System

Abstract

An object of a system according to an embodiment is to enable participants to efficiently learn various educational contents.SOLUTION: A system includes a reception unit, a generation unit, a provision unit, a training unit, and a learning unit. The reception unit receives an input from a participant. The generation unit generates a portrait based on the information received by the reception unit. The providing section provides advice based on the portrait generated by the generating section. The practice part practices the English conversation based on the information received by the reception part. The learning unit supports learning of programming on the basis of the information received by the reception unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback of making it difficult for participants to efficiently learn a variety of educational content using a single system.

[0005] The system according to the embodiment aims to enable participants to efficiently learn a variety of educational content. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a provision unit, a practice unit, and a learning unit. The reception unit receives input from participants. The generation unit generates a portrait based on the information received by the reception unit. The provision unit provides advice based on the portrait generated by the generation unit. The practice unit practices English conversation based on the information received by the reception unit. The learning unit supports learning programming based on the information received by the reception unit. [Effects of the Invention]

[0007] The system according to the embodiment can enable participants to efficiently learn a variety of educational content. [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) An event proposal system according to an embodiment of the present invention accepts participants' input, and a generative AI creates a futuristic-designed event kit. The system plans events focused on areas likely to interest participants as part of their educational programs. The event proposal system accepts participants' input, and a generative AI creates a futuristic-designed event kit. The system plans events incorporating themes such as art, language, and programming. For example, participants upload their own photos, and a generative AI generates a portrait based on the photos. The generative AI also provides advice on the participants' drawings to help them improve their skills. Next, the event proposal system uses the generative AI as a practice partner for English conversation. Participants can improve their English skills by interacting with the generative AI. For example, they can engage in conversations tailored to various situations, such as everyday conversation and business English. Furthermore, the event proposal system uses the generative AI to support programming learning. Participants can learn the basics of programming through interaction with the generative AI and try writing actual code. For example, the generative AI guides participants through the programming steps for creating a simple game. This allows participants to have fun while learning new skills, potentially creating and acquiring customer service opportunities. This allows participants to have fun while learning new skills, and is expected to create and acquire customer service opportunities. For example, when participants upload their own photos, the generative AI generates a caricature based on the photo. The generative AI also provides advice on the drawings participants make, helping them improve their skills. Next, the generative AI acts as an English conversation practice partner, allowing participants to improve their English skills by interacting with the generative AI. Furthermore, the generative AI supports programming learning, allowing participants to learn the basics of programming through interaction with the generative AI and actually write code. This allows participants to have fun while learning new skills, and is expected to create and acquire customer service opportunities.

[0029] An event proposal system according to an embodiment includes a reception unit, a generation unit, a provision unit, a practice unit, and a learning unit. The reception unit receives input from participants. The participant's input includes, but is not limited to, text input, voice input, and image input. For example, the reception unit receives a question input by the participant in text. The reception unit can also receive a question input by voice from the participant. The reception unit can also receive an image uploaded by the participant. The generation unit generates a caricature based on the information received by the reception unit using a generation AI. For example, the generation unit generates a caricature based on a photo uploaded by the participant. The generation unit can also generate a caricature that reflects the participant's characteristics using the generation AI. The generation unit can also adjust the expression style of the caricature based on the participant's emotions using the generation AI. The provision unit provides advice based on the caricature generated by the generation unit. For example, the provision unit provides advice for a drawing drawn by the participant. The provision unit can also adjust the expression style of the advice based on the participant's emotions using the generation AI. The providing unit can also use the generation AI to improve the accuracy of advice by referring to the participant's past advice results. The practice unit provides English conversation practice based on the information received by the reception unit. The practice unit provides English conversation practice according to situations such as everyday conversation and business English, for example. The practice unit can also use the generation AI to adjust the way the English conversation practice is presented based on the participant's emotions. The practice unit can also use the generation AI to improve the accuracy of the practice by referring to the participant's past practice results. The learning unit supports programming learning based on the information received by the reception unit. The learning unit guides the participant through programming steps for creating a simple game, for example. The learning unit can also use the generation AI to adjust the way the programming practice is presented based on the participant's emotions. The learning unit can also use the generation AI to improve the accuracy of the learning by referring to the participant's past learning results.As a result, the event proposal system according to the embodiment can effectively implement various events by accepting input from participants and generating, providing, practicing, and learning from them.

[0030] The generation unit can generate caricatures based on photos uploaded by participants. For example, the generation unit generates caricatures based on photos uploaded by participants. The generation unit can also use a generation AI to generate caricatures that reflect the characteristics of the participants. The generation unit can also use the generation AI to adjust the expression method of the caricature based on the emotions of the participants. This makes it possible to provide a more personalized service by generating caricatures based on photos uploaded by participants. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input photos uploaded by participants into the generation AI and cause the generation AI to generate caricatures from the photos.

[0031] The providing unit can provide advice for a picture drawn by a participant. For example, the providing unit provides advice for a picture drawn by a participant. The providing unit can also use the generation AI to adjust the way the advice is expressed based on the participant's emotions. The providing unit can also use the generation AI to improve the accuracy of the advice by referring to past advice results given to the participant. This makes it possible to support skill improvement by providing advice for a picture drawn by a participant. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit can input a picture drawn by a participant into the generation AI and cause the generation AI to generate advice for the picture.

[0032] The practice unit can provide English conversation practice tailored to situations such as everyday conversation or business English. The practice unit provides English conversation practice tailored to situations such as everyday conversation or business English. The practice unit can also use a generation AI to adjust the way the English conversation practice is presented based on the participant's emotions. The practice unit can also use a generation AI to improve the accuracy of the practice by referring to the participant's past practice results. This allows participants to improve their English skills by practicing English conversation tailored to situations such as everyday conversation or business English. Some or all of the above-described processing in the practice unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the practice unit can input the content of the participant's English conversation practice into the generation AI and have the generation AI generate the practice content.

[0033] The learning unit can guide participants through the programming steps for creating a simple game. For example, the learning unit guides participants through the programming steps for creating a simple game. The learning unit can also use the generation AI to adjust the way programming learning is expressed based on the participants' emotions. The learning unit can also use the generation AI to improve the accuracy of learning by referring to the participants' past learning results. This allows participants to improve their programming skills by guiding them through the programming steps for creating a simple game. Some or all of the above-described processing in the learning unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the learning unit can input programming steps into the generation AI and have the generation AI execute the procedural guidance.

[0034] The reception unit can analyze the participant's past event participation history and select an appropriate reception method. For example, the reception unit can suggest the optimal reception method based on data on events the participant has previously participated in. The reception unit can also prioritize the selection of a reception method that the participant has previously preferred. The reception unit can also customize the optimal reception method by referring to the participant's past feedback. In this way, the optimal reception method can be selected by analyzing the participant's past event participation history. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the participant's past event participation history into the generation AI and have the generation AI select the optimal reception method.

[0035] The reception unit can perform filtering based on the participant's current interests or fields of interest. For example, the reception unit displays only events related to the participant's current fields of interest. The reception unit can also preferentially suggest related events based on the participant's fields of interest. The reception unit can also customize the content of the event according to the participant's interests. In this way, by filtering based on the participant's current interests and fields of interest, highly relevant events can be suggested. Some or all of the above-described processing in the reception unit may be performed using, or without, the generation AI, for example. For example, the reception unit can input data on the participant's interests and fields of interest into the generation AI and have the generation AI perform the filtering.

[0036] The reception unit can select the optimal reception means depending on the participant's input method. For example, if a participant desires voice input, the reception unit provides a reception means using voice recognition. If a participant desires text input, the reception unit can also provide an interface optimized for text input. Furthermore, if a participant uploads an image, the reception unit can also provide a reception means using image recognition. This allows for smoother reception by selecting the optimal reception means depending on the participant's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input data on the participant's input method into the generation AI and have the generation AI select the optimal reception means.

[0037] The reception unit can prioritize receiving highly relevant information by taking into account the geographical location information of the participant. For example, the reception unit prioritizes receiving nearby event information based on the participant's current location. The reception unit can also prioritize receiving related event information based on the participant's geographical location information. The reception unit can also suggest optimal event information by taking into account the participant's location information. In this way, highly relevant information can be prioritized by taking into account the participant's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, or without, the generation AI. For example, the reception unit can input the participant's geographical location information to the generation AI and cause the generation AI to receive highly relevant information.

[0038] The reception unit can analyze the participants' social media activities and receive related information. The reception unit can, for example, analyze the content of the participants' social media posts and receive related event information. The reception unit can also receive related event information by referring to the activities of the participants' friends on social media. The reception unit can also receive related event information based on the participants' social media check-in information. In this way, by analyzing the participants' social media activities, related information can be efficiently received. Some or all of the above-described processing in the reception unit can be performed using, or without, the generation AI. For example, the reception unit can input data on the participants' social media activities into the generation AI and have the generation AI receive the related information.

[0039] The reception unit can customize the reception method by reflecting the participants' past feedback. The reception unit can, for example, propose an optimal reception method based on feedback provided by the participants in the past. The reception unit can also customize the reception method by referring to the participants' past feedback. The reception unit can also optimize the reception procedure by reflecting the participants' feedback. In this way, the optimal reception method can be provided by reflecting the participants' past feedback. Some or all of the above-described processing in the reception unit may be performed using, or without, the generation AI, for example. For example, the reception unit can input data of the participants' past feedback into the generation AI and have the generation AI customize the reception method.

[0040] When generating a caricature, the generation unit can adjust the level of detail of the generation based on the features of the participant. For example, the generation unit generates a caricature that reflects the facial features of the participant in detail. The generation unit can also generate a caricature that reflects the features of the participant's hairstyle and clothing. The generation unit can also generate a caricature that reflects the facial expression and pose of the participant. By adjusting the level of detail of the generation based on the features of the participant, a more accurate caricature can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input feature data of the participant into the generation AI and cause the generation AI to generate a caricature.

[0041] When generating a caricature, the generation unit can apply different generation algorithms depending on the category of the participant. For example, the generation unit applies a caricature generation algorithm for children. The generation unit can also apply a caricature generation algorithm for adults. The generation unit can also apply a caricature generation algorithm for professionals. In this way, by applying different generation algorithms depending on the category of the participant, a more appropriate caricature can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input category data of the participant into the generation AI and cause the generation AI to apply the generation algorithm.

[0042] When generating a portrait, the generation unit can improve the accuracy of the generation by referring to the participant's past portrait results. The generation unit can improve the accuracy of the generation, for example, based on the participant's past portrait results. The generation unit can also improve the accuracy of the generation by referring to the participant's past feedback. The generation unit can also analyze the participant's past portrait results and apply an optimal generation algorithm. In this way, the generation accuracy can be improved by referring to the participant's past portrait results. Some or all of the above-mentioned processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the participant's past portrait result data into the generation AI and cause the generation AI to improve the accuracy of the generation.

[0043] When generating caricatures, the generation unit can determine the generation priority based on the time of submission of the participants. For example, if a participant submits early, the generation unit can generate a caricature with priority. If a participant is approaching the submission deadline, the generation unit can also generate a caricature quickly. The generation unit can also adjust the generation order based on the time of submission of the participants. This allows for appropriate responses according to the submission time by determining the generation priority based on the time of submission of the participants. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input participant submission time data into the generation AI and have the generation AI determine the generation priority.

[0044] When generating caricatures, the generation unit can adjust the order of generation based on the relevance of participants. For example, if a participant is attending an important event, the generation unit can prioritize generating caricatures. The generation unit can also prioritize generating caricatures if the participant belongs to a specific category. The generation unit can also adjust the order of generation based on the relevance of participants. This allows important participants to be given priority by adjusting the order of generation based on the relevance of participants. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input participant relevance data into the generation AI and cause the generation AI to adjust the order of generation.

[0045] When generating a caricature, the generation unit can adjust the use of technical terminology in the generation according to the participant's level of expertise. For example, if the participant is a beginner, the generation unit can provide an explanation that avoids technical terminology. If the participant is an intermediate learner, the generation unit can also provide an explanation that uses appropriate technical terminology. Furthermore, if the participant is an advanced learner, the generation unit can provide a detailed explanation that makes heavy use of technical terminology. In this way, by adjusting the use of technical terminology according to the participant's level of expertise, it is possible to provide a more easily understandable explanation. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the participant's level of expertise data into the generation AI and have the generation AI execute the use of technical terminology.

[0046] When providing advice, the providing unit can adjust the level of detail of the advice based on the importance of the picture drawn by the participant. For example, if the participant draws an important work, the providing unit can provide detailed advice. If the participant draws a practice work, the providing unit can also provide simplified advice. The providing unit can also adjust the level of detail of the advice based on the importance of the picture drawn by the participant. In this way, more appropriate advice can be provided by adjusting the level of detail of the advice based on the importance of the picture drawn by the participant. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, the generation AI, for example. For example, the providing unit can input importance data of the picture drawn by the participant to the generation AI and cause the generation AI to adjust the level of detail of the advice.

[0047] When providing advice, the providing unit can apply different advice algorithms depending on the category of the participant. For example, the providing unit applies an advice algorithm for children. The providing unit can also apply an advice algorithm for adults. The providing unit can also apply an advice algorithm for professionals. In this way, by applying different advice algorithms depending on the category of the participant, more appropriate advice can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input category data of the participant into the generation AI and cause the generation AI to apply the advice algorithm.

[0048] When providing advice, the providing unit can improve the accuracy of the advice by referring to the participant's past advice results. The providing unit improves the accuracy of the advice, for example, based on the participant's past advice results. The providing unit can also improve the accuracy of the advice by referring to the participant's past feedback. The providing unit can also analyze the participant's past advice results and apply an optimal advice algorithm. In this way, the accuracy of the advice can be improved by referring to the participant's past advice results. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the participant's past advice result data into the generation AI and cause the generation AI to improve the accuracy of the advice.

[0049] When providing advice, the providing unit can determine the priority of advice based on the participant's submission time. For example, if a participant submits early, the providing unit can provide advice preferentially. If a participant is approaching the submission deadline, the providing unit can also provide advice quickly. The providing unit can also adjust the order of advice based on the participant's submission time. In this way, by determining the priority of advice based on the participant's submission time, appropriate measures can be taken according to the submission time. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can input participant's submission time data into the generation AI and have the generation AI determine the priority of advice.

[0050] When providing advice, the providing unit can adjust the order of advice based on the relevance of the participant. For example, if the participant is attending an important event, the providing unit can provide advice preferentially. If the participant belongs to a specific category, the providing unit can also adjust the order of advice based on the relevance of the participant. In this way, by adjusting the order of advice based on the relevance of the participant, it is possible to give priority to important participants. Some or all of the above-mentioned processing in the providing unit may be performed using, or without using, the generation AI. For example, the providing unit can input participant relevance data into the generation AI and cause the generation AI to adjust the order of advice.

[0051] When providing advice, the providing unit can adjust the use of technical terminology in the advice according to the participant's level of expertise. For example, if the participant is a beginner, the providing unit can provide an explanation that avoids technical terminology. If the participant is an intermediate learner, the providing unit can also provide an explanation that uses appropriate technical terminology. Furthermore, if the participant is an advanced learner, the providing unit can also provide a detailed explanation that makes heavy use of technical terminology. In this way, by adjusting the use of technical terminology according to the participant's level of expertise, more understandable advice can be provided. Some or all of the above-described processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the participant's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0052] The practice unit can adjust the level of detail of the practice based on the participant's skill level during English conversation practice. For example, if the participant is a beginner, the practice unit can provide basic English conversation practice. If the participant is intermediate, the practice unit can also provide English conversation practice at a daily conversation level. Furthermore, if the participant is advanced, the practice unit can also provide business English or specialized English conversation practice. This allows for more appropriate practice by adjusting the level of detail of the practice based on the participant's skill level. Some or all of the above-described processing in the practice unit can be performed using, or without, a generation AI. For example, the practice unit can input the participant's skill level data into the generation AI and have the generation AI adjust the level of detail of the practice.

[0053] The practice unit can apply different practice algorithms depending on the participant's category during English conversation practice. For example, the practice unit applies an English conversation practice algorithm for children. The practice unit can also apply an English conversation practice algorithm for adults. The practice unit can also apply an English conversation practice algorithm for business. This allows for more appropriate practice by applying different practice algorithms depending on the participant's category. Some or all of the above-mentioned processing in the practice unit can be performed using, or without, a generation AI. For example, the practice unit can input participant category data into the generation AI and have the generation AI apply the practice algorithm.

[0054] The practice unit can improve the accuracy of English conversation practice by referring to the participant's past practice results. For example, the practice unit improves the accuracy of practice based on the participant's past practice results. The practice unit can also improve the accuracy of practice by referring to the participant's past feedback. The practice unit can also analyze the participant's past practice results and apply an optimal practice algorithm. In this way, the accuracy of practice can be improved by referring to the participant's past practice results. Some or all of the above-mentioned processing in the practice unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the practice unit can input the participant's past practice result data into the generation AI and have the generation AI improve the accuracy of the practice.

[0055] The practice unit can determine the priority of practice based on the participant's submission time during English conversation practice. For example, if a participant submits early, the practice unit can provide English conversation practice on a priority basis. The practice unit can also provide English conversation practice quickly if the participant is close to the submission deadline. The practice unit can also adjust the order of practice based on the participant's submission time. This allows for appropriate responses according to the submission time by determining the priority of practice based on the participant's submission time. Some or all of the above-mentioned processing in the practice unit may be performed using, or without, a generation AI. For example, the practice unit can input participant submission time data into the generation AI and have the generation AI determine the priority of practice.

[0056] The practice unit can adjust the order of practice based on the relevance of the participants during English conversation practice. For example, if a participant is attending an important event, the practice unit can prioritize providing English conversation practice. The practice unit can also prioritize providing English conversation practice if the participant belongs to a specific category. The practice unit can also adjust the order of practice based on the relevance of the participants. This allows important participants to be given priority by adjusting the order of practice based on the relevance of the participants. Some or all of the above-mentioned processing in the practice unit may be performed using, or without, a generation AI. For example, the practice unit can input participant relevance data into the generation AI and have the generation AI adjust the order of practice.

[0057] During English conversation practice, the practice unit can adjust the use of technical terminology in the practice according to the participant's level of expertise. For example, if the participant is a beginner, the practice unit can provide explanations that avoid technical terminology. If the participant is an intermediate learner, the practice unit can also provide explanations that use appropriate technical terminology. Furthermore, if the participant is an advanced learner, the practice unit can provide detailed explanations that make use of technical terminology. This allows for more understandable practice by adjusting the use of technical terminology according to the participant's level of expertise. Some or all of the above-described processing in the practice unit can be performed, for example, using or without the use of a generation AI. For example, the practice unit can input the participant's level of expertise data into the generation AI and have the generation AI execute the use of technical terminology.

[0058] The learning unit can adjust the level of detail of the learning based on the skill level of the participant during programming learning. For example, if the participant is a beginner, the learning unit can provide basic programming learning. If the participant is an intermediate learner, the learning unit can also provide applied programming learning. Furthermore, if the participant is an advanced learner, the learning unit can also provide specialized programming learning. In this way, by adjusting the level of detail of the learning based on the skill level of the participant, more appropriate learning can be provided. Some or all of the above-mentioned processing in the learning unit may be performed using, or without, a generation AI. For example, the learning unit can input the participant's skill level data into the generation AI and have the generation AI adjust the level of detail of the learning.

[0059] The learning unit can apply different learning algorithms depending on the category of the participant when learning programming. For example, the learning unit applies a programming learning algorithm for children. The learning unit can also apply a programming learning algorithm for adults. The learning unit can also apply a programming learning algorithm for professionals. This makes it possible to provide more appropriate learning by applying different learning algorithms depending on the category of the participant. Some or all of the above-mentioned processing in the learning unit may be performed using, or without, the generation AI, for example. For example, the learning unit can input the category data of the participant into the generation AI and cause the generation AI to apply the learning algorithm.

[0060] The learning unit can improve the accuracy of learning by referring to the participants' past learning results when learning programming. The learning unit improves the accuracy of learning, for example, based on the participants' past learning results. The learning unit can also improve the accuracy of learning by referring to the participants' past feedback. The learning unit can also analyze the participants' past learning results and apply an optimal learning algorithm. In this way, the accuracy of learning can be improved by referring to the participants' past learning results. Some or all of the above-mentioned processing in the learning unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the learning unit can input the participants' past learning result data into the generation AI and cause the generation AI to improve the accuracy of learning.

[0061] The learning unit can determine the priority of learning based on the time of submission of the participant when learning programming. For example, if a participant submits early, the learning unit can provide programming learning preferentially. If a participant is approaching the submission deadline, the learning unit can also provide programming learning quickly. The learning unit can also adjust the order of learning based on the time of submission of the participant. In this way, by determining the priority of learning based on the time of submission of the participant, appropriate measures can be taken according to the submission time. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the learning unit can input the submission time data of the participant into the generation AI and have the generation AI determine the priority of learning.

[0062] The learning unit can adjust the order of learning based on the relevance of the participants when learning programming. For example, if a participant is participating in an important project, the learning unit can provide programming learning preferentially. The learning unit can also provide programming learning preferentially if the participant belongs to a specific category. The learning unit can also adjust the order of learning based on the relevance of the participants. In this way, by adjusting the order of learning based on the relevance of the participants, important participants can be given priority. Some or all of the above-mentioned processing in the learning unit may be performed using, or without, the generation AI. For example, the learning unit can input the relevance data of the participants into the generation AI and cause the generation AI to adjust the order of learning.

[0063] The learning unit can adjust the use of technical terminology during programming learning according to the participant's level of expertise. For example, if the participant is a beginner, the learning unit can provide explanations that avoid technical terminology. If the participant is an intermediate learner, the learning unit can also provide explanations that use appropriate technical terminology. Furthermore, if the participant is an advanced learner, the learning unit can provide detailed explanations that make use of technical terminology. This makes it possible to provide learning that is easier to understand by adjusting the use of technical terminology according to the participant's level of expertise. Some or all of the above-mentioned processing in the learning unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the learning unit can input the participant's level of expertise data into the generation AI and have the generation AI execute the use of technical terminology.

[0064] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0065] The generation unit can also generate caricatures customized based on the participant's hobbies and interests. For example, if the participant likes sports, a sports-themed caricature can be generated. If the participant likes music, a caricature holding an instrument can be generated. Furthermore, if the participant likes a particular character or anime, a caricature incorporating that character can be generated. This allows for a more personalized service by providing caricatures that match the participant's hobbies and interests.

[0066] The advice provider can also adjust the format of advice according to the participant's learning style. For example, advice using diagrams and illustrations can be provided to visual learners. Audio advice can also be provided to auditory learners. Furthermore, advice for hands-on learning can be provided to experiential learners. This allows for more effective learning support by providing advice according to the participant's learning style.

[0067] The practice section can also customize the content of English conversation practice according to the participant's cultural background. For example, if a participant is from Asia, English conversation practice related to Asian culture and customs can be provided. If a participant is from Europe, English conversation practice related to European culture and customs can be provided. Furthermore, if a participant is interested in a particular country or region, English conversation practice related to that country or region can be provided. This makes it possible to provide English conversation practice that is tailored to the participant's cultural background, making the practice more interesting.

[0068] The learning department can also adjust the difficulty of programming lessons according to the participant's learning progress. For example, if a participant has mastered the basics, it can provide applied content as the next step. Also, if a participant is struggling with a particular task, it can provide supplementary explanations and additional practice problems for that task. Furthermore, if a participant is interested in a particular field, it can provide programming tasks related to that field. This allows for more effective learning support by providing programming lessons according to the participant's learning progress.

[0069] The reception unit can analyze the participant's past event participation history and select an appropriate reception method. For example, the reception unit can suggest the optimal reception method based on data on events the participant has previously participated in. The reception unit can also prioritize the selection of a reception method that the participant has previously preferred. The reception unit can also customize the optimal reception method by referring to the participant's past feedback. In this way, the optimal reception method can be selected by analyzing the participant's past event participation history. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the participant's past event participation history into the generation AI and have the generation AI select the optimal reception method.

[0070] The reception unit can perform filtering based on the participant's current interests or fields of interest. For example, it can display only events related to the participant's current fields of interest. The reception unit can also preferentially suggest related events based on the participant's fields of interest. The reception unit can also customize the content of the event according to the participant's interests. In this way, by filtering based on the participant's current interests and fields of interest, it is possible to suggest highly relevant events. Some or all of the above-described processing in the reception unit may be performed using, or without, the generation AI, for example. For example, the reception unit can input data on the participant's interests and fields of interest into the generation AI and have the generation AI perform the filtering.

[0071] The reception unit can select the optimal reception means depending on the participant's input method. For example, if a participant desires voice input, the reception unit can provide a reception means using voice recognition. If a participant desires text input, the reception unit can also provide an interface optimized for text input. Furthermore, if a participant uploads an image, the reception unit can also provide a reception means using image recognition. This allows for smoother reception by selecting the optimal reception means depending on the participant's input method. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input data on the participant's input method into the generation AI and have the generation AI select the optimal reception means.

[0072] The processing flow of the first embodiment will be briefly explained below.

[0073] Step 1: The reception unit receives input from participants. The input from participants includes text input, voice input, image input, etc. For example, the reception unit receives input of questions by text, voice input, and image upload from participants. Step 2: The generation unit uses generation AI to generate a caricature based on the information received by the reception unit. For example, the generation unit can generate a caricature based on a photo uploaded by the participant, or adjust the caricature's expression based on the participant's characteristics and emotions. Step 3: The providing unit provides advice based on the portrait generated by the generating unit. For example, the providing unit can provide advice based on the drawing of the participant, or can adjust the way the advice is expressed based on the participant's emotions and past advice results. Step 4: The practice section conducts English conversation practice based on the information received by the reception section. For example, they practice English conversation according to situations such as everyday conversation or business English, and can adjust the way they practice based on the participants' emotions and past practice results. Step 5: The learning unit supports programming learning based on the information received by the reception unit. For example, it can guide participants through the programming steps for creating a simple game and adjust the way they express their learning based on their emotions and past learning results.

[0074] (Example 2) An event proposal system according to an embodiment of the present invention accepts participants' input, and a generative AI creates a futuristic-designed event kit. The system plans events focused on areas likely to interest participants as part of their educational programs. The event proposal system accepts participants' input, and a generative AI creates a futuristic-designed event kit. The system plans events incorporating themes such as art, language, and programming. For example, participants upload their own photos, and a generative AI generates a portrait based on the photos. The generative AI also provides advice on the participants' drawings to help them improve their skills. Next, the event proposal system uses the generative AI as a practice partner for English conversation. Participants can improve their English skills by interacting with the generative AI. For example, they can engage in conversations tailored to various situations, such as everyday conversation and business English. Furthermore, the event proposal system uses the generative AI to support programming learning. Participants can learn the basics of programming through interaction with the generative AI and try writing actual code. For example, the generative AI guides participants through the programming steps for creating a simple game. This allows participants to have fun while learning new skills, potentially creating and acquiring customer service opportunities. This allows participants to have fun while learning new skills, and is expected to create and acquire customer service opportunities. For example, when participants upload their own photos, the generative AI generates a caricature based on the photo. The generative AI also provides advice on the drawings participants make, helping them improve their skills. Next, the generative AI acts as an English conversation practice partner, allowing participants to improve their English skills by interacting with the generative AI. Furthermore, the generative AI supports programming learning, allowing participants to learn the basics of programming through interaction with the generative AI and actually write code. This allows participants to have fun while learning new skills, and is expected to create and acquire customer service opportunities.

[0075] An event proposal system according to an embodiment includes a reception unit, a generation unit, a provision unit, a practice unit, and a learning unit. The reception unit receives input from participants. The participant's input includes, but is not limited to, text input, voice input, and image input. For example, the reception unit receives a question input by the participant in text. The reception unit can also receive a question input by voice from the participant. The reception unit can also receive an image uploaded by the participant. The generation unit generates a caricature based on the information received by the reception unit using a generation AI. For example, the generation unit generates a caricature based on a photo uploaded by the participant. The generation unit can also generate a caricature that reflects the participant's characteristics using the generation AI. The generation unit can also adjust the expression style of the caricature based on the participant's emotions using the generation AI. The provision unit provides advice based on the caricature generated by the generation unit. For example, the provision unit provides advice for a drawing drawn by the participant. The provision unit can also adjust the expression style of the advice based on the participant's emotions using the generation AI. The providing unit can also use the generation AI to improve the accuracy of advice by referring to the participant's past advice results. The practice unit provides English conversation practice based on the information received by the reception unit. The practice unit provides English conversation practice according to situations such as everyday conversation and business English, for example. The practice unit can also use the generation AI to adjust the way the English conversation practice is presented based on the participant's emotions. The practice unit can also use the generation AI to improve the accuracy of the practice by referring to the participant's past practice results. The learning unit supports programming learning based on the information received by the reception unit. The learning unit guides the participant through programming steps for creating a simple game, for example. The learning unit can also use the generation AI to adjust the way the programming practice is presented based on the participant's emotions. The learning unit can also use the generation AI to improve the accuracy of the learning by referring to the participant's past learning results.As a result, the event proposal system according to the embodiment can effectively implement various events by accepting input from participants and generating, providing, practicing, and learning from them.

[0076] The generation unit can generate caricatures based on photos uploaded by participants. For example, the generation unit generates caricatures based on photos uploaded by participants. The generation unit can also use a generation AI to generate caricatures that reflect the characteristics of the participants. The generation unit can also use the generation AI to adjust the expression method of the caricature based on the emotions of the participants. This makes it possible to provide a more personalized service by generating caricatures based on photos uploaded by participants. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input photos uploaded by participants into the generation AI and cause the generation AI to generate caricatures from the photos.

[0077] The providing unit can provide advice for a picture drawn by a participant. For example, the providing unit provides advice for a picture drawn by a participant. The providing unit can also use the generation AI to adjust the way the advice is expressed based on the participant's emotions. The providing unit can also use the generation AI to improve the accuracy of the advice by referring to past advice results given to the participant. This makes it possible to support skill improvement by providing advice for a picture drawn by a participant. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit can input a picture drawn by a participant into the generation AI and cause the generation AI to generate advice for the picture.

[0078] The practice unit can provide English conversation practice tailored to situations such as everyday conversation or business English. The practice unit provides English conversation practice tailored to situations such as everyday conversation or business English. The practice unit can also use a generation AI to adjust the way the English conversation practice is presented based on the participant's emotions. The practice unit can also use a generation AI to improve the accuracy of the practice by referring to the participant's past practice results. This allows participants to improve their English skills by practicing English conversation tailored to situations such as everyday conversation or business English. Some or all of the above-described processing in the practice unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the practice unit can input the content of the participant's English conversation practice into the generation AI and have the generation AI generate the practice content.

[0079] The learning unit can guide participants through the programming steps for creating a simple game. For example, the learning unit guides participants through the programming steps for creating a simple game. The learning unit can also use the generation AI to adjust the way programming learning is expressed based on the participants' emotions. The learning unit can also use the generation AI to improve the accuracy of learning by referring to the participants' past learning results. This allows participants to improve their programming skills by guiding them through the programming steps for creating a simple game. Some or all of the above-described processing in the learning unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the learning unit can input programming steps into the generation AI and have the generation AI execute the procedural guidance.

[0080] The reception unit can estimate the emotions of participants and adjust the timing of input reception based on the estimated emotions of the participants. For example, if a participant is nervous, the reception unit can delay the timing of input reception to allow the participant to relax. If a participant is excited, the reception unit can also advance the timing to quickly accept input. Furthermore, if a participant is tired, the reception unit can accept input after a break. In this way, by adjusting the timing of input reception according to the participant's emotions, input can be accepted at a more appropriate timing. 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-mentioned processing in the reception unit can be performed using, for example, the generation AI. For example, the reception unit can input participant emotion data to the generation AI and have the generation AI execute emotion estimation.

[0081] The reception unit can analyze the participant's past event participation history and select an appropriate reception method. For example, the reception unit can suggest the optimal reception method based on data on events the participant has previously participated in. The reception unit can also prioritize the selection of a reception method that the participant has previously preferred. The reception unit can also customize the optimal reception method by referring to the participant's past feedback. In this way, the optimal reception method can be selected by analyzing the participant's past event participation history. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the participant's past event participation history into the generation AI and have the generation AI select the optimal reception method.

[0082] The reception unit can perform filtering based on the participant's current interests or fields of interest. For example, the reception unit displays only events related to the participant's current fields of interest. The reception unit can also preferentially suggest related events based on the participant's fields of interest. The reception unit can also customize the content of the event according to the participant's interests. In this way, by filtering based on the participant's current interests and fields of interest, highly relevant events can be suggested. Some or all of the above-described processing in the reception unit may be performed using, or without, the generation AI, for example. For example, the reception unit can input data on the participant's interests and fields of interest into the generation AI and have the generation AI perform the filtering.

[0083] The reception unit can select the optimal reception means depending on the participant's input method. For example, if a participant desires voice input, the reception unit provides a reception means using voice recognition. If a participant desires text input, the reception unit can also provide an interface optimized for text input. Furthermore, if a participant uploads an image, the reception unit can also provide a reception means using image recognition. This allows for smoother reception by selecting the optimal reception means depending on the participant's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input data on the participant's input method into the generation AI and have the generation AI select the optimal reception means.

[0084] The reception unit can estimate the emotions of participants and determine the priority of information to be received based on the estimated emotions of the participants. For example, if a participant is nervous, the reception unit can prioritize receiving important information. If a participant is relaxed, the reception unit can also prioritize receiving detailed information. Furthermore, if a participant is in a hurry, the reception unit can prioritize receiving the minimum necessary information. In this way, by determining the priority of information to be received according to the emotions of participants, important information can be received preferentially. 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-mentioned processing in the reception unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the reception unit can input emotion data of participants into the generation AI and have the generation AI execute emotion estimation.

[0085] The reception unit can prioritize receiving highly relevant information by taking into account the geographical location information of the participant. For example, the reception unit prioritizes receiving nearby event information based on the participant's current location. The reception unit can also prioritize receiving related event information based on the participant's geographical location information. The reception unit can also suggest optimal event information by taking into account the participant's location information. In this way, highly relevant information can be prioritized by taking into account the participant's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, or without, the generation AI. For example, the reception unit can input the participant's geographical location information to the generation AI and cause the generation AI to receive highly relevant information.

[0086] The reception unit can analyze the participants' social media activities and receive related information. The reception unit can, for example, analyze the content of the participants' social media posts and receive related event information. The reception unit can also receive related event information by referring to the activities of the participants' friends on social media. The reception unit can also receive related event information based on the participants' social media check-in information. In this way, by analyzing the participants' social media activities, related information can be efficiently received. Some or all of the above-described processing in the reception unit can be performed using, or without, the generation AI. For example, the reception unit can input data on the participants' social media activities into the generation AI and have the generation AI receive the related information.

[0087] The reception unit can customize the reception method by reflecting the participants' past feedback. The reception unit can, for example, propose an optimal reception method based on feedback provided by the participants in the past. The reception unit can also customize the reception method by referring to the participants' past feedback. The reception unit can also optimize the reception procedure by reflecting the participants' feedback. In this way, the optimal reception method can be provided by reflecting the participants' past feedback. Some or all of the above-described processing in the reception unit may be performed using, or without, the generation AI, for example. For example, the reception unit can input data of the participants' past feedback into the generation AI and have the generation AI customize the reception method.

[0088] The generation unit can estimate the participant's emotions and adjust the caricature expression method based on the estimated participant's emotions. For example, if the participant is relaxed, the generation unit can generate a caricature with a soft touch. If the participant is excited, the generation unit can also generate a caricature using vivid colors. If the participant is nervous, the generation unit can also generate a caricature using subdued colors. This allows for adjusting the caricature expression method according to the participant's emotions, thereby providing a more appropriate caricature. 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 generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can input the participant's emotion data into the generation AI and cause the generation AI to adjust the caricature expression method.

[0089] When generating a caricature, the generation unit can adjust the level of detail of the generation based on the features of the participant. For example, the generation unit generates a caricature that reflects the facial features of the participant in detail. The generation unit can also generate a caricature that reflects the features of the participant's hairstyle and clothing. The generation unit can also generate a caricature that reflects the facial expression and pose of the participant. By adjusting the level of detail of the generation based on the features of the participant, a more accurate caricature can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input feature data of the participant into the generation AI and cause the generation AI to generate a caricature.

[0090] When generating a caricature, the generation unit can apply different generation algorithms depending on the category of the participant. For example, the generation unit applies a caricature generation algorithm for children. The generation unit can also apply a caricature generation algorithm for adults. The generation unit can also apply a caricature generation algorithm for professionals. In this way, by applying different generation algorithms depending on the category of the participant, a more appropriate caricature can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input category data of the participant into the generation AI and cause the generation AI to apply the generation algorithm.

[0091] When generating a portrait, the generation unit can improve the accuracy of the generation by referring to the participant's past portrait results. The generation unit can improve the accuracy of the generation, for example, based on the participant's past portrait results. The generation unit can also improve the accuracy of the generation by referring to the participant's past feedback. The generation unit can also analyze the participant's past portrait results and apply an optimal generation algorithm. In this way, the generation accuracy can be improved by referring to the participant's past portrait results. Some or all of the above-mentioned processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the participant's past portrait result data into the generation AI and cause the generation AI to improve the accuracy of the generation.

[0092] The generation unit can estimate the emotion of the participant and adjust the length of the caricature based on the estimated emotion of the participant. For example, if the participant is relaxed, the generation unit can generate a detailed caricature. If the participant is in a hurry, the generation unit can also generate a simplified caricature. Furthermore, if the participant is excited, the generation unit can generate a visually stimulating caricature. This allows for adjusting the length of the caricature according to the emotion of the participant, thereby providing a more appropriate caricature. Emotion estimation is achieved 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 generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can input emotion data of the participant into the generation AI and cause the generation AI to adjust the length of the caricature.

[0093] When generating caricatures, the generation unit can determine the generation priority based on the time of submission of the participants. For example, if a participant submits early, the generation unit can generate a caricature with priority. If a participant is approaching the submission deadline, the generation unit can also generate a caricature quickly. The generation unit can also adjust the generation order based on the time of submission of the participants. This allows for appropriate responses according to the submission time by determining the generation priority based on the time of submission of the participants. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input participant submission time data into the generation AI and have the generation AI determine the generation priority.

[0094] When generating caricatures, the generation unit can adjust the order of generation based on the relevance of participants. For example, if a participant is attending an important event, the generation unit can prioritize generating caricatures. The generation unit can also prioritize generating caricatures if the participant belongs to a specific category. The generation unit can also adjust the order of generation based on the relevance of participants. This allows important participants to be given priority by adjusting the order of generation based on the relevance of participants. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input participant relevance data into the generation AI and cause the generation AI to adjust the order of generation.

[0095] When generating a caricature, the generation unit can adjust the use of technical terminology in the generation according to the participant's level of expertise. For example, if the participant is a beginner, the generation unit can provide an explanation that avoids technical terminology. If the participant is an intermediate learner, the generation unit can also provide an explanation that uses appropriate technical terminology. Furthermore, if the participant is an advanced learner, the generation unit can provide a detailed explanation that makes heavy use of technical terminology. In this way, by adjusting the use of technical terminology according to the participant's level of expertise, it is possible to provide a more easily understandable explanation. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the participant's level of expertise data into the generation AI and have the generation AI execute the use of technical terminology.

[0096] The providing unit can estimate the emotion of a participant and adjust the way in which advice is expressed based on the estimated emotion of the participant. For example, if the participant is relaxed, the providing unit can provide advice using soft expressions. If the participant is nervous, the providing unit can also provide advice using calm expressions. Furthermore, if the participant is excited, the providing unit can also provide advice using visually stimulating expressions. This allows for adjusting the way in which advice is expressed according to the emotion of the participant, thereby providing more appropriate advice. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may 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 providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit can input emotion data of the participant into the generation AI and cause the generation AI to adjust the way in which advice is expressed.

[0097] When providing advice, the providing unit can adjust the level of detail of the advice based on the importance of the picture drawn by the participant. For example, if the participant draws an important work, the providing unit can provide detailed advice. If the participant draws a practice work, the providing unit can also provide simplified advice. The providing unit can also adjust the level of detail of the advice based on the importance of the picture drawn by the participant. In this way, more appropriate advice can be provided by adjusting the level of detail of the advice based on the importance of the picture drawn by the participant. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, the generation AI, for example. For example, the providing unit can input importance data of the picture drawn by the participant to the generation AI and cause the generation AI to adjust the level of detail of the advice.

[0098] When providing advice, the providing unit can apply different advice algorithms depending on the category of the participant. For example, the providing unit applies an advice algorithm for children. The providing unit can also apply an advice algorithm for adults. The providing unit can also apply an advice algorithm for professionals. In this way, by applying different advice algorithms depending on the category of the participant, more appropriate advice can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input category data of the participant into the generation AI and cause the generation AI to apply the advice algorithm.

[0099] When providing advice, the providing unit can improve the accuracy of the advice by referring to the participant's past advice results. The providing unit improves the accuracy of the advice, for example, based on the participant's past advice results. The providing unit can also improve the accuracy of the advice by referring to the participant's past feedback. The providing unit can also analyze the participant's past advice results and apply an optimal advice algorithm. In this way, the accuracy of the advice can be improved by referring to the participant's past advice results. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the participant's past advice result data into the generation AI and cause the generation AI to improve the accuracy of the advice.

[0100] The providing unit can estimate the emotion of a participant and adjust the length of advice based on the estimated emotion of the participant. For example, if the participant is relaxed, the providing unit can provide detailed advice. If the participant is in a hurry, the providing unit can also provide simplified advice. Furthermore, if the participant is excited, the providing unit can also provide visually stimulating advice. This allows for adjusting the length of advice according to the emotion of the participant, thereby providing more appropriate advice. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, 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 providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit can input emotion data of the participant into the generation AI and cause the generation AI to adjust the length of the advice.

[0101] When providing advice, the providing unit can determine the priority of advice based on the participant's submission time. For example, if a participant submits early, the providing unit can provide advice preferentially. If a participant is approaching the submission deadline, the providing unit can also provide advice quickly. The providing unit can also adjust the order of advice based on the participant's submission time. In this way, by determining the priority of advice based on the participant's submission time, appropriate measures can be taken according to the submission time. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can input participant's submission time data into the generation AI and have the generation AI determine the priority of advice.

[0102] When providing advice, the providing unit can adjust the order of advice based on the relevance of the participant. For example, if the participant is attending an important event, the providing unit can provide advice preferentially. If the participant belongs to a specific category, the providing unit can also adjust the order of advice based on the relevance of the participant. In this way, by adjusting the order of advice based on the relevance of the participant, it is possible to give priority to important participants. Some or all of the above-mentioned processing in the providing unit may be performed using, or without using, the generation AI. For example, the providing unit can input participant relevance data into the generation AI and cause the generation AI to adjust the order of advice.

[0103] When providing advice, the providing unit can adjust the use of technical terminology in the advice according to the participant's level of expertise. For example, if the participant is a beginner, the providing unit can provide an explanation that avoids technical terminology. If the participant is an intermediate learner, the providing unit can also provide an explanation that uses appropriate technical terminology. Furthermore, if the participant is an advanced learner, the providing unit can also provide a detailed explanation that makes heavy use of technical terminology. In this way, by adjusting the use of technical terminology according to the participant's level of expertise, more understandable advice can be provided. Some or all of the above-described processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the participant's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0104] The practice unit can estimate the participant's emotions and adjust the way the English conversation practice is presented based on the estimated participant's emotions. For example, if the participant is relaxed, the practice unit can provide the English conversation practice using softer expressions. If the participant is nervous, the practice unit can provide the English conversation practice using calmer expressions. Furthermore, if the participant is excited, the practice unit can provide the English conversation practice using visually stimulating expressions. This allows for more appropriate practice by adjusting the way the English conversation practice is presented based on the participant'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-described processing in the practice unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the practice unit can input the participant's emotion data into the generation AI and have the generation AI adjust the way the English conversation practice is presented.

[0105] The practice unit can adjust the level of detail of the practice based on the participant's skill level during English conversation practice. For example, if the participant is a beginner, the practice unit can provide basic English conversation practice. If the participant is intermediate, the practice unit can also provide English conversation practice at a daily conversation level. Furthermore, if the participant is advanced, the practice unit can also provide business English or specialized English conversation practice. This allows for more appropriate practice by adjusting the level of detail of the practice based on the participant's skill level. Some or all of the above-described processing in the practice unit can be performed using, or without, a generation AI. For example, the practice unit can input the participant's skill level data into the generation AI and have the generation AI adjust the level of detail of the practice.

[0106] The practice unit can apply different practice algorithms depending on the participant's category during English conversation practice. For example, the practice unit applies an English conversation practice algorithm for children. The practice unit can also apply an English conversation practice algorithm for adults. The practice unit can also apply an English conversation practice algorithm for business. This allows for more appropriate practice by applying different practice algorithms depending on the participant's category. Some or all of the above-mentioned processing in the practice unit can be performed using, or without, a generation AI. For example, the practice unit can input participant category data into the generation AI and have the generation AI apply the practice algorithm.

[0107] The practice unit can improve the accuracy of English conversation practice by referring to the participant's past practice results. For example, the practice unit improves the accuracy of practice based on the participant's past practice results. The practice unit can also improve the accuracy of practice by referring to the participant's past feedback. The practice unit can also analyze the participant's past practice results and apply an optimal practice algorithm. In this way, the accuracy of practice can be improved by referring to the participant's past practice results. Some or all of the above-mentioned processing in the practice unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the practice unit can input the participant's past practice result data into the generation AI and have the generation AI improve the accuracy of the practice.

[0108] The practice unit can estimate the participant's emotions and adjust the length of the English conversation practice based on the estimated participant's emotions. For example, if the participant is relaxed, the practice unit can provide detailed English conversation practice. If the participant is in a hurry, the practice unit can also provide simplified English conversation practice. Furthermore, if the participant is excited, the practice unit can provide visually stimulating English conversation practice. This allows for more appropriate practice by adjusting the length of the English conversation practice according to the participant'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 practice unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the practice unit can input the participant's emotion data into the generation AI and have the generation AI adjust the length of the English conversation practice.

[0109] The practice unit can determine the priority of practice based on the participant's submission time during English conversation practice. For example, if a participant submits early, the practice unit can provide English conversation practice on a priority basis. The practice unit can also provide English conversation practice quickly if the participant is close to the submission deadline. The practice unit can also adjust the order of practice based on the participant's submission time. This allows for appropriate responses according to the submission time by determining the priority of practice based on the participant's submission time. Some or all of the above-mentioned processing in the practice unit may be performed using, or without, a generation AI. For example, the practice unit can input participant submission time data into the generation AI and have the generation AI determine the priority of practice.

[0110] The practice unit can adjust the order of practice based on the relevance of the participants during English conversation practice. For example, if a participant is attending an important event, the practice unit can prioritize providing English conversation practice. The practice unit can also prioritize providing English conversation practice if the participant belongs to a specific category. The practice unit can also adjust the order of practice based on the relevance of the participants. This allows important participants to be given priority by adjusting the order of practice based on the relevance of the participants. Some or all of the above-mentioned processing in the practice unit may be performed using, or without, a generation AI. For example, the practice unit can input participant relevance data into the generation AI and have the generation AI adjust the order of practice.

[0111] During English conversation practice, the practice unit can adjust the use of technical terminology in the practice according to the participant's level of expertise. For example, if the participant is a beginner, the practice unit can provide explanations that avoid technical terminology. If the participant is an intermediate learner, the practice unit can also provide explanations that use appropriate technical terminology. Furthermore, if the participant is an advanced learner, the practice unit can provide detailed explanations that make use of technical terminology. This allows for more understandable practice by adjusting the use of technical terminology according to the participant's level of expertise. Some or all of the above-described processing in the practice unit can be performed, for example, using or without the use of a generation AI. For example, the practice unit can input the participant's level of expertise data into the generation AI and have the generation AI execute the use of technical terminology.

[0112] The learning unit can estimate the emotions of the participants and adjust the expression method of the programming lesson based on the estimated emotions of the participants. For example, if the participants are relaxed, the learning unit can use soft expressions to teach the programming lesson. If the participants are nervous, the learning unit can use calm expressions to teach the programming lesson. Furthermore, if the participants are excited, the learning unit can use visually stimulating expressions to teach the programming lesson. This allows for more appropriate learning by adjusting the expression method of the programming lesson according to the participants' emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 learning unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the learning unit can input the participants' emotion data into the generation AI and cause the generation AI to adjust the expression method of the programming lesson.

[0113] The learning unit can adjust the level of detail of the learning based on the skill level of the participant during programming learning. For example, if the participant is a beginner, the learning unit can provide basic programming learning. If the participant is an intermediate learner, the learning unit can also provide applied programming learning. Furthermore, if the participant is an advanced learner, the learning unit can also provide specialized programming learning. In this way, by adjusting the level of detail of the learning based on the skill level of the participant, more appropriate learning can be provided. Some or all of the above-mentioned processing in the learning unit may be performed using, or without, a generation AI. For example, the learning unit can input the participant's skill level data into the generation AI and have the generation AI adjust the level of detail of the learning.

[0114] The learning unit can apply different learning algorithms depending on the category of the participant when learning programming. For example, the learning unit applies a programming learning algorithm for children. The learning unit can also apply a programming learning algorithm for adults. The learning unit can also apply a programming learning algorithm for professionals. This makes it possible to provide more appropriate learning by applying different learning algorithms depending on the category of the participant. Some or all of the above-mentioned processing in the learning unit may be performed using, or without, the generation AI, for example. For example, the learning unit can input the category data of the participant into the generation AI and cause the generation AI to apply the learning algorithm.

[0115] The learning unit can improve the accuracy of learning by referring to the participants' past learning results when learning programming. The learning unit improves the accuracy of learning, for example, based on the participants' past learning results. The learning unit can also improve the accuracy of learning by referring to the participants' past feedback. The learning unit can also analyze the participants' past learning results and apply an optimal learning algorithm. In this way, the accuracy of learning can be improved by referring to the participants' past learning results. Some or all of the above-mentioned processing in the learning unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the learning unit can input the participants' past learning result data into the generation AI and cause the generation AI to improve the accuracy of learning.

[0116] The learning unit can estimate the emotions of the participants and adjust the length of the programming lesson based on the estimated emotions of the participants. For example, if the participants are relaxed, the learning unit can provide detailed programming lessons. If the participants are in a hurry, the learning unit can also provide simplified programming lessons. Furthermore, if the participants are excited, the learning unit can also provide visually stimulating programming lessons. This allows for more appropriate learning by adjusting the length of the programming lesson according to the participants' 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 learning unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the learning unit can input the participants' emotion data into the generation AI and cause the generation AI to adjust the length of the programming lesson.

[0117] The learning unit can determine the priority of learning based on the time of submission of the participant when learning programming. For example, if a participant submits early, the learning unit can provide programming learning preferentially. If a participant is approaching the submission deadline, the learning unit can also provide programming learning quickly. The learning unit can also adjust the order of learning based on the time of submission of the participant. In this way, by determining the priority of learning based on the time of submission of the participant, appropriate measures can be taken according to the submission time. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the learning unit can input the submission time data of the participant into the generation AI and have the generation AI determine the priority of learning.

[0118] The learning unit can adjust the order of learning based on the relevance of the participants when learning programming. For example, if a participant is participating in an important project, the learning unit can provide programming learning preferentially. The learning unit can also provide programming learning preferentially if the participant belongs to a specific category. The learning unit can also adjust the order of learning based on the relevance of the participants. In this way, by adjusting the order of learning based on the relevance of the participants, important participants can be given priority. Some or all of the above-mentioned processing in the learning unit may be performed using, or without, the generation AI. For example, the learning unit can input the relevance data of the participants into the generation AI and cause the generation AI to adjust the order of learning.

[0119] The learning unit can adjust the use of technical terminology during programming learning according to the participant's level of expertise. For example, if the participant is a beginner, the learning unit can provide explanations that avoid technical terminology. If the participant is an intermediate learner, the learning unit can also provide explanations that use appropriate technical terminology. Furthermore, if the participant is an advanced learner, the learning unit can provide detailed explanations that make use of technical terminology. This makes it possible to provide learning that is easier to understand by adjusting the use of technical terminology according to the participant's level of expertise. Some or all of the above-mentioned processing in the learning unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the learning unit can input the participant's level of expertise data into the generation AI and have the generation AI execute the use of technical terminology. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, generation unit, provision unit, practice unit, and learning unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can receive participant input via the reception device 38 of the smart device 14 or the communication I / F 26 of the data processing device 12. The generation unit can generate a portrait using a generation AI, for example, by the specific processing unit 290 of the data processing device 12. The provision unit can provide advice, for example, by the output device 40 of the smart device 14 or the specific processing unit 290 of the data processing device 12. The practice unit can practice English conversation, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The learning unit can support programming learning, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, generation unit, provision unit, practice unit, and learning unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can receive input from participants via the microphone 238 of the smart glasses 214 or the communication I / F 26 of the data processing device 12. The generation unit can generate a portrait using a generation AI, for example, by the specific processing unit 290 of the data processing device 12. The provision unit can provide advice, for example, by the speaker 240 of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The practice unit can practice English conversation, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The learning unit can support programming learning, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-described reception unit, generation unit, provision unit, practice unit, and learning 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 can receive input from participants via the microphone 238 of the headset-type terminal 314 or the communication I / F 26 of the data processing device 12. The generation unit can generate a portrait using a generation AI, for example, by the specific processing unit 290 of the data processing device 12. The provision unit can provide advice, for example, by the speaker 240 of the headset-type terminal 314 or the specific processing unit 290 of the data processing device 12. The practice unit can practice English conversation, for example, by the control unit 46A of the headset-type terminal 314 or the specific processing unit 290 of the data processing device 12. The learning unit can support programming learning, for example, by the control unit 46A of the headset-type terminal 314 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-described reception unit, generation unit, provision unit, practice unit, and learning unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can receive input from participants via the microphone 238 of the robot 414 or the communication I / F 26 of the data processing device 12. The generation unit can generate a portrait using a generation AI, for example, by the specific processing unit 290 of the data processing device 12. The provision unit can provide advice, for example, by the speaker 240 of the robot 414 or the specific processing unit 290 of the data processing device 12. The practice unit can practice English conversation, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The learning unit can support programming learning, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12.

[0120] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0121] The event suggestion system can also be equipped with a health management unit that monitors the health of participants. The health management unit measures participants' heart rates and stress levels and adjusts the content and progress of the event based on this. For example, if a participant's heart rate is high, it can suggest a relaxing activity. It can also instruct participants to take breaks if their stress level is high. Furthermore, the health management unit can accumulate participants' health data and use it for long-term health management. This makes it possible to provide events that are tailored to the participants' health conditions, resulting in more personalized services.

[0122] The generation unit can also generate caricatures customized based on the participant's hobbies and interests. For example, if the participant likes sports, a sports-themed caricature can be generated. If the participant likes music, a caricature holding an instrument can be generated. Furthermore, if the participant likes a particular character or anime, a caricature incorporating that character can be generated. This allows for a more personalized service by providing caricatures that match the participant's hobbies and interests.

[0123] The advice provider can also adjust the format of advice according to the participant's learning style. For example, advice using diagrams and illustrations can be provided to visual learners. Audio advice can also be provided to auditory learners. Furthermore, advice for hands-on learning can be provided to experiential learners. This allows for more effective learning support by providing advice according to the participant's learning style.

[0124] The practice section can also customize the content of English conversation practice according to the participant's cultural background. For example, if a participant is from Asia, English conversation practice related to Asian culture and customs can be provided. If a participant is from Europe, English conversation practice related to European culture and customs can be provided. Furthermore, if a participant is interested in a particular country or region, English conversation practice related to that country or region can be provided. This makes it possible to provide English conversation practice that is tailored to the participant's cultural background, making the practice more interesting.

[0125] The learning department can also adjust the difficulty of programming lessons according to the participant's learning progress. For example, if a participant has mastered the basics, it can provide applied content as the next step. Also, if a participant is struggling with a particular task, it can provide supplementary explanations and additional practice problems for that task. Furthermore, if a participant is interested in a particular field, it can provide programming tasks related to that field. This allows for more effective learning support by providing programming lessons according to the participant's learning progress.

[0126] The reception unit can also estimate the emotions of participants and suggest event themes based on the estimated emotions of the participants. For example, if a participant is relaxed, it can suggest an event with a relaxing theme. If a participant is excited, it can suggest an event with an active theme. Also, if a participant is sad, it can suggest an event with a fun theme to lift their spirits. In this way, by suggesting event themes according to the emotions of participants, it is possible to provide more appropriate events.

[0127] The reception unit can analyze the participant's past event participation history and select an appropriate reception method. For example, the reception unit can suggest the optimal reception method based on data on events the participant has previously participated in. The reception unit can also prioritize the selection of a reception method that the participant has previously preferred. The reception unit can also customize the optimal reception method by referring to the participant's past feedback. In this way, the optimal reception method can be selected by analyzing the participant's past event participation history. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the participant's past event participation history into the generation AI and have the generation AI select the optimal reception method.

[0128] The reception unit can perform filtering based on the participant's current interests or fields of interest. For example, it can display only events related to the participant's current fields of interest. The reception unit can also preferentially suggest related events based on the participant's fields of interest. The reception unit can also customize the content of the event according to the participant's interests. In this way, by filtering based on the participant's current interests and fields of interest, it is possible to suggest highly relevant events. Some or all of the above-described processing in the reception unit may be performed using, or without, the generation AI, for example. For example, the reception unit can input data on the participant's interests and fields of interest into the generation AI and have the generation AI perform the filtering.

[0129] The reception unit can select the optimal reception means depending on the participant's input method. For example, if a participant desires voice input, the reception unit can provide a reception means using voice recognition. If a participant desires text input, the reception unit can also provide an interface optimized for text input. Furthermore, if a participant uploads an image, the reception unit can also provide a reception means using image recognition. This allows for smoother reception by selecting the optimal reception means depending on the participant's input method. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input data on the participant's input method into the generation AI and have the generation AI select the optimal reception means.

[0130] The reception unit can estimate the emotions of participants and determine the priority of information to be received based on the estimated emotions of the participants. For example, if a participant is nervous, important information can be received first. If a participant is relaxed, the reception unit can also prioritize detailed information. Furthermore, if a participant is in a hurry, the reception unit can also prioritize the minimum necessary information. This allows important information to be received first by determining the priority of information to be received according to the emotions of the participants. 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 reception unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the reception unit can input emotion data of participants into the generation AI and have the generation AI perform emotion estimation.

[0131] The processing flow of the second embodiment will be briefly explained below.

[0132] Step 1: The reception unit receives input from participants. The input from participants includes text input, voice input, image input, etc. For example, the reception unit receives input of questions by text, voice input, and image upload from participants. Step 2: The generation unit uses generation AI to generate a caricature based on the information received by the reception unit. For example, the generation unit can generate a caricature based on a photo uploaded by the participant, or adjust the caricature's expression based on the participant's characteristics and emotions. Step 3: The providing unit provides advice based on the portrait generated by the generating unit. For example, the providing unit can provide advice based on the drawing of the participant, or can adjust the way the advice is expressed based on the participant's emotions and past advice results. Step 4: The practice section conducts English conversation practice based on the information received by the reception section. For example, they practice English conversation according to situations such as everyday conversation or business English, and can adjust the way they practice based on the participants' emotions and past practice results. Step 5: The learning unit supports programming learning based on the information received by the reception unit. For example, it can guide participants through the programming steps for creating a simple game and adjust the way they express their learning based on their emotions and past learning results.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0137] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0138] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0153] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0167] 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.

[0168] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0169] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0184] 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.

[0185] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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).

[0190] 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.

[0191] 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."

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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, in order to avoid confusion and to 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.

[0203] 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.

[0204] [Explanation of symbols]

[0205] 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 unit that receives input from participants; a generation unit that generates a portrait based on the information received by the reception unit; a providing unit that provides advice based on the portrait generated by the generating unit; a practice unit that practices English conversation based on the information received by the reception unit; a learning unit that supports learning programming based on the information received by the receiving unit. A system characterized by:

2. The generation unit Generate caricatures based on photos uploaded by participants The system of claim 1 .

3. The providing unit Providing advice on participants' drawings The system of claim 1 .

4. The practice section: Practice English conversation in everyday or business situations The system of claim 1 .

5. The learning unit Guided programming steps to create a simple game The system of claim 1 .

6. The reception unit Estimate participants' emotions and adjust the timing of input acceptance based on the estimated emotions of the participants. The system of claim 1 .

7. The reception unit Analyze participants' past event participation history and select the appropriate reception method The system of claim 1 .

8. The reception unit Filtering based on participants' current interests or areas of concern The system of claim 1 .

Images