation unit can analyze the user's past learning history at the time of generation and generate appropriate teaching materials. The generation unit analyzes the user's past learning history at the time of generation and generate appropriate teaching materials. For example, optimal teaching materials are generated based on the user's past learning history. It is also possible to analyze the user's past learning history and generate effective teaching materials. It is also possible to customize the content of the teaching materials by referring to the user's past learning history. In this way, optimal teaching materials are generated by analyzing the user's past learning history. The analysis of the learning history is performed, for example, by collecting and analyzing log data.

[0045] The generation unit can customize the learning material content based on the user's current skill level at the time of generation. The generation unit customizes the learning material content based on the user's current skill level at the time of generation. For example, the learning material content is adjusted according to the user's current skill level. Learning material with an appropriate level of difficulty can also be generated based on the user's skill level. It is also possible to evaluate the user's skill level and generate optimal learning material. This enables more effective learning by customizing the learning material content based on the user's current skill level. The method of skill level evaluation and customization needs to be clarified, for example, by clarifying the skill evaluation criteria and customization method.

[0046] The generation unit can improve the content of the learning material by reflecting user feedback at the time of generation. The generation unit improves the content of the learning material by reflecting user feedback at the time of generation. For example, the content of the learning material can be adjusted based on user feedback. The content of the learning material can also be improved by reflecting user feedback. The generation unit can also analyze user feedback and generate optimal learning material. In this way, the content of the learning material can be improved by reflecting user feedback, enabling more effective learning. The process of collecting feedback and improving the learning material needs to be clarified, for example, the method of collecting feedback and the process of improvement.

[0047] The dialogue unit can analyze the user's past dialogue history during dialogue and provide optimal dialogue content. The dialogue unit can analyze the user's past dialogue history during dialogue and provide optimal dialogue content. For example, optimal dialogue content is provided based on the user's past dialogue history. The dialogue unit can also analyze the user's past dialogue history and provide effective dialogue content. The dialogue content can also be customized with reference to the user's past dialogue history. In this way, optimal dialogue content is provided by analyzing the user's past dialogue history. The dialogue history is analyzed, for example, using a method of collecting and analyzing log data.

[0048] The dialogue unit can customize dialogue content based on the user's current interests during dialogue. The dialogue unit customizes dialogue content based on the user's current interests during dialogue. For example, the dialogue content is adjusted according to the user's current interests. Appropriate dialogue content can also be provided based on the user's interests. The user's interests can also be evaluated and optimal dialogue content can be provided. This enables more appropriate dialogue by customizing dialogue content based on the user's current interests. Interest evaluation and customization can be performed using, for example, a questionnaire or an analysis of behavioral history.

[0049] The dialogue unit can improve the dialogue method by reflecting user feedback during the dialogue. The dialogue unit can improve the dialogue method by reflecting user feedback during the dialogue. For example, the dialogue method can be adjusted based on user feedback. The dialogue content can also be improved by reflecting user feedback. The dialogue unit can also analyze user feedback and propose an optimal dialogue method. In this way, the dialogue method can be improved by reflecting user feedback, enabling more effective dialogue. The process for collecting and improving feedback needs to be clarified, for example, the feedback collection method and the improvement process.

[0050] The story generation unit can analyze the user's past story history and generate an appropriate story when generating a story. The story generation unit analyzes the user's past story history and generates an appropriate story when generating a story. For example, the unit generates an optimal story based on the user's past story history. It can also analyze the user's past story history and generate an effective story. It can also customize the content of the story by referring to the user's past story history. In this way, the optimal story is generated by analyzing the user's past story history. The story history is analyzed, for example, using a method of collecting and analyzing log data.

[0051] The story generation unit can customize the story content based on the user's current interests when generating a story. The story generation unit customizes the story content based on the user's current interests when generating a story. For example, the story content is adjusted according to the user's current interests. Appropriate story content can also be provided based on the user's interests. The user's interests can also be evaluated and optimal story content can be provided. In this way, by customizing the story content based on the user's current interests, a more appropriate story can be provided. Methods for evaluating and customizing interests include, for example, using a questionnaire or an analysis of behavioral history.

[0052] The story generation unit can improve the story content by reflecting user feedback when generating a story. The story generation unit improves the story content by reflecting user feedback when generating a story. For example, the story content can be adjusted based on user feedback. The story content can also be improved by reflecting user feedback. The user feedback can also be analyzed to provide optimal story content. In this way, the story content can be improved by reflecting user feedback, and a more effective story can be provided. The process of collecting feedback and improving it needs to be clarified, for example, the feedback collection method and the improvement process.

[0053] The community unit can analyze the user's past community activity history when creating a community and provide the optimal community. The community unit can analyze the user's past community activity history when creating a community and provide the optimal community. For example, the optimal community is provided based on the user's past community activity history. The unit can also analyze the user's past community activity history to provide an effective community. The unit can also customize the content of the community by referring to the user's past community activity history. In this way, the optimal community is provided by analyzing the user's past community activity history. The analysis of the community activity history is performed, for example, using a method of collecting and analyzing log data.

[0054] The community unit can customize community content based on the user's current interests when creating a community. The community unit customizes community content based on the user's current interests when creating a community. For example, the community content is adjusted according to the user's current interests. Appropriate community content can also be provided based on the user's interests. The user's interests can also be evaluated and optimal community content can be provided. In this way, a more appropriate community can be provided by customizing community content based on the user's current interests. Methods for evaluating and customizing interests include, for example, using a survey or an analysis of behavioral history.

[0055] The community unit can improve the community content by reflecting user feedback when creating a community. The community unit can improve the community content by reflecting user feedback when creating a community. For example, the community content can be adjusted based on user feedback. The community content can also be improved by reflecting user feedback. The community content can also be analyzed and optimal community content can be provided. In this way, the community content can be improved by reflecting user feedback, and a more effective community can be provided. The process for collecting and improving feedback needs to be clarified, for example, the method for collecting feedback and the improvement process.

[0056] The interest grasping unit can analyze the user's past interest history when grasping an interest and provide the optimal interest grasping method. The interest grasping unit can analyze the user's past interest history when grasping an interest and provide the optimal interest grasping method. For example, the optimal interest grasping method is provided based on the user's past interest history. It is also possible to analyze the user's past interest history and provide an effective interest grasping method. It is also possible to customize the interest grasping method by referring to the user's past interest history. In this way, the optimal interest grasping method is provided by analyzing the user's past interest history. Analysis of the interest history is performed, for example, using log data collection and analysis methods.

[0057] The interest grasping unit can customize the content of interests based on the user's current interests when grasping the user's interests. The interest grasping unit customizes the content of interests based on the user's current interests when grasping the user's interests. For example, the content of interests is adjusted according to the user's current interests. Appropriate content of interests can also be provided based on the user's interests. The user's interests can also be evaluated and optimal content of interests can be provided. This makes it possible to grasp the user's interests more appropriately by customizing the content of interests based on the user's current interests. Methods for evaluating and customizing the interests are performed using, for example, a questionnaire or an analysis of behavioral history.

[0058] The interest identification unit can improve the interest identification method by reflecting user feedback when identifying interests. The interest identification unit improves the interest identification method by reflecting user feedback when identifying interests. For example, the interest identification method can be adjusted based on user feedback. The interest content can also be improved by reflecting user feedback. The user feedback can also be analyzed to provide an optimal interest identification method. In this way, by reflecting user feedback, the interest identification method can be improved and more effective interest identification becomes possible. The process of collecting and improving feedback needs to be clarified, for example, the feedback collection method and the improvement process.

[0059] The data protection unit can analyze the user's past data protection history and provide the optimal data protection method when protecting data. The data protection unit can analyze the user's past data protection history and provide the optimal data protection method when protecting data. For example, the optimal data protection method is provided based on the user's past data protection history. The data protection unit can also analyze the user's past data protection history and provide an effective data protection method. The data protection method can also be customized by referring to the user's past data protection history. In this way, the optimal data protection method is provided by analyzing the user's past data protection history. The analysis of the data protection history is performed, for example, using log data collection and analysis methods.

[0060] The data protection unit can customize the protection content based on the user's current data protection needs when protecting data. The data protection unit customizes the protection content based on the user's current data protection needs when protecting data. For example, the protection content is adjusted according to the user's current data protection needs. The data protection unit can also provide appropriate protection content based on the user's data protection needs. The data protection unit can also evaluate the user's data protection needs and provide optimal protection content. This enables more appropriate data protection by customizing the protection content based on the user's current data protection needs. It is necessary to clarify the methods for evaluating and customizing data protection needs, for example, the methods for evaluating needs and the methods for customizing.

[0061] The data protection unit can improve the data protection method by reflecting user feedback during data protection. The data protection unit can improve the data protection method by reflecting user feedback during data protection. For example, the data protection method can be adjusted based on user feedback. The data protection content can also be improved by reflecting user feedback. The data protection unit can also analyze user feedback and provide an optimal data protection method. In this way, by reflecting user feedback, the data protection method can be improved and more effective data protection can be achieved. The process for collecting and improving feedback needs to be clarified, for example, the feedback collection method and the improvement process.

[0062] The communication unit can analyze the user's past communication history during communication and provide the optimal communication method. The communication unit can analyze the user's past communication history during communication and provide the optimal communication method. For example, the optimal communication method is provided based on the user's past communication history. The communication unit can also analyze the user's past communication history and provide an effective communication method. The communication method can also be customized by referring to the user's past communication history. In this way, the optimal communication method is provided by analyzing the user's past communication history. Analysis of the communication history is performed, for example, using log data collection and analysis methods.

[0063] The communication unit can customize the communication content based on the user's current interests during communication. The communication unit customizes the communication content based on the user's current interests during communication. For example, the communication content is adjusted according to the user's current interests. Appropriate communication content can also be provided based on the user's interests. The user's interests can also be evaluated and optimal communication content can be provided. This enables more appropriate communication by customizing the communication content based on the user's current interests. Methods for evaluating and customizing interests include, for example, using a questionnaire or an analysis of behavioral history.

[0064] The communication unit can improve the communication method by reflecting user feedback during communication. The communication unit can improve the communication method by reflecting user feedback during communication. For example, the communication method can be adjusted based on user feedback. The content of communication can also be improved by reflecting user feedback. The communication unit can also analyze user feedback and provide the optimal communication method. In this way, by reflecting user feedback, the communication method can be improved and more effective communication can be achieved. The process for collecting and improving feedback needs to be clarified, for example, the feedback collection method and the improvement process.

[0065] The quest unit can analyze the user's past quest history as the quest progresses and provide the most appropriate quest. The quest unit can analyze the user's past quest history as the quest progresses and provide the most appropriate quest. For example, the quest unit can provide the most appropriate quest based on the user's past quest history. The quest unit can also analyze the user's past quest history and provide an effective quest. The quest content can also be customized by referring to the user's past quest history. In this way, the most appropriate quest can be provided by analyzing the user's past quest history. The quest history is analyzed, for example, using log data collection and analysis methods.

[0066] The quest unit can customize the quest content based on the user's current interests as the quest progresses. The quest unit customizes the quest content based on the user's current interests as the quest progresses. For example, the quest content is adjusted according to the user's current interests. Appropriate quest content can also be provided based on the user's interests. The user's interests can also be evaluated and optimal quest content can be provided. In this way, by customizing the quest content based on the user's current interests, a more appropriate quest can be provided. Methods for evaluating and customizing interests include, for example, using a questionnaire or an analysis of behavioral history.

[0067] The quest unit can improve the quest content by reflecting user feedback as the quest progresses. The quest unit can improve the quest content by reflecting user feedback as the quest progresses. For example, the quest content can be adjusted based on user feedback. The quest content can also be improved by reflecting user feedback. The quest unit can also analyze user feedback and provide optimal quest content. In this way, the quest content can be improved by reflecting user feedback, and a more effective quest can be provided. The process for collecting feedback and improving it needs to be clarified, for example, the method for collecting feedback and the improvement process.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The reskilling support system can further include a data analysis unit that analyzes the user's learning data. The data analysis unit collects the user's learning data and analyzes the effectiveness of the learning. For example, it analyzes data on study time and acquired skills to evaluate the effectiveness of the learning. The data analysis unit can also analyze the user's learning patterns and suggest optimal learning methods. This allows the user to proceed with effective learning based on data.

[0070] The reskilling support system can further include a goal setting unit that sets learning goals for the user. The goal setting unit sets learning goals for the user and manages the degree of achievement. For example, it sets short-term and long-term goals and visualizes the degree of achievement. The goal setting unit can also adjust goals according to the user's learning progress. This allows the user to proceed with their learning with a clear goal in mind.

[0071] The reskilling support system can further include a schedule management unit that manages the user's study schedule. The schedule management unit manages the user's study schedule and supports efficient study. For example, it can incorporate study time and break times into the schedule and set reminders. The schedule management unit can also adjust the schedule according to the user's study progress. This allows the user to study in a planned manner.

[0072] The reskilling support system can further include a resource management unit that manages the user's learning resources. The resource management unit manages the user's learning resources and provides the most appropriate resources. For example, it manages teaching materials and reference materials and provides the necessary resources. The resource management unit can also update resources according to the user's learning progress. This allows the user to efficiently use the necessary resources to advance their learning.

[0073] The reskilling support system can further include a community support unit that supports users' learning communities. The community support unit supports communication between users and increases their motivation to learn. For example, it provides forums and chat functions that allow users to share information with each other. The community support unit can also suggest community activities based on the user's learning progress. This allows users to cooperate with other users and advance their learning.

[0074] The processing flow of the first embodiment will be briefly explained below.

[0075] Step 1: The visualization unit visualizes the user as a character. For example, the user can customize their own character and select the type of avatar and visualization accuracy. The visualization unit can also customize the character based on the user's occupation and hobbies. Step 2: The training unit trains the character visualized by the visualization unit. For example, when a user acquires a new skill, that skill is reflected in the character, and the character evolves. The training unit can also analyze the user's past activity history and optimize the character's growth process. Step 3: The suggestion unit suggests learning methods for the subject in which the user wants to improve their skills. For example, the AI ​​can understand the user's interests and goals and suggest optimal learning methods based on those. The suggestion unit can also adjust the use of technical terminology in its suggestions based on the user's level of expertise. Step 4: The generator generates learning materials unique to the user based on the learning methods proposed by the suggester. For example, AI analyzes the user's past learning history and generates optimal learning materials. The generator can also estimate the user's emotions and adjust the content of the generated learning materials based on the estimated user emotions. Step 5: The dialogue unit engages in a dialogue with the user based on the learning materials generated by the generation unit. For example, the AI ​​can clarify the user's goals and interests through dialogue with the user and customize the dialogue content based on that. The dialogue unit can also estimate the user's emotions and adjust the way the dialogue proceeds based on the estimated user emotions. Step 6: The story generation unit generates the game story and character evolution based on the information obtained by the dialogue unit. For example, the AI ​​estimates the user's emotions and adjusts the story progression based on the estimated user emotions. The story generation unit can also analyze the user's past story history to generate the optimal story. Step 7: The community section creates a community that can be shared with other users. For example, it provides a chat function and forum for users to communicate with each other. The community section also includes a quest section where users can collaborate on quests.

[0076] (Example 2) A reskilling support system according to an embodiment of the present invention visualizes a user as a character and provides an interface for developing that character. This system proposes learning methods for the genre in which the user wishes to improve their skills and introduces a function for generating user-specific learning materials. Furthermore, the system understands the user's interests and goals through dialogue with the user and generates a game story and character evolution with a role-playing game feel. Furthermore, a function is added for creating a community that can be shared with other users and for users with the same goals and interests to collaborate on quests. For example, the reskilling support system allows users to customize their own characters and develop them by acquiring skills and experience. Next, AI understands the user's interests and goals and suggests optimal learning methods based on that understanding. Furthermore, through dialogue with the user, the user clarifies their goals and interests, and the game story progresses based on that understanding. Finally, the user can acquire practical skills by working on projects with other users. This allows the reskilling support system to provide a multifunctional interface for supporting user learning and promoting growth. For example, the system allows users to customize their own characters and develop them by acquiring skills and experience. Furthermore, AI understands the user's interests and goals and suggests optimal learning methods based on that understanding. Furthermore, through dialogue with other users, players clarify their own goals and interests, and the game story progresses based on these. Finally, by working on projects together with other users, players can acquire practical skills.

[0077] A reskilling support system according to an embodiment includes a visualization unit, a training unit, a proposal unit, a generation unit, a dialogue unit, a story generation unit, and a community unit. The visualization unit visualizes a user as a character. For example, a user can customize their own character and select the type of avatar and the visualization accuracy. The visualization unit can also customize the character based on the user's occupation or hobbies. The training unit develops the character visualized by the visualization unit. For example, when a user acquires a new skill, that skill is reflected in the character, causing the character to evolve. The training unit can also analyze the user's past activity history and optimize the character's growth process. The proposal unit suggests a learning method for the genre in which the user wants to improve their skills. For example, AI can understand the user's interests and goals and suggest an optimal learning method based on that. The proposal unit can also adjust the use of suggested technical terms depending on the user's level of expertise. The generation unit generates unique learning materials for the user based on the learning methods suggested by the proposal unit. For example, AI can analyze the user's past learning history and generate optimal learning materials. The generation unit can also estimate the user's emotions and adjust the content of the generated learning materials based on the estimated user emotions. The dialogue unit engages in dialogue with the user based on the learning materials generated by the generation unit. For example, the AI ​​can clarify the user's goals and interests through dialogue with the user and customize the dialogue content based on that. The dialogue unit can also estimate the user's emotions and adjust the way the dialogue progresses based on the estimated user emotions. The story generation unit generates a game story and character evolution based on the information obtained by the dialogue unit. For example, the AI ​​can estimate the user's emotions and adjust the way the story progresses based on the estimated user emotions. The story generation unit can also analyze the user's past story history and generate an optimal story. The community unit creates a community that can be shared with other users. For example, it provides a chat function and forum for users to communicate with each other. The community unit also has a quest unit for users to collaborate on quests.As a result, the reskilling support system according to the embodiment can provide a multifunctional interface for supporting the user's learning and promoting their growth.

[0078] The suggestion unit may include an interest understanding unit that understands the user's interests. The interest understanding unit understands the user's interests. For example, the user's interests are collected using a questionnaire. The interest understanding unit can also analyze the user's behavioral history to understand the interests. For example, the interests are identified based on data on content the user has previously viewed or events the user has attended. The interest understanding unit can also estimate the user's emotions and adjust the method of understanding the interests based on the estimated user emotions. For example, if the user is relaxed, detailed interest understanding is performed. In this way, the user's interests can be understood and a more appropriate learning method can be suggested.

[0079] The generation unit may include a data protection unit that protects the user's data. The data protection unit protects the user's data. For example, the data protection unit protects the user's data using encryption technology. The data protection unit may also perform access control to prevent unauthorized access to the user's data. For example, different access rights may be set for each user, restricting the viewing and editing of data. The data protection unit may also estimate the user's emotions and adjust the data protection method based on the estimated user's emotions. For example, if the user is relaxed, a detailed data protection method may be provided. This protects the user's data and ensures privacy.

[0080] The story generation unit can generate a game story. The story generation unit generates a game story. For example, AI can grasp the user's interests and desired direction and generate a story based on that. The story generation unit can also estimate the user's emotions and adjust the way the story progresses based on the estimated user's emotions. For example, if the user is relaxed, the story progresses at a leisurely pace. The story generation unit can also analyze the user's past story history and generate an optimal story. For example, the optimal story is generated based on the user's past story history. In this way, generating a game story makes learning more enjoyable for the user.

[0081] The community unit may include a communication unit for users to communicate with each other. The communication unit provides a function for users to communicate with each other. For example, users can communicate with each other in real time using a chat function. The communication unit may also provide a forum for users to share information with each other. For example, a user may post a question or opinion, and other users may respond or comment on it. The communication unit may also estimate a user's emotions and adjust the method of communication based on the estimated user's emotions. For example, if a user is relaxed, the communication may be conducted at a leisurely pace. This allows users to communicate with each other, thereby increasing their motivation to learn.

[0082] The community unit may include a quest unit for collaboratively completing quests. The quest unit provides a function for users to collaboratively complete quests. For example, a quest for achieving a specific goal can be set, and users can cooperate to progress through the quest. The quest unit can also estimate the user's emotions and adjust the way the quest progresses based on the estimated user's emotions. For example, if the user is relaxed, the quest can progress at a leisurely pace. The quest unit can also analyze the user's past quest history and provide the most appropriate quest. For example, the quest can be provided based on the user's past quest history. This allows users to collaborate on quests and advance their learning.

[0083] The visualization unit can estimate the user's emotions and adjust the character's facial expressions and movements based on the estimated user emotions. The visualization unit estimates the user's emotions and adjusts the character's facial expressions and movements based on the estimated user emotions. For example, if the user is happy, the visualization unit adjusts the character's facial expressions and movements to be smiling. If the user is sad, the visualization unit can adjust the character's facial expressions and movements to be depressed. If the user is excited, the visualization unit can adjust the character's movements to be more active. This provides a more realistic experience by adjusting the character's facial expressions and movements according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0084] The visualization unit can analyze the user's past activity history and optimally adjust the character's growth process. The visualization unit analyzes the user's past activity history and optimally adjusts the character's growth process. For example, the visualization unit reflects the character's growth based on skills the user has learned in the past. The visualization unit can also optimize the timing for improving the character's skills from the user's past activity history. The visualization unit can also analyze the user's past activity history and suggest skills necessary for the character's growth. In this way, the character's growth is optimized by analyzing the user's past activity history. The analysis of the activity history is performed, for example, using log data collection and analysis methods.

[0085] The visualization unit can provide customizable options based on the user's occupation and hobbies when visualizing the character. The visualization unit provides customizable options based on the user's occupation and hobbies when visualizing the character. For example, the character's clothing and tools can be customized according to the user's occupation. The character's background and environment can also be customized based on the user's hobbies. The character can also be equipped with items related to the user's occupation and hobbies. This provides a more personalized experience by customizing the character based on the user's occupation and hobbies. The specific content and criteria of the customizable options need to be clarified, for example, the types of options and the customization method.

[0086] The visualization unit can select an appropriate display means according to the user's input method when visualizing a character. The visualization unit selects an appropriate display means according to the user's input method (voice, text, image, etc.) when visualizing a character. For example, when a user uses voice input, the character's movements can be displayed in accordance with the voice. Also, when a user uses text input, the character's movements can be displayed in accordance with the text. Also, when a user uses image input, the character's movements can be displayed in accordance with the image. This allows for more intuitive operation by selecting the optimal display means according to the user's input method. When selecting a display means according to the input method, it is necessary to clarify the specific method and criteria, such as voice input, text input, image input, etc.

[0087] The training unit can estimate the user's emotions and adjust the training progress speed based on the estimated user emotions. The training unit can estimate the user's emotions and adjust the training progress speed based on the estimated user emotions. For example, if the user is relaxed, the training progress speed can be slowed down. Also, if the user is in a hurry, the training progress speed can be increased. Also, if the user is excited, the training progress speed can be adjusted to an appropriate pace. In this way, by adjusting the training progress speed according to the user's emotions, learning can proceed at a more appropriate pace. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0088] During training, the training unit can analyze the user's past learning history and provide an appropriate training plan. During training, the training unit can analyze the user's past learning history and provide an appropriate training plan. For example, the training unit can propose an optimal training plan based on the skills the user has learned in the past. It can also propose an effective training method based on the user's past learning history. It can also analyze the user's past learning history and customize the training plan. In this way, the optimal training plan can be provided by analyzing the user's past learning history. The analysis of the learning history is performed, for example, by collecting and analyzing log data.

[0089] The training unit can customize the training content based on the user's current skill level during training. The training unit customizes the training content based on the user's current skill level during training. For example, the training content is adjusted according to the user's current skill level. It can also provide training content of an appropriate level of difficulty based on the user's skill level. It can also evaluate the user's skill level and propose an optimal training plan. This allows for more effective learning by customizing the training content based on the user's current skill level. The method of skill level evaluation and customization needs to be clarified, for example, by clarifying the skill evaluation criteria and customization method.

[0090] The training department can improve the training method by reflecting user feedback during training. The training department can improve the training method by reflecting user feedback during training. For example, the training method can be adjusted based on user feedback. The training content can also be improved by reflecting user feedback. The training method can also be proposed by analyzing user feedback. In this way, by reflecting user feedback, the training method can be improved and more effective learning can be achieved. The process for collecting and improving feedback needs to be clarified, for example, the method for collecting feedback and the process for improvement.

[0091] The suggestion unit can estimate the user's emotion and adjust the way the suggestion is expressed based on the estimated user's emotion. The suggestion unit can estimate the user's emotion and adjust the way the suggestion is expressed based on the estimated user's emotion. For example, if the user is relaxed, a detailed suggestion can be made. If the user is in a hurry, a concise suggestion can be made. If the user is excited, a visually stimulating suggestion can be made. This allows for more appropriate suggestions to be made by adjusting the way the suggestion is expressed according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0092] The suggestion unit can adjust the level of detail of the suggestion based on the user's interests and desired direction when making a suggestion. The suggestion unit adjusts the level of detail of the suggestion based on the user's interests and desired direction when making a suggestion. For example, the suggestion unit makes detailed suggestions based on the user's interests. The suggestion unit can also adjust the level of detail of the suggestion based on the user's desired direction. The suggestion unit can also understand the user's interests and desired direction and make optimal suggestions. By adjusting the level of detail of the suggestion based on the user's interests and desired direction, more appropriate suggestions can be made. The interests and desired direction can be understood, for example, by using a questionnaire or an analysis of behavioral history.

[0093] The suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. ... For example, the suggestion unit improves the accuracy of the suggestion based on the user's past suggestion results. The suggestion unit can also analyze the user's past suggestion results and make an optimal suggestion. The suggestion content can also be improved by referring to the user's past suggestion results. In this way, the suggestion accuracy is improved by referring to the user's past suggestion results. The process for evaluating the suggestion results and improving the accuracy needs to be clarified, for example, the method for evaluating the suggestion results and the process for improving the accuracy.

[0094] The suggestion unit can adjust the use of technical terms in the proposal according to the user's level of expertise when making a proposal. The suggestion unit adjusts the use of technical terms in the proposal according to the user's level of expertise when making a proposal. For example, technical terms are used according to the user's level of expertise. The suggestion unit can also adjust the content of the proposal based on the user's level of expertise. The suggestion unit can also evaluate the user's level of expertise and make optimal suggestions. In this way, by adjusting the use of technical terms in the proposal according to the user's level of expertise, it is possible to make suggestions that are easier to understand. The method for evaluating the level of expertise and selecting terms needs to be clarified, for example, by clarifying the evaluation criteria for expertise and the method for selecting terms.

[0095] The generation unit can estimate the user's emotions and adjust the content of the generated learning materials based on the estimated user emotions. The generation unit can estimate the user's emotions and adjust the content of the generated learning materials based on the estimated user emotions. For example, if the user is relaxed, detailed learning materials can be generated. If the user is in a hurry, concise learning materials can be generated. If the user is excited, visually stimulating learning materials can be generated. This allows for more effective learning by adjusting the content of the learning materials according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0096] The generation unit can analyze the user's past learning history at the time of generation and generate appropriate teaching materials. The generation unit analyzes the user's past learning history at the time of generation and generate appropriate teaching materials. For example, optimal teaching materials are generated based on the user's past learning history. It is also possible to analyze the user's past learning history and generate effective teaching materials. It is also possible to customize the content of the teaching materials by referring to the user's past learning history. In this way, optimal teaching materials are generated by analyzing the user's past learning history. The analysis of the learning history is performed, for example, by collecting and analyzing log data.

[0097] The generation unit can customize the learning material content based on the user's current skill level at the time of generation. The generation unit customizes the learning material content based on the user's current skill level at the time of generation. For example, the learning material content is adjusted according to the user's current skill level. Learning material with an appropriate level of difficulty can also be generated based on the user's skill level. It is also possible to evaluate the user's skill level and generate optimal learning material. This enables more effective learning by customizing the learning material content based on the user's current skill level. The method of skill level evaluation and customization needs to be clarified, for example, by clarifying the skill evaluation criteria and customization method.

[0098] The generation unit can improve the content of the learning material by reflecting user feedback at the time of generation. The generation unit improves the content of the learning material by reflecting user feedback at the time of generation. For example, the content of the learning material can be adjusted based on user feedback. The content of the learning material can also be improved by reflecting user feedback. The generation unit can also analyze user feedback and generate optimal learning material. In this way, the content of the learning material can be improved by reflecting user feedback, enabling more effective learning. The process of collecting feedback and improving the learning material needs to be clarified, for example, the method of collecting feedback and the process of improvement.

[0099] The dialogue unit can estimate the user's emotions and adjust the way the dialogue proceeds based on the estimated user emotions. The dialogue unit can estimate the user's emotions and adjust the way the dialogue proceeds based on the estimated user emotions. For example, if the user is relaxed, the dialogue can proceed at a leisurely pace. If the user is in a hurry, the dialogue can proceed quickly. If the user is excited, the dialogue can proceed in a visually stimulating manner. This allows for a more appropriate dialogue by adjusting the way the dialogue proceeds according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0100] The dialogue unit can analyze the user's past dialogue history during dialogue and provide optimal dialogue content. The dialogue unit can analyze the user's past dialogue history during dialogue and provide optimal dialogue content. For example, optimal dialogue content is provided based on the user's past dialogue history. The dialogue unit can also analyze the user's past dialogue history and provide effective dialogue content. The dialogue content can also be customized with reference to the user's past dialogue history. In this way, optimal dialogue content is provided by analyzing the user's past dialogue history. The dialogue history is analyzed, for example, using a method of collecting and analyzing log data.

[0101] The dialogue unit can customize dialogue content based on the user's current interests during dialogue. The dialogue unit customizes dialogue content based on the user's current interests during dialogue. For example, the dialogue content is adjusted according to the user's current interests. Appropriate dialogue content can also be provided based on the user's interests. The user's interests can also be evaluated and optimal dialogue content can be provided. This enables more appropriate dialogue by customizing dialogue content based on the user's current interests. Interest evaluation and customization can be performed using, for example, a questionnaire or an analysis of behavioral history.

[0102] The dialogue unit can improve the dialogue method by reflecting user feedback during the dialogue. The dialogue unit can improve the dialogue method by reflecting user feedback during the dialogue. For example, the dialogue method can be adjusted based on user feedback. The dialogue content can also be improved by reflecting user feedback. The dialogue unit can also analyze user feedback and propose an optimal dialogue method. In this way, the dialogue method can be improved by reflecting user feedback, enabling more effective dialogue. The process for collecting and improving feedback needs to be clarified, for example, the feedback collection method and the improvement process.

[0103] The story generation unit can estimate the user's emotions and adjust the way the story progresses based on the estimated user emotions. The story generation unit estimates the user's emotions and adjusts the way the story progresses based on the estimated user emotions. For example, if the user is relaxed, the story can progress at a leisurely pace. If the user is in a hurry, the story can progress quickly. If the user is excited, the story can progress in a visually stimulating manner. In this way, by adjusting the way the story progresses according to the user's emotions, a more appropriate story can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0104] The story generation unit can analyze the user's past story history and generate an appropriate story when generating a story. The story generation unit analyzes the user's past story history and generates an appropriate story when generating a story. For example, the unit generates an optimal story based on the user's past story history. It can also analyze the user's past story history and generate an effective story. It can also customize the content of the story by referring to the user's past story history. In this way, the optimal story is generated by analyzing the user's past story history. The story history is analyzed, for example, using a method of collecting and analyzing log data.

[0105] The story generation unit can customize the story content based on the user's current interests when generating a story. The story generation unit customizes the story content based on the user's current interests when generating a story. For example, the story content is adjusted according to the user's current interests. Appropriate story content can also be provided based on the user's interests. The user's interests can also be evaluated and optimal story content can be provided. In this way, by customizing the story content based on the user's current interests, a more appropriate story can be provided. Methods for evaluating and customizing interests include, for example, using a questionnaire or an analysis of behavioral history.

[0106] The story generation unit can improve the story content by reflecting user feedback when generating a story. The story generation unit improves the story content by reflecting user feedback when generating a story. For example, the story content can be adjusted based on user feedback. The story content can also be improved by reflecting user feedback. The user feedback can also be analyzed to provide optimal story content. In this way, the story content can be improved by reflecting user feedback, and a more effective story can be provided. The process of collecting feedback and improving it needs to be clarified, for example, the feedback collection method and the improvement process.

[0107] The community unit can estimate the user's emotions and adjust the display method of the community based on the estimated user's emotions. The community unit can estimate the user's emotions and adjust the display method of the community based on the estimated user's emotions. For example, if the user is relaxed, the community can be displayed at a leisurely pace. If the user is in a hurry, the community can be displayed quickly. If the user is excited, a visually stimulating community can be displayed. In this way, by adjusting the display method of the community according to the user's emotions, a more appropriate community can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0108] The community unit can analyze the user's past community activity history when creating a community and provide the optimal community. The community unit can analyze the user's past community activity history when creating a community and provide the optimal community. For example, the optimal community is provided based on the user's past community activity history. The unit can also analyze the user's past community activity history to provide an effective community. The unit can also customize the content of the community by referring to the user's past community activity history. In this way, the optimal community is provided by analyzing the user's past community activity history. The analysis of the community activity history is performed, for example, using a method of collecting and analyzing log data.

[0109] The community unit can customize community content based on the user's current interests when creating a community. The community unit customizes community content based on the user's current interests when creating a community. For example, the community content is adjusted according to the user's current interests. Appropriate community content can also be provided based on the user's interests. The user's interests can also be evaluated and optimal community content can be provided. In this way, a more appropriate community can be provided by customizing community content based on the user's current interests. Methods for evaluating and customizing interests include, for example, using a survey or an analysis of behavioral history.

[0110] The community unit can improve the community content by reflecting user feedback when creating a community. The community unit can improve the community content by reflecting user feedback when creating a community. For example, the community content can be adjusted based on user feedback. The community content can also be improved by reflecting user feedback. The community content can also be analyzed and optimal community content can be provided. In this way, the community content can be improved by reflecting user feedback, and a more effective community can be provided. The process for collecting and improving feedback needs to be clarified, for example, the method for collecting feedback and the improvement process.

[0111] The interest grasping unit can estimate the user's emotion and adjust the interest grasping method based on the estimated user emotion. The interest grasping unit can estimate the user's emotion and adjust the interest grasping method based on the estimated user emotion. For example, if the user is relaxed, detailed interest grasping can be performed. If the user is in a hurry, brief interest grasping can be performed. If the user is excited, visually stimulating interest grasping can be performed. This allows for more appropriate interest grasping by adjusting the interest grasping method according to the user's emotion. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0112] The interest grasping unit can analyze the user's past interest history when grasping an interest and provide the optimal interest grasping method. The interest grasping unit can analyze the user's past interest history when grasping an interest and provide the optimal interest grasping method. For example, the optimal interest grasping method is provided based on the user's past interest history. It is also possible to analyze the user's past interest history and provide an effective interest grasping method. It is also possible to customize the interest grasping method by referring to the user's past interest history. In this way, the optimal interest grasping method is provided by analyzing the user's past interest history. Analysis of the interest history is performed, for example, using log data collection and analysis methods.

[0113] The interest grasping unit can customize the content of interests based on the user's current interests when grasping the user's interests. The interest grasping unit customizes the content of interests based on the user's current interests when grasping the user's interests. For example, the content of interests is adjusted according to the user's current interests. Appropriate content of interests can also be provided based on the user's interests. The user's interests can also be evaluated and optimal content of interests can be provided. This makes it possible to grasp the user's interests more appropriately by customizing the content of interests based on the user's current interests. Methods for evaluating and customizing the interests are performed using, for example, a questionnaire or an analysis of behavioral history.

[0114] The interest identification unit can improve the interest identification method by reflecting user feedback when identifying interests. The interest identification unit improves the interest identification method by reflecting user feedback when identifying interests. For example, the interest identification method can be adjusted based on user feedback. The interest content can also be improved by reflecting user feedback. The user feedback can also be analyzed to provide an optimal interest identification method. In this way, by reflecting user feedback, the interest identification method can be improved and more effective interest identification becomes possible. The process of collecting and improving feedback needs to be clarified, for example, the feedback collection method and the improvement process.

[0115] The data protection unit can estimate the user's emotions and adjust the data protection method based on the estimated user emotions. The data protection unit can estimate the user's emotions and adjust the data protection method based on the estimated user emotions. For example, if the user is relaxed, a detailed data protection method can be provided. If the user is in a hurry, a concise data protection method can be provided. If the user is excited, a visually stimulating data protection method can be provided. This allows for more appropriate data protection by adjusting the data protection method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0116] The data protection unit can analyze the user's past data protection history and provide the optimal data protection method when protecting data. The data protection unit can analyze the user's past data protection history and provide the optimal data protection method when protecting data. For example, the optimal data protection method is provided based on the user's past data protection history. The data protection unit can also analyze the user's past data protection history and provide an effective data protection method. The data protection method can also be customized by referring to the user's past data protection history. In this way, the optimal data protection method is provided by analyzing the user's past data protection history. The analysis of the data protection history is performed, for example, using log data collection and analysis methods.

[0117] The data protection unit can customize the protection content based on the user's current data protection needs when protecting data. The data protection unit customizes the protection content based on the user's current data protection needs when protecting data. For example, the protection content is adjusted according to the user's current data protection needs. The data protection unit can also provide appropriate protection content based on the user's data protection needs. The data protection unit can also evaluate the user's data protection needs and provide optimal protection content. This enables more appropriate data protection by customizing the protection content based on the user's current data protection needs. It is necessary to clarify the methods for evaluating and customizing data protection needs, for example, the methods for evaluating needs and the methods for customizing.

[0118] The data protection unit can improve the data protection method by reflecting user feedback during data protection. The data protection unit can improve the data protection method by reflecting user feedback during data protection. For example, the data protection method can be adjusted based on user feedback. The data protection content can also be improved by reflecting user feedback. The data protection unit can also analyze user feedback and provide an optimal data protection method. In this way, by reflecting user feedback, the data protection method can be improved and more effective data protection can be achieved. The process for collecting and improving feedback needs to be clarified, for example, the feedback collection method and the improvement process.

[0119] The communication unit can estimate the user's emotions and adjust the communication method based on the estimated user's emotions. The communication unit can estimate the user's emotions and adjust the communication method based on the estimated user's emotions. For example, if the user is relaxed, communication can be performed at a leisurely pace. If the user is in a hurry, communication can be performed quickly. If the user is excited, communication can be performed in a visually stimulating manner. This enables more appropriate communication by adjusting the communication method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0120] The communication unit can analyze the user's past communication history during communication and provide the optimal communication method. The communication unit can analyze the user's past communication history during communication and provide the optimal communication method. For example, the optimal communication method is provided based on the user's past communication history. The communication unit can also analyze the user's past communication history and provide an effective communication method. The communication method can also be customized by referring to the user's past communication history. In this way, the optimal communication method is provided by analyzing the user's past communication history. Analysis of the communication history is performed, for example, using log data collection and analysis methods.

[0121] The communication unit can customize the communication content based on the user's current interests during communication. The communication unit customizes the communication content based on the user's current interests during communication. For example, the communication content is adjusted according to the user's current interests. Appropriate communication content can also be provided based on the user's interests. The user's interests can also be evaluated and optimal communication content can be provided. This enables more appropriate communication by customizing the communication content based on the user's current interests. Methods for evaluating and customizing interests include, for example, using a questionnaire or an analysis of behavioral history.

[0122] The communication unit can improve the communication method by reflecting user feedback during communication. The communication unit can improve the communication method by reflecting user feedback during communication. For example, the communication method can be adjusted based on user feedback. The content of communication can also be improved by reflecting user feedback. The communication unit can also analyze user feedback and provide the optimal communication method. In this way, by reflecting user feedback, the communication method can be improved and more effective communication can be achieved. The process for collecting and improving feedback needs to be clarified, for example, the feedback collection method and the improvement process.

[0123] The quest unit can estimate the user's emotions and adjust the way the quest progresses based on the estimated user emotions. The quest unit can estimate the user's emotions and adjust the way the quest progresses based on the estimated user emotions. For example, if the user is relaxed, the quest can progress at a leisurely pace. If the user is in a hurry, the quest can progress quickly. If the user is excited, the quest can progress in a visually stimulating manner. In this way, by adjusting the way the quest progresses according to the user's emotions, a more appropriate quest can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0124] The quest unit can analyze the user's past quest history as the quest progresses and provide the most appropriate quest. The quest unit can analyze the user's past quest history as the quest progresses and provide the most appropriate quest. For example, the quest unit can provide the most appropriate quest based on the user's past quest history. The quest unit can also analyze the user's past quest history and provide an effective quest. The quest content can also be customized by referring to the user's past quest history. In this way, the most appropriate quest can be provided by analyzing the user's past quest history. The quest history is analyzed, for example, using log data collection and analysis methods.

[0125] The quest unit can customize the quest content based on the user's current interests as the quest progresses. The quest unit customizes the quest content based on the user's current interests as the quest progresses. For example, the quest content is adjusted according to the user's current interests. Appropriate quest content can also be provided based on the user's interests. The user's interests can also be evaluated and optimal quest content can be provided. In this way, by customizing the quest content based on the user's current interests, a more appropriate quest can be provided. Methods for evaluating and customizing interests include, for example, using a questionnaire or an analysis of behavioral history.

[0126] The quest unit can improve the quest content by reflecting user feedback as the quest progresses. The quest unit can improve the quest content by reflecting user feedback as the quest progresses. For example, the quest content can be adjusted based on user feedback. The quest content can also be improved by reflecting user feedback. The quest unit can also analyze user feedback and provide optimal quest content. In this way, the quest content can be improved by reflecting user feedback, and a more effective quest can be provided. The process for collecting feedback and improving it needs to be clarified, for example, the method for collecting feedback and the improvement process. === Hard Collateral 1-1 === Each of the multiple elements, including the visualization unit, development unit, suggestion unit, generation unit, dialogue unit, story generation unit, and community unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the visualization unit displays the user's character using the display 40A of the smart device 14. The development unit manages the character's growth using the specific processing unit 290 of the data processing device 12. The suggestion unit suggests the user's optimal learning method using the specific processing unit 290 of the data processing device 12. The generation unit generates user-specific learning materials using the specific processing unit 290 of the data processing device 12. The dialogue unit interacts with the user using the microphone 38B of the smart device 14. The story generation unit generates a game story and character evolution using the specific processing unit 290 of the data processing device 12. The community unit creates a community that can be shared with other users using the communication I / F 44 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the visualization unit, development unit, suggestion unit, generation unit, dialogue unit, story generation unit, and community unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the visualization unit displays the user's character using the display of the smart glasses 214. The development unit manages the character's growth via the specific processing unit 290 of the data processing device 12. The suggestion unit suggests an optimal learning method for the user via the specific processing unit 290 of the data processing device 12. The generation unit generates user-specific learning materials via the specific processing unit 290 of the data processing device 12. The dialogue unit interacts with the user using the microphone 238 of the smart glasses 214. The story generation unit generates a game story and character evolution via the specific processing unit 290 of the data processing device 12. The community unit creates a community that can be shared with other users via the communication I / F 44 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the visualization unit, development unit, suggestion unit, generation unit, dialogue unit, story generation unit, and community unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the visualization unit displays the user's character using the display 343 of the headset terminal 314. The development unit manages the character's growth using the specific processing unit 290 of the data processing device 12. The suggestion unit suggests an optimal learning method for the user using the specific processing unit 290 of the data processing device 12. The generation unit generates user-specific learning materials using the specific processing unit 290 of the data processing device 12. The dialogue unit interacts with the user using the microphone 238 of the headset terminal 314. The story generation unit generates a game story and character evolution using the specific processing unit 290 of the data processing device 12. The community unit creates a community that can be shared with other users using the communication I / F 44 of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the visualization unit, training unit, suggestion unit, generation unit, dialogue unit, story generation unit, and community unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the visualization unit displays the user's character using the display of the robot 414. The training unit manages the character's growth using the specific processing unit 290 of the data processing device 12. The suggestion unit suggests an optimal learning method for the user using the specific processing unit 290 of the data processing device 12. The generation unit generates user-specific teaching materials using the specific processing unit 290 of the data processing device 12. The dialogue unit engages in dialogue with the user using the microphone 238 of the robot 414. The story generation unit generates a game story and character evolution using the specific processing unit 290 of the data processing device 12. The community unit creates a community that can be shared with other users using the communication I / F 44 of the robot 414.

[0127] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0128] The reskilling support system can further include a health management unit that monitors the user's health condition. The health management unit collects the user's health data and suggests appropriate breaks and exercises according to the user's learning progress. For example, it may recommend stretching or light exercise after a long period of learning. The health management unit can also estimate the user's emotions and suggest activities to relax if the user's stress level is high. This can support effective learning while maintaining the user's health.

[0129] The reskilling support system can further include a progress display unit that visually displays the user's learning progress. The progress display unit displays the user's learning progress in graphs and charts, visualizing the degree of goal achievement. For example, it displays the study time and progress of acquired skills in real time. The progress display unit can also estimate the user's emotions and display encouraging messages if motivation is declining. This allows the user to continue learning while checking their own progress.

[0130] The reskilling support system can further include an environmental adjustment unit that optimizes the user's learning environment. The environmental adjustment unit monitors the user's learning environment and provides an optimal environment. For example, it adjusts lighting and music to create an environment that is easy to concentrate in. The environmental adjustment unit can also estimate the user's emotions and provide a relaxing environment if the user is relaxed. This allows the user to study in a comfortable environment.

[0131] The reskilling support system can further include a learning style adaptation unit that customizes the system according to the user's learning style. The learning style adaptation unit analyzes the user's learning style and suggests the optimal learning method. For example, it provides visual learning materials to a user who is good at visual learning, and audio learning materials to a user who is good at auditory learning. The learning style adaptation unit can also estimate the user's emotions and customize the system according to the user's learning style. This allows the user to effectively study using a learning method that suits them.

[0132] The reskilling support system can further include an evaluation unit that evaluates the user's learning outcomes. The evaluation unit evaluates the user's learning outcomes and provides feedback. For example, the evaluation unit evaluates the learning outcomes through tests and quizzes and provides feedback based on the results. The evaluation unit can also estimate the user's emotions and display encouraging messages if the user's motivation is declining. This allows the user to continue studying while checking their learning outcomes.

[0133] The reskilling support system can further include a data analysis unit that analyzes the user's learning data. The data analysis unit collects the user's learning data and analyzes the effectiveness of the learning. For example, it analyzes data on study time and acquired skills to evaluate the effectiveness of the learning. The data analysis unit can also analyze the user's learning patterns and suggest optimal learning methods. This allows the user to proceed with effective learning based on data.

[0134] The reskilling support system can further include a goal setting unit that sets learning goals for the user. The goal setting unit sets learning goals for the user and manages the degree of achievement. For example, it sets short-term and long-term goals and visualizes the degree of achievement. The goal setting unit can also adjust goals according to the user's learning progress. This allows the user to proceed with their learning with a clear goal in mind.

[0135] The reskilling support system can further include a schedule management unit that manages the user's study schedule. The schedule management unit manages the user's study schedule and supports efficient study. For example, it can incorporate study time and break times into the schedule and set reminders. The schedule management unit can also adjust the schedule according to the user's study progress. This allows the user to study in a planned manner.

[0136] The reskilling support system can further include a resource management unit that manages the user's learning resources. The resource management unit manages the user's learning resources and provides the most appropriate resources. For example, it manages teaching materials and reference materials and provides the necessary resources. The resource management unit can also update resources according to the user's learning progress. This allows the user to efficiently use the necessary resources to advance their learning.

[0137] The reskilling support system can further include a community support unit that supports users' learning communities. The community support unit supports communication between users and increases their motivation to learn. For example, it provides forums and chat functions that allow users to share information with each other. The community support unit can also suggest community activities based on the user's learning progress. This allows users to cooperate with other users and advance their learning.

[0138] The processing flow of the second embodiment will be briefly explained below.

[0139] Step 1: The visualization unit visualizes the user as a character. For example, the user can customize their own character and select the type of avatar and visualization accuracy. The visualization unit can also customize the character based on the user's occupation and hobbies. Step 2: The training unit trains the character visualized by the visualization unit. For example, when a user acquires a new skill, that skill is reflected in the character, and the character evolves. The training unit can also analyze the user's past activity history and optimize the character's growth process. Step 3: The suggestion unit suggests learning methods for the subject in which the user wants to improve their skills. For example, the AI ​​can understand the user's interests and goals and suggest optimal learning methods based on those. The suggestion unit can also adjust the use of technical terminology in its suggestions based on the user's level of expertise. Step 4: The generator generates learning materials unique to the user based on the learning methods proposed by the suggester. For example, AI analyzes the user's past learning history and generates optimal learning materials. The generator can also estimate the user's emotions and adjust the content of the generated learning materials based on the estimated user emotions. Step 5: The dialogue unit engages in a dialogue with the user based on the learning materials generated by the generation unit. For example, the AI ​​can clarify the user's goals and interests through dialogue with the user and customize the dialogue content based on that. The dialogue unit can also estimate the user's emotions and adjust the way the dialogue proceeds based on the estimated user emotions. Step 6: The story generation unit generates the game story and character evolution based on the information obtained by the dialogue unit. For example, the AI ​​estimates the user's emotions and adjusts the story progression based on the estimated user emotions. The story generation unit can also analyze the user's past story history to generate the optimal story. Step 7: The community section creates a community that can be shared with other users. For example, it provides a chat function and forum for users to communicate with each other. The community section also includes a quest section where users can collaborate on quests.

[0140] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0144] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0145] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0147] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

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

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0150] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0151] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0152] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0153] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0154] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0156] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0158] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0159] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0160] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0161] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0162] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0163] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0164] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0166] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0167] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0168] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0169] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0170] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0171] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0172] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0174] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0175] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0176] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0177] 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.

[0178] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0179] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0180] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0181] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0182] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0183] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0184] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0185] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0186] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0187] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0188] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0189] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0190] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0191] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0192] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0193] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0194] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0195] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0196] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0197] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0198] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0199] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0200] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0201] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0202] 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.

[0203] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0204] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0205] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0206] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0207] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0208] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0209] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0210] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0211] [Explanation of symbols]

[0212] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a visualization unit that visualizes the user as a character; a development unit that develops the character visualized by the visualization unit; A suggestion section that suggests learning methods for the genre in which the user wants to improve their skills; a generation unit that generates user-original learning materials based on the learning methods suggested by the suggestion unit; a dialogue unit that dialogues with a user based on the teaching material generated by the generation unit; a story generation unit that generates a game story or character evolution based on the information obtained by the dialogue unit; A community section where users can create communities that can be shared with other users. Equipped with A system characterized by:

2. The proposal unit An interest grasping unit is provided to grasp the user's interests.

2. The system of claim 1.

3. The generation unit Equipped with a data protection unit that protects user data 2. The system of claim 1.

4. The story generation unit Generate a game story 2. The system of claim 1.

5. The community section Equipped with a communication section for users to communicate with each other 2. The system of claim 1.

6. The community section A quest section will be set up to allow players to complete quests collaboratively.

2. The system of claim 1.

7. The visualization unit Estimate the user's emotions and adjust the character's facial expressions and movements based on the estimated user emotions.

2. The system of claim 1.

8. The visualization unit Analyze the user's past activity history and optimize the character's growth process 2. The system of claim 1.

Images