system

Abstract

The system according to the embodiment aims to reproduce the speaking style and knowledge of great people, and to provide the user with the feeling that they are having a conversation with a great person. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a reproduction unit, a reception unit, and an answering unit. The collection unit collects voice data. The analysis unit analyzes the data collected by the collection unit. The reproduction unit reproduces the speaking style and knowledge of the great person based on the data analyzed by the analysis unit. The reception unit accepts input from a user. The answering unit answers like the great person based on the input accepted by the reception unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not allow users to converse with deceased great figures or celebrities, and there is room for improvement in providing new entertainment experiences.

[0005] The system according to the embodiment aims to reproduce the speaking style and knowledge of great people, and to provide the user with the feeling that they are having a conversation with a great person. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a reproduction unit, a reception unit, and an answering unit. The collection unit collects voice data. The analysis unit analyzes the data collected by the collection unit. The reproduction unit reproduces the speaking style and knowledge of the great person based on the data analyzed by the analysis unit. The reception unit receives input from a user. The answering unit responds like the great person based on the input received by the reception unit. [Effects of the Invention]

[0007] The system according to the embodiment can reproduce the speaking style and knowledge of great people, providing users with the feeling that they are having a conversation with the great people. [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 conversation system according to an embodiment of the present invention accumulates data such as the voices and books of great figures of the past, enabling users to converse as if they were the great figures themselves. This conversation system collects data such as voices and books of great figures of the past and trains AI to imitate their speaking style and knowledge. Users can initiate conversations with great figures through the app, inputting questions and topics, and the AI ​​responds accordingly, just like the great figures. For example, if a user asks, "What event has had the greatest impact on you?", the AI ​​generates an answer based on the great figure's life and thoughts. Users can also use this entertainment service by paying a service fee. Copyright royalties are paid to the great figures or their families. This allows service providers to earn revenue by providing the platform and service. This app offers a new entertainment experience, allowing users to converse with great figures of the past, making it a highly attractive service. The conversation system collects voice data and book data of great figures of the past and trains AI to imitate their speaking style and knowledge, enabling users to converse with them.

[0029] A conversation system according to an embodiment includes a collection unit, an analysis unit, a reproduction unit, a reception unit, and a response unit. The collection unit collects audio data and book data of great figures of the past. The collection unit can use, for example, publicly available audio data or audio data collected from libraries. For example, the collection unit collects audio data from online libraries and podcasts. The collection unit can also collect historical recordings from library archives. The collection unit can also collect non-audio data, such as handwritten notes and letters by great figures, and use this data for analysis. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit converts audio data into text data using speech recognition technology and analyzes the text data. For example, the analysis unit converts audio data into text data using deep learning-based speech recognition technology. The analysis unit also uses natural language processing technology to analyze book data and understand the knowledge and thoughts of great figures. For example, the analysis unit analyzes the book data using morphological analysis and grammatical analysis. The reproducing unit reproduces the speaking style and knowledge of the great person based on the data analyzed by the analyzing unit. The reproducing unit reproduces the speaking style of the great person using, for example, speech synthesis technology. The reproducing unit can also build a knowledge base and reproduce the knowledge of the great person. The receiving unit receives input from the user. The receiving unit can receive, for example, speech input or text input. For example, when a user inputs a question by voice, the receiving unit converts it into text data using speech recognition technology. The answering unit responds in the manner of the great person based on the input received by the receiving unit. The answering unit generates an answer based on the great person's thoughts using, for example, natural language generation technology. For example, the answering unit references a knowledge base of great people to generate an appropriate answer in response to a user's question. As a result, the conversation system according to the embodiment can collect, analyze, and reproduce speech data and book data of great people of the past, thereby realizing a dialogue with the user.

[0030] The collection unit may use publicly available audio data or audio data collected from a library. Examples of publicly available audio data include online libraries and podcasts. For example, the collection unit may collect audio data of speeches and interviews by famous people from online libraries. The collection unit may also collect audio data of dialogues and lectures by famous people from podcasts. Examples of audio data collected from libraries include historical recordings and archival materials. For example, the collection unit may collect recordings of speeches and lectures by famous people from library archives. The collection unit may also collect audio data of interviews by famous people from library digital archives. This allows the collection unit to expand the scope of its collection by using publicly available audio data and library data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit may collect audio data using an AI model that automatically collects audio data from online libraries and podcasts.

[0031] The analysis unit can use speech recognition technology to convert speech data into text data and analyze the text data. Examples of speech recognition technology include deep learning-based speech recognition technology and HMM-based speech recognition technology. The analysis unit can use deep learning-based speech recognition technology to convert speech data into text data. Deep learning-based speech recognition technology learns from large amounts of speech data to achieve highly accurate speech recognition. The analysis unit can also use HMM-based speech recognition technology to convert speech data into text data. HMM-based speech recognition technology models temporal changes in speech to achieve highly accurate speech recognition. The analysis unit then analyzes the data converted into text data using speech recognition technology. The analysis unit can use natural language processing technology to analyze text data and understand the knowledge and thoughts of great figures. Natural language processing technology includes morphological analysis, grammatical analysis, and semantic analysis. The analysis unit can use morphological analysis to analyze text data and understand the meaning of words and sentence structure. The analysis unit can also use grammatical analysis to analyze text data and understand sentence structure and grammatical relationships. Additionally, the analyzer may analyze text data using semantic analysis to understand the meaning and context of a sentence. This allows the analyzer to convert speech data into text data, making analysis easier. Some or all of the above-described processing in the analyzer may be performed using, for example, AI, or may be performed without AI. For example, the analyzer may analyze speech data using an AI model that converts speech data into text data.

[0032] The analysis unit uses natural language processing technology to analyze book data and understand the knowledge and thoughts of great figures. Natural language processing technology includes, for example, morphological analysis, grammatical analysis, and semantic analysis. The analysis unit uses, for example, morphological analysis to analyze book data and understand the meaning of words and sentence structure. Morphological analysis is a technology that divides sentences into words and analyzes the meaning and parts of speech of each word. The analysis unit can also analyze book data using grammatical analysis to understand sentence structure and grammatical relationships. Grammatical analysis is a technology that analyzes sentence structure and clarifies grammatical relationships such as subject, predicate, and object. The analysis unit can also analyze book data using semantic analysis to understand the meaning and context of sentences. Semantic analysis is a technology that analyzes the meaning of sentences and provides appropriate interpretations based on the context. This enables the analysis unit to analyze book data using natural language processing technology. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without AI. For example, the analysis unit can use an AI model that analyzes book data to understand the knowledge and thoughts of great people.

[0033] The answering unit can provide an answer based on the great person's thoughts based on the user's question or topic. The answering unit generates an answer based on the great person's thoughts using, for example, natural language generation technology. Natural language generation technology includes, for example, text generation AI (e.g., LLM) and multimodal generation AI. The answering unit generates an answer to the user's question using, for example, text generation AI. Text generation AI learns large amounts of text data and has advanced natural language processing capabilities. The answering unit can also generate an answer to the user's question using multimodal generation AI. Multimodal generation AI can handle multiple modalities, including not only text but also images and audio. Furthermore, the answering unit references a knowledge base of great people to generate an appropriate answer to the user's question. The knowledge base contains information about the great person's life and thoughts, and the answering unit generates an answer based on this information. For example, if a user asks, "What event has had the greatest impact on you?", the answering unit generates an answer based on the great person's life and thoughts. This allows the answering unit to provide an answer to the user's question based on the great person's thoughts. Some or all of the above-described processing in the answering unit may be performed using, for example, AI, or may be performed without using AI. For example, the answering unit may generate an answer using an AI model that takes a user's question as input and outputs an answer based on the ideas of great people.

[0034] The conversation system includes a storage unit that stores and manages data on great people. The storage unit stores and manages the data on great people. The storage unit stores the data on great people using, for example, a database. The database includes audio data of the great people, book data, and non-audio data such as handwritten notes and letters. The storage unit can also store the data on great people using cloud storage. Cloud storage is a technology for safely storing large amounts of data and making it easy to access. Furthermore, the storage unit can organize the data on great people in chronological order to make it easy to search. For example, the storage unit can organize the data on great people in chronological order to make it easy to search. The storage unit can also organize the data on great people by category to make it easy for users to access. For example, the storage unit can organize the data on great people by theme to make it easy for users to access. In this way, the storage unit can ensure the consistency and reliability of the data by storing and managing the data on great people. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can store and manage data using an AI model that automatically organizes and manages data on great people.

[0035] During collection, the collection unit can collect data by focusing on a specific period in the great person's life. For example, the collection unit collects audio data from the great person's youth and analyzes their growth process. For example, the collection unit collects audio data of speeches and interviews from the great person's youth and analyzes their growth process. The collection unit can also collect audio data from the great person's peak career and analyze their knowledge and thoughts during that period. For example, the collection unit collects audio data of lectures and conversations from the great person's peak career and analyzes their knowledge and thoughts during that period. Furthermore, the collection unit can collect audio data from the great person's later years and analyze their experiences and wisdom during that period. For example, the collection unit collects audio data of speeches and interviews from the great person's later years and analyzes their experiences and wisdom during that period. This allows the collection unit to collect more detailed data by focusing on a specific period in the great person's life. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection department could collect audio data using an AI model that focuses data collection on specific periods in the lives of great figures.

[0036] During collection, the collection unit can collect not only the voice data of great people, but also non-voice data such as handwritten notes or letters, and use them for analysis. For example, the collection unit collects handwritten notes of great people, analyzes their contents, and combines them with voice data. For example, the collection unit scans handwritten notes of great people, converts them into text data, and analyzes them. The collection unit can also collect letters of great people, analyze their writing style and expressions, and reflect them in the voice data. For example, the collection unit scans letters of great people, converts them into text data, and analyzes them. Furthermore, the collection unit can collect handwritten notes of great people, analyze their knowledge and thoughts, and integrate them into the voice data. For example, the collection unit scans handwritten notes of great people, converts them into text data, and analyzes them. This allows the collection unit to collect non-voice data, enabling more multifaceted analysis. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection department can collect non-audio data such as handwritten notes and letters from great figures and use AI models to analyze the data.

[0037] During collection, the collection unit can collect testimonies from the great person's family or friends and use them to gain a deeper understanding of the great person's personality. For example, the collection unit collects testimonies from the great person's family and analyzes the content of the testimonies to recreate the great person's personality. For example, the collection unit collects interviews with the great person's family and analyzes the content of the testimonies to recreate the great person's personality. The collection unit can also collect testimonies from the great person's friends and analyze the content of the testimonies to recreate the great person's personal relationships. For example, the collection unit collects interviews with the great person's friends and analyzes the content of the testimonies to recreate the great person's personal relationships. Furthermore, the collection unit can collect testimonies from the great person's colleagues and analyze the content of the testimonies to recreate the great person's professional aspects. For example, the collection unit collects interviews with the great person's colleagues and analyzes the content of the testimonies to recreate the great person's professional aspects. In this way, the collection unit can gain a deeper understanding of the great person's personality by collecting testimonies from the great person's family and friends. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection department could collect testimonies from the great person's family and friends and use an AI model to recreate the great person's personality.

[0038] During collection, the collection unit can collect video data related to the great person and combine it with audio data for analysis. For example, the collection unit collects video data of a lecture by the great person, analyzes the content, and combines it with audio data. For example, the collection unit collects video data of a lecture by the great person, analyzes the content, and combines it with audio data. The collection unit can also collect video data of an interview with the great person, analyze the content, and combine it with audio data. For example, the collection unit collects video data of an interview with the great person, analyzes the content, and combines it with audio data. The collection unit can also collect documentary footage of the great person, analyze the content, and combine it with audio data. For example, the collection unit collects documentary footage of the great person, analyzes the content, and combines it with audio data. This allows the collection unit to collect video data and combine it with audio data for more detailed analysis. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can collect data using an AI model that collects video data related to the great person and analyzes it in combination with audio data.

[0039] During analysis, the analysis unit can focus on analyzing the great person's knowledge of a particular theme or field. For example, the analysis unit can focus on analyzing the great person's scientific knowledge and provide information on that field. For example, the analysis unit can analyze the content of the great person's scientific papers and lectures and provide information on that field. The analysis unit can also focus on analyzing the great person's literary knowledge and provide information on that field. For example, the analysis unit can analyze the content of the great person's literary works and critiques and provide information on that field. The analysis unit can also focus on analyzing the great person's historical knowledge and provide information on that field. For example, the analysis unit can analyze the content of the great person's historical speeches and writings and provide information on that field. In this way, the analysis unit can provide more specialized information by focusing on analyzing knowledge on a particular theme or field. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can perform the analysis using an AI model that focuses on analyzing the great person's knowledge of a particular theme or field.

[0040] The analysis unit can perform the analysis by taking into account the background or context of the great person's statement. For example, the analysis unit can perform the analysis by taking into account the historical background of the great person's statement. For example, the analysis unit can perform the analysis by taking into account the time and circumstances under which the great person's statement was made. The analysis unit can also perform the analysis by taking into account the cultural background of the great person's statement. For example, the analysis unit can perform the analysis by taking into account the cultural background and social circumstances under which the great person's statement was made. Furthermore, the analysis unit can perform the analysis by taking into account the social background of the great person's statement. For example, the analysis unit can perform the analysis by taking into account the social background and political circumstances under which the great person's statement was made. In this way, the analysis unit can perform a more accurate analysis by taking into account the background and context of the statement. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that performs analysis by taking into account the background and context of the great person's statement.

[0041] During analysis, the analysis unit can analyze the frequency and patterns of statements made by great people and extract their characteristics. For example, the analysis unit analyzes the frequency of statements made by great people and extracts their characteristics. The analysis unit can also analyze the patterns of statements made by great people and extract their characteristics. For example, the analysis unit analyzes the patterns of statements made by great people and extracts their characteristics. The analysis unit can also analyze the tone and rhythm of statements made by great people and extract their characteristics. For example, the analysis unit analyzes the tone and rhythm of statements made by great people and extracts their characteristics. In this way, the analysis unit can more clearly grasp the characteristics of great people by analyzing the frequency and patterns of statements. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform analysis using an AI model that analyzes the frequency and patterns of statements made by great people and extracts their characteristics.

[0042] During the analysis, the analysis unit can analyze the emotional tone of the great person's statement and provide the information to the user. For example, the analysis unit can analyze the emotional tone of the great person's statement and provide the information to the user. For example, the analysis unit can analyze the emotional tone of the great person's statement and provide the information to the user. The analysis unit can also analyze the emotional intensity of the great person's statement and provide the information to the user. For example, the analysis unit can analyze the emotional intensity of the great person's statement and provide the information to the user. Furthermore, the analysis unit can analyze emotional changes in the great person's statement and provide the information to the user. For example, the analysis unit can analyze emotional changes in the great person's statement and provide the information to the user. In this way, the analysis unit can provide the user with a deeper understanding by analyzing the emotional tone of the statement. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that analyzes the emotional tone of the great person's statement and provides the information to the user.

[0043] The reenactment unit can generate a scenario for reenacting a specific situation or scene of a great person during the reenactment. For example, the reenactment unit generates a scenario for reenacting a scene of a lecture by a great person. For example, the reenactment unit generates a scenario based on the content and background of the lecture by the great person and reenacts that scene. The reenactment unit can also generate a scenario for reenacting a scene of an interview by the great person. For example, the reenactment unit generates a scenario based on the content and background of the interview by the great person and reenacts that scene. The reenactment unit can also generate a scenario for reenacting a scene of a dialogue between the great person. For example, the reenactment unit generates a scenario based on the content and background of the dialogue between the great person and reenacts that scene. In this way, the reenactment unit can provide a more realistic experience by reenacting a specific situation or scene. Some or all of the above-described processing in the reenactment unit may be performed using, for example, AI, or may be performed without using AI. For example, the reenactment unit can generate a scenario using an AI model that generates a scenario for reenacting a specific situation or scene of a great person.

[0044] During reproduction, the reproducing unit can reproduce background sounds and environmental sounds of the great person's speech, thereby providing a more realistic experience. For example, the reproducing unit can reproduce background sounds of the great person's lecture, thereby providing a more realistic experience. For example, the reproducing unit can reproduce background sounds of the great person's lecture, thereby reproducing the scene more realistically. The reproducing unit can also reproduce environmental sounds of the great person's interview, thereby providing a more realistic experience. For example, the reproducing unit can reproduce environmental sounds of the great person's interview, thereby reproducing the scene more realistically. Furthermore, the reproducing unit can reproduce background sounds of the great person's dialogue, thereby providing a more realistic experience. For example, the reproducing unit can reproduce background sounds of the great person's dialogue, thereby reproducing the scene more realistically. In this way, the reproducing unit can provide a more realistic experience by reproducing background sounds and environmental sounds. Some or all of the above-described processing in the reproducing unit may be performed using, for example, AI, or may be performed without using AI. For example, the reproducing unit can reproduce audio using an AI model that reproduces background sounds and environmental sounds of the great person's speech.

[0045] The reproducing unit can provide visual information by displaying visual content related to the statements of a great person during the reproducing process. For example, the reproducing unit can display related visual content when reproducing a speech by a great person. For example, the reproducing unit can display related visual content when reproducing a speech by a great person, thereby more realistically reproducing the scene. The reproducing unit can also display related visual content when reproducing an interview by a great person. For example, the reproducing unit can display related visual content when reproducing an interview by a great person, thereby more realistically reproducing the scene. The reproducing unit can also display related visual content when reproducing a dialogue by a great person. For example, the reproducing unit can display related visual content when reproducing a dialogue by a great person, thereby more realistically reproducing the scene. In this way, the reproducing unit can provide visual information and deepen the user's understanding by displaying visual content. Some or all of the above-described processing in the reproducing unit can be performed using, for example, AI, or can be performed without AI. For example, the reproducing unit can display visual content using an AI model that displays visual content related to the statements of a great person.

[0046] The recapitulation unit can provide additional information and annotations related to the statements of a great person during the recapitulation, thereby deepening the user's understanding. For example, the recapitulation unit can provide related additional information and annotations when recapturing a speech by a great person. For example, the recapitulation unit can provide related additional information and annotations when recapturing a speech by a great person, thereby providing a deeper understanding of the scene. The recapitulation unit can also provide related additional information and annotations when recapturing an interview by a great person. For example, the recapitulation unit can provide related additional information and annotations when recapturing an interview by a great person, thereby providing a deeper understanding of the scene. The recapitulation unit can also provide related additional information and annotations when recapturing a dialogue by a great person. For example, the recapitulation unit can provide related additional information and annotations when recapturing a dialogue by a great person, thereby providing a deeper understanding of the scene. In this way, the recapitulation unit can deepen the user's understanding by providing additional information and annotations. Some or all of the above-described processing in the recapitulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recapitulation unit can provide the additional information and annotations using an AI model that provides additional information and annotations related to the statements of a great person.

[0047] The reception unit can suggest appropriate questions by referring to the user's past question history when receiving the request. The reception unit can suggest appropriate questions by referring to the user's past question history, for example. The reception unit can automatically display, for example, questions that the user has frequently asked in the past as candidates. For example, the reception unit can analyze the user's past question history and automatically display frequently asked questions as candidates. The reception unit can also suggest related questions from the user's past question history. For example, the reception unit can analyze the user's past question history and suggest related questions. The reception unit can also analyze the user's past question history and suggest the most appropriate question. For example, the reception unit can analyze the user's past question history and suggest the most appropriate question. In this way, the reception unit can suggest appropriate questions by referring to the past question history. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can suggest questions by referring to the user's past question history and using an AI model that suggests appropriate questions.

[0048] The reception unit can analyze the user's input content in real time upon reception and provide appropriate feedback. The reception unit, for example, analyzes the user's input content in real time and provides appropriate feedback. The reception unit, for example, analyzes the user's input content in real time and provides related information. For example, the reception unit analyzes the user's input content in real time and provides related information. The reception unit can also suggest appropriate questions or topics based on the user's input content. For example, the reception unit analyzes the user's input content and suggests appropriate questions or topics. The reception unit can also analyze the user's input content and provide immediate feedback. For example, the reception unit analyzes the user's input content and provides immediate feedback. In this way, the reception unit can provide appropriate feedback by analyzing in real time. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can analyze the user's input content in real time and provide feedback using an AI model that provides appropriate feedback.

[0049] The reception unit can suggest related topics and questions based on the user's input content when receiving the input. The reception unit, for example, suggests related topics and questions based on the user's input content. The reception unit, for example, suggests related topics based on the user's input content. For example, the reception unit analyzes the user's input content and suggests related topics. The reception unit can also suggest related questions from the user's input content. For example, the reception unit analyzes the user's input content and suggests related questions. The reception unit can also analyze the user's input content and suggest the most relevant topic or question. For example, the reception unit analyzes the user's input content and suggests the most relevant topic or question. This allows the reception unit to make more appropriate suggestions by suggesting related topics and questions based on the input content. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can make suggestions using an AI model that suggests related topics and questions based on the user's input content.

[0050] The reception unit can analyze the user's input content upon reception and collect data for providing an appropriate answer. The reception unit, for example, analyzes the user's input content and collects data for providing an appropriate answer. The reception unit, for example, analyzes the user's input content and collects related data. For example, the reception unit analyzes the user's input content and collects related data. The reception unit can also collect information for providing an appropriate answer based on the user's input content. For example, the reception unit analyzes the user's input content and collects information for providing an appropriate answer. The reception unit can also analyze the user's input content and collect most appropriate data. For example, the reception unit analyzes the user's input content and collects most appropriate data. As a result, the reception unit can analyze the input content and collect data for providing an appropriate answer, thereby providing a more appropriate answer. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can collect data using an AI model that analyzes the user's input content and collects data for providing an appropriate answer.

[0051] When answering a question, the answering unit can provide a more appropriate answer by taking into account the background and context of the great person's statement. For example, the answering unit can provide an answer by taking into account the historical background of the great person's statement. For example, the answering unit can provide an answer by taking into account the time and circumstances under which the great person's statement was made. The answering unit can also provide an answer by taking into account the cultural background of the great person's statement. For example, the answering unit can provide an answer by taking into account the cultural background and social circumstances under which the great person's statement was made. Furthermore, the answering unit can also provide an answer by taking into account the social background of the great person's statement. For example, the answering unit can provide an answer by taking into account the social background and political circumstances under which the great person's statement was made. In this way, the answering unit can provide a more appropriate answer by taking into account the background and context of the statement. Some or all of the above-described processing in the answering unit may be performed using, for example, AI, or may be performed without using AI. For example, the answering unit can provide an answer using an AI model that provides an answer by taking into account the background and context of the great person's statement.

[0052] When answering, the answering unit can provide additional information and annotations related to the statement of a great person, thereby deepening the user's understanding. The answering unit, for example, provides additional historical information related to the statement of a great person. For example, the answering unit can provide additional historical information related to the statement of a great person, thereby providing a deeper understanding of the background of the statement. The answering unit can also provide cultural annotations related to the statement of a great person. For example, the answering unit can provide cultural annotations related to the statement of a great person, thereby providing a deeper understanding of the background of the statement. Furthermore, the answering unit can also provide social background information related to the statement of a great person. For example, the answering unit can provide social background information related to the statement of a great person, thereby providing a deeper understanding of the background of the statement. In this way, the answering unit can deepen the user's understanding by providing additional information and annotations. Some or all of the above-described processing in the answering unit may be performed using, for example, AI, or may be performed without using AI. For example, the answering unit can provide additional information and annotations using an AI model that provides additional information and annotations related to the statement of a great person.

[0053] When answering, the answering unit can display visual content related to the great person's statement to provide visual information. The answering unit, for example, displays an image related to the great person's statement. For example, the answering unit can display an image related to the great person's statement to visually understand the background of the statement. The answering unit can also display a video related to the great person's statement. For example, the answering unit can display a video related to the great person's statement to visually understand the background of the statement. The answering unit can also display a chart related to the great person's statement. For example, the answering unit can display a chart related to the great person's statement to visually understand the background of the statement. In this way, the answering unit can provide visual information and deepen the user's understanding by displaying visual content. Some or all of the above-mentioned processing in the answering unit may be performed, for example, using AI or without AI. For example, the answering unit can display visual content using an AI model that displays visual content related to the great person's statement.

[0054] When answering, the answering unit can provide additional information and annotations related to the statement of a great person, thereby deepening the user's understanding. The answering unit, for example, provides additional historical information related to the statement of a great person. For example, the answering unit can provide additional historical information related to the statement of a great person, thereby providing a deeper understanding of the background of the statement. The answering unit can also provide cultural annotations related to the statement of a great person. For example, the answering unit can provide cultural annotations related to the statement of a great person, thereby providing a deeper understanding of the background of the statement. Furthermore, the answering unit can also provide social background information related to the statement of a great person. For example, the answering unit can provide social background information related to the statement of a great person, thereby providing a deeper understanding of the background of the statement. In this way, the answering unit can deepen the user's understanding by providing additional information and annotations. Some or all of the above-described processing in the answering unit may be performed using, for example, AI, or may be performed without using AI. For example, the answering unit can provide additional information and annotations using an AI model that provides additional information and annotations related to the statement of a great person.

[0055] The storage unit can organize the data of great people in chronological order during storage to make it easier to search. For example, the storage unit can organize the data of great people in chronological order to make it easier to search. For example, the storage unit can organize the data of great people in chronological order to make it easier to search. The storage unit can also organize the data of great people by event to make it easier to search. For example, the storage unit can organize the data of great people by event to make it easier to search. The storage unit can also organize the data of great people by the time of their statements to make it easier to search. For example, the storage unit can organize the data of great people by the time of their statements to make it easier to search. In this way, the storage unit organizes the data in chronological order, making it easier to search. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can organize the data using an AI model that organizes the data of great people in chronological order to make it easier to search.

[0056] The storage unit may organize the data of famous people by category during storage, allowing users to easily access the data. For example, the storage unit may organize the data of famous people by theme, allowing users to easily access the data. For example, the storage unit may organize the data of famous people by theme, allowing users to easily access the data. The storage unit may also organize the data of famous people by field, allowing users to easily access the data. For example, the storage unit may organize the data of famous people by field, allowing users to easily access the data. The storage unit may also organize the data of famous people by topic, allowing users to easily access the data. For example, the storage unit may organize the data of famous people by topic, allowing users to easily access the data. In this way, the storage unit organizes the data by category, allowing users to easily access the data. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit may organize the data using an AI model that organizes the data of famous people by category, allowing users to easily access the data.

[0057] When storing the data of famous people, the storage unit can organize the data of famous people by related topic or theme, allowing the user to easily access it. For example, the storage unit can organize the data of famous people by related topic, allowing the user to easily access it. Furthermore, the storage unit can organize the data of famous people by related theme, allowing the user to easily access it. For example, the storage unit can organize the data of famous people by related theme, allowing the user to easily access it. Furthermore, the storage unit can organize the data of famous people by related field, allowing the user to easily access it. For example, the storage unit can organize the data of famous people by related field, allowing the user to easily access it. In this way, the storage unit organizes the data by topic or theme, allowing the user to easily access it. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can organize the data of famous people using an AI model that organizes the data of famous people by related topic or theme, allowing the user to easily access it.

[0058] The storage unit may link the data of the great person with other related data during storage, thereby enabling a user to gain a deeper understanding. For example, the storage unit may link the data of the great person with related historical data. For example, the storage unit may link the data of the great person with related historical data to enable a deeper understanding of the data. The storage unit may also link the data of the great person with related cultural data. For example, the storage unit may link the data of the great person with related cultural data to enable a deeper understanding of the data. Furthermore, the storage unit may also link the data of the great person with related social data. For example, the storage unit may link the data of the great person with related social data to enable a deeper understanding of the data. In this way, the storage unit may link the data with related data to enable a deeper understanding of the data. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit may link the data using an AI model that links the data of the great person with other related data.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The conversation system can refer to the user's past conversation history and customize the content of the conversation based on the user's interests and concerns. For example, if the user has frequently selected science-related topics in the past, the system can prioritize providing stories about famous scientific figures. Specifically, if the user has frequently asked science-related questions in the past, the system can provide stories about famous scientific figures' scientific discoveries and research. Furthermore, if the user has frequently selected literature-related topics in the past, the system can also provide stories about famous literary figures. For example, if the user has frequently asked literature-related questions in the past, the system can provide stories about literary works and critiques by famous figures. Furthermore, if the user has frequently selected history-related topics in the past, the system can also provide stories about famous historical figures. For example, if the user has frequently asked history-related questions in the past, the system can provide stories about historical events and speeches by famous figures. This allows the conversation system to provide customized conversations based on the user's interests and concerns.

[0061] A conversation system can analyze user input in real time and provide appropriate feedback. For example, when a user inputs a question, the system can generate an appropriate answer in real time. For example, if a user asks, "What event has had the greatest impact on you?", the system can instantly generate an answer based on the life and thoughts of great people. Furthermore, when a user inputs an impression or opinion, the system can provide appropriate feedback in response to that content. For example, if a user inputs, "This episode was very moving," the system can provide feedback such as, "Thank you. There are many inspiring episodes in the lives of great people." Furthermore, when a user inputs a question, the system can provide an appropriate explanation for that question. For example, if a user inputs, "What is the background of this event?", the system can explain the historical and cultural background of the event. This allows the conversation system to provide appropriate feedback in real time based on the user's input.

[0062] The conversation system can analyze the user's past conversation history and suggest conversation topics based on the user's interests and concerns. For example, it can suggest new related topics based on topics that the user has asked many questions about in the past. Specifically, if the user has asked many questions about science in the past, the system can suggest new topics related to science. Also, if the user has asked many questions about literature in the past, the system can suggest new topics related to literature. For example, if the user has asked many questions about literature in the past, the system can suggest new topics related to literary works and criticisms of famous people. Furthermore, if the user has asked many questions about history in the past, the system can suggest new topics related to history. For example, if the user has asked many questions about history in the past, the system can suggest new topics related to historical events and speeches of famous people. In this way, the conversation system can suggest new topics based on the user's interests and concerns.

[0063] A conversation system can analyze a user's input and provide relevant visual content. For example, if a user asks about a particular famous person, the system can provide images and videos related to that person. For example, if a user asks, "Tell me about Albert Einstein," the system can provide photos of Einstein and videos of his lectures. Also, if a user asks about a specific event, the system can provide visual content related to that event. For example, if a user asks, "Tell me about the Apollo 11 moon landing," the system can provide footage and photos of the moon landing. Furthermore, if a user asks about a specific place, the system can provide visual content related to that place. For example, if a user asks, "Tell me about the pyramids," the system can provide photos of the pyramids and documentary footage. This allows the conversation system to provide relevant visual content based on the user's input.

[0064] The conversation system can analyze the user's past conversation history and suggest conversation topics based on the user's interests and concerns. For example, it can suggest new related topics based on topics that the user has asked many questions about in the past. Specifically, if the user has asked many questions about science in the past, the system can suggest new topics related to science. Also, if the user has asked many questions about literature in the past, the system can suggest new topics related to literature. For example, if the user has asked many questions about literature in the past, the system can suggest new topics related to literary works and criticisms of famous people. Furthermore, if the user has asked many questions about history in the past, the system can suggest new topics related to history. For example, if the user has asked many questions about history in the past, the system can suggest new topics related to historical events and speeches of famous people. In this way, the conversation system can suggest new topics based on the user's interests and concerns.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The collection department collects audio data and book data of great figures of the past. The collection department can use, for example, publicly available audio data or audio data collected from libraries. For example, the collection department can collect audio data from online libraries or podcasts. The collection department can also collect historical recordings from library archives. In addition, the collection department can collect non-audio data, such as handwritten notes and letters of great figures, and use them for analysis. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses voice recognition technology to convert voice data into text data and analyzes the text data. For example, the analysis unit uses deep learning-based voice recognition technology to convert voice data into text data. The analysis unit also uses natural language processing technology to analyze book data and understand the knowledge and thoughts of great people. For example, the analysis unit analyzes book data using morphological analysis and grammatical analysis. Step 3: The reproducing unit reproduces the speaking style and knowledge of the great person based on the data analyzed by the analyzing unit. For example, the reproducing unit reproduces the speaking style of the great person using speech synthesis technology. The reproducing unit can also build a knowledge base and reproduce the knowledge of the great person. Step 4: The reception unit receives input from the user. The reception unit can receive, for example, voice input or text input. For example, when the user inputs a question by voice, the reception unit converts it into text data using voice recognition technology. Step 5: The answering unit answers in the style of the great person based on the input received by the receiving unit. The answering unit generates an answer based on the great person's ideas using, for example, natural language generation technology. For example, the answering unit refers to a knowledge base of great people in response to the user's question and generates an appropriate answer.

[0067] (Example 2) A conversation system according to an embodiment of the present invention accumulates data such as the voices and books of great figures of the past, enabling users to converse as if they were the great figures themselves. This conversation system collects data such as voices and books of great figures of the past and trains AI to imitate their speaking style and knowledge. Users can initiate conversations with great figures through the app, inputting questions and topics, and the AI ​​responds accordingly, just like the great figures. For example, if a user asks, "What event has had the greatest impact on you?", the AI ​​generates an answer based on the great figure's life and thoughts. Users can also use this entertainment service by paying a service fee. Copyright royalties are paid to the great figures or their families. This allows service providers to earn revenue by providing the platform and service. This app offers a new entertainment experience, allowing users to converse with great figures of the past, making it a highly attractive service. The conversation system collects voice data and book data of great figures of the past and trains AI to imitate their speaking style and knowledge, enabling users to converse with them.

[0068] A conversation system according to an embodiment includes a collection unit, an analysis unit, a reproduction unit, a reception unit, and a response unit. The collection unit collects audio data and book data of great figures of the past. The collection unit can use, for example, publicly available audio data or audio data collected from libraries. For example, the collection unit collects audio data from online libraries and podcasts. The collection unit can also collect historical recordings from library archives. The collection unit can also collect non-audio data, such as handwritten notes and letters by great figures, and use this data for analysis. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit converts audio data into text data using speech recognition technology and analyzes the text data. For example, the analysis unit converts audio data into text data using deep learning-based speech recognition technology. The analysis unit also uses natural language processing technology to analyze book data and understand the knowledge and thoughts of great figures. For example, the analysis unit analyzes the book data using morphological analysis and grammatical analysis. The reproducing unit reproduces the speaking style and knowledge of the great person based on the data analyzed by the analyzing unit. The reproducing unit reproduces the speaking style of the great person using, for example, speech synthesis technology. The reproducing unit can also build a knowledge base and reproduce the knowledge of the great person. The receiving unit receives input from the user. The receiving unit can receive, for example, speech input or text input. For example, when a user inputs a question by voice, the receiving unit converts it into text data using speech recognition technology. The answering unit responds in the manner of the great person based on the input received by the receiving unit. The answering unit generates an answer based on the great person's thoughts using, for example, natural language generation technology. For example, the answering unit references a knowledge base of great people to generate an appropriate answer in response to a user's question. As a result, the conversation system according to the embodiment can collect, analyze, and reproduce speech data and book data of great people of the past, thereby realizing a dialogue with the user.

[0069] The collection unit may use publicly available audio data or audio data collected from a library. Examples of publicly available audio data include online libraries and podcasts. For example, the collection unit may collect audio data of speeches and interviews by famous people from online libraries. The collection unit may also collect audio data of dialogues and lectures by famous people from podcasts. Examples of audio data collected from libraries include historical recordings and archival materials. For example, the collection unit may collect recordings of speeches and lectures by famous people from library archives. The collection unit may also collect audio data of interviews by famous people from library digital archives. This allows the collection unit to expand the scope of its collection by using publicly available audio data and library data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit may collect audio data using an AI model that automatically collects audio data from online libraries and podcasts.

[0070] The analysis unit can use speech recognition technology to convert speech data into text data and analyze the text data. Examples of speech recognition technology include deep learning-based speech recognition technology and HMM-based speech recognition technology. The analysis unit can use deep learning-based speech recognition technology to convert speech data into text data. Deep learning-based speech recognition technology learns from large amounts of speech data to achieve highly accurate speech recognition. The analysis unit can also use HMM-based speech recognition technology to convert speech data into text data. HMM-based speech recognition technology models temporal changes in speech to achieve highly accurate speech recognition. The analysis unit then analyzes the data converted into text data using speech recognition technology. The analysis unit can use natural language processing technology to analyze text data and understand the knowledge and thoughts of great figures. Natural language processing technology includes morphological analysis, grammatical analysis, and semantic analysis. The analysis unit can use morphological analysis to analyze text data and understand the meaning of words and sentence structure. The analysis unit can also use grammatical analysis to analyze text data and understand sentence structure and grammatical relationships. Additionally, the analyzer may analyze text data using semantic analysis to understand the meaning and context of a sentence. This allows the analyzer to convert speech data into text data, making analysis easier. Some or all of the above-described processing in the analyzer may be performed using, for example, AI, or may be performed without AI. For example, the analyzer may analyze speech data using an AI model that converts speech data into text data.

[0071] The analysis unit uses natural language processing technology to analyze book data and understand the knowledge and thoughts of great figures. Natural language processing technology includes, for example, morphological analysis, grammatical analysis, and semantic analysis. The analysis unit uses, for example, morphological analysis to analyze book data and understand the meaning of words and sentence structure. Morphological analysis is a technology that divides sentences into words and analyzes the meaning and parts of speech of each word. The analysis unit can also analyze book data using grammatical analysis to understand sentence structure and grammatical relationships. Grammatical analysis is a technology that analyzes sentence structure and clarifies grammatical relationships such as subject, predicate, and object. The analysis unit can also analyze book data using semantic analysis to understand the meaning and context of sentences. Semantic analysis is a technology that analyzes the meaning of sentences and provides appropriate interpretations based on the context. This enables the analysis unit to analyze book data using natural language processing technology. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without AI. For example, the analysis unit can use an AI model that analyzes book data to understand the knowledge and thoughts of great people.

[0072] The answering unit can provide an answer based on the great person's thoughts based on the user's question or topic. The answering unit generates an answer based on the great person's thoughts using, for example, natural language generation technology. Natural language generation technology includes, for example, text generation AI (e.g., LLM) and multimodal generation AI. The answering unit generates an answer to the user's question using, for example, text generation AI. Text generation AI learns large amounts of text data and has advanced natural language processing capabilities. The answering unit can also generate an answer to the user's question using multimodal generation AI. Multimodal generation AI can handle multiple modalities, including not only text but also images and audio. Furthermore, the answering unit references a knowledge base of great people to generate an appropriate answer to the user's question. The knowledge base contains information about the great person's life and thoughts, and the answering unit generates an answer based on this information. For example, if a user asks, "What event has had the greatest impact on you?", the answering unit generates an answer based on the great person's life and thoughts. This allows the answering unit to provide an answer to the user's question based on the great person's thoughts. Some or all of the above-described processing in the answering unit may be performed using, for example, AI, or may be performed without using AI. For example, the answering unit may generate an answer using an AI model that takes a user's question as input and outputs an answer based on the ideas of great people.

[0073] The conversation system includes a storage unit that stores and manages data on great people. The storage unit stores and manages the data on great people. The storage unit stores the data on great people using, for example, a database. The database includes audio data of the great people, book data, and non-audio data such as handwritten notes and letters. The storage unit can also store the data on great people using cloud storage. Cloud storage is a technology for safely storing large amounts of data and making it easy to access. Furthermore, the storage unit can organize the data on great people in chronological order to make it easy to search. For example, the storage unit can organize the data on great people in chronological order to make it easy to search. The storage unit can also organize the data on great people by category to make it easy for users to access. For example, the storage unit can organize the data on great people by theme to make it easy for users to access. In this way, the storage unit can ensure the consistency and reliability of the data by storing and managing the data on great people. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can store and manage data using an AI model that automatically organizes and manages data on great people.

[0074] The collection unit can estimate the user's emotions and determine the priority of the voice data to be collected based on the estimated user emotions. For example, the collection unit estimates the user's emotions and determines the priority of the voice data to be collected based on the estimated user emotions. For example, voice tone analysis and facial expression recognition technology are used to estimate the user's emotions. For example, if the user is excited, the collection unit preferentially collects voice data of speeches or addresses by famous people that are more emotional. For example, the collection unit analyzes the user's voice tone and determines that the user is excited, and collects voice data of emotional speeches. Furthermore, if the user is relaxed, the collection unit can preferentially collect voice data of calm dialogues or interviews by famous people. For example, the collection unit recognizes the user's facial expressions and determines that the user is relaxed, and collects voice data of calm dialogues. Furthermore, if the user is sad, the collection unit can preferentially collect voice data containing words of encouragement or comfort from famous people. For example, the collection unit analyzes the user's voice tone and determines that the user is sad, and collects voice data containing words of encouragement. This allows the collection unit to prioritize voice data based on the user's emotions, thereby collecting more appropriate data. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit may collect voice data using an AI model that estimates the user's emotions and prioritizes the voice data to be collected.

[0075] During collection, the collection unit can collect data by focusing on a specific period in the great person's life. For example, the collection unit collects audio data from the great person's youth and analyzes their growth process. For example, the collection unit collects audio data of speeches and interviews from the great person's youth and analyzes their growth process. The collection unit can also collect audio data from the great person's peak career and analyze their knowledge and thoughts during that period. For example, the collection unit collects audio data of lectures and conversations from the great person's peak career and analyzes their knowledge and thoughts during that period. Furthermore, the collection unit can collect audio data from the great person's later years and analyze their experiences and wisdom during that period. For example, the collection unit collects audio data of speeches and interviews from the great person's later years and analyzes their experiences and wisdom during that period. This allows the collection unit to collect more detailed data by focusing on a specific period in the great person's life. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection department could collect audio data using an AI model that focuses data collection on specific periods in the lives of great figures.

[0076] During collection, the collection unit can collect not only the voice data of great people, but also non-voice data such as handwritten notes or letters, and use them for analysis. For example, the collection unit collects handwritten notes of great people, analyzes their contents, and combines them with voice data. For example, the collection unit scans handwritten notes of great people, converts them into text data, and analyzes them. The collection unit can also collect letters of great people, analyze their writing style and expressions, and reflect them in the voice data. For example, the collection unit scans letters of great people, converts them into text data, and analyzes them. Furthermore, the collection unit can collect handwritten notes of great people, analyze their knowledge and thoughts, and integrate them into the voice data. For example, the collection unit scans handwritten notes of great people, converts them into text data, and analyzes them. This allows the collection unit to collect non-voice data, enabling more multifaceted analysis. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection department can collect non-audio data such as handwritten notes and letters from great figures and use AI models to analyze the data.

[0077] The collection unit can estimate the user's emotions and select the type of data to collect based on the estimated user emotions. For example, the collection unit estimates the user's emotions and selects the type of data to collect based on the estimated user emotions. For example, voice tone analysis and facial expression recognition technology are used to estimate the user's emotions. For example, if the user is excited, the collection unit preferentially collects audio data of emotional speeches or speeches by great figures. For example, the collection unit analyzes the user's voice tone and determines that the user is excited, and collects audio data of emotional speeches. Furthermore, the collection unit can preferentially collect audio data of calm dialogues or interviews by great figures when the user is relaxed. For example, the collection unit recognizes the user's facial expressions and determines that the user is relaxed, and collects audio data of calm dialogues. Furthermore, the collection unit can preferentially collect audio data containing encouraging or comforting words from great figures when the user is sad. For example, the collection unit analyzes the user's voice tone and determines that the user is sad, and collects audio data containing encouraging words. This allows the collection unit to select the type of data based on the user's emotions, thereby collecting more appropriate data. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may collect data using an AI model that estimates the user's emotions and selects the type of data to collect.

[0078] During collection, the collection unit can collect testimonies from the great person's family or friends and use them to gain a deeper understanding of the great person's personality. For example, the collection unit collects testimonies from the great person's family and analyzes the content of the testimonies to recreate the great person's personality. For example, the collection unit collects interviews with the great person's family and analyzes the content of the testimonies to recreate the great person's personality. The collection unit can also collect testimonies from the great person's friends and analyze the content of the testimonies to recreate the great person's personal relationships. For example, the collection unit collects interviews with the great person's friends and analyzes the content of the testimonies to recreate the great person's personal relationships. Furthermore, the collection unit can collect testimonies from the great person's colleagues and analyze the content of the testimonies to recreate the great person's professional aspects. For example, the collection unit collects interviews with the great person's colleagues and analyzes the content of the testimonies to recreate the great person's professional aspects. In this way, the collection unit can gain a deeper understanding of the great person's personality by collecting testimonies from the great person's family and friends. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection department could collect testimonies from the great person's family and friends and use an AI model to recreate the great person's personality.

[0079] During collection, the collection unit can collect video data related to the great person and combine it with audio data for analysis. For example, the collection unit collects video data of a lecture by the great person, analyzes the content, and combines it with audio data. For example, the collection unit collects video data of a lecture by the great person, analyzes the content, and combines it with audio data. The collection unit can also collect video data of an interview with the great person, analyze the content, and combine it with audio data. For example, the collection unit collects video data of an interview with the great person, analyzes the content, and combines it with audio data. The collection unit can also collect documentary footage of the great person, analyze the content, and combine it with audio data. For example, the collection unit collects documentary footage of the great person, analyzes the content, and combines it with audio data. This allows the collection unit to collect video data and combine it with audio data for more detailed analysis. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can collect data using an AI model that collects video data related to the great person and analyzes it in combination with audio data.

[0080] The analysis unit can estimate the user's emotion and adjust the accuracy of the analysis based on the estimated user's emotion. For example, the analysis unit estimates the user's emotion and adjusts the accuracy of the analysis based on the estimated user's emotion. For example, voice tone analysis or facial expression recognition technology is used to estimate the user's emotion. For example, if the user is excited, the analysis unit increases the accuracy of the analysis to provide more detailed information. For example, the analysis unit analyzes the user's voice tone and determines that the user is excited, increases the accuracy of the analysis to provide more detailed information. The analysis unit can also adjust the accuracy of the analysis to provide more concise information if the user is relaxed. For example, the analysis unit recognizes the user's facial expression and determines that the user is relaxed, adjusts the accuracy of the analysis to provide more concise information. Furthermore, the analysis unit can adjust the accuracy of the analysis to provide comforting or encouraging information if the user is sad. For example, the analysis unit analyzes the user's voice tone and determines that the user is sad, and provides comforting or encouraging information. In this way, the analysis unit can adjust the accuracy of the analysis based on the user's emotion to provide more appropriate analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may perform analysis using an AI model that estimates the user's emotions and adjusts the accuracy of the analysis.

[0081] During analysis, the analysis unit can focus on analyzing the great person's knowledge of a particular theme or field. For example, the analysis unit can focus on analyzing the great person's scientific knowledge and provide information on that field. For example, the analysis unit can analyze the content of the great person's scientific papers and lectures and provide information on that field. The analysis unit can also focus on analyzing the great person's literary knowledge and provide information on that field. For example, the analysis unit can analyze the content of the great person's literary works and critiques and provide information on that field. The analysis unit can also focus on analyzing the great person's historical knowledge and provide information on that field. For example, the analysis unit can analyze the content of the great person's historical speeches and writings and provide information on that field. In this way, the analysis unit can provide more specialized information by focusing on analyzing knowledge on a particular theme or field. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can perform the analysis using an AI model that focuses on analyzing the great person's knowledge of a particular theme or field.

[0082] The analysis unit can perform the analysis by taking into account the background or context of the great person's statement. For example, the analysis unit can perform the analysis by taking into account the historical background of the great person's statement. For example, the analysis unit can perform the analysis by taking into account the time and circumstances under which the great person's statement was made. The analysis unit can also perform the analysis by taking into account the cultural background of the great person's statement. For example, the analysis unit can perform the analysis by taking into account the cultural background and social circumstances under which the great person's statement was made. Furthermore, the analysis unit can perform the analysis by taking into account the social background of the great person's statement. For example, the analysis unit can perform the analysis by taking into account the social background and political circumstances under which the great person's statement was made. In this way, the analysis unit can perform a more accurate analysis by taking into account the background and context of the statement. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that performs analysis by taking into account the background and context of the great person's statement.

[0083] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. For example, the analysis unit estimates the user's emotion and adjusts the display method of the analysis result based on the estimated user's emotion. For example, voice tone analysis or facial expression recognition technology is used to estimate the user's emotion. For example, the analysis unit displays a detailed analysis result when the user is excited. For example, the analysis unit analyzes the user's voice tone and determines that the user is excited, and displays a detailed analysis result. The analysis unit can also display a concise analysis result when the user is relaxed. For example, the analysis unit recognizes the user's facial expression and determines that the user is relaxed, and displays a concise analysis result. Furthermore, the analysis unit can display a comforting or encouraging analysis result when the user is sad. For example, the analysis unit analyzes the user's voice tone and determines that the user is sad, and displays a comforting or encouraging analysis result. In this way, the analysis unit can provide more appropriate analysis results by adjusting the display method based on the user's emotion. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may perform analysis using an AI model that estimates the user's emotions and adjusts the display method of the analysis results.

[0084] During analysis, the analysis unit can analyze the frequency and patterns of statements made by great people and extract their characteristics. For example, the analysis unit analyzes the frequency of statements made by great people and extracts their characteristics. The analysis unit can also analyze the patterns of statements made by great people and extract their characteristics. For example, the analysis unit analyzes the patterns of statements made by great people and extracts their characteristics. The analysis unit can also analyze the tone and rhythm of statements made by great people and extract their characteristics. For example, the analysis unit analyzes the tone and rhythm of statements made by great people and extracts their characteristics. In this way, the analysis unit can more clearly grasp the characteristics of great people by analyzing the frequency and patterns of statements. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform analysis using an AI model that analyzes the frequency and patterns of statements made by great people and extracts their characteristics.

[0085] During the analysis, the analysis unit can analyze the emotional tone of the great person's statement and provide the information to the user. For example, the analysis unit can analyze the emotional tone of the great person's statement and provide the information to the user. For example, the analysis unit can analyze the emotional tone of the great person's statement and provide the information to the user. The analysis unit can also analyze the emotional intensity of the great person's statement and provide the information to the user. For example, the analysis unit can analyze the emotional intensity of the great person's statement and provide the information to the user. Furthermore, the analysis unit can analyze emotional changes in the great person's statement and provide the information to the user. For example, the analysis unit can analyze emotional changes in the great person's statement and provide the information to the user. In this way, the analysis unit can provide the user with a deeper understanding by analyzing the emotional tone of the statement. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that analyzes the emotional tone of the great person's statement and provides the information to the user.

[0086] The reproduction unit can estimate the user's emotion and adjust the tone and tempo of the reproduced audio based on the estimated user's emotion. The reproduction unit, for example, estimates the user's emotion and adjusts the tone and tempo of the reproduced audio based on the estimated user's emotion. For example, audio tone analysis and facial expression recognition technology are used to estimate the user's emotion. For example, if the user is excited, the reproduction unit increases the tone and speeds up the tempo of the reproduced audio. For example, the reproduction unit analyzes the user's audio tone and determines that the user is excited, and increases the tone and speeds up the tempo of the reproduced audio. The reproduction unit can also calm the tone and slow the tempo of the reproduced audio when the user is relaxed. For example, the reproduction unit recognizes the user's facial expression and determines that the user is relaxed, and decreases the tone and speeds up the tempo of the reproduced audio. Furthermore, the reproduction unit can also soften the tone and slow the tempo of the reproduced audio when the user is sad. For example, the reproduction unit analyzes the user's audio tone and determines that the user is sad, and decreases the tone and speeds up the tempo of the reproduced audio. This allows the reproduction unit to adjust the tone and tempo of the audio based on the user's emotions, thereby enabling more appropriate reproduction. Some or all of the above-described processing in the reproduction unit may be performed using, for example, AI, or may be performed without using AI. For example, the reproduction unit may reproduce audio using an AI model that estimates the user's emotions and adjusts the tone and tempo of the audio to be reproduced.

[0087] The reenactment unit can generate a scenario for reenacting a specific situation or scene of a great person during the reenactment. For example, the reenactment unit generates a scenario for reenacting a scene of a lecture by a great person. For example, the reenactment unit generates a scenario based on the content and background of the lecture by the great person and reenacts that scene. The reenactment unit can also generate a scenario for reenacting a scene of an interview by the great person. For example, the reenactment unit generates a scenario based on the content and background of the interview by the great person and reenacts that scene. The reenactment unit can also generate a scenario for reenacting a scene of a dialogue between the great person. For example, the reenactment unit generates a scenario based on the content and background of the dialogue between the great person and reenacts that scene. In this way, the reenactment unit can provide a more realistic experience by reenacting a specific situation or scene. Some or all of the above-described processing in the reenactment unit may be performed using, for example, AI, or may be performed without using AI. For example, the reenactment unit can generate a scenario using an AI model that generates a scenario for reenacting a specific situation or scene of a great person.

[0088] During reproduction, the reproducing unit can reproduce background sounds and environmental sounds of the great person's speech, thereby providing a more realistic experience. For example, the reproducing unit can reproduce background sounds of the great person's lecture, thereby providing a more realistic experience. For example, the reproducing unit can reproduce background sounds of the great person's lecture, thereby reproducing the scene more realistically. The reproducing unit can also reproduce environmental sounds of the great person's interview, thereby providing a more realistic experience. For example, the reproducing unit can reproduce environmental sounds of the great person's interview, thereby reproducing the scene more realistically. Furthermore, the reproducing unit can reproduce background sounds of the great person's dialogue, thereby providing a more realistic experience. For example, the reproducing unit can reproduce background sounds of the great person's dialogue, thereby reproducing the scene more realistically. In this way, the reproducing unit can provide a more realistic experience by reproducing background sounds and environmental sounds. Some or all of the above-described processing in the reproducing unit may be performed using, for example, AI, or may be performed without using AI. For example, the reproducing unit can reproduce audio using an AI model that reproduces background sounds and environmental sounds of the great person's speech.

[0089] The reproducing unit can estimate the user's emotions and determine the priority of the content to be reproduced based on the estimated user's emotions. For example, the reproducing unit estimates the user's emotions and determines the priority of the content to be reproduced based on the estimated user's emotions. For example, voice tone analysis and facial expression recognition technology are used to estimate the user's emotions. For example, the reproducing unit prioritizes the reproduction of emotional speeches and speeches when the user is excited. For example, the reproducing unit analyzes the user's voice tone and determines that the user is excited, and prioritizes the reproduction of emotional speeches and speeches. The reproducing unit can also prioritize the reproduction of calm dialogues and interviews when the user is relaxed. For example, the reproducing unit recognizes the user's facial expressions and determines that the user is relaxed, and prioritizes the reproduction of calm dialogues and interviews. Furthermore, the reproducing unit can prioritize the reproduction of encouraging and comforting words when the user is sad. For example, the reproducing unit analyzes the user's voice tone and determines that the user is sad, and prioritizes the reproduction of encouraging and comforting words. This allows the reproduction unit to perform more appropriate reproduction by determining the priority of the reproduction content based on the user's emotions. Some or all of the above-described processing in the reproduction unit may be performed using, for example, AI, or may be performed without using AI. For example, the reproduction unit may perform reproduction using an AI model that estimates the user's emotions and determines the priority of the content to be reproduced.

[0090] The reproducing unit can provide visual information by displaying visual content related to the statements of a great person during the reproducing process. For example, the reproducing unit can display related visual content when reproducing a speech by a great person. For example, the reproducing unit can display related visual content when reproducing a speech by a great person, thereby more realistically reproducing the scene. The reproducing unit can also display related visual content when reproducing an interview by a great person. For example, the reproducing unit can display related visual content when reproducing an interview by a great person, thereby more realistically reproducing the scene. The reproducing unit can also display related visual content when reproducing a dialogue by a great person. For example, the reproducing unit can display related visual content when reproducing a dialogue by a great person, thereby more realistically reproducing the scene. In this way, the reproducing unit can provide visual information and deepen the user's understanding by displaying visual content. Some or all of the above-described processing in the reproducing unit can be performed using, for example, AI, or can be performed without AI. For example, the reproducing unit can display visual content using an AI model that displays visual content related to the statements of a great person.

[0091] The recapitulation unit can provide additional information and annotations related to the statements of a great person during the recapitulation, thereby deepening the user's understanding. For example, the recapitulation unit can provide related additional information and annotations when recapturing a speech by a great person. For example, the recapitulation unit can provide related additional information and annotations when recapturing a speech by a great person, thereby providing a deeper understanding of the scene. The recapitulation unit can also provide related additional information and annotations when recapturing an interview by a great person. For example, the recapitulation unit can provide related additional information and annotations when recapturing an interview by a great person, thereby providing a deeper understanding of the scene. The recapitulation unit can also provide related additional information and annotations when recapturing a dialogue by a great person. For example, the recapitulation unit can provide related additional information and annotations when recapturing a dialogue by a great person, thereby providing a deeper understanding of the scene. In this way, the recapitulation unit can deepen the user's understanding by providing additional information and annotations. Some or all of the above-described processing in the recapitulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recapitulation unit can provide the additional information and annotations using an AI model that provides additional information and annotations related to the statements of a great person.

[0092] The reception unit can estimate the user's emotion and adjust the reception interface based on the estimated user's emotion. For example, the reception unit estimates the user's emotion and adjusts the reception interface based on the estimated user's emotion. For example, voice tone analysis or facial expression recognition technology is used to estimate the user's emotion. For example, if the user is excited, the reception unit provides a simple and intuitive interface. For example, the reception unit analyzes the user's voice tone and determines that the user is excited, and provides a simple and intuitive interface. Furthermore, the reception unit can provide an interface with detailed options if the user is relaxed. For example, the reception unit recognizes the user's facial expression and determines that the user is relaxed, and provides an interface with detailed options. Furthermore, the reception unit can provide an interface with a calm color scheme if the user is sad. For example, the reception unit analyzes the user's voice tone and determines that the user is sad, and provides an interface with a calm color scheme. This allows the reception unit to adjust the interface based on the user's emotion, thereby providing a more appropriate reception. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can provide an interface using an AI model that estimates the user's emotions and adjusts the interface at the time of reception.

[0093] The reception unit can suggest appropriate questions by referring to the user's past question history when receiving the request. The reception unit can suggest appropriate questions by referring to the user's past question history, for example. The reception unit can automatically display, for example, questions that the user has frequently asked in the past as candidates. For example, the reception unit can analyze the user's past question history and automatically display frequently asked questions as candidates. The reception unit can also suggest related questions from the user's past question history. For example, the reception unit can analyze the user's past question history and suggest related questions. The reception unit can also analyze the user's past question history and suggest the most appropriate question. For example, the reception unit can analyze the user's past question history and suggest the most appropriate question. In this way, the reception unit can suggest appropriate questions by referring to the past question history. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can suggest questions by referring to the user's past question history and using an AI model that suggests appropriate questions.

[0094] The reception unit can analyze the user's input content in real time upon reception and provide appropriate feedback. The reception unit, for example, analyzes the user's input content in real time and provides appropriate feedback. The reception unit, for example, analyzes the user's input content in real time and provides related information. For example, the reception unit analyzes the user's input content in real time and provides related information. The reception unit can also suggest appropriate questions or topics based on the user's input content. For example, the reception unit analyzes the user's input content and suggests appropriate questions or topics. The reception unit can also analyze the user's input content and provide immediate feedback. For example, the reception unit analyzes the user's input content and provides immediate feedback. In this way, the reception unit can provide appropriate feedback by analyzing in real time. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can analyze the user's input content in real time and provide feedback using an AI model that provides appropriate feedback.

[0095] The reception unit can estimate the user's emotion and adjust the response speed at the time of reception based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and adjusts the response speed at the time of reception based on the estimated user's emotion. The user's emotion can be estimated using, for example, voice tone analysis or facial expression recognition technology. The reception unit provides a quick response when the user is excited. For example, the reception unit analyzes the user's voice tone and provides a quick response when it determines that the user is excited. The reception unit can also provide a slower response when the user is relaxed. For example, the reception unit recognizes the user's facial expression and provides a slower response when it determines that the user is relaxed. Furthermore, the reception unit can respond in a gentler tone when the user is sad. For example, the reception unit analyzes the user's voice tone and responds in a gentler tone when it determines that the user is sad. This allows the reception unit to adjust the response speed based on the user's emotion, thereby providing a more appropriate response. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can respond using an AI model that estimates the user's emotions and adjusts the response speed when receiving a call.

[0096] The reception unit can suggest related topics and questions based on the user's input content when receiving the input. The reception unit, for example, suggests related topics and questions based on the user's input content. The reception unit, for example, suggests related topics based on the user's input content. For example, the reception unit analyzes the user's input content and suggests related topics. The reception unit can also suggest related questions from the user's input content. For example, the reception unit analyzes the user's input content and suggests related questions. The reception unit can also analyze the user's input content and suggest the most relevant topic or question. For example, the reception unit analyzes the user's input content and suggests the most relevant topic or question. This allows the reception unit to make more appropriate suggestions by suggesting related topics and questions based on the input content. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can make suggestions using an AI model that suggests related topics and questions based on the user's input content.

[0097] The reception unit can analyze the user's input content upon reception and collect data for providing an appropriate answer. The reception unit, for example, analyzes the user's input content and collects data for providing an appropriate answer. The reception unit, for example, analyzes the user's input content and collects related data. For example, the reception unit analyzes the user's input content and collects related data. The reception unit can also collect information for providing an appropriate answer based on the user's input content. For example, the reception unit analyzes the user's input content and collects information for providing an appropriate answer. The reception unit can also analyze the user's input content and collect most appropriate data. For example, the reception unit analyzes the user's input content and collects most appropriate data. As a result, the reception unit can analyze the input content and collect data for providing an appropriate answer, thereby providing a more appropriate answer. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can collect data using an AI model that analyzes the user's input content and collects data for providing an appropriate answer.

[0098] The answering unit can estimate the user's emotions and adjust the tone and content of the answer based on the estimated user's emotions. The answering unit, for example, estimates the user's emotions and adjusts the tone and content of the answer based on the estimated user's emotions. For example, voice tone analysis and facial expression recognition technology are used to estimate the user's emotions. For example, if the user is excited, the answering unit answers in a bright and cheerful tone. For example, if the answering unit analyzes the user's voice tone and determines that the user is excited, it answers in a bright and cheerful tone. The answering unit can also answer in a gentle tone if the user is relaxed. For example, if the answering unit recognizes the user's facial expression and determines that the user is relaxed, it answers in a gentle tone. Furthermore, the answering unit can also answer in a gentle tone if the user is sad. For example, if the answering unit analyzes the user's voice tone and determines that the user is sad, it answers in a gentle tone. In this way, the answering unit can adjust the tone and content of the answer based on the user's emotions, thereby providing a more appropriate answer. Some or all of the above-described processing in the answering unit may be performed using, for example, AI, or may be performed without AI. For example, the answering unit may use an AI model to estimate the user's emotions and adjust the tone and content of the answer.

[0099] When answering a question, the answering unit can provide a more appropriate answer by taking into account the background and context of the great person's statement. For example, the answering unit can provide an answer by taking into account the historical background of the great person's statement. For example, the answering unit can provide an answer by taking into account the time and circumstances under which the great person's statement was made. The answering unit can also provide an answer by taking into account the cultural background of the great person's statement. For example, the answering unit can provide an answer by taking into account the cultural background and social circumstances under which the great person's statement was made. Furthermore, the answering unit can also provide an answer by taking into account the social background of the great person's statement. For example, the answering unit can provide an answer by taking into account the social background and political circumstances under which the great person's statement was made. In this way, the answering unit can provide a more appropriate answer by taking into account the background and context of the statement. Some or all of the above-described processing in the answering unit may be performed using, for example, AI, or may be performed without using AI. For example, the answering unit can provide an answer using an AI model that provides an answer by taking into account the background and context of the great person's statement.

[0100] When answering, the answering unit can provide additional information and annotations related to the statement of a great person, thereby deepening the user's understanding. The answering unit, for example, provides additional historical information related to the statement of a great person. For example, the answering unit can provide additional historical information related to the statement of a great person, thereby providing a deeper understanding of the background of the statement. The answering unit can also provide cultural annotations related to the statement of a great person. For example, the answering unit can provide cultural annotations related to the statement of a great person, thereby providing a deeper understanding of the background of the statement. Furthermore, the answering unit can also provide social background information related to the statement of a great person. For example, the answering unit can provide social background information related to the statement of a great person, thereby providing a deeper understanding of the background of the statement. In this way, the answering unit can deepen the user's understanding by providing additional information and annotations. Some or all of the above-described processing in the answering unit may be performed using, for example, AI, or may be performed without using AI. For example, the answering unit can provide additional information and annotations using an AI model that provides additional information and annotations related to the statement of a great person.

[0101] The answering unit can estimate the user's emotions and determine the priority of answers based on the estimated user's emotions. The answering unit, for example, estimates the user's emotions and determines the priority of answers based on the estimated user's emotions. For example, voice tone analysis or facial expression recognition technology is used to estimate the user's emotions. For example, if the user is excited, the answering unit prioritizes answers to emotional questions. For example, if the answering unit analyzes the user's voice tone and determines that the user is excited, it prioritizes answers to emotional questions. The answering unit can also prioritize answers to calm questions if the user is relaxed. For example, if the answering unit recognizes the user's facial expression and determines that the user is relaxed, it prioritizes answers to calm questions. Furthermore, if the user is sad, the answering unit can prioritize answers to comforting or encouraging questions. For example, if the answering unit analyzes the user's voice tone and determines that the user is sad, it prioritizes answers to comforting or encouraging questions. This allows the answering unit to determine the priority of answers based on the user's emotions, thereby providing more appropriate answers. Some or all of the above-described processing in the answering unit may be performed using, for example, AI, or may be performed without using AI. For example, the answering unit may provide an answer using an AI model that estimates the user's emotions and determines the priority of answers.

[0102] When answering, the answering unit can display visual content related to the great person's statement to provide visual information. The answering unit, for example, displays an image related to the great person's statement. For example, the answering unit can display an image related to the great person's statement to visually understand the background of the statement. The answering unit can also display a video related to the great person's statement. For example, the answering unit can display a video related to the great person's statement to visually understand the background of the statement. The answering unit can also display a chart related to the great person's statement. For example, the answering unit can display a chart related to the great person's statement to visually understand the background of the statement. In this way, the answering unit can provide visual information and deepen the user's understanding by displaying visual content. Some or all of the above-mentioned processing in the answering unit may be performed, for example, using AI or without AI. For example, the answering unit can display visual content using an AI model that displays visual content related to the great person's statement.

[0103] When answering, the answering unit can provide additional information and annotations related to the statement of a great person, thereby deepening the user's understanding. The answering unit, for example, provides additional historical information related to the statement of a great person. For example, the answering unit can provide additional historical information related to the statement of a great person, thereby providing a deeper understanding of the background of the statement. The answering unit can also provide cultural annotations related to the statement of a great person. For example, the answering unit can provide cultural annotations related to the statement of a great person, thereby providing a deeper understanding of the background of the statement. Furthermore, the answering unit can also provide social background information related to the statement of a great person. For example, the answering unit can provide social background information related to the statement of a great person, thereby providing a deeper understanding of the background of the statement. In this way, the answering unit can deepen the user's understanding by providing additional information and annotations. Some or all of the above-described processing in the answering unit may be performed using, for example, AI, or may be performed without using AI. For example, the answering unit can provide additional information and annotations using an AI model that provides additional information and annotations related to the statement of a great person.

[0104] The storage unit can estimate the user's emotions and determine the priority of data to be stored based on the estimated user emotions. For example, the storage unit estimates the user's emotions and determines the priority of data to be stored based on the estimated user emotions. For example, voice tone analysis or facial expression recognition technology is used to estimate the user's emotions. For example, the storage unit prioritizes storing data of emotional speeches or speeches when the user is excited. For example, the storage unit analyzes the user's voice tone and determines that the user is excited, and prioritizes storing data of emotional speeches or speeches. The storage unit can also prioritize storing data of calm conversations or interviews when the user is relaxed. For example, the storage unit recognizes the user's facial expressions and determines that the user is relaxed, and prioritizes storing data of calm conversations or interviews. Furthermore, the storage unit can prioritize storing data of encouraging or comforting words when the user is sad. For example, the storage unit analyzes the user's voice tone and determines that the user is sad, and prioritizes storing data of encouraging or comforting words. This allows the storage unit to determine the priority of data based on the user's emotions, thereby enabling more appropriate data storage. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit may store data using an AI model that estimates the user's emotions and determines the priority of data to be stored.

[0105] The storage unit can organize the data of great people in chronological order during storage to make it easier to search. For example, the storage unit can organize the data of great people in chronological order to make it easier to search. For example, the storage unit can organize the data of great people in chronological order to make it easier to search. The storage unit can also organize the data of great people by event to make it easier to search. For example, the storage unit can organize the data of great people by event to make it easier to search. The storage unit can also organize the data of great people by the time of their statements to make it easier to search. For example, the storage unit can organize the data of great people by the time of their statements to make it easier to search. In this way, the storage unit organizes the data in chronological order, making it easier to search. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can organize the data using an AI model that organizes the data of great people in chronological order to make it easier to search.

[0106] The storage unit may organize the data of famous people by category during storage, allowing users to easily access the data. For example, the storage unit may organize the data of famous people by theme, allowing users to easily access the data. For example, the storage unit may organize the data of famous people by theme, allowing users to easily access the data. The storage unit may also organize the data of famous people by field, allowing users to easily access the data. For example, the storage unit may organize the data of famous people by field, allowing users to easily access the data. The storage unit may also organize the data of famous people by topic, allowing users to easily access the data. For example, the storage unit may organize the data of famous people by topic, allowing users to easily access the data. In this way, the storage unit organizes the data by category, allowing users to easily access the data. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit may organize the data using an AI model that organizes the data of famous people by category, allowing users to easily access the data.

[0107] The storage unit can estimate the user's emotions and adjust the display method of the stored data based on the estimated user's emotions. For example, the storage unit estimates the user's emotions and adjusts the display method of the stored data based on the estimated user's emotions. For example, voice tone analysis or facial expression recognition technology is used to estimate the user's emotions. For example, if the user is excited, the storage unit provides a visually stimulating display method. For example, the storage unit analyzes the user's voice tone and determines that the user is excited, and provides a visually stimulating display method. The storage unit can also provide a calm display method if the user is relaxed. For example, the storage unit recognizes the user's facial expression and determines that the user is relaxed, and provides a calm display method. Furthermore, the storage unit can also provide a calm display method if the user is sad. For example, the storage unit analyzes the user's voice tone and determines that the user is sad, and provides a calm display method. In this way, the storage unit can adjust the display method based on the user's emotions to display data more appropriately. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can display data using an AI model that estimates a user's emotions and adjusts how the stored data is displayed.

[0108] When storing the data of famous people, the storage unit can organize the data of famous people by related topic or theme, allowing the user to easily access it. For example, the storage unit can organize the data of famous people by related topic, allowing the user to easily access it. Furthermore, the storage unit can organize the data of famous people by related theme, allowing the user to easily access it. For example, the storage unit can organize the data of famous people by related theme, allowing the user to easily access it. Furthermore, the storage unit can organize the data of famous people by related field, allowing the user to easily access it. For example, the storage unit can organize the data of famous people by related field, allowing the user to easily access it. In this way, the storage unit organizes the data by topic or theme, allowing the user to easily access it. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can organize the data of famous people using an AI model that organizes the data of famous people by related topic or theme, allowing the user to easily access it.

[0109] The storage unit may link the data of the great person with other related data during storage, thereby enabling a user to gain a deeper understanding. For example, the storage unit may link the data of the great person with related historical data. For example, the storage unit may link the data of the great person with related historical data to enable a deeper understanding of the data. The storage unit may also link the data of the great person with related cultural data. For example, the storage unit may link the data of the great person with related cultural data to enable a deeper understanding of the data. Furthermore, the storage unit may also link the data of the great person with related social data. For example, the storage unit may link the data of the great person with related social data to enable a deeper understanding of the data. In this way, the storage unit may link the data with related data to enable a deeper understanding of the data. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit may link the data using an AI model that links the data of the great person with other related data. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, reproduction unit, reception unit, response unit, and storage unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects voice data and book data using the camera 42 and microphone 38B of the smart device 14, and the collected data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The reproduction unit is realized by the specific processing unit 290 of the data processing device 12 and reproduces the speaking style and knowledge of the great person based on the analyzed data. The reception unit is realized by the control unit 46A of the smart device 14 and receives input from the user. The response unit is realized by the specific processing unit 290 of the data processing device 12 and responds like the great person based on the user's input. The storage unit is realized by the database 24 of the data processing device 12 and stores and manages data on the great person. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, reproduction unit, reception unit, response unit, and storage unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects voice data and book data using the camera 42 and microphone 238 of the smart glasses 214, and the collected data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The reproduction unit is realized by the specific processing unit 290 of the data processing device 12 and reproduces the speaking style and knowledge of the great person based on the analyzed data. The reception unit is realized by the control unit 46A of the smart glasses 214 and receives input from the user. The response unit is realized by the specific processing unit 290 of the data processing device 12 and responds like the great person based on the user's input. The storage unit is realized by the database 24 of the data processing device 12 and stores and manages data on the great person. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, reproduction unit, reception unit, response unit, and storage unit, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects voice data and book data using the camera 42 and microphone 238 of the headset terminal 314, and the collected data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The reproduction unit is realized by the specific processing unit 290 of the data processing device 12 and reproduces the speaking style and knowledge of the great person based on the analyzed data. The reception unit is realized by the control unit 46A of the headset terminal 314 and receives input from the user. The response unit is realized by the specific processing unit 290 of the data processing device 12 and responds like the great person based on the user's input. The storage unit is realized by the database 24 of the data processing device 12 and stores and manages data on the great person. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, reproduction unit, reception unit, response unit, and storage unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects voice data and book data using the camera 42 and microphone 238 of the robot 414, and the collected data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The reproduction unit is realized by the specific processing unit 290 of the data processing device 12 and reproduces the speaking style and knowledge of the great person based on the analyzed data. The reception unit is realized by the control unit 46A of the robot 414 and receives input from the user. The response unit is realized by the specific processing unit 290 of the data processing device 12 and responds like the great person based on the user's input. The storage unit is realized by the database 24 of the data processing device 12 and stores and manages data on the great person.

[0110] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0111] The conversation system can estimate the user's emotions and dynamically change the conversation topic based on the estimated emotions. For example, if the user is excited, the system can provide more stimulating topics. Specifically, if the user is determined to be excited, the system can preferentially provide stories about the adventures and challenges of great people. Alternatively, if the user is relaxed, the system can provide calming topics. For example, if the user is determined to be relaxed, the system can provide stories about the philosophies of great people or stories about everyday life. Furthermore, if the user is sad, the system can provide topics containing words of comfort and encouragement. For example, if the user is determined to be sad, the system can provide stories about great people overcoming difficulties or words of encouragement. This allows the conversation system to provide appropriate topics according to the user's emotions.

[0112] The conversation system can refer to the user's past conversation history and customize the content of the conversation based on the user's interests and concerns. For example, if the user has frequently selected science-related topics in the past, the system can prioritize providing stories about famous scientific figures. Specifically, if the user has frequently asked science-related questions in the past, the system can provide stories about famous scientific discoveries and research. Furthermore, if the user has frequently selected literature-related topics in the past, the system can also provide stories about famous literary figures. For example, if the user has frequently asked literature-related questions in the past, the system can provide stories about literary works and critiques by famous figures. Furthermore, if the user has frequently selected history-related topics in the past, the system can also provide stories about famous historical figures. For example, if the user has frequently asked history-related questions in the past, the system can provide stories about historical events and speeches by famous figures. This allows the conversation system to provide customized conversations based on the user's interests and concerns.

[0113] The conversation system can estimate the user's emotions and adjust the tempo and tone of the conversation based on the estimated emotions. For example, if the user is excited, the system can speed up the conversation tempo and lighten the tone. Specifically, if the user is determined to be excited, the system can provide stories about great people at a fast tempo and lighten the tone. Also, if the user is relaxed, the system can slow down the conversation tempo and lighten the tone. For example, if the user is determined to be relaxed, the system can provide stories about great people at a slow tempo and lighten the tone. Furthermore, if the user is sad, the system can slow down the conversation tempo and lighten the tone. For example, if the user is determined to be sad, the system can provide stories about great people at a slow tempo and lighten the tone. This allows the conversation system to provide conversation at an appropriate tempo and tone according to the user's emotions.

[0114] A conversation system can analyze user input in real time and provide appropriate feedback. For example, when a user inputs a question, the system can generate an appropriate answer in real time. For example, if a user asks, "What event has had the greatest impact on you?", the system can instantly generate an answer based on the life and thoughts of great people. Furthermore, when a user inputs an impression or opinion, the system can provide appropriate feedback in response to that content. For example, if a user inputs, "This episode was very moving," the system can provide feedback such as, "Thank you. There are many inspiring episodes in the lives of great people." Furthermore, when a user inputs a question, the system can provide an appropriate explanation for that question. For example, if a user inputs, "What is the background of this event?", the system can explain the historical and cultural background of the event. This allows the conversation system to provide appropriate feedback in real time based on the user's input.

[0115] The conversation system can estimate the user's emotions and adjust the content of the conversation based on the estimated emotions. For example, if the user is excited, the system can provide more emotional episodes. Specifically, if the user is determined to be excited, the system can provide episodes related to emotional speeches or public addresses by great people. Also, if the user is relaxed, the system can provide calm episodes. For example, if the user is determined to be relaxed, the system can provide episodes related to calm conversations or interviews by great people. Furthermore, if the user is sad, the system can provide episodes containing encouraging or comforting words. For example, if the user is determined to be sad, the system can provide experiences of great people overcoming difficulties or encouraging words. This allows the conversation system to provide appropriate content according to the user's emotions.

[0116] The conversation system can analyze the user's past conversation history and suggest conversation topics based on the user's interests and concerns. For example, it can suggest new related topics based on topics that the user has asked many questions about in the past. Specifically, if the user has asked many questions about science in the past, the system can suggest new topics related to science. Also, if the user has asked many questions about literature in the past, the system can suggest new topics related to literature. For example, if the user has asked many questions about literature in the past, the system can suggest new topics related to literary works and criticisms of famous people. Furthermore, if the user has asked many questions about history in the past, the system can suggest new topics related to history. For example, if the user has asked many questions about history in the past, the system can suggest new topics related to historical events and speeches of famous people. In this way, the conversation system can suggest new topics based on the user's interests and concerns.

[0117] The conversation system can estimate the user's emotions and adjust the length of the conversation based on the estimated emotions. For example, if the user is excited, the system can continue the conversation for a longer period of time. Specifically, if the user is determined to be excited, the system can provide detailed anecdotes about great people and continue the conversation for a longer period of time. Also, if the user is relaxed, the system can shorten the conversation. For example, if the user is determined to be relaxed, the system can briefly provide anecdotes about great people and shorten the conversation. Furthermore, if the user is sad, the system can proceed with the conversation at a slower pace. For example, if the user is determined to be sad, the system can slowly provide anecdotes about great people and proceed with the conversation at a slower pace. This allows the conversation system to provide an appropriate length of conversation according to the user's emotions.

[0118] A conversation system can analyze a user's input and provide relevant visual content. For example, if a user asks about a particular famous person, the system can provide images and videos related to that person. For example, if a user asks, "Tell me about Albert Einstein," the system can provide photos of Einstein and videos of his lectures. Also, if a user asks about a specific event, the system can provide visual content related to that event. For example, if a user asks, "Tell me about the Apollo 11 moon landing," the system can provide footage and photos of the moon landing. Furthermore, if a user asks about a specific place, the system can provide visual content related to that place. For example, if a user asks, "Tell me about the pyramids," the system can provide photos of the pyramids and documentary footage. This allows the conversation system to provide relevant visual content based on the user's input.

[0119] The conversation system can estimate the user's emotions and personalize the content of the conversation based on the estimated emotions. For example, if the user is excited, the system can provide more emotional episodes. Specifically, if the user is determined to be excited, the system can provide episodes related to emotional speeches or public addresses by great people. Alternatively, if the user is relaxed, the system can provide calm episodes. For example, if the user is determined to be relaxed, the system can provide episodes related to calm conversations or interviews by great people. Furthermore, if the user is sad, the system can provide episodes containing encouraging or comforting words. For example, if the user is determined to be sad, the system can provide encouraging words or stories about great people's experiences of overcoming difficulties. This allows the conversation system to provide appropriate content according to the user's emotions.

[0120] The conversation system can analyze the user's past conversation history and suggest conversation topics based on the user's interests and concerns. For example, it can suggest new related topics based on topics that the user has asked many questions about in the past. Specifically, if the user has asked many questions about science in the past, the system can suggest new topics related to science. Also, if the user has asked many questions about literature in the past, the system can suggest new topics related to literature. For example, if the user has asked many questions about literature in the past, the system can suggest new topics related to literary works and criticisms of famous people. Furthermore, if the user has asked many questions about history in the past, the system can suggest new topics related to history. For example, if the user has asked many questions about history in the past, the system can suggest new topics related to historical events and speeches of famous people. In this way, the conversation system can suggest new topics based on the user's interests and concerns.

[0121] The processing flow of the second embodiment will be briefly explained below.

[0122] Step 1: The collection department collects audio data and book data of great figures of the past. The collection department can use, for example, publicly available audio data or audio data collected from libraries. For example, the collection department can collect audio data from online libraries or podcasts. The collection department can also collect historical recordings from library archives. In addition, the collection department can collect non-audio data, such as handwritten notes and letters of great figures, and use them for analysis. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses voice recognition technology to convert voice data into text data and analyzes the text data. For example, the analysis unit uses deep learning-based voice recognition technology to convert voice data into text data. The analysis unit also uses natural language processing technology to analyze book data and understand the knowledge and thoughts of great people. For example, the analysis unit analyzes book data using morphological analysis and grammatical analysis. Step 3: The reproducing unit reproduces the speaking style and knowledge of the great person based on the data analyzed by the analyzing unit. For example, the reproducing unit reproduces the speaking style of the great person using speech synthesis technology. The reproducing unit can also build a knowledge base and reproduce the knowledge of the great person. Step 4: The reception unit receives input from the user. The reception unit can receive, for example, voice input or text input. For example, when the user inputs a question by voice, the reception unit converts it into text data using voice recognition technology. Step 5: The answering unit answers in the style of the great person based on the input received by the receiving unit. The answering unit generates an answer based on the great person's ideas using, for example, natural language generation technology. For example, the answering unit refers to a knowledge base of great people in response to the user's question and generates an appropriate answer.

[0123] 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.

[0124] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0125] 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.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0141] 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.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0144] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[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 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.

[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 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.

[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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0157] 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.

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

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

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

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

[0170] In the 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.

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

[0172] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0173] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0174] 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.

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

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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).

[0180] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0193] 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.

[0194] [Explanation of symbols]

[0195] 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 collection unit that collects voice data; an analysis unit that analyzes the data collected by the collection unit; a reproducing unit that reproduces the speaking style and knowledge of the great person based on the data analyzed by the analyzing unit; a reception unit that receives input from a user; an answering unit that answers like a great person based on the input received by the receiving unit; Equipped with A system characterized by:

2. The collecting unit Use publicly available audio data or audio data collected from libraries The system of claim 1 .

3. The analysis unit Use voice recognition technology to convert voice data into text data and analyze the text data. The system of claim 1 .

4. The analysis unit Using natural language processing technology to analyze book data and understand the knowledge and thoughts of great people The system of claim 1 .

5. The answering section Answers based on the thoughts of great people based on user questions or topics The system of claim 1 .

6. Equipped with a storage unit to store and manage data on great people The system of claim 1 .

7. The collecting unit Estimate the user's emotions and prioritize the voice data to be collected based on the estimated user emotions. The system of claim 1 .

8. The collecting unit When collecting data, focus on a specific period in the life of a great person. The system of claim 1 .

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