System

Abstract

An object of the system according to the embodiment is to enhance the motivation for learning by being taught history in an interactive manner from a historical person.SOLUTION: A system includes a reception unit, a generation unit, and a progress unit. The receiving unit receives an input from a user. The generation unit generates a voice of a historical person on the basis of the information received by the reception unit. The progression unit progresses the interaction based on the voice generated by the generation 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 technologies have limited means of increasing learners' motivation in history learning, and there is room for improvement.

[0005] The system according to the embodiment aims to increase motivation to learn by having historical figures teach history in an interactive format. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a progression unit. The reception unit receives user input. The generation unit generates a voice of a historical figure based on the information received by the reception unit. The progression unit progresses a dialogue based on the voice generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can increase motivation to learn by having historical figures teach history in an interactive format. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention allows users to learn history through dialogue with historical figures in a VR environment. This system creates a virtual space where users can interact with historical figures, uses a synthetic voice AI to realistically reproduce the historical figures' voices, and uses a conversational AI to naturally engage in dialogue with the user. For example, a user puts on a VR device and enters the virtual space. Next, the user selects the historical figure with whom they want to interact. The selected figure begins speaking in a realistic voice using the synthetic voice AI. The conversational AI generates appropriate answers to the user's questions and advances the dialogue. This allows users to learn history directly from historical figures, increasing their motivation to learn. For example, if a user asks, "Tell me about this historical figure," the character's character will respond, "I am a historical figure who played an active role in a specific era." Furthermore, if a user asks, "Tell me about events from that era," the character will respond, "There were important events in that era." This allows the system to deepen users' interest in history and increase their motivation to learn by learning directly from historical figures. Furthermore, the use of VR technology can provide an immersive learning experience.

[0029] A history learning system according to an embodiment includes a reception unit, a generation unit, and a progression unit. The reception unit receives user input. The user input includes, but is not limited to, text input and voice input. The reception unit may also provide an interface for the user to select a historical figure with which the user wants to converse. The generation unit uses a synthetic voice AI to generate the voice of the historical figure based on the information received by the reception unit. For example, the generation unit uses voice synthesis technology to realistically reproduce the voice of the selected historical figure. The generation unit may also generate the voice of the historical figure by referencing a voice database. The progression unit uses a conversation AI to progress the dialogue based on the voice generated by the generation unit. For example, the progression unit generates appropriate answers to user questions and progresses the dialogue. The progression unit may also generate responses based on a dialogue scenario. This allows the history learning system according to an embodiment to enable the user to learn history interactively with historical figures, thereby improving motivation for learning. Some or all of the above-described processing performed by the progression unit may be performed using, for example, AI, or may be performed without AI. For example, the progress unit can progress the dialogue using an AI model that takes a user's question as input and outputs an appropriate answer.

[0030] The reception unit allows the user to select a historical figure with which the user wants to have a conversation. For example, the reception unit provides an interface for the user to select a historical figure with which the user wants to have a conversation from a list. The reception unit also provides a search function, allowing the user to search for and select a specific historical figure. For example, when the user enters "Napoleon," the reception unit displays information about Napoleon and allows the user to select it. The reception unit can also recommend related historical figures based on the user's past selection history. This allows the user to select a historical figure with which the user wants to have a conversation.

[0031] The generation unit can generate the voice of the selected historical figure. The generation unit generates the voice of the selected historical figure, for example, using voice synthesis technology. For example, the generation unit references a voice database and realistically reproduces the voice of the selected figure. The generation unit can also generate the voice of the selected figure using synthetic voice AI. For example, the generation unit reproduces the tone and rhythm of the selected figure's voice using voice synthesis technology. The generation unit can also extract features of the selected figure's voice based on the voice database and reproduce them using synthetic voice AI. This allows the voice of the selected historical figure to be realistically reproduced.

[0032] The facilitator can generate appropriate answers to the user's questions and progress the dialogue. The facilitator can generate appropriate answers to the user's questions using, for example, a conversational AI. For example, the facilitator can generate answers to the user's questions using a response generation algorithm based on the content of the question. The facilitator can also generate responses based on a dialogue scenario. For example, if the user asks, "Tell me about the events of that era," the conversational AI can respond with, "There were important events in that era." The facilitator can also explain historical events based on the user's questions. For example, if the user asks, "Tell me about the wars of that era," the conversational AI can respond with, "There were major wars in that era." This allows for a natural dialogue to be held in response to the user's questions.

[0033] The facilitator can explain historical events based on the user's questions. The facilitator can, for example, use a conversational AI to explain historical events based on the user's questions. For example, the facilitator can use text generation technology to generate answers to the user's questions. The facilitator can also use speech synthesis technology to provide answers to the user's questions by voice. For example, if the user asks, "Tell me about the wars of that era," the conversational AI can respond, "There was a big war in that era." The facilitator can also explain details of historical events based on the user's questions. For example, if the user asks, "Tell me about the causes of that war," the conversational AI can respond, "The causes of that war are complex and involve many factors." This allows the user to learn about historical events based on their questions.

[0034] The facilitator can engage in dialogue to increase the user's motivation to learn. For example, the facilitator can use conversational AI to engage in dialogue to increase the user's motivation to learn. For example, the facilitator can use words of encouragement to increase the user's motivation to learn. The facilitator can also provide interesting topics to increase the user's motivation to learn. For example, if the user feels that learning is difficult, the conversational AI can encourage the user by saying, "You're making great progress. Keep up the good work." The facilitator can also provide interesting topics to increase the user's motivation to learn. For example, if the user feels that they are not interested in history, the conversational AI can suggest, "There are many interesting episodes in history. Why not learn about Napoleon's strategies, for example?" This can increase the user's motivation to learn.

[0035] The reception unit can analyze the user's past dialogue history and recommend the most suitable historical figure. The reception unit can analyze the user's past dialogue history using, for example, text mining technology. For example, the reception unit can analyze the tendencies of people with whom the user has previously dialogued and recommend people from the same era or related figures. The reception unit can also recommend historical figures related to topics in which the user has previously shown interest. For example, the reception unit can recommend similar figures based on dialogue content that the user has previously rated highly. Furthermore, the reception unit can also recommend the most suitable historical figure based on the user's dialogue history using a machine learning algorithm. This makes it possible to recommend the most suitable historical figure based on the user's past dialogue history.

[0036] The reception unit can select an appropriate historical figure based on the user's current learning progress. The reception unit evaluates the user's current learning progress based on, for example, the learning history. For example, if the user is a beginner, the reception unit selects a figure with basic historical knowledge. Furthermore, if the user is an intermediate learner, the reception unit can also select a figure with deeper knowledge. Furthermore, if the user is an advanced learner, the reception unit can also select a figure with specialized knowledge. For example, the reception unit selects an appropriate historical figure based on the user's test results. In this way, it is possible to select a historical figure that corresponds to the user's learning progress.

[0037] The reception unit can filter historical figures with which the user wants to have a conversation based on the user's interests and concerns. The reception unit evaluates the user's interests and concerns based on, for example, survey results. For example, the reception unit filters figures based on the eras and regions in which the user is interested. The reception unit can also filter figures based on topics in which the user is interested (war, culture, science, etc.). Furthermore, if the user is interested in people of a specific gender or age group, the reception unit can filter based on those conditions. For example, if the user inputs "I'm interested in warlords from the Sengoku period," the reception unit filters and displays people related to warlords from the Sengoku period. The reception unit can also perform filtering based on the user's interests and concerns based on the user's past selection history. This makes it possible to filter historical figures based on the user's interests and concerns.

[0038] The reception unit can suggest relevant historical figures by taking into account the user's geographical location information. The reception unit, for example, acquires the user's geographical location information using GPS data. For example, the reception unit can suggest historical figures related to the area where the user is currently located. If the user is traveling, the reception unit can also suggest figures related to the history of that area. If the user is interested in a specific country or area, the reception unit can also suggest figures related to that area. For example, if the user inputs "I'm interested in France," the reception unit can suggest historical figures related to France. The reception unit can also acquire the user's geographical location information using a location information service and suggest relevant historical figures. This makes it possible to suggest relevant historical figures based on the user's geographical location information.

[0039] The reception unit can analyze the user's social media activity and suggest related historical figures. The reception unit can analyze the user's social media activity, for example, using social media activity analysis technology. For example, the reception unit can suggest people based on history-related accounts that the user follows on social media. The reception unit can also analyze the content of the user's posts and suggest historical figures that the user may be interested in. The reception unit can also make suggestions based on historical figures that the user's friends are interested in. For example, if the reception unit analyzes that the user "makes many posts about history," it can suggest historical figures. The reception unit can also suggest related historical figures based on an analysis of the user's followers. In this way, related historical figures can be suggested based on the user's social media activity.

[0040] The reception unit can customize a method for selecting a historical figure with which the user wants to have a conversation by reflecting the user's past feedback. The reception unit can analyze the user's past feedback using, for example, feedback analysis technology. For example, the reception unit can suggest similar figures based on the characteristics of figures that the user has previously rated highly. The reception unit can also make suggestions while avoiding the characteristics of figures that the user has previously rated poorly. Furthermore, the reception unit can adjust the selection algorithm and make suggestions based on the user's feedback. For example, if the user gives feedback that "this person was interesting," the reception unit can suggest a similar figure. Also, if the user gives feedback that "this person was not interesting," the reception unit can make suggestions while avoiding those characteristics. In this way, the method for selecting historical figures can be customized based on the user's past feedback.

[0041] When generating the voice of a historical figure, the generation unit can reproduce an accent based on the historical background and culture of that person. When generating the voice of a historical figure, the generation unit, for example, uses speech synthesis technology to reproduce an accent based on the historical background and culture of that person. For example, in the case of an ancient Roman figure, the generation unit reproduces a Latin accent. Furthermore, in the case of a Japanese figure from the Edo period, the generation unit can also reproduce the Japanese accent of that time. Furthermore, in the case of an Italian figure from the Renaissance period, the generation unit can also reproduce an Italian accent. For example, the generation unit references a speech database and reproduces an accent based on historical materials. Furthermore, the generation unit can also use speech synthesis technology to reproduce an accent based on the historical background and culture. This allows the voice of a historical figure to be reproduced realistically.

[0042] When generating the voice of a historical figure, the generation unit can improve the naturalness of the voice by referring to the user's past dialogue history. The generation unit can analyze the user's past dialogue history, for example, using text mining technology. For example, the generation unit can generate a voice by referring to the tones and rhythms of the voices that the user previously preferred. The generation unit can also generate a voice that reflects the voice characteristics of figures with whom the user previously conversed. Furthermore, the generation unit can improve the naturalness of the voice based on the user's past feedback. For example, if the user provides feedback such as "this voice was natural," the generation unit can generate a voice that reflects the characteristics. Also, if the user provides feedback such as "this voice was unnatural," the generation unit can generate a voice that avoids the characteristics. In this way, the naturalness of the voice can be improved based on the user's past dialogue history.

[0043] When generating the voice of a historical figure, the generation unit can adjust the voice speed and rhythm according to the user's learning progress. The generation unit evaluates the user's current learning progress based on, for example, the learning history. For example, the generation unit can make the user speak at a slow speed if the user is a beginner. Furthermore, the generation unit can also make the user speak at a moderate speed if the user is an intermediate learner. Furthermore, the generation unit can also make the user speak at a fast speed if the user is an advanced learner. For example, the generation unit adjusts the voice speed and rhythm based on the user's test results. Furthermore, the generation unit can use speech synthesis technology to adjust the voice speed and rhythm according to the user's learning progress. This makes it possible to adjust the voice speed and rhythm according to the user's learning progress.

[0044] When generating the voice of a historical figure, the generation unit can add a regional accent by taking into account the user's geographical location information. The generation unit, for example, acquires the user's geographical location information using GPS data. For example, if the user is in France, the generation unit can add a French accent. Also, if the user is in the United States, the generation unit can add an American English accent. Furthermore, if the user is in China, the generation unit can add a Chinese accent. For example, the generation unit references a voice database and generates a voice based on a regional accent. Also, the generation unit can add a regional accent using speech synthesis technology. In this way, a regional accent can be added based on the user's geographical location information.

[0045] When generating a voice of a historical figure, the generation unit can analyze the user's social media activity and incorporate related topics. The generation unit can analyze the user's social media activity, for example, using social media activity analysis technology. For example, the generation unit can generate a voice that reflects topics that the user is talking about on social media. The generation unit can also generate a voice that reflects topics that the user's friends are interested in. Furthermore, the generation unit can generate a voice that reflects the content of accounts that the user follows. For example, if the generation unit analyzes that the user "posts many things related to history," it can generate a voice that incorporates topics related to history. The generation unit can also generate a voice that incorporates related topics based on an analysis of the user's followers. This makes it possible to incorporate related topics based on the user's social media activity.

[0046] When generating the voice of a historical figure, the generation unit can customize the quality of the voice by reflecting the user's past feedback. The generation unit can analyze the user's past feedback using, for example, feedback analysis technology. For example, the generation unit generates a voice by reflecting voice features that the user has previously rated highly. The generation unit can also generate a voice by avoiding voice features that the user has previously rated poorly. Furthermore, the generation unit can improve the quality of the voice based on the user's feedback. For example, if the user provides feedback that "this voice was natural," the generation unit can generate a voice by reflecting those features. Also, if the user provides feedback that "this voice was unnatural," the generation unit can generate a voice by avoiding those features. This allows the quality of the voice to be customized based on the user's past feedback.

[0047] The moderator can analyze the user's past dialogue history and select an optimal dialogue progression method. The moderator can analyze the user's past dialogue history using, for example, text mining technology. For example, the moderator can proceed with the dialogue by referring to the dialogue progression methods that the user preferred in the past. The moderator can also select related topics based on the content of the user's past dialogues and proceed with the dialogue. Furthermore, the moderator can adjust the dialogue progression method based on the user's past feedback. For example, if the user gives feedback that "this progression method was good," the moderator can proceed with the dialogue by reflecting those characteristics. Also, if the user gives feedback that "this progression method was not good," the moderator can proceed with the dialogue by avoiding those characteristics. In this way, an optimal dialogue progression method can be selected based on the user's past dialogue history.

[0048] The facilitator can customize the content of the dialogue based on the user's current learning progress. The facilitator can evaluate the user's current learning progress based on, for example, the learning history. For example, if the user is a beginner, the facilitator can start the dialogue with basic content. Furthermore, if the user is an intermediate learner, the facilitator can move on to more in-depth content. Furthermore, if the user is an advanced learner, the facilitator can hold a dialogue including specialized content. For example, the facilitator can customize the content of the dialogue based on the user's test results. Furthermore, the facilitator can use conversational AI to customize the content of the dialogue based on the user's learning progress. This makes it possible to customize the content of the dialogue according to the user's learning progress.

[0049] The moderator can select a topic for the conversation based on the user's interests. The moderator can evaluate the user's interests based on, for example, survey results. For example, the moderator can select a topic based on a time period or region in which the user is interested. The moderator can also select a topic based on topics in which the user is interested (war, culture, science, etc.). If the user is interested in people of a particular gender or age group, the moderator can select a topic based on those conditions. For example, if the user inputs, "I'm interested in warlords from the Warring States period," the moderator can select a topic related to warlords from the Warring States period and continue the conversation. The moderator can also select a topic based on the user's interests based on the user's past selection history. This allows the conversation topic to be selected based on the user's interests.

[0050] The progression unit can explain relevant historical events taking into account the user's geographical location information. The progression unit, for example, uses GPS data to acquire the user's geographical location information. For example, the progression unit explains historical events related to the area where the user is currently located. If the user is traveling, the progression unit can also explain events related to the history of that area. If the user is interested in a particular country or area, the progression unit can also explain events related to that area. For example, if the user inputs "I'm interested in France," the progression unit can explain historical events related to France. The progression unit can also use a location information service to acquire the user's geographical location information and explain relevant historical events. This makes it possible to explain relevant historical events based on the user's geographical location information.

[0051] The facilitator can analyze the user's social media activity and explain related historical events. The facilitator can, for example, use social media activity analysis technology to analyze the user's social media activity. For example, the facilitator can explain historical events related to topics the user is talking about on social media. The facilitator can also explain historical events that the user's friends are interested in. The facilitator can also explain historical events related to the content of accounts the user follows. For example, if the facilitator analyzes that the user "posts a lot about history," it can explain events related to history. The facilitator can also explain related historical events based on an analysis of the user's followers. This makes it possible to explain related historical events based on the user's social media activity.

[0052] The facilitator can customize the dialogue progression method by reflecting the user's past feedback. The facilitator can analyze the user's past feedback using, for example, feedback analysis technology. For example, the facilitator can proceed with the dialogue by referring to dialogue progression methods that the user previously rated highly. The facilitator can also proceed by avoiding dialogue progression methods that the user previously rated poorly. Furthermore, the facilitator can adjust the dialogue progression method based on the user's feedback. For example, if the user gives feedback that "this progression method was good," the facilitator can proceed by reflecting that feature. Also, if the user gives feedback that "this progression method was not good," the facilitator can proceed by avoiding that feature. In this way, the dialogue progression method can be customized based on the user's past feedback.

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

[0054] The reception unit can suggest relevant historical figures by taking into account the user's geographical location information. For example, the reception unit can acquire the user's geographical location information using GPS data and suggest historical figures related to the area where the user is currently located. Also, if the user is traveling, it can suggest people related to the history of that area. Furthermore, if the user is interested in a particular country or area, it can suggest people related to that area. In this way, it is possible to suggest relevant historical figures based on the user's geographical location information.

[0055] When generating the voices of historical figures, the generation unit can reproduce accents based on the historical background and culture of the person. For example, in the case of a person from ancient Rome, the generation unit can reproduce a Latin accent. In addition, in the case of a person from Japan in the Edo period, the generation unit can also reproduce the Japanese accent of that time. Furthermore, in the case of a person from Italy in the Renaissance period, the generation unit can also reproduce an Italian accent. This allows the voices of historical figures to be reproduced realistically.

[0056] The moderator can analyze the user's past dialogue history and select the optimal dialogue progression method. For example, the moderator can proceed with the dialogue by referring to the dialogue progression method that the user preferred in the past. The moderator can also select and proceed with a related topic based on the content of the user's past dialogue. Furthermore, the moderator can adjust the dialogue progression method based on the user's past feedback. In this way, the optimal dialogue progression method can be selected based on the user's past dialogue history.

[0057] The progress manager can customize the content of the dialogue based on the user's current learning progress. For example, the progress manager evaluates the user's current learning progress based on the learning history. If the user is a beginner, the dialogue can start with basic content. If the user is an intermediate learner, the dialogue can progress to more in-depth content. Furthermore, if the user is an advanced learner, the dialogue can include specialized content. This allows the dialogue content to be customized according to the user's learning progress.

[0058] The reception unit can analyze the user's social media activity and suggest related historical figures. For example, the reception unit can analyze the user's social media activity using social media activity analysis technology. The reception unit can suggest people based on history-related accounts the user follows on social media. The reception unit can also analyze the content of the user's posts and suggest historical figures that the user may be interested in. This makes it possible to suggest related historical figures based on the user's social media activity.

[0059] When generating the voice of a historical figure, the generation unit can improve the naturalness of the voice by referring to the user's past dialogue history. For example, the generation unit can use text mining technology to analyze the user's past dialogue history. The generation unit can generate a voice by referring to the voice tones and rhythms that the user previously preferred. The generation unit can also generate a voice that reflects the voice characteristics of figures with whom the user previously conversed. This makes it possible to improve the naturalness of the voice based on the user's past dialogue history.

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

[0061] Step 1: The reception unit receives user input. User input can include text input, voice input, etc. The reception unit can also provide an interface for the user to select the historical figure with which they want to interact. Step 2: The generation unit uses a synthetic voice AI to generate the voice of the historical figure based on the information received by the reception unit. The generation unit uses voice synthesis technology to realistically reproduce the voice of the selected historical figure, and can also generate the voice by referencing a voice database. Step 3: The moderator uses conversational AI to advance the dialogue based on the voice generated by the generator. The moderator generates appropriate answers to the user's questions and advances the dialogue. It can also generate responses based on the dialogue scenario.

[0062] (Example 2) A system according to an embodiment of the present invention allows users to learn history through dialogue with historical figures in a VR environment. This system creates a virtual space where users can interact with historical figures, uses a synthetic voice AI to realistically reproduce the historical figures' voices, and uses a conversational AI to naturally engage in dialogue with the user. For example, a user puts on a VR device and enters the virtual space. Next, the user selects the historical figure with whom they want to interact. The selected figure begins speaking in a realistic voice using the synthetic voice AI. The conversational AI generates appropriate answers to the user's questions and advances the dialogue. This allows users to learn history directly from historical figures, increasing their motivation to learn. For example, if a user asks, "Tell me about this historical figure," the character's character will respond, "I am a historical figure who played an active role in a specific era." Furthermore, if a user asks, "Tell me about events from that era," the character will respond, "There were important events in that era." This allows the system to deepen users' interest in history and increase their motivation to learn by learning directly from historical figures. Furthermore, the use of VR technology can provide an immersive learning experience.

[0063] A history learning system according to an embodiment includes a reception unit, a generation unit, and a progression unit. The reception unit receives user input. The user input includes, but is not limited to, text input and voice input. The reception unit may also provide an interface for the user to select a historical figure with which the user wants to converse. The generation unit uses a synthetic voice AI to generate the voice of the historical figure based on the information received by the reception unit. For example, the generation unit uses voice synthesis technology to realistically reproduce the voice of the selected historical figure. The generation unit may also generate the voice of the historical figure by referencing a voice database. The progression unit uses a conversation AI to progress the dialogue based on the voice generated by the generation unit. For example, the progression unit generates appropriate answers to user questions and progresses the dialogue. The progression unit may also generate responses based on a dialogue scenario. This allows the history learning system according to an embodiment to enable the user to learn history interactively with historical figures, thereby improving motivation for learning. Some or all of the above-described processing performed by the progression unit may be performed using, for example, AI, or may be performed without AI. For example, the progress unit can progress the dialogue using an AI model that takes a user's question as input and outputs an appropriate answer.

[0064] The reception unit allows the user to select a historical figure with which the user wants to have a conversation. For example, the reception unit provides an interface for the user to select a historical figure with which the user wants to have a conversation from a list. The reception unit also provides a search function, allowing the user to search for and select a specific historical figure. For example, when the user enters "Napoleon," the reception unit displays information about Napoleon and allows the user to select it. The reception unit can also recommend related historical figures based on the user's past selection history. This allows the user to select a historical figure with which the user wants to have a conversation.

[0065] The generation unit can generate the voice of the selected historical figure. The generation unit generates the voice of the selected historical figure, for example, using voice synthesis technology. For example, the generation unit references a voice database and realistically reproduces the voice of the selected figure. The generation unit can also generate the voice of the selected figure using synthetic voice AI. For example, the generation unit reproduces the tone and rhythm of the selected figure's voice using voice synthesis technology. The generation unit can also extract features of the selected figure's voice based on the voice database and reproduce them using synthetic voice AI. This allows the voice of the selected historical figure to be realistically reproduced.

[0066] The facilitator can generate appropriate answers to the user's questions and progress the dialogue. The facilitator can generate appropriate answers to the user's questions using, for example, a conversational AI. For example, the facilitator can generate answers to the user's questions using a response generation algorithm based on the content of the question. The facilitator can also generate responses based on a dialogue scenario. For example, if the user asks, "Tell me about the events of that era," the conversational AI can respond with, "There were important events in that era." The facilitator can also explain historical events based on the user's questions. For example, if the user asks, "Tell me about the wars of that era," the conversational AI can respond with, "There were major wars in that era." This allows for a natural dialogue to be held in response to the user's questions.

[0067] The facilitator can explain historical events based on the user's questions. The facilitator can, for example, use a conversational AI to explain historical events based on the user's questions. For example, the facilitator can use text generation technology to generate answers to the user's questions. The facilitator can also use speech synthesis technology to provide answers to the user's questions by voice. For example, if the user asks, "Tell me about the wars of that era," the conversational AI can respond, "There was a big war in that era." The facilitator can also explain details of historical events based on the user's questions. For example, if the user asks, "Tell me about the causes of that war," the conversational AI can respond, "The causes of that war are complex and involve many factors." This allows the user to learn about historical events based on their questions.

[0068] The facilitator can engage in dialogue to increase the user's motivation to learn. For example, the facilitator can use conversational AI to engage in dialogue to increase the user's motivation to learn. For example, the facilitator can use words of encouragement to increase the user's motivation to learn. The facilitator can also provide interesting topics to increase the user's motivation to learn. For example, if the user feels that learning is difficult, the conversational AI can encourage the user by saying, "You're making great progress. Keep up the good work." The facilitator can also provide interesting topics to increase the user's motivation to learn. For example, if the user feels that they are not interested in history, the conversational AI can suggest, "There are many interesting episodes in history. Why not learn about Napoleon's strategies, for example?" This can increase the user's motivation to learn.

[0069] The reception unit can estimate the user's emotions and suggest historical figures with whom the user would like to converse based on the estimated user emotions. The reception unit, for example, uses an emotion estimation algorithm to estimate the user's emotions. For example, the reception unit can use facial expression recognition technology to estimate emotions from the user's facial expressions. The reception unit can also use voice analysis technology to estimate emotions from the tone and speed of the user's voice. For example, if the user is excited, the reception unit can suggest active historical figures such as Sengoku warlords and adventurers. If the user is calm, the reception unit can also suggest quiet historical figures such as philosophers and poets. Furthermore, if the user is sad, the reception unit can suggest historical figures with encouraging words. This allows the system to suggest historical figures that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0070] The reception unit can analyze the user's past dialogue history and recommend the most suitable historical figure. The reception unit can analyze the user's past dialogue history using, for example, text mining technology. For example, the reception unit can analyze the tendencies of people with whom the user has previously dialogued and recommend people from the same era or related figures. The reception unit can also recommend historical figures related to topics in which the user has previously shown interest. For example, the reception unit can recommend similar figures based on dialogue content that the user has previously rated highly. Furthermore, the reception unit can also recommend the most suitable historical figure based on the user's dialogue history using a machine learning algorithm. This makes it possible to recommend the most suitable historical figure based on the user's past dialogue history.

[0071] The reception unit can select an appropriate historical figure based on the user's current learning progress. The reception unit evaluates the user's current learning progress based on, for example, the learning history. For example, if the user is a beginner, the reception unit selects a figure with basic historical knowledge. Furthermore, if the user is an intermediate learner, the reception unit can also select a figure with deeper knowledge. Furthermore, if the user is an advanced learner, the reception unit can also select a figure with specialized knowledge. For example, the reception unit selects an appropriate historical figure based on the user's test results. In this way, it is possible to select a historical figure that corresponds to the user's learning progress.

[0072] The reception unit can filter historical figures with which the user wants to have a conversation based on the user's interests and concerns. The reception unit evaluates the user's interests and concerns based on, for example, survey results. For example, the reception unit filters figures based on the eras and regions in which the user is interested. The reception unit can also filter figures based on topics in which the user is interested (war, culture, science, etc.). Furthermore, if the user is interested in people of a specific gender or age group, the reception unit can filter based on those conditions. For example, if the user inputs "I'm interested in warlords from the Sengoku period," the reception unit filters and displays people related to warlords from the Sengoku period. The reception unit can also perform filtering based on the user's interests and concerns based on the user's past selection history. This makes it possible to filter historical figures based on the user's interests and concerns.

[0073] The reception unit can estimate the user's emotion and adjust the timing of starting the dialogue based on the estimated user emotion. The reception unit estimates the user's emotion using, for example, an emotion estimation algorithm. For example, the reception unit can estimate the emotion from the user's facial expression using facial expression recognition technology. The reception unit can also estimate the emotion from the tone and speed of the user's voice using voice analysis technology. For example, the reception unit can start the dialogue immediately if the user is relaxed. If the user is nervous, the reception unit can start the dialogue after allowing the user time to prepare for relaxation. Furthermore, the reception unit can start the dialogue quickly if the user is in a hurry. This makes it possible to adjust the timing of starting the dialogue according to the user's emotion. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0074] The reception unit can suggest relevant historical figures by taking into account the user's geographical location information. The reception unit, for example, acquires the user's geographical location information using GPS data. For example, the reception unit can suggest historical figures related to the area where the user is currently located. If the user is traveling, the reception unit can also suggest figures related to the history of that area. If the user is interested in a specific country or area, the reception unit can also suggest figures related to that area. For example, if the user inputs "I'm interested in France," the reception unit can suggest historical figures related to France. The reception unit can also acquire the user's geographical location information using a location information service and suggest relevant historical figures. This makes it possible to suggest relevant historical figures based on the user's geographical location information.

[0075] The reception unit can analyze the user's social media activity and suggest related historical figures. The reception unit can analyze the user's social media activity, for example, using social media activity analysis technology. For example, the reception unit can suggest people based on history-related accounts that the user follows on social media. The reception unit can also analyze the content of the user's posts and suggest historical figures that the user may be interested in. The reception unit can also make suggestions based on historical figures that the user's friends are interested in. For example, if the reception unit analyzes that the user "makes many posts about history," it can suggest historical figures. The reception unit can also suggest related historical figures based on an analysis of the user's followers. In this way, related historical figures can be suggested based on the user's social media activity.

[0076] The reception unit can customize a method for selecting a historical figure with which the user wants to have a conversation by reflecting the user's past feedback. The reception unit can analyze the user's past feedback using, for example, feedback analysis technology. For example, the reception unit can suggest similar figures based on the characteristics of figures that the user has previously rated highly. The reception unit can also make suggestions while avoiding the characteristics of figures that the user has previously rated poorly. Furthermore, the reception unit can adjust the selection algorithm and make suggestions based on the user's feedback. For example, if the user gives feedback that "this person was interesting," the reception unit can suggest a similar figure. Also, if the user gives feedback that "this person was not interesting," the reception unit can make suggestions while avoiding those characteristics. In this way, the method for selecting historical figures can be customized based on the user's past feedback.

[0077] The generation unit can estimate the user's emotion and adjust the tone of voice and speaking style based on the estimated user emotion. The generation unit estimates the user's emotion using, for example, an emotion estimation algorithm. For example, the generation unit can estimate the emotion from the user's facial expression using facial expression recognition technology. The generation unit can also estimate the emotion from the tone and speed of the user's voice using voice analysis technology. For example, the generation unit can speak in a calm tone if the user is relaxed. The generation unit can also speak in a lively tone if the user is excited. Furthermore, the generation unit can speak in a gentle tone if the user is sad. This makes it possible to adjust the tone of voice and speaking style according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0078] When generating the voice of a historical figure, the generation unit can reproduce an accent based on the historical background and culture of that person. When generating the voice of a historical figure, the generation unit, for example, uses speech synthesis technology to reproduce an accent based on the historical background and culture of that person. For example, in the case of an ancient Roman figure, the generation unit reproduces a Latin accent. Furthermore, in the case of a Japanese figure from the Edo period, the generation unit can also reproduce the Japanese accent of that time. Furthermore, in the case of an Italian figure from the Renaissance period, the generation unit can also reproduce an Italian accent. For example, the generation unit references a speech database and reproduces an accent based on historical materials. Furthermore, the generation unit can also use speech synthesis technology to reproduce an accent based on the historical background and culture. This allows the voice of a historical figure to be reproduced realistically.

[0079] When generating the voice of a historical figure, the generation unit can improve the naturalness of the voice by referring to the user's past dialogue history. The generation unit can analyze the user's past dialogue history, for example, using text mining technology. For example, the generation unit can generate a voice by referring to the tones and rhythms of the voices that the user previously preferred. The generation unit can also generate a voice that reflects the voice characteristics of figures with whom the user previously conversed. Furthermore, the generation unit can improve the naturalness of the voice based on the user's past feedback. For example, if the user provides feedback such as "this voice was natural," the generation unit can generate a voice that reflects the characteristics. Also, if the user provides feedback such as "this voice was unnatural," the generation unit can generate a voice that avoids the characteristics. In this way, the naturalness of the voice can be improved based on the user's past dialogue history.

[0080] When generating the voice of a historical figure, the generation unit can adjust the voice speed and rhythm according to the user's learning progress. The generation unit evaluates the user's current learning progress based on, for example, the learning history. For example, the generation unit can make the user speak at a slow speed if the user is a beginner. Furthermore, the generation unit can also make the user speak at a moderate speed if the user is an intermediate learner. Furthermore, the generation unit can also make the user speak at a fast speed if the user is an advanced learner. For example, the generation unit adjusts the voice speed and rhythm based on the user's test results. Furthermore, the generation unit can use speech synthesis technology to adjust the voice speed and rhythm according to the user's learning progress. This makes it possible to adjust the voice speed and rhythm according to the user's learning progress.

[0081] The generation unit can estimate the user's emotion and adjust the voice volume based on the estimated user's emotion. The generation unit estimates the user's emotion using, for example, an emotion estimation algorithm. For example, the generation unit can estimate the emotion from the user's facial expression using facial expression recognition technology. The generation unit can also estimate the emotion from the tone and speed of the user's voice using voice analysis technology. For example, the generation unit can speak at a gentle volume when the user is relaxed. The generation unit can also speak at a slightly louder volume when the user is excited. Furthermore, the generation unit can also speak at a quieter volume when the user is sad. This allows the voice volume to be adjusted according to the user's emotion. Emotion estimation is realized using, for example, an emotion engine or generation AI with an emotion estimation function. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0082] When generating the voice of a historical figure, the generation unit can add a regional accent by taking into account the user's geographical location information. The generation unit, for example, acquires the user's geographical location information using GPS data. For example, if the user is in France, the generation unit can add a French accent. Also, if the user is in the United States, the generation unit can add an American English accent. Furthermore, if the user is in China, the generation unit can add a Chinese accent. For example, the generation unit references a voice database and generates a voice based on a regional accent. Also, the generation unit can add a regional accent using speech synthesis technology. In this way, a regional accent can be added based on the user's geographical location information.

[0083] When generating a voice of a historical figure, the generation unit can analyze the user's social media activity and incorporate related topics. The generation unit can analyze the user's social media activity, for example, using social media activity analysis technology. For example, the generation unit can generate a voice that reflects topics that the user is talking about on social media. The generation unit can also generate a voice that reflects topics that the user's friends are interested in. Furthermore, the generation unit can generate a voice that reflects the content of accounts that the user follows. For example, if the generation unit analyzes that the user "posts many things related to history," it can generate a voice that incorporates topics related to history. The generation unit can also generate a voice that incorporates related topics based on an analysis of the user's followers. This makes it possible to incorporate related topics based on the user's social media activity.

[0084] When generating the voice of a historical figure, the generation unit can customize the quality of the voice by reflecting the user's past feedback. The generation unit can analyze the user's past feedback using, for example, feedback analysis technology. For example, the generation unit generates a voice by reflecting voice features that the user has previously rated highly. The generation unit can also generate a voice by avoiding voice features that the user has previously rated poorly. Furthermore, the generation unit can improve the quality of the voice based on the user's feedback. For example, if the user provides feedback that "this voice was natural," the generation unit can generate a voice by reflecting those features. Also, if the user provides feedback that "this voice was unnatural," the generation unit can generate a voice by avoiding those features. This allows the quality of the voice to be customized based on the user's past feedback.

[0085] The facilitator can estimate the user's emotion and adjust the dialogue progression method based on the estimated user emotion. The facilitator can estimate the user's emotion using, for example, an emotion estimation algorithm. For example, the facilitator can estimate the emotion from the user's facial expression using facial expression recognition technology. The facilitator can also estimate the emotion from the tone and speed of the user's voice using voice analysis technology. For example, the facilitator can proceed with the dialogue at a slow pace if the user is relaxed. The facilitator can also proceed with the dialogue at a lively pace if the user is excited. Furthermore, the facilitator can proceed with the dialogue in a gentle tone if the user is sad. This allows the dialogue progression method to be adjusted according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0086] The moderator can analyze the user's past dialogue history and select an optimal dialogue progression method. The moderator can analyze the user's past dialogue history using, for example, text mining technology. For example, the moderator can proceed with the dialogue by referring to the dialogue progression methods that the user preferred in the past. The moderator can also select related topics based on the content of the user's past dialogues and proceed with the dialogue. Furthermore, the moderator can adjust the dialogue progression method based on the user's past feedback. For example, if the user gives feedback that "this progression method was good," the moderator can proceed with the dialogue by reflecting those characteristics. Also, if the user gives feedback that "this progression method was not good," the moderator can proceed with the dialogue by avoiding those characteristics. In this way, an optimal dialogue progression method can be selected based on the user's past dialogue history.

[0087] The facilitator can customize the content of the dialogue based on the user's current learning progress. The facilitator can evaluate the user's current learning progress based on, for example, the learning history. For example, if the user is a beginner, the facilitator can start the dialogue with basic content. Furthermore, if the user is an intermediate learner, the facilitator can move on to more in-depth content. Furthermore, if the user is an advanced learner, the facilitator can hold a dialogue including specialized content. For example, the facilitator can customize the content of the dialogue based on the user's test results. Furthermore, the facilitator can use conversational AI to customize the content of the dialogue based on the user's learning progress. This makes it possible to customize the content of the dialogue according to the user's learning progress.

[0088] The moderator can select a topic for the conversation based on the user's interests. The moderator can evaluate the user's interests based on, for example, survey results. For example, the moderator can select a topic based on a time period or region in which the user is interested. The moderator can also select a topic based on topics in which the user is interested (war, culture, science, etc.). If the user is interested in people of a particular gender or age group, the moderator can select a topic based on those conditions. For example, if the user inputs, "I'm interested in warlords from the Warring States period," the moderator can select a topic related to warlords from the Warring States period and continue the conversation. The moderator can also select a topic based on the user's interests based on the user's past selection history. This allows the conversation topic to be selected based on the user's interests.

[0089] The facilitator can estimate the user's emotion and adjust the tempo of the dialogue based on the estimated user emotion. The facilitator can estimate the user's emotion using, for example, an emotion estimation algorithm. For example, the facilitator can estimate the emotion from the user's facial expression using facial expression recognition technology. The facilitator can also estimate the emotion from the tone and speed of the user's voice using voice analysis technology. For example, the facilitator can proceed with the dialogue at a slower tempo if the user is relaxed. The facilitator can also proceed with the dialogue at a faster tempo if the user is excited. Furthermore, the facilitator can proceed with the dialogue at a calmer tempo if the user is sad. This allows the tempo of the dialogue to be adjusted according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0090] The progression unit can explain relevant historical events taking into account the user's geographical location information. The progression unit, for example, uses GPS data to acquire the user's geographical location information. For example, the progression unit explains historical events related to the area where the user is currently located. If the user is traveling, the progression unit can also explain events related to the history of that area. If the user is interested in a particular country or area, the progression unit can also explain events related to that area. For example, if the user inputs "I'm interested in France," the progression unit can explain historical events related to France. The progression unit can also use a location information service to acquire the user's geographical location information and explain relevant historical events. This makes it possible to explain relevant historical events based on the user's geographical location information.

[0091] The facilitator can analyze the user's social media activity and explain related historical events. The facilitator can, for example, use social media activity analysis technology to analyze the user's social media activity. For example, the facilitator can explain historical events related to topics the user is talking about on social media. The facilitator can also explain historical events that the user's friends are interested in. The facilitator can also explain historical events related to the content of accounts the user follows. For example, if the facilitator analyzes that the user "posts a lot about history," it can explain events related to history. The facilitator can also explain related historical events based on an analysis of the user's followers. This makes it possible to explain related historical events based on the user's social media activity.

[0092] The facilitator can customize the dialogue progression method by reflecting the user's past feedback. The facilitator can analyze the user's past feedback using, for example, feedback analysis technology. For example, the facilitator can proceed with the dialogue by referring to dialogue progression methods that the user previously rated highly. The facilitator can also proceed by avoiding dialogue progression methods that the user previously rated poorly. Furthermore, the facilitator can adjust the dialogue progression method based on the user's feedback. For example, if the user gives feedback that "this progression method was good," the facilitator can proceed by reflecting that feature. Also, if the user gives feedback that "this progression method was not good," the facilitator can proceed by avoiding that feature. In this way, the dialogue progression method can be customized based on the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and progression unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives user input using a touch panel 38A or a microphone 38B of the smart device 14. The generation unit generates the voice of a historical figure using a synthetic voice AI by the specific processing unit 290 of the data processing device 12. The progression unit progresses the dialogue using a conversation AI by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and progression unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives user input using the microphone 238 of the smart glasses 214. The generation unit generates the voice of a historical figure using a synthetic voice AI by the specific processing unit 290 of the data processing device 12. The progression unit progresses the dialogue using a conversation AI by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and progression unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit receives user input using the microphone 238 of the headset-type terminal 314. The generation unit generates the voice of a historical figure using a synthetic voice AI by the specific processing unit 290 of the data processing device 12. The progression unit progresses the dialogue using a conversation AI by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and progression unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives user input using the microphone 238 of the robot 414. The generation unit generates the voice of a historical figure using a synthetic voice AI by the specific processing unit 290 of the data processing device 12. The progression unit progresses the dialogue using a conversation AI by the specific processing unit 290 of the data processing device 12.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The facilitator can estimate the user's emotions and adjust the way the dialogue proceeds based on the estimated user's emotions. For example, if the user is relaxed, the facilitator can proceed with the dialogue at a slow pace. If the user is excited, the facilitator can proceed with the dialogue at a lively pace. Furthermore, if the user is sad, the facilitator can proceed with the dialogue in a gentle tone. In this way, the way the dialogue proceeds can be adjusted according to the user's emotions.

[0095] The reception unit can suggest relevant historical figures by taking into account the user's geographical location information. For example, the reception unit can acquire the user's geographical location information using GPS data and suggest historical figures related to the area where the user is currently located. Also, if the user is traveling, it can suggest people related to the history of that area. Furthermore, if the user is interested in a particular country or area, it can suggest people related to that area. In this way, it is possible to suggest relevant historical figures based on the user's geographical location information.

[0096] When generating the voices of historical figures, the generation unit can reproduce accents based on the historical background and culture of the person. For example, in the case of a person from ancient Rome, the generation unit can reproduce a Latin accent. In addition, in the case of a person from Japan in the Edo period, the generation unit can also reproduce the Japanese accent of that time. Furthermore, in the case of a person from Italy in the Renaissance period, the generation unit can also reproduce an Italian accent. This allows the voices of historical figures to be reproduced realistically.

[0097] The moderator can analyze the user's past dialogue history and select the optimal dialogue progression method. For example, the moderator can proceed with the dialogue by referring to the dialogue progression method that the user preferred in the past. The moderator can also select and proceed with a related topic based on the content of the user's past dialogue. Furthermore, the moderator can adjust the dialogue progression method based on the user's past feedback. In this way, the optimal dialogue progression method can be selected based on the user's past dialogue history.

[0098] The reception unit can estimate the user's emotions and suggest historical figures with whom the user would like to have a conversation based on the estimated user emotions. For example, the reception unit can use facial expression recognition technology to estimate emotions from the user's facial expressions. It can also use voice analysis technology to estimate emotions from the tone and speed of the user's voice. If the user is excited, it can suggest active historical figures such as Sengoku warlords and adventurers. If the user is calm, it can suggest quiet historical figures such as philosophers and poets. In this way, it is possible to suggest historical figures that match the user's emotions.

[0099] The generation unit can estimate the user's emotion and adjust the tone of voice and speaking style based on the estimated user emotion. For example, the generation unit can use facial expression recognition technology to estimate the emotion from the user's facial expression. It can also use voice analysis technology to estimate the emotion from the tone and speed of the user's voice. If the user is relaxed, the generation unit can speak in a calm tone. If the user is excited, the generation unit can speak in a lively tone. Furthermore, if the user is sad, the generation unit can speak in a gentle tone. In this way, the generation unit can adjust the tone of voice and speaking style according to the user's emotion.

[0100] The progress manager can customize the content of the dialogue based on the user's current learning progress. For example, the progress manager evaluates the user's current learning progress based on the learning history. If the user is a beginner, the dialogue can start with basic content. If the user is an intermediate learner, the dialogue can progress to more in-depth content. Furthermore, if the user is an advanced learner, the dialogue can include specialized content. This allows the dialogue content to be customized according to the user's learning progress.

[0101] The reception unit can analyze the user's social media activity and suggest related historical figures. For example, the reception unit can analyze the user's social media activity using social media activity analysis technology. The reception unit can suggest people based on history-related accounts the user follows on social media. The reception unit can also analyze the content of the user's posts and suggest historical figures that the user may be interested in. This makes it possible to suggest related historical figures based on the user's social media activity.

[0102] The facilitator can estimate the user's emotions and adjust the tempo of the dialogue based on the estimated user emotions. For example, the facilitator can use facial expression recognition technology to estimate emotions from the user's facial expressions. It can also use voice analysis technology to estimate emotions from the tone and speed of the user's voice. If the user is relaxed, the dialogue can proceed at a slower tempo. If the user is excited, the dialogue can proceed at a faster tempo. This makes it possible to adjust the tempo of the dialogue according to the user's emotions.

[0103] When generating the voice of a historical figure, the generation unit can improve the naturalness of the voice by referring to the user's past dialogue history. For example, the generation unit can use text mining technology to analyze the user's past dialogue history. The generation unit can generate a voice by referring to the voice tones and rhythms that the user previously preferred. The generation unit can also generate a voice that reflects the voice characteristics of figures with whom the user previously conversed. This makes it possible to improve the naturalness of the voice based on the user's past dialogue history.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The reception unit receives user input. User input can include text input, voice input, etc. The reception unit can also provide an interface for the user to select the historical figure with which they want to interact. Step 2: The generation unit uses a synthetic voice AI to generate the voice of the historical figure based on the information received by the reception unit. The generation unit uses voice synthesis technology to realistically reproduce the voice of the selected historical figure, and can also generate the voice by referencing a voice database. Step 3: The moderator uses conversational AI to advance the dialogue based on the voice generated by the generator. The moderator generates appropriate answers to the user's questions and advances the dialogue. It can also generate responses based on the dialogue scenario.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0176] 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.

[0177] [Explanation of symbols]

[0178] 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 a user; a generation unit that generates a voice of a historical figure based on the information received by the reception unit; a progression unit that progresses the dialogue based on the voice generated by the generation unit. A system characterized by:

2. The reception unit The user selects the historical figure they want to interact with.

2. The system of claim 1.

3. The generation unit Generate the voice of a selected historical figure 2. The system of claim 1.

4. The progression section Generate appropriate answers to user questions and advance the dialogue 2. The system of claim 1.

5. The progression section Explain historical events based on user questions 2. The system of claim 1.

6. The progression section Engage in dialogue to increase user motivation to learn 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and suggest historical figures to interact with based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyzes the user's past interaction history and recommends the most suitable historical figure 2. The system of claim 1.

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