system

Abstract

The system according to the embodiment aims to provide learning content on public manners that meets the needs of users. [Solution] The system according to the embodiment comprises a reception unit, a collection unit, a generation unit, a simulation unit, and a feedback unit. The reception unit receives input from the user regarding a public manners topic they wish to learn. The collection unit collects relevant information based on the topic received by the reception unit. The generation unit generates learning content based on the information collected by the collection unit. The simulation unit performs a simulation based on the content generated by the generation unit to allow the user to actually practice public manners. The feedback unit provides feedback based on the results of the simulation performed by the simulation unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to provide effective learning content that met individual needs when learning public etiquette.

[0005] The system according to the embodiment aims to provide learning content on public manners that meets the needs of users. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a collection unit, a generation unit, a simulation unit, and a feedback unit. The reception unit inputs a topic of public etiquette that a user wants to learn. The collection unit collects related information based on the topic received by the reception unit. The generation unit generates learning content based on the information collected by the collection unit. The simulation unit performs a simulation for the user to actually practice public etiquette based on the content generated by the generation unit. The feedback unit provides feedback based on the results of the simulation performed by the simulation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide learning content on public manners that meets the needs of the user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The public manners learning system according to an embodiment of the present invention is a system in which a user inputs a theme of public manners they wish to learn, and a generating AI collects relevant information based on that theme and generates learning content. This system comprises a reception unit for inputting a theme of public manners the user wishes to learn, a collection unit for collecting relevant information based on the theme received by the reception unit, a generation unit for generating learning content based on the information collected by the collection unit, a simulation unit for the user to actually practice public manners based on the content generated by the generation unit, and a feedback unit for providing feedback based on the results of the simulation performed by the simulation unit. For example, when a user inputs a theme of public manners they wish to learn, the generating AI collects information from the internet based on that theme and generates learning content in the form of text, images, videos, etc. Based on the generated content, the user performs a simulation, and the generating AI analyzes the user's actions and provides feedback, enabling the user to acquire correct public manners. As a result, the public manners learning system enables users to efficiently learn and practice public manners.

[0029] A public manners learning system according to an embodiment includes a reception unit, a collection unit, a generation unit, a simulation unit, and a feedback unit. The reception unit inputs a topic of public manners that a user wants to learn. For example, if a user wants to learn about manners on trains, the user can input the topic to the reception unit. The collection unit collects related information based on the topic received by the reception unit. For example, the collection unit collects information from the Internet and acquires the latest news articles, academic papers, video content, and the like. The generation unit generates learning content based on the information collected by the collection unit. For example, the generation unit generates learning content in the form of text, images, video, and the like based on the collected information. The simulation unit performs a simulation for a user to actually practice public manners based on the content generated by the generation unit. For example, the simulation unit analyzes the user's behavior and provides a virtual reality simulation or a scenario-based simulation. The feedback unit provides feedback based on the results of the simulation performed by the simulation unit. For example, the feedback unit analyzes the results of the simulation and provides text feedback, audio feedback, an evaluation score, and the like. This allows the public manners learning system according to an embodiment to efficiently learn and practice public manners.

[0030] The collection unit can collect information from the internet. For example, the collection unit can collect information from websites, social media, online databases, etc. The collection unit obtains the latest news articles, academic papers, video content, etc., and uses them to generate learning content. In this way, by collecting information from the internet, learning content can be generated based on the latest information. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can efficiently collect information using an AI model for collecting information from the internet.

[0031] The generation unit can generate learning content in the form of text, images, and videos based on collected information. For example, the generation unit can generate learning content in the form of text, images, and videos based on collected information. For example, the generation unit can generate learning content by adjusting the text format, image resolution, video length, etc., based on collected information. The generation unit can generate learning content in the form of text, images, videos, etc., based on collected information using a generation AI. For example, the generation unit can have a generation AI analyze the collected information and generate text content using a text generation AI (e.g., LLM). The generation unit can also automatically select relevant images using image recognition technology and generate image content. Furthermore, the generation unit can generate visually appealing video content using a video generation algorithm. This allows for the generation of diverse learning content formats, thereby enhancing the user's learning effectiveness. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can efficiently generate content using an AI model for generating learning content in the form of text, images, and videos based on collected information.

[0032] The simulation unit can analyze a user's behavior and perform a simulation. For example, the simulation unit analyzes a user's behavior and performs a simulation. For example, the simulation unit records the user's behavior and analyzes the behavior using an analysis algorithm. The simulation unit can provide a simulation using virtual reality or a scenario-based simulation. For example, the simulation unit simulates a user's behavior in a public place and analyzes the results. The simulation unit can also analyze the user's behavior in real time and adjust the progress of the simulation. This allows for a more realistic simulation to be provided by analyzing the user's behavior. Some or all of the above-described processing in the simulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the simulation unit can input the user's behavior data into a generation AI and have the generation AI perform an analysis of the behavior.

[0033] The feedback unit can provide feedback to the user based on the results of the simulation. The feedback unit provides feedback to the user based on, for example, the results of the simulation. For example, the feedback unit analyzes the results of the simulation and provides text feedback, audio feedback, an evaluation score, or the like. The feedback unit can evaluate the user's behavior and point out specific areas for improvement. For example, the feedback unit can explain in detail the good points and areas for improvement of the user's behavior and provide specific advice. The feedback unit can also evaluate the user's behavior and score it. For example, the feedback unit can evaluate the user's behavior and assign a score ranging from 0 to 100 points. By providing feedback based on the results of the simulation, the user's learning effect can be improved. Some or all of the above-mentioned processing in the feedback unit can be performed using, for example, AI, or can be performed without using AI. For example, the feedback unit can input the results of the simulation to a generation AI and cause the generation AI to generate feedback.

[0034] The reception unit can analyze the user's past theme input history and suggest the optimal theme input method. For example, the reception unit can analyze the user's past theme input history and suggest the optimal theme input method. For example, the reception unit can automatically display themes that the user has frequently input in the past as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest themes to be used in a specific time period based on the user's past input history. This can improve the user's input efficiency by suggesting the optimal input method based on the past input history. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without AI. For example, the reception unit can input the user's past theme input history into a generation AI and have the generation AI suggest the optimal theme input method.

[0035] The reception unit can automatically suggest related themes based on the user's current areas of interest when the user inputs a theme. For example, the reception unit automatically suggests related themes based on the user's current areas of interest when the user inputs a theme. For example, the reception unit can suggest related themes based on keywords recently searched by the user. The reception unit can also analyze content previously viewed by the user and suggest related themes. Furthermore, the reception unit can suggest related themes based on topics in online communities in which the user participates. This can increase the user's motivation to learn by suggesting related themes based on the user's areas of interest. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input data related to the user's areas of interest to a generation AI and cause the generation AI to suggest related themes.

[0036] When a theme is input, the reception unit can prioritize suggesting highly relevant themes by taking into account the user's geographical location information. For example, when a theme is input, the reception unit prioritizes suggesting highly relevant themes by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can suggest a theme about public etiquette related to that area. Furthermore, if the user is traveling, the reception unit can also suggest a theme about public etiquette in the destination. Furthermore, if the user is participating in a specific event, the reception unit can also suggest a theme about public etiquette related to the event. This can improve the user's learning effectiveness by suggesting highly relevant themes based on the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's geographical location information to a generation AI and cause the generation AI to suggest highly relevant themes.

[0037] The reception unit can analyze the user's social media activity when a theme is input and suggest related themes. For example, the reception unit can analyze the user's social media activity when a theme is input and suggest related themes. For example, the reception unit can suggest related public etiquette themes based on posts shared by the user on social media. The reception unit can also analyze the content of posts from accounts the user follows and suggest related themes. Furthermore, the reception unit can suggest related themes based on the topics of groups the user participates in. This can increase the user's motivation to learn by suggesting related themes based on the user's social media activity. Some or all of the above-described processing by the reception unit can be performed using, or without, AI. For example, the reception unit can input the user's social media activity data into a generation AI and cause the generation AI to suggest related themes.

[0038] The collection unit can select an appropriate information source by referring to the user's past learning history when collecting information. For example, the collection unit selects an appropriate information source by referring to the user's past learning history when collecting information. For example, the collection unit selects an optimal information source based on information sources used by the user in the past. The collection unit can also prioritize selecting highly reliable information sources based on the user's past learning history. Furthermore, the collection unit can analyze the user's past learning history and select the most efficient information source. This allows the user's learning efficiency to be improved by selecting an optimal information source based on the past learning history. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's past learning history data into the generation AI and cause the generation AI to select an appropriate information source.

[0039] The collection unit can filter information based on the user's current areas of interest when collecting information. For example, the collection unit filters information based on the user's current areas of interest when collecting information. For example, the collection unit filters related information based on keywords recently searched by the user. The collection unit can also analyze content previously viewed by the user and filter related information. Furthermore, the collection unit can filter related information based on topics in online communities in which the user participates. This can improve the user's learning effect by filtering information based on the user's areas of interest. Some or all of the above-described processing in the collection unit can be performed using, or without, AI. For example, the collection unit can input data related to the user's areas of interest to a generation AI and cause the generation AI to filter the information.

[0040] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, when collecting information, the collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit collects information on public manners related to that area. Furthermore, when the user is traveling, the collection unit can collect information on public manners in the destination. Furthermore, when the user is participating in a specific event, the collection unit can collect information on public manners related to the event. In this way, by collecting highly relevant information based on the user's geographical location information, the user's learning effect can be improved. Some or all of the above-mentioned processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant information.

[0041] The collection unit can analyze the user's social media activities and collect related information when collecting information. For example, the collection unit can analyze the user's social media activities and collect related information when collecting information. For example, the collection unit can collect related public etiquette information based on posts shared by the user on social media. The collection unit can also analyze the content of posts from accounts the user follows and collect related information. Furthermore, the collection unit can also collect related information based on the topics of groups the user participates in. By collecting related information based on the user's social media activities, the user's motivation to learn can be increased. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's social media activity data into a generation AI and cause the generation AI to collect related information.

[0042] The generation unit can adjust the level of detail of the content based on the importance of the information when generating the content. For example, the generation unit can adjust the level of detail of the content based on the importance of the information when generating the content. For example, the generation unit can provide detailed explanations for highly important information and summarize less important information concisely. The generation unit can also add images or videos to highly important information to make it easier to understand visually. Furthermore, the generation unit can highlight highly important information to attract the user's attention. In this way, adjusting the level of detail of the content based on the importance of the information can improve the user's learning effect. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can adjust the level of detail of the content using an AI model for evaluating the importance of information.

[0043] The generation unit can apply different generation algorithms depending on the category of information when generating content. For example, the generation unit can apply a natural language processing algorithm to text information to generate easy-to-read sentences. It can also apply an image recognition algorithm to image information to automatically select relevant images. Furthermore, it can apply a video generation algorithm to video information to generate visually appealing videos. By applying different generation algorithms depending on the category of information, the user's learning effect can be enhanced. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can efficiently generate content using an AI model for applying different generation algorithms depending on the category of information.

[0044] The generation unit can determine the priority of content based on the timing of information collection when generating content. For example, the generation unit can prioritize the inclusion of the latest information in the content. The generation unit can also summarize older information concisely and explain newer information in detail. Furthermore, the generation unit can prioritize the display of highly important information based on the timing of information collection. This enhances the user's learning effect by prioritizing content based on the timing of information collection. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can determine the priority of content using an AI model to evaluate the timing of information collection.

[0045] The generation unit can adjust the order of content based on the relevance of information when generating content. For example, the generation unit can adjust the order of content based on the relevance of information when generating content. For example, the generation unit can prioritize displaying highly relevant information to help a user understand. The generation unit can also postpone less relevant information and display important information first. Furthermore, the generation unit can create a natural flow of content based on the relevance of information. This can improve the user's learning effect by adjusting the order of content based on the relevance of information. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can adjust the order of content using an AI model for evaluating the relevance of information.

[0046] The simulation unit can select an appropriate simulation method by referring to the user's past behavioral history during a simulation. For example, the simulation unit can select an appropriate simulation method by referring to the user's past behavioral history during a simulation. For example, the simulation unit can select an optimal simulation method based on simulations the user has performed in the past. The simulation unit can also select an effective simulation method from the user's past behavioral history. Furthermore, the simulation unit can analyze the user's past behavioral history and select the most efficient simulation method. By selecting an optimal simulation method based on the past behavioral history, the user's learning effect can be improved. Some or all of the above-described processing in the simulation unit can be performed using, for example, AI, or without AI. For example, the simulation unit can input the user's past behavioral history data into a generation AI and cause the generation AI to select an appropriate simulation method.

[0047] The simulation unit can customize the simulation means based on the user's current living situation during the simulation. For example, the simulation unit can customize the simulation means based on the user's current living situation during the simulation. For example, if the user is busy, the simulation unit can provide a simulation that can be completed in a short time. Furthermore, if the user is relaxed, the simulation unit can provide a detailed simulation. Furthermore, if the user is in a specific location, the simulation unit can provide a simulation related to that location. This can enhance the user's learning effect by providing a simulation means that suits the user's living situation. Some or all of the above-described processing in the simulation unit can be performed using, or without, AI. For example, the simulation unit can input the user's living situation data into a generation AI and have the generation AI customize the simulation means.

[0048] The simulation unit can select an appropriate simulation method during a simulation by taking into account the user's geographical location information. For example, the simulation unit can select an appropriate simulation method by taking into account the user's geographical location information during a simulation. For example, if the user is in a specific area, the simulation unit can provide a simulation related to that area. Furthermore, if the user is traveling, the simulation unit can provide a simulation related to the public manners of the destination. Furthermore, if the user is participating in a specific event, the simulation unit can provide a simulation related to the event. This can enhance the user's learning effect by providing an optimal simulation method based on the user's geographical location information. Some or all of the above-described processing in the simulation unit can be performed using, or without, AI. For example, the simulation unit can input the user's geographical location information into a generation AI and cause the generation AI to select an appropriate simulation method.

[0049] The simulation unit can analyze the user's social media activity during the simulation and suggest simulation methods. For example, the simulation unit can analyze the user's social media activity during the simulation and suggest simulation methods. For example, the simulation unit can provide related simulations based on posts shared by the user on social media. The simulation unit can also analyze the content of posts from accounts the user follows and provide related simulations. Furthermore, the simulation unit can provide related simulations based on the topics of groups the user participates in. This can improve the user's learning effect by providing optimal simulation methods based on the user's social media activity. Some or all of the above-described processing in the simulation unit can be performed using, or without, AI. For example, the simulation unit can input the user's social media activity data into a generation AI and cause the generation AI to execute the proposed simulation methods.

[0050] The feedback unit can select an appropriate feedback method by referring to the user's past behavioral history when providing feedback. For example, the feedback unit can select an appropriate feedback method by referring to the user's past behavioral history when providing feedback. For example, the feedback unit selects an optimal feedback method based on feedback the user has received in the past. The feedback unit can also select an effective feedback method from the user's past behavioral history. Furthermore, the feedback unit can analyze the user's past behavioral history and select the most efficient feedback method. This can enhance the user's learning effect by selecting an optimal feedback method based on the past behavioral history. Some or all of the above-described processing in the feedback unit can be performed using, for example, AI, or can be performed without using AI. For example, the feedback unit can input the user's past behavioral history data into the generation AI and cause the generation AI to select an appropriate feedback method.

[0051] The feedback unit can customize the feedback means based on the user's current living situation when providing feedback. For example, the feedback unit customizes the feedback means based on the user's current living situation when providing feedback. For example, if the user is busy, the feedback unit can provide feedback that can be completed in a short time. Also, if the user is relaxed, the feedback unit can provide detailed feedback. Furthermore, if the user is in a specific location, the feedback unit can provide feedback related to the location. This can improve the user's learning effect by providing feedback means that correspond to the user's living situation. Some or all of the above-described processing in the feedback unit can be performed using, or without, AI. For example, the feedback unit can input the user's living situation data into a generation AI and cause the generation AI to customize the feedback means.

[0052] The feedback unit may select an appropriate feedback method by taking into account the user's geographical location information when providing feedback. For example, the feedback unit may select an appropriate feedback method by taking into account the user's geographical location information when providing feedback. For example, if the user is in a specific area, the feedback unit may provide feedback related to the area. Also, if the user is traveling, the feedback unit may provide feedback regarding public manners in the destination. Furthermore, if the user is participating in a specific event, the feedback unit may provide feedback related to the event. This allows the user's learning effect to be improved by providing an optimal feedback method based on the user's geographical location information. Some or all of the above-described processing in the feedback unit may be performed using, or without, AI. For example, the feedback unit may input the user's geographical location information to a generation AI and cause the generation AI to select an appropriate feedback method.

[0053] The feedback unit may analyze the user's social media activity and suggest a means of feedback when providing feedback. For example, the feedback unit may analyze the user's social media activity and suggest a means of feedback when providing feedback. For example, the feedback unit may provide relevant feedback based on posts shared by the user on social media. The feedback unit may also analyze the content of posts from accounts the user follows and provide relevant feedback. Furthermore, the feedback unit may provide relevant feedback based on the topics of groups the user participates in. This allows the user to improve their learning effectiveness by providing optimal feedback means based on the user's social media activity. Some or all of the above-described processing in the feedback unit may be performed using, or without, AI. For example, the feedback unit may input the user's social media activity data into a generation AI and cause the generation AI to suggest feedback means.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The reception unit can analyze the user's past learning history and suggest the optimal theme input method. For example, the reception unit can automatically display themes that the user has frequently input in the past as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest themes to be used in a specific time period based on the user's past input history. This can improve the user's input efficiency by suggesting the optimal input method based on the past input history. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's past theme input history into a generation AI and have the generation AI suggest the optimal theme input method.

[0056] When collecting information, the collection unit can select an appropriate information source by referring to the user's past learning history. For example, the collection unit selects the most appropriate information source based on information sources used by the user in the past. The collection unit can also prioritize selecting highly reliable information sources based on the user's past learning history. Furthermore, the collection unit can analyze the user's past learning history and select the most efficient information source. This allows the user's learning efficiency to be improved by selecting the most appropriate information source based on the past learning history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past learning history data into the generation AI and cause the generation AI to select an appropriate information source.

[0057] When generating content, the generation unit can adjust the level of detail of the content based on the importance of the information. For example, the generation unit can provide detailed explanations for highly important information and summarize less important information concisely. The generation unit can also add images or videos to highly important information to make it easier to understand visually. Furthermore, the generation unit can highlight highly important information to attract the user's attention. By adjusting the level of detail of the content based on the importance of the information, the user's learning effect can be improved. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can adjust the level of detail of the content using an AI model for evaluating the importance of information.

[0058] During a simulation, the simulation unit can select an appropriate simulation method by referring to the user's past behavioral history. For example, the simulation unit selects an optimal simulation method based on simulations the user has performed in the past. The simulation unit can also select an effective simulation method from the user's past behavioral history. Furthermore, the simulation unit can analyze the user's past behavioral history and select the most efficient simulation method. This allows the user's learning effect to be improved by selecting an optimal simulation method based on the past behavioral history. Some or all of the above-described processing in the simulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the simulation unit can input the user's past behavioral history data into a generation AI and have the generation AI select an appropriate simulation method.

[0059] When providing feedback, the feedback unit can select an appropriate feedback method by referring to the user's past behavioral history. For example, the feedback unit selects the optimal feedback method based on feedback the user has received in the past. The feedback unit can also select an effective feedback method from the user's past behavioral history. Furthermore, the feedback unit can analyze the user's past behavioral history and select the most efficient feedback method. This allows the user's learning effect to be improved by selecting the optimal feedback method based on the past behavioral history. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's past behavioral history data into the generation AI and cause the generation AI to select an appropriate feedback method.

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

[0061] Step 1: The reception desk receives the topic of public manners that the user wants to learn about. For example, if a user wants to learn about manners on trains, they can enter that topic into the reception desk. Step 2: The collection department collects relevant information based on the themes received by the reception department. For example, the collection department collects information from the internet, such as the latest news articles, academic papers, and video content. Step 3: The generation unit generates learning content based on the information collected by the collection unit. For example, the generation unit generates learning content in formats such as text, images, and videos based on the collected information. Step 4: The simulation unit performs a simulation based on the content generated by the generation unit to allow the user to actually practice public manners. For example, the simulation unit analyzes the user's behavior and provides a virtual reality simulation or a scenario-based simulation. Step 5: The feedback unit provides feedback based on the results of the simulation performed by the simulation unit. For example, the feedback unit analyzes the simulation results and provides text feedback, voice feedback, evaluation scores, etc.

[0062] (Example 2) The public manners learning system according to an embodiment of the present invention is a system in which a user inputs a theme of public manners they wish to learn, and a generating AI collects relevant information based on that theme and generates learning content. This system comprises a reception unit for inputting a theme of public manners the user wishes to learn, a collection unit for collecting relevant information based on the theme received by the reception unit, a generation unit for generating learning content based on the information collected by the collection unit, a simulation unit for the user to actually practice public manners based on the content generated by the generation unit, and a feedback unit for providing feedback based on the results of the simulation performed by the simulation unit. For example, when a user inputs a theme of public manners they wish to learn, the generating AI collects information from the internet based on that theme and generates learning content in the form of text, images, videos, etc. Based on the generated content, the user performs a simulation, and the generating AI analyzes the user's actions and provides feedback, enabling the user to acquire correct public manners. As a result, the public manners learning system enables users to efficiently learn and practice public manners.

[0063] A public manners learning system according to an embodiment includes a reception unit, a collection unit, a generation unit, a simulation unit, and a feedback unit. The reception unit inputs a topic of public manners that a user wants to learn. For example, if a user wants to learn about manners on trains, the user can input the topic to the reception unit. The collection unit collects related information based on the topic received by the reception unit. For example, the collection unit collects information from the Internet and acquires the latest news articles, academic papers, video content, and the like. The generation unit generates learning content based on the information collected by the collection unit. For example, the generation unit generates learning content in the form of text, images, video, and the like based on the collected information. The simulation unit performs a simulation for a user to actually practice public manners based on the content generated by the generation unit. For example, the simulation unit analyzes the user's behavior and provides a virtual reality simulation or a scenario-based simulation. The feedback unit provides feedback based on the results of the simulation performed by the simulation unit. For example, the feedback unit analyzes the results of the simulation and provides text feedback, audio feedback, an evaluation score, and the like. This allows the public manners learning system according to an embodiment to efficiently learn and practice public manners.

[0064] The collection unit can collect information from the internet. For example, the collection unit can collect information from websites, social media, online databases, etc. The collection unit obtains the latest news articles, academic papers, video content, etc., and uses them to generate learning content. In this way, by collecting information from the internet, learning content can be generated based on the latest information. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can efficiently collect information using an AI model for collecting information from the internet.

[0065] The generation unit can generate learning content in the form of text, images, and videos based on collected information. For example, the generation unit can generate learning content in the form of text, images, and videos based on collected information. For example, the generation unit can generate learning content by adjusting the text format, image resolution, video length, etc., based on collected information. The generation unit can generate learning content in the form of text, images, videos, etc., based on collected information using a generation AI. For example, the generation unit can have a generation AI analyze the collected information and generate text content using a text generation AI (e.g., LLM). The generation unit can also automatically select relevant images using image recognition technology and generate image content. Furthermore, the generation unit can generate visually appealing video content using a video generation algorithm. This allows for the generation of diverse learning content formats, thereby enhancing the user's learning effectiveness. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can efficiently generate content using an AI model for generating learning content in the form of text, images, and videos based on collected information.

[0066] The simulation unit can analyze a user's behavior and perform a simulation. For example, the simulation unit analyzes a user's behavior and performs a simulation. For example, the simulation unit records the user's behavior and analyzes the behavior using an analysis algorithm. The simulation unit can provide a simulation using virtual reality or a scenario-based simulation. For example, the simulation unit simulates a user's behavior in a public place and analyzes the results. The simulation unit can also analyze the user's behavior in real time and adjust the progress of the simulation. This allows for a more realistic simulation to be provided by analyzing the user's behavior. Some or all of the above-described processing in the simulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the simulation unit can input the user's behavior data into a generation AI and have the generation AI perform an analysis of the behavior.

[0067] The feedback unit can provide feedback to the user based on the results of the simulation. The feedback unit provides feedback to the user based on, for example, the results of the simulation. For example, the feedback unit analyzes the results of the simulation and provides text feedback, audio feedback, an evaluation score, or the like. The feedback unit can evaluate the user's behavior and point out specific areas for improvement. For example, the feedback unit can explain in detail the good points and areas for improvement of the user's behavior and provide specific advice. The feedback unit can also evaluate the user's behavior and score it. For example, the feedback unit can evaluate the user's behavior and assign a score ranging from 0 to 100 points. By providing feedback based on the results of the simulation, the user's learning effect can be improved. Some or all of the above-mentioned processing in the feedback unit can be performed using, for example, AI, or can be performed without using AI. For example, the feedback unit can input the results of the simulation to a generation AI and cause the generation AI to generate feedback.

[0068] The reception unit can estimate the user's emotions and adjust the theme input method based on the estimated emotions. For example, the reception unit can estimate the user's emotions and adjust the theme input method based on the estimated emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and enable quick theme input. This improves user convenience by providing an input method that suits the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0069] The reception unit can analyze the user's past theme input history and suggest the optimal theme input method. For example, the reception unit can analyze the user's past theme input history and suggest the optimal theme input method. For example, the reception unit can automatically display themes that the user has frequently input in the past as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest themes to be used in a specific time period based on the user's past input history. This can improve the user's input efficiency by suggesting the optimal input method based on the past input history. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without AI. For example, the reception unit can input the user's past theme input history into a generation AI and have the generation AI suggest the optimal theme input method.

[0070] The reception unit can automatically suggest related themes based on the user's current areas of interest when the user inputs a theme. For example, the reception unit automatically suggests related themes based on the user's current areas of interest when the user inputs a theme. For example, the reception unit can suggest related themes based on keywords recently searched by the user. The reception unit can also analyze content previously viewed by the user and suggest related themes. Furthermore, the reception unit can suggest related themes based on topics in online communities in which the user participates. This can increase the user's motivation to learn by suggesting related themes based on the user's areas of interest. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input data related to the user's areas of interest to a generation AI and cause the generation AI to suggest related themes.

[0071] The reception unit can estimate the user's emotions and prioritize themes based on the estimated emotions. For example, the reception unit estimates the user's emotions and prioritizes themes based on the estimated emotions. For example, if the user is excited, the reception unit can prioritize displaying interesting themes. Furthermore, if the user is tired, the reception unit can prioritize displaying simple and easy-to-understand themes. Furthermore, if the user is relaxed, the reception unit can prioritize displaying in-depth themes. This allows the user's learning efficiency to be improved by prioritizing themes according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or without an AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the themes.

[0072] When a theme is input, the reception unit can prioritize suggesting highly relevant themes by taking into account the user's geographical location information. For example, when a theme is input, the reception unit prioritizes suggesting highly relevant themes by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can suggest a theme about public etiquette related to that area. Furthermore, if the user is traveling, the reception unit can also suggest a theme about public etiquette in the destination. Furthermore, if the user is participating in a specific event, the reception unit can also suggest a theme about public etiquette related to the event. This can improve the user's learning effectiveness by suggesting highly relevant themes based on the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's geographical location information to a generation AI and cause the generation AI to suggest highly relevant themes.

[0073] The reception unit can analyze the user's social media activity when a theme is input and suggest related themes. For example, the reception unit can analyze the user's social media activity when a theme is input and suggest related themes. For example, the reception unit can suggest related public etiquette themes based on posts shared by the user on social media. The reception unit can also analyze the content of posts from accounts the user follows and suggest related themes. Furthermore, the reception unit can suggest related themes based on the topics of groups the user participates in. This can increase the user's motivation to learn by suggesting related themes based on the user's social media activity. Some or all of the above-described processing by the reception unit can be performed using, or without, AI. For example, the reception unit can input the user's social media activity data into a generation AI and cause the generation AI to suggest related themes.

[0074] The collection unit can estimate the user's emotions and adjust the information collection method based on the estimated emotions. For example, the collection unit estimates the user's emotions and adjusts the information collection method based on the estimated emotions. For example, when the user is relaxed, the collection unit collects information from a wide range of information sources. Furthermore, when the user is in a hurry, the collection unit can quickly collect information from reliable information sources. Furthermore, when the user is excited, the collection unit can prioritize collecting visually appealing information. This improves user convenience by providing an information collection method that corresponds to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the information collection method.

[0075] The collection unit can select an appropriate information source by referring to the user's past learning history when collecting information. For example, the collection unit selects an appropriate information source by referring to the user's past learning history when collecting information. For example, the collection unit selects an optimal information source based on information sources used by the user in the past. The collection unit can also prioritize selecting highly reliable information sources based on the user's past learning history. Furthermore, the collection unit can analyze the user's past learning history and select the most efficient information source. This allows the user's learning efficiency to be improved by selecting an optimal information source based on the past learning history. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's past learning history data into the generation AI and cause the generation AI to select an appropriate information source.

[0076] The collection unit can filter information based on the user's current areas of interest when collecting information. For example, the collection unit filters information based on the user's current areas of interest when collecting information. For example, the collection unit filters related information based on keywords recently searched by the user. The collection unit can also analyze content previously viewed by the user and filter related information. Furthermore, the collection unit can filter related information based on topics in online communities in which the user participates. This can improve the user's learning effect by filtering information based on the user's areas of interest. Some or all of the above-described processing in the collection unit can be performed using, or without, AI. For example, the collection unit can input data related to the user's areas of interest to a generation AI and cause the generation AI to filter the information.

[0077] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated emotions. For example, the collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated emotions. For example, when the user is excited, the collection unit can prioritize collecting visually appealing information. Furthermore, when the user is tired, the collection unit can prioritize collecting simple and easy-to-understand information. Furthermore, when the user is relaxed, the collection unit can prioritize collecting in-depth information. This allows the user's learning efficiency to be improved by prioritizing information according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of information.

[0078] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, when collecting information, the collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit collects information on public manners related to that area. Furthermore, when the user is traveling, the collection unit can collect information on public manners in the destination. Furthermore, when the user is participating in a specific event, the collection unit can collect information on public manners related to the event. In this way, by collecting highly relevant information based on the user's geographical location information, the user's learning effect can be improved. Some or all of the above-mentioned processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant information.

[0079] The collection unit can analyze the user's social media activities and collect related information when collecting information. For example, the collection unit can analyze the user's social media activities and collect related information when collecting information. For example, the collection unit can collect related public etiquette information based on posts shared by the user on social media. The collection unit can also analyze the content of posts from accounts the user follows and collect related information. Furthermore, the collection unit can also collect related information based on the topics of groups the user participates in. By collecting related information based on the user's social media activities, the user's motivation to learn can be increased. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's social media activity data into a generation AI and cause the generation AI to collect related information.

[0080] The generation unit can estimate the user's emotions and adjust the content presentation method based on the estimated emotions. For example, the generation unit can estimate the user's emotions and adjust the content presentation method based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate content that progresses at a leisurely pace. If the user is in a hurry, the generation unit can generate concise content that focuses on the main points. Furthermore, if the user is excited, the generation unit can generate content that adds visually stimulating effects. This can improve the user's learning effect by providing a content presentation method that corresponds to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the content presentation method.

[0081] The generation unit can adjust the level of detail of the content based on the importance of the information when generating the content. For example, the generation unit can adjust the level of detail of the content based on the importance of the information when generating the content. For example, the generation unit can provide detailed explanations for highly important information and summarize less important information concisely. The generation unit can also add images or videos to highly important information to make it easier to understand visually. Furthermore, the generation unit can highlight highly important information to attract the user's attention. In this way, adjusting the level of detail of the content based on the importance of the information can improve the user's learning effect. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can adjust the level of detail of the content using an AI model for evaluating the importance of information.

[0082] The generation unit can apply different generation algorithms depending on the category of information when generating content. For example, the generation unit can apply a natural language processing algorithm to text information to generate easy-to-read sentences. It can also apply an image recognition algorithm to image information to automatically select relevant images. Furthermore, it can apply a video generation algorithm to video information to generate visually appealing videos. By applying different generation algorithms depending on the category of information, the user's learning effect can be enhanced. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can efficiently generate content using an AI model for applying different generation algorithms depending on the category of information.

[0083] The generation unit can estimate the user's emotions and adjust the length of the content based on the estimated emotions. For example, the generation unit can estimate the user's emotions and adjust the length of the content based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate short, to-the-point content. If the user is relaxed, the generation unit can generate longer content with detailed explanations. Furthermore, if the user is excited, the generation unit can generate content with visually stimulating effects. This can enhance the user's learning effect by providing content of a length that corresponds to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the content.

[0084] The generation unit can determine the priority of content based on the timing of information collection when generating content. For example, the generation unit can prioritize the inclusion of the latest information in the content. The generation unit can also summarize older information concisely and explain newer information in detail. Furthermore, the generation unit can prioritize the display of highly important information based on the timing of information collection. This enhances the user's learning effect by prioritizing content based on the timing of information collection. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can determine the priority of content using an AI model to evaluate the timing of information collection.

[0085] The generation unit can adjust the order of content based on the relevance of information when generating content. For example, the generation unit can adjust the order of content based on the relevance of information when generating content. For example, the generation unit can prioritize displaying highly relevant information to help a user understand. The generation unit can also postpone less relevant information and display important information first. Furthermore, the generation unit can create a natural flow of content based on the relevance of information. This can improve the user's learning effect by adjusting the order of content based on the relevance of information. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can adjust the order of content using an AI model for evaluating the relevance of information.

[0086] The simulation unit can estimate the user's emotions and adjust the simulation method based on the estimated emotions. For example, the simulation unit can estimate the user's emotions and adjust the simulation method based on the estimated emotions. For example, if the user is relaxed, the simulation unit can provide a simulation that proceeds at a leisurely pace. If the user is in a hurry, the simulation unit can provide a concise simulation that focuses on the main points. Furthermore, if the user is excited, the simulation unit can provide a simulation that adds visually stimulating effects. This can improve the user's learning effect by providing a simulation method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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. Some or all of the above-mentioned processing in the simulation unit can be performed using an AI, for example, or without an AI. For example, the simulation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the simulation method.

[0087] The simulation unit can select an appropriate simulation method by referring to the user's past behavioral history during a simulation. For example, the simulation unit can select an appropriate simulation method by referring to the user's past behavioral history during a simulation. For example, the simulation unit can select an optimal simulation method based on simulations the user has performed in the past. The simulation unit can also select an effective simulation method from the user's past behavioral history. Furthermore, the simulation unit can analyze the user's past behavioral history and select the most efficient simulation method. By selecting an optimal simulation method based on the past behavioral history, the user's learning effect can be improved. Some or all of the above-described processing in the simulation unit can be performed using, for example, AI, or without AI. For example, the simulation unit can input the user's past behavioral history data into a generation AI and cause the generation AI to select an appropriate simulation method.

[0088] The simulation unit can customize the simulation means based on the user's current living situation during the simulation. For example, the simulation unit can customize the simulation means based on the user's current living situation during the simulation. For example, if the user is busy, the simulation unit can provide a simulation that can be completed in a short time. Furthermore, if the user is relaxed, the simulation unit can provide a detailed simulation. Furthermore, if the user is in a specific location, the simulation unit can provide a simulation related to that location. This can enhance the user's learning effect by providing a simulation means that suits the user's living situation. Some or all of the above-described processing in the simulation unit can be performed using, or without, AI. For example, the simulation unit can input the user's living situation data into a generation AI and have the generation AI customize the simulation means.

[0089] The simulation unit can estimate the user's emotions and prioritize the simulations based on the estimated emotions. For example, the simulation unit can estimate the user's emotions and prioritize the simulations based on the estimated emotions. For example, if the user is excited, the simulation unit can prioritize visually stimulating simulations. Furthermore, if the user is tired, the simulation unit can prioritize simple and easy-to-understand simulations. Furthermore, if the user is relaxed, the simulation unit can prioritize in-depth simulations. This can improve the user's learning effect by prioritizing simulations according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the simulation unit can be performed using, for example, an AI, or without an AI. For example, the simulation unit can input the user's emotion data into the generation AI and have the generation AI determine the priorities of the simulations.

[0090] The simulation unit can select an appropriate simulation method during a simulation by taking into account the user's geographical location information. For example, the simulation unit can select an appropriate simulation method by taking into account the user's geographical location information during a simulation. For example, if the user is in a specific area, the simulation unit can provide a simulation related to that area. Furthermore, if the user is traveling, the simulation unit can provide a simulation related to the public manners of the destination. Furthermore, if the user is participating in a specific event, the simulation unit can provide a simulation related to the event. This can enhance the user's learning effect by providing an optimal simulation method based on the user's geographical location information. Some or all of the above-described processing in the simulation unit can be performed using, or without, AI. For example, the simulation unit can input the user's geographical location information into a generation AI and cause the generation AI to select an appropriate simulation method.

[0091] The simulation unit can analyze the user's social media activity during the simulation and suggest simulation methods. For example, the simulation unit can analyze the user's social media activity during the simulation and suggest simulation methods. For example, the simulation unit can provide related simulations based on posts shared by the user on social media. The simulation unit can also analyze the content of posts from accounts the user follows and provide related simulations. Furthermore, the simulation unit can provide related simulations based on the topics of groups the user participates in. This can improve the user's learning effect by providing optimal simulation methods based on the user's social media activity. Some or all of the above-described processing in the simulation unit can be performed using, or without, AI. For example, the simulation unit can input the user's social media activity data into a generation AI and cause the generation AI to execute the proposed simulation methods.

[0092] The feedback unit can estimate the user's emotions and adjust the feedback method based on the estimated emotions. For example, the feedback unit can estimate the user's emotions and adjust the feedback method based on the estimated emotions. For example, the feedback unit can provide detailed feedback when the user is relaxed. Furthermore, the feedback unit can provide concise feedback that focuses on the main points when the user is in a hurry. Furthermore, the feedback unit can provide feedback with visually stimulating effects when the user is excited. This can improve the user's learning effect by providing a feedback method that corresponds to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a 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. Some or all of the above-described processing in the feedback unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the feedback unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the feedback method.

[0093] The feedback unit can select an appropriate feedback method by referring to the user's past behavioral history when providing feedback. For example, the feedback unit can select an appropriate feedback method by referring to the user's past behavioral history when providing feedback. For example, the feedback unit selects an optimal feedback method based on feedback the user has received in the past. The feedback unit can also select an effective feedback method from the user's past behavioral history. Furthermore, the feedback unit can analyze the user's past behavioral history and select the most efficient feedback method. This can enhance the user's learning effect by selecting an optimal feedback method based on the past behavioral history. Some or all of the above-described processing in the feedback unit can be performed using, for example, AI, or can be performed without using AI. For example, the feedback unit can input the user's past behavioral history data into the generation AI and cause the generation AI to select an appropriate feedback method.

[0094] The feedback unit can customize the feedback means based on the user's current living situation when providing feedback. For example, the feedback unit customizes the feedback means based on the user's current living situation when providing feedback. For example, if the user is busy, the feedback unit can provide feedback that can be completed in a short time. Also, if the user is relaxed, the feedback unit can provide detailed feedback. Furthermore, if the user is in a specific location, the feedback unit can provide feedback related to the location. This can improve the user's learning effect by providing feedback means that correspond to the user's living situation. Some or all of the above-described processing in the feedback unit can be performed using, or without, AI. For example, the feedback unit can input the user's living situation data into a generation AI and cause the generation AI to customize the feedback means.

[0095] The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated emotions. For example, the feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated emotions. For example, if the user is excited, the feedback unit can prioritize visually stimulating feedback. Also, if the user is tired, the feedback unit can prioritize simple and easy-to-understand feedback. Furthermore, if the user is relaxed, the feedback unit can prioritize in-depth feedback. This can improve the user's learning effect by determining the priority of feedback according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the feedback unit can be performed using, for example, an AI. For example, the feedback unit can input the user's emotion data into the generation AI and cause the generation AI to determine the priority of feedback.

[0096] The feedback unit may select an appropriate feedback method by taking into account the user's geographical location information when providing feedback. For example, the feedback unit may select an appropriate feedback method by taking into account the user's geographical location information when providing feedback. For example, if the user is in a specific area, the feedback unit may provide feedback related to the area. Also, if the user is traveling, the feedback unit may provide feedback regarding public manners in the destination. Furthermore, if the user is participating in a specific event, the feedback unit may provide feedback related to the event. This allows the user's learning effect to be improved by providing an optimal feedback method based on the user's geographical location information. Some or all of the above-described processing in the feedback unit may be performed using, or without, AI. For example, the feedback unit may input the user's geographical location information to a generation AI and cause the generation AI to select an appropriate feedback method.

[0097] The feedback unit may analyze the user's social media activity and suggest a means of feedback when providing feedback. For example, the feedback unit may analyze the user's social media activity and suggest a means of feedback when providing feedback. For example, the feedback unit may provide relevant feedback based on posts shared by the user on social media. The feedback unit may also analyze the content of posts from accounts the user follows and provide relevant feedback. Furthermore, the feedback unit may provide relevant feedback based on the topics of groups the user participates in. This allows the user to improve their learning effectiveness by providing optimal feedback means based on the user's social media activity. Some or all of the above-described processing in the feedback unit may be performed using, or without, AI. For example, the feedback unit may input the user's social media activity data into a generation AI and cause the generation AI to suggest feedback means. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, collection unit, generation unit, simulation unit, and feedback unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives user input using the touch panel 38A or microphone 38B of the smart device 14. The collection unit collects information on the Internet using the specific processing unit 290 of the data processing device 12. The generation unit generates learning content based on the information collected by the specific processing unit 290 of the data processing device 12. The simulation unit provides virtual reality or scenario-based simulation using the control unit 46A of the smart device 14. The feedback unit analyzes the results of the simulation using the specific processing unit 290 of the data processing device 12 and provides feedback. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, collection unit, generation unit, simulation unit, and feedback unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives user input using the microphone 238 of the smart glasses 214. The collection unit collects information on the Internet using the specific processing unit 290 of the data processing device 12. The generation unit generates learning content based on the information collected by the specific processing unit 290 of the data processing device 12. The simulation unit provides virtual reality or scenario-based simulation using the control unit 46A of the smart glasses 214. The feedback unit analyzes the results of the simulation using the specific processing unit 290 of the data processing device 12 and provides feedback. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, collection unit, generation unit, simulation unit, and feedback unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit receives user input using the microphone 238 of the headset-type terminal 314. The collection unit collects information on the Internet using the specific processing unit 290 of the data processing device 12. The generation unit generates learning content based on the information collected by the specific processing unit 290 of the data processing device 12. The simulation unit provides virtual reality or scenario-based simulation using the control unit 46A of the headset-type terminal 314. The feedback unit analyzes the results of the simulation using the specific processing unit 290 of the data processing device 12 and provides feedback. === Hard Collateral 1-4 === Each of the multiple elements described above, including the reception unit, collection unit, generation unit, simulation unit, and feedback unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the reception unit receives user input using the microphone 238 of the robot 414. The collection unit collects information from the internet using the specific processing unit 290 of the data processing unit 12. The generation unit generates learning content based on the information collected by the specific processing unit 290 of the data processing unit 12. The simulation unit provides virtual reality or scenario-based simulations using the control unit 46A of the robot 414. The feedback unit analyzes the simulation results using the specific processing unit 290 of the data processing unit 12 and provides feedback.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The reception unit can analyze the user's past learning history and suggest the optimal theme input method. For example, the reception unit can automatically display themes that the user has frequently input in the past as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest themes to be used in a specific time period based on the user's past input history. This can improve the user's input efficiency by suggesting the optimal input method based on the past input history. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's past theme input history into a generation AI and have the generation AI suggest the optimal theme input method.

[0100] When collecting information, the collection unit can select an appropriate information source by referring to the user's past learning history. For example, the collection unit selects the most appropriate information source based on information sources used by the user in the past. The collection unit can also prioritize selecting highly reliable information sources based on the user's past learning history. Furthermore, the collection unit can analyze the user's past learning history and select the most efficient information source. This allows the user's learning efficiency to be improved by selecting the most appropriate information source based on the past learning history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past learning history data into the generation AI and cause the generation AI to select an appropriate information source.

[0101] When generating content, the generation unit can adjust the level of detail of the content based on the importance of the information. For example, the generation unit can provide detailed explanations for highly important information and summarize less important information concisely. The generation unit can also add images or videos to highly important information to make it easier to understand visually. Furthermore, the generation unit can highlight highly important information to attract the user's attention. By adjusting the level of detail of the content based on the importance of the information, the user's learning effect can be improved. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can adjust the level of detail of the content using an AI model for evaluating the importance of information.

[0102] During a simulation, the simulation unit can select an appropriate simulation method by referring to the user's past behavioral history. For example, the simulation unit selects an optimal simulation method based on simulations the user has performed in the past. The simulation unit can also select an effective simulation method from the user's past behavioral history. Furthermore, the simulation unit can analyze the user's past behavioral history and select the most efficient simulation method. This allows the user's learning effect to be improved by selecting an optimal simulation method based on the past behavioral history. Some or all of the above-described processing in the simulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the simulation unit can input the user's past behavioral history data into a generation AI and have the generation AI select an appropriate simulation method.

[0103] When providing feedback, the feedback unit can select an appropriate feedback method by referring to the user's past behavioral history. For example, the feedback unit selects the optimal feedback method based on feedback the user has received in the past. The feedback unit can also select an effective feedback method from the user's past behavioral history. Furthermore, the feedback unit can analyze the user's past behavioral history and select the most efficient feedback method. This allows the user's learning effect to be improved by selecting the optimal feedback method based on the past behavioral history. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's past behavioral history data into the generation AI and cause the generation AI to select an appropriate feedback method.

[0104] The reception unit can estimate the user's emotions and adjust the theme input method based on the estimated emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Alternatively, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and enable quick theme input. This improves user convenience by providing an input method that suits the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0105] The collection unit can estimate the user's emotions and adjust the information collection method based on the estimated emotions. For example, when the user is relaxed, the collection unit collects information from a wide range of information sources. Furthermore, when the user is in a hurry, the collection unit can also quickly collect information from reliable information sources. Furthermore, when the user is excited, the collection unit can prioritize collecting visually appealing information. This improves user convenience by providing an information collection method that corresponds to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the information collection method.

[0106] The generation unit can estimate the user's emotions and adjust the way the content is presented based on those emotions. For example, if the user is relaxed, the generation unit can generate content that proceeds at a leisurely pace. If the user is in a hurry, the generation unit can also generate concise content that gets straight to the point. Furthermore, if the user is excited, the generation unit can generate content with visually stimulating effects. This enhances the user's learning effect by providing content presentation methods that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the way the content is presented.

[0107] The simulation unit can estimate the user's emotions and adjust the simulation method based on the estimated emotions. For example, if the user is relaxed, the simulation unit can provide a simulation that proceeds at a leisurely pace. If the user is in a hurry, the simulation unit can also provide a concise simulation that gets straight to the point. Furthermore, if the user is excited, the simulation unit can provide a simulation with visually stimulating effects. By providing a simulation method that responds to the user's emotions, the user's learning effect can be enhanced. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the simulation unit may be performed using AI, for example, or not using AI. For example, the simulation unit can input user emotion data into the generative AI and have the generative AI adjust the simulation method.

[0108] The feedback unit can estimate the user's emotions and adjust the feedback method based on the estimated emotions. For example, if the user is relaxed, the feedback unit can provide detailed feedback. If the user is in a hurry, the feedback unit can also provide concise feedback that gets straight to the point. Furthermore, if the user is excited, the feedback unit can provide feedback with visually stimulating effects. This enhances the user's learning effect by providing feedback methods that are tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input the user's emotional data into the generation AI and cause the generation AI to adjust the feedback method.

[0109] The processing flow of the second embodiment will be briefly explained below.

[0110] Step 1: The reception desk receives the topic of public manners that the user wants to learn about. For example, if a user wants to learn about manners on trains, they can enter that topic into the reception desk. Step 2: The collection department collects relevant information based on the themes received by the reception department. For example, the collection department collects information from the internet, such as the latest news articles, academic papers, and video content. Step 3: The generation unit generates learning content based on the information collected by the collection unit. For example, the generation unit generates learning content in formats such as text, images, and videos based on the collected information. Step 4: The simulation unit performs a simulation based on the content generated by the generation unit to allow the user to actually practice public manners. For example, the simulation unit analyzes the user's behavior and provides a virtual reality simulation or a scenario-based simulation. Step 5: The feedback unit provides feedback based on the results of the simulation performed by the simulation unit. For example, the feedback unit analyzes the simulation results and provides text feedback, voice feedback, evaluation scores, etc.

[0111] 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.

[0112] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0113] 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.

[0114] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0129] 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.

[0130] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0145] 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.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 7, a 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0162] 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.

[0163] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] [Explanation of symbols]

[0183] 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 reception section for inputting the topic of public manners that a user wants to learn; a collection unit that collects related information based on the theme received by the reception unit; a generation unit that generates study content based on the information collected by the collection unit; a simulation unit that performs a simulation for a user to actually practice public manners based on the content generated by the generation unit; a feedback unit that provides feedback based on the results of the simulation performed by the simulation unit. A system characterized by:

2. The collecting unit Collecting information from the Internet 2. The system of claim 1.

3. The generation unit Generate learning content in the form of text, images, and videos based on collected information 2. The system of claim 1.

4. The simulation unit Analyze user behavior and perform simulations 2. The system of claim 1.

5. The feedback unit Providing feedback to the user based on the results of the simulation 2. The system of claim 1.

6. The reception unit Inferring the user's emotions and adjusting the theme input method based on the inferred emotions 2. The system of claim 1.

7. The reception unit Analyze the user's past theme input history and suggest the appropriate theme input method 2. The system of claim 1.

8. The reception unit As you type a topic, auto-suggest related topics based on your current interests 2. The system of claim 1.

9. The reception unit Estimate user emotions and prioritize topics based on the estimated emotions 2. The system of claim 1.

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