System

Abstract

An object of a system according to an embodiment is to resolve loneliness and provide mental support through a conversation with a bedridden person.SOLUTION: A system includes an interaction part, a selection part, an input part, an imitation part, a reading part, a quiz part, and a music part. The interaction unit performs voice interaction. The selection unit selects a character for the dialogue performed by the dialogue unit. The input unit inputs voice data of relatives and friends. The imitator performs a dialogue based on the data input by the inputter. The reading unit reads a specific book. The quiz section sets a quiz. The music unit plays favorite music.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not been able to adequately facilitate communication with bedridden people or alleviate their feelings of loneliness, and there is room for improvement.

[0005] The system according to the embodiment aims to alleviate feelings of loneliness and provide psychological support to bedridden people through dialogue with them. [Means for solving the problem]

[0006] The system according to the embodiment includes a dialogue unit, a selection unit, an input unit, an imitation unit, a reading unit, a quiz unit, and a music unit. The dialogue unit performs voice dialogue. The selection unit selects characters for dialogue performed by the dialogue unit. The input unit inputs voice data of relatives or friends. The imitation unit performs dialogue based on the data input by the input unit. The reading unit reads a specific book. The quiz unit asks quizzes. The music unit plays favorite music. [Effects of the Invention]

[0007] The system according to the embodiment can relieve the feeling of loneliness through dialogue with bedridden people and provide them with psychological support. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The dialogue partner system of an embodiment of the present invention utilizes a generation AI to engage in dialogue with bedridden individuals. This system allows for voice dialogue and allows users to choose from several existing characters. Relatives and friends can also input and provide their own data. This helps alleviate loneliness in bedridden individuals and, in the case of elderly individuals, helps prevent dementia. It also has functions for reading books aloud, providing quizzes, and playing favorite music. For example, the dialogue partner system allows users to interact with the generation AI via voice. The generation AI responds appropriately to everyday conversations and questions. Next, users can select a character based on their preferences. For example, characters with gentle voices or lively voices are available. Furthermore, by recording the voices and speaking styles of relatives and friends and inputting them into the generation AI, the generation AI can imitate those voices and speaking styles to engage in dialogue. The generation AI can also be instructed to recite specific books. For example, users can have their favorite novels or poems recited. Furthermore, the generation AI can pose various quizzes to the user. For example, users can have fun while deepening their knowledge by answering quizzes about general knowledge or hobbies. Finally, the generation AI can be instructed to play specific music. For example, it can play songs by a favorite artist or relaxing music. In this way, the conversation partner system can alleviate loneliness in bedridden people and help prevent dementia in elderly people. It also has functions such as reading books aloud, asking quizzes, and playing favorite music, so it can provide a variety of entertainment.

[0029] A dialogue partner system according to an embodiment includes a dialogue unit, a selection unit, an input unit, an imitation unit, a reading unit, a quiz unit, and a music unit. The dialogue unit uses a generation AI to conduct voice dialogue. The dialogue unit provides appropriate responses to, for example, everyday conversations and questions. The dialogue unit uses a generation AI to analyze a user's speech using voice recognition technology and generate appropriate responses using natural language processing technology. For example, when a user asks, "What's the weather like today?", the dialogue unit generates weather information and responds with, "It's sunny today." Furthermore, when a user asks, "How are you?", the dialogue unit can also respond with, "I'm fine. How about you?" The selection unit selects a character based on the user's preferences. The selection unit provides multiple characters, such as characters with gentle voices and characters with lively voices, and allows the user to select one. The selection unit can also suggest the most suitable character based on the user's selection history and survey results. For example, the selection unit suggests a new character based on characters previously selected by the user. The input unit records the voices and speaking styles of relatives and friends and inputs them into the generation AI. For example, the input unit records the voices of relatives and friends and saves them as audio files. The input unit analyzes the recorded data using voice recognition technology to generate data for the generation AI to imitate the speaking styles of relatives and friends. For example, the input unit extracts characteristics of the tone of voice and speaking style of the relatives and friends and inputs this into the generation AI. The imitation unit engages in a dialogue based on the input data. For example, the imitation unit engages in a dialogue by imitating the voices and speaking styles of relatives and friends. The imitation unit reproduces the voices and speaking styles of relatives and friends based on the input data. For example, the imitation unit imitates the tone of voice and speaking style of relatives and friends and engages in a dialogue with the user. The reading unit recites a specific book specified by the user. For example, if the user commands the generation AI to "read this book," the reading unit recites the book. The reading section uses a generative AI to analyze text data and recites it using voice synthesis technology. For example, the reading section may recite a favorite novel or poem. The quiz section presents various quizzes to users.The quiz section, for example, poses quizzes about general knowledge or hobbies, allowing users to deepen their knowledge while having fun by answering them. In the quiz section, a generation AI generates quiz content and poses it to the user. For example, the quiz section poses a question such as, "Which of the following mountains is the highest?" The music section plays specific music specified by the user. For example, if a user instructs the music section to "play this song," the generation AI plays that song. The music section acquires music data and plays it. For example, the music section plays songs by a favorite artist or relaxing music. This allows the dialogue partner system according to the embodiment to alleviate loneliness in bedridden people and prevent dementia in elderly people. It also has functions for reading books aloud, asking quizzes, and playing favorite music, providing a variety of entertainment.

[0030] The dialogue unit can respond to everyday conversations and questions. For example, the dialogue unit provides appropriate responses to everyday conversations and questions. In the dialogue unit, the generation AI analyzes the user's speech using speech recognition technology and generates an appropriate response using natural language processing technology. For example, if the user asks, "What's the weather like today?", the generation AI obtains the weather information and responds, "It's sunny today." Also, if the user asks, "How are you?", the dialogue unit can respond, "I'm fine. How about you?" This enables natural dialogue with the user by providing appropriate responses to everyday conversations and questions.

[0031] The selection unit can select a character according to the user's preferences. For example, the selection unit prepares multiple characters, such as characters with gentle voices and characters with lively voices, so that the user can select one. The selection unit can also suggest the most suitable character based on the user's selection history and survey results using the generation AI. For example, the selection unit can suggest a new character based on characters the user has previously selected. This makes it possible to select a character that suits the user's preferences.

[0032] The input unit can record the voices and speaking styles of relatives and friends and input them into the voice recognition system. For example, the input unit records the voices of relatives and friends and saves them as audio files. The input unit then analyzes the recorded data using voice recognition technology to generate data that the generation AI uses to imitate the speaking styles of relatives and friends. For example, the input unit extracts the characteristics of the tone of voice and speaking style of the relatives and friends and inputs them into the generation AI. This makes it possible to have more friendly conversations by inputting the voices and speaking styles of relatives and friends.

[0033] The mimicry unit can conduct a conversation based on input data. For example, the mimicry unit conducts a conversation by imitating the voice and speaking style of a relative or friend. The mimicry unit reproduces the voice and speaking style of a relative or friend based on the data input by the generation AI. For example, the mimicry unit imitates the tone of voice and speaking style of a relative or friend to converse with the user. This enables a friendly conversation by conducting a conversation based on input data.

[0034] The reading unit can recite a specific book specified by the user. For example, if the user instructs the reading unit to "read this book," the generation AI will recite the book. The reading unit uses the generation AI to analyze text data and perform the reading using speech synthesis technology. For example, the reading unit may recite a favorite novel or poem. This allows the user to enjoy reading by having the specific book specified by the user recited.

[0035] The quiz section can ask multiple quizzes to users. For example, the quiz section can ask quizzes about general knowledge or hobbies, and users can deepen their knowledge while having fun by answering them. In the quiz section, the generation AI generates the content of the quiz and asks it to users. For example, the quiz section asks a question such as, "Which of the following is the highest mountain?" This allows users to deepen their knowledge while having fun by asking them a variety of quizzes.

[0036] The music section can play specific music specified by the user. For example, if the user requests, "Play this song," the generation AI will play that song. The music section uses the generation AI to obtain music data and play it. For example, the music section can play songs by a favorite artist or relaxing music. This allows users to enjoy music by playing the specific music they request.

[0037] The dialogue unit can analyze the user's past dialogue history and select dialogue content. For example, the dialogue unit allows the generation AI to provide new related topics based on topics the user has previously discussed. The dialogue unit collects the user's past dialogue history as log data and analyzes it using text analysis technology. For example, if the user previously said, "I like traveling," the generation AI would provide a new topic by asking, "Where did you travel recently?" The dialogue unit also advances the dialogue based on themes the user has previously shown interest in. For example, if the user previously said, "I like movies," the generation AI would advance the dialogue by asking, "What movie have you seen recently?" The dialogue unit also adjusts the dialogue content to avoid topics the user has avoided in the past. For example, if the user previously said, "I don't like talking about sports," the generation AI would avoid topics related to sports. This makes it possible to select optimal dialogue content based on past dialogue history.

[0038] The dialogue unit can customize the dialogue content based on the user's current health condition and mood. For example, if the user is feeling unwell, the generation AI provides health advice. The dialogue unit determines the user's health condition and mood based on the user's self-reported health and vital signs. For example, if the user says, "I'm not feeling well today," the generation AI provides advice such as, "Don't push yourself, and get plenty of rest." Furthermore, if the user is in a good mood, the dialogue unit offers a fun topic. For example, if the user says, "I'm feeling good today," the generation AI offers a topic such as, "That's great. Did anything fun happen?" Furthermore, if the user is feeling stressed, the dialogue unit selects a relaxing topic. For example, if the user says, "I'm feeling stressed today," the generation AI suggests, "Shall I play some relaxing music?" This makes it possible to customize the dialogue content according to the user's health condition and mood.

[0039] During a dialogue, the dialogue unit can select a dialogue topic based on the user's interests and concerns. For example, the dialogue unit provides a generation AI with topics related to the user's hobbies. The dialogue unit identifies the user's interests and concerns based on the user's survey results and past behavioral history. For example, if the user says, "I like reading," the dialogue unit provides a topic such as, "What book have you read recently?" The dialogue unit also provides a topic such as, "What book have you read recently?" The dialogue unit also provides a topic such as news or topics that the user is interested in. For example, if the user says, "I'm interested in technology," the dialogue unit provides a topic such as, "Let's talk about recent technology news." The dialogue unit also provides a topic such as the user's favorite movies or music. For example, if the user says, "I like movies," the dialogue unit provides a topic such as, "What movie have you seen recently?" This makes it possible to select a dialogue topic based on the user's interests and concerns.

[0040] During a dialogue, the dialogue unit can select relevant topics based on the user's geographic location information. For example, the dialogue unit provides the generation AI with topics related to news and events in the user's area. The dialogue unit identifies the user's geographic location based on the user's GPS data and location information services. For example, if the user says, "I live in Tokyo," the generation AI suggests, "Let's talk about recent Tokyo news." Furthermore, if the user is traveling, the dialogue unit provides tourist information about the destination. For example, if the user says, "I'm currently traveling in Kyoto," the generation AI suggests, "Let's talk about recommended tourist spots in Kyoto." Furthermore, if the user is in a specific location, the dialogue unit selects topics related to that location. For example, if the user says, "I'm currently at a cafe," the generation AI suggests, "Let's talk about recommended ways to spend time at a cafe." This makes it possible to select topics based on the user's geographic location information.

[0041] The dialogue unit can analyze the user's social media activity during dialogue and provide related topics. For example, the dialogue unit provides topics based on articles the user shared on social media. The dialogue unit uses the generation AI to analyze the user's social media activity based on the content of the user's posts and the number of likes. For example, if the user says, "I recently shared this article," the dialogue unit will suggest, "Let's talk more about that article." The dialogue unit also selects topics related to accounts the user follows on social media. For example, if the user says, "I follow this account," the dialogue unit will suggest, "Let's talk about topics related to that account." The dialogue unit also provides topics related to events the user is participating in on social media. For example, if the user says, "I'm attending this event," the dialogue unit will suggest, "Let's talk more about that event." This makes it possible to provide topics based on the user's social media activity.

[0042] The dialogue unit can customize the dialogue content by reflecting the user's past feedback. For example, the dialogue unit allows the generation AI to suggest new topics based on topics the user has liked in the past. The dialogue unit allows the generation AI to collect past feedback based on user survey results and comments. For example, if the user says, "I like this topic," the dialogue unit may suggest, "Let's talk about a new, related topic." The dialogue unit may also adjust the dialogue content to avoid topics the user has avoided in the past. For example, if the user says, "I'm not good at this topic," the dialogue unit may respond, "I understand. Let's talk about a different topic." Furthermore, the dialogue unit may adjust the tone and style of the dialogue based on the user's past feedback. For example, if the user provides feedback such as, "Please speak in a gentler tone," the dialogue unit may respond, "I understand. I will speak in a gentler tone from now on." This makes it possible to customize the dialogue content based on the user's past feedback.

[0043] When selecting a character, the selection unit can suggest a character by referring to the user's past selection history. For example, the selection unit allows the generation AI to suggest a new character based on characters the user has previously selected. The selection unit allows the generation AI to collect and analyze the user's selection history as log data. For example, if the selection unit says, "I chose this character in the past," the generation AI responds, "In that case, I'll suggest a new character with similar characteristics." The selection unit also suggests the optimal character based on the characteristics of characters the user has previously preferred. For example, if the selection unit says, "I like this character's voice," the generation AI responds, "In that case, I'll suggest another character played by the same voice actor." The selection unit also allows the generation AI to suggest characters to avoid characters the user has avoided in the past. For example, if the selection unit says, "I don't like this character," the generation AI responds, "I understand. I'll suggest another character." This enables the generation AI to suggest optimal characters based on the user's past selection history.

[0044] When selecting a character, the selection unit can customize the character based on the user's current mood and situation. For example, if the user is tired, the generation AI will suggest a relaxing character. In the selection unit, the generation AI identifies the mood and situation based on the user's self-reporting and vital data. For example, if the user says, "I'm tired today," the selection unit will suggest, "Then, let's choose a relaxing character." In addition, if the user is in good spirits, the selection unit will suggest an active character. For example, if the user says, "I'm in good spirits today," the selection unit will suggest, "Then, let's choose an active character." In addition, if the user is feeling stressed, the selection unit will suggest a soothing character. For example, if the user says, "I'm stressed today," the selection unit will suggest, "Then, let's choose a soothing character." This makes it possible to customize characters according to the user's current mood and situation.

[0045] When selecting a character, the selection unit can suggest characters based on the user's interests and concerns. For example, the selection unit uses the generation AI to suggest characters similar to the user's favorite anime character. The selection unit uses the generation AI to identify the user's interests and concerns based on the user's survey results and past behavioral history. For example, if the user says, "I like this anime character," the selection unit uses the generation AI to respond, "In that case, I'll suggest a character with similar characteristics." The selection unit also uses the generation AI to suggest characters related to the user's interests. For example, if the user says, "I'm interested in history," the selection unit uses the generation AI to respond, "In that case, I'll suggest a character modeled after a historical figure." The selection unit also uses the generation AI to suggest new characters based on the characteristics of characters the user has previously liked. For example, if the user says, "I like this character's personality," the selection unit uses the generation AI to respond, "In that case, I'll suggest a new character with the same personality." This enables character suggestions based on the user's interests and concerns.

[0046] When selecting a character, the selection unit can suggest highly relevant characters based on the user's geographic location information. For example, the selection unit uses the generation AI to suggest characters related to the area where the user lives. The selection unit uses the generation AI to identify the user's geographic location information based on the user's GPS data or location information services. For example, if the user says, "I live in Tokyo," the selection unit uses the generation AI to respond, "In that case, I'll suggest a character related to Tokyo." Furthermore, if the user is traveling, the selection unit uses the generation AI to suggest characters related to the user's travel destination. For example, if the user says, "I'm currently traveling in Kyoto," the selection unit uses the generation AI to respond, "In that case, I'll suggest a character related to Kyoto." Furthermore, if the user is in a specific location, the selection unit uses the generation AI to suggest characters related to that location. For example, if the user says, "I'm currently at a cafe," the selection unit uses the generation AI to respond, "In that case, I'll suggest a character related to cafes." This enables character suggestions based on the user's geographic location information.

[0047] When selecting a character, the selection unit can analyze the user's social media activity and suggest related characters. For example, the selection unit uses the generation AI to suggest characters related to accounts the user follows on social media. The selection unit analyzes the user's activity based on the content of their social media posts and the number of likes. For example, if the selection unit says, "I follow this account," the generation AI responds, "Then, I will suggest a character related to that account." The selection unit also uses the generation AI to suggest characters based on articles the user shared on social media. For example, if the selection unit says, "I recently shared this article," the generation AI responds, "Then, I will suggest a character related to that article." The selection unit also uses the generation AI to suggest characters related to events the user is participating in on social media. For example, if the selection unit says, "I am participating in this event," the generation AI responds, "Then, I will suggest a character related to that event." This enables character suggestions based on the user's social media activity.

[0048] The selection unit can customize characters by reflecting the user's past feedback when selecting a character. For example, the selection unit allows the generation AI to suggest new characters based on the characteristics of characters the user previously liked. The selection unit allows the generation AI to collect past feedback based on user survey results and comments. For example, if the user says, "I like this character's voice," the selection unit allows the generation AI to respond, "In that case, I'll suggest another character played by the same voice actor." The selection unit also allows the generation AI to suggest characters to avoid characters the user previously avoided. For example, if the user says, "I don't like this character," the selection unit allows the generation AI to respond, "I understand. I'll suggest a different character." Furthermore, the selection unit allows the generation AI to adjust the character's tone and style based on the user's past feedback. For example, if the user provides feedback such as, "Please speak in a gentler tone," the selection unit allows the generation AI to respond, "I understand. I'll speak in a gentler tone from now on." This enables character customization based on the user's past feedback.

[0049] When inputting data, the input unit can select the input method by referring to the past dialogue history of relatives and friends. For example, the input unit allows the generation AI to suggest the optimal input method based on the input methods used by relatives and friends in the past. The input unit allows the generation AI to collect and analyze the dialogue history of relatives and friends as log data. For example, if a relative or friend says, "I input data using this method in the past," the generation AI responds, "Then, I will input data using the same method." The input unit also selects the optimal input timing from the past dialogue history of relatives and friends. For example, if a relative or friend says, "I input data at this timing," the generation AI responds, "Then, I will input data at the same timing." Furthermore, the input unit analyzes the past dialogue history of relatives and friends, and the generation AI improves the accuracy of the input. For example, if a relative or friend says, "This method improved the accuracy of my input," the input unit will respond by saying, "Then I'll input using the same method." This makes it possible to select the optimal input method based on the past conversation history of the relative or friend.

[0050] The input section can customize the input content based on the current situation and interests of relatives and friends at the time of input. For example, the generation AI customizes the input content based on the topics that relatives and friends are currently interested in. In the input section, the generation AI identifies the current situation and interests based on the relatives' and friends' self-reporting and vital data. For example, if a relative or friend says, "I'm interested in this right now," the generation AI responds, "I'll input content related to that topic." In addition, the generation AI adjusts the input content based on the relatives' and friends' current situation. For example, if a relative or friend says, "I'm busy right now," the generation AI responds, "I'll input brief content." In addition, the generation AI optimizes the input content based on the relatives' and friends' current interests. For example, if a relative or friend says, "I want to relax right now," the input section of the AI ​​will respond by saying, "Then I'll input something that will help you relax." This makes it possible to customize the input content based on the current situation and interests of the relative or friend.

[0051] The input unit can improve the accuracy of the input by analyzing the tone of voice and speaking style of relatives and friends during input. For example, the input unit analyzes the tone of voice of relatives and friends, and the generation AI improves the accuracy of the input. In the input unit, the generation AI uses voice analysis technology to extract the characteristics of the tone of voice and speaking style of relatives and friends. For example, the input unit analyzes the tone of voice of relatives and friends and responds, "I will input in this tone." The input unit also analyzes the characteristics of the speaking style of relatives and friends, and the generation AI improves the accuracy of the input. For example, the input unit analyzes the characteristics of the speaking style of relatives and friends and responds, "I will input in this speaking style." The input unit also analyzes the tone of voice and speaking style of relatives and friends in combination, and the generation AI improves the accuracy of the input. For example, the input unit analyzes the tone of voice and speaking style of a relative or friend and responds, "I will input using this tone and speaking style." This makes it possible to improve the accuracy of input based on the analysis of the tone of voice and speaking style of a relative or friend.

[0052] The input unit can input highly relevant data based on the geographic location information of relatives and friends at the time of input. For example, the generation AI inputs data related to the current location of the relatives and friends. The input unit determines the geographic location information based on the relatives' and friends' GPS data and location information services. For example, if the relatives or friends say, "I'm in Tokyo now," the generation AI responds, "Then, I'll input data related to Tokyo." The input unit also inputs data related to places the relatives or friends have visited in the past. For example, if the relatives or friends say, "I went to Kyoto in the past," the generation AI responds, "Then, I'll input data related to Kyoto." The input unit also inputs data related to travel destinations the relatives or friends are planning. For example, if a relative or friend says, "I'm going to Hokkaido next time," the input section of the AI ​​will respond by saying, "Then, I'll input data related to Hokkaido." This makes it possible to input data based on the geographic location information of relatives and friends.

[0053] The input unit can analyze the social media activities of relatives and friends at the time of input and input related data. For example, the generation AI inputs data based on articles shared by relatives and friends on social media. The input unit analyzes activities based on the content of relatives' and friends' social media posts and the number of likes. For example, if a relative or friend says, "I recently shared this article," the generation AI responds, "Then, I will input data related to that article." The input unit also inputs data related to accounts that relatives and friends follow on social media. For example, if a relative or friend says, "I'm following this account," the generation AI responds, "Then, I will input data related to that account." The input unit also inputs data related to events that relatives and friends are attending on social media. For example, if a relative or friend says, "I'm attending this event," the input unit will respond by saying, "Then I'll input data related to that event." This makes it possible to input data based on the social media activity of relatives and friends.

[0054] The input unit can customize the input method by reflecting past feedback from relatives and friends. For example, the generation AI suggests new input methods based on input methods that relatives and friends have preferred in the past. The generation AI collects past feedback from relatives and friends based on survey results and comments. For example, if a relative or friend says, "I input using this method," the generation AI responds, "I'll input using the same method." The generation AI also customizes the input method to avoid input methods that relatives and friends have avoided in the past. For example, if a relative or friend says, "I'm not good at this method," the generation AI responds, "I'll input using a different method." The generation AI also adjusts the timing and style of input based on feedback provided by relatives and friends in the past. For example, if a relative or friend gives feedback such as "Please input more slowly," the generation AI will respond with "I understand. I will input more slowly from now on." This makes it possible to customize the input method based on past feedback from relatives and friends.

[0055] During imitation, the imitation unit can improve the accuracy of imitation based on the level of detail of the input data. For example, if the input data is detailed, the generation AI performs high-accuracy imitation. The imitation unit evaluates the level of detail of the input data and adjusts the accuracy of imitation. For example, if the imitation unit evaluates the input data as "detailed voice data," the generation AI responds, "Then, I will perform high-accuracy imitation." Furthermore, if the input data is sparse, the generation AI uses complementary data to improve the accuracy of imitation. For example, if the imitation unit evaluates the input data as "sparse voice data," the generation AI responds, "Then, I will perform imitation using complementary data." Furthermore, the imitation unit adjusts the accuracy of imitation based on the level of detail of the input data. For example, if the imitation unit evaluates the input data as "medium level of detail," the generation AI responds, "Then, I will perform imitation with medium accuracy." This allows for improved imitation accuracy based on the level of detail of the input data.

[0056] When imitating, the imitation unit can analyze the tone of voice and speaking style of a relative or friend to improve the accuracy of the imitation. For example, the imitation unit analyzes the tone of voice of a relative or friend, and the generation AI improves the accuracy of the imitation. In the imitation unit, the generation AI uses voice analysis technology to extract the characteristics of the tone of voice and speaking style of a relative or friend. For example, the imitation unit analyzes the tone of voice of a relative or friend and responds, "I will imitate you using this tone." The imitation unit also analyzes the characteristics of the speaking style of a relative or friend, and the generation AI improves the accuracy of the imitation. For example, the imitation unit analyzes the characteristics of the speaking style of a relative or friend and responds, "I will imitate you using this tone and speaking style." Furthermore, the imitation unit analyzes the tone of voice and speaking style of a relative or friend in combination, and the generation AI improves the accuracy of the imitation. For example, the imitation unit analyzes the tone of voice and speaking style of a relative or friend and responds, "I will imitate you using this tone and speaking style." This makes it possible to improve the accuracy of the imitation based on the analysis of the tone of voice and speaking style of a relative or friend.

[0057] The imitation unit can improve the accuracy of the imitation by referring to the user's past dialogue history. For example, the imitation unit allows the generation AI to improve the accuracy of the imitation based on the user's past dialogue. In the imitation unit, the generation AI collects and analyzes the user's dialogue history as log data. For example, if the user says, "I spoke like this in the past," the generation AI responds, "Then, I will imitate you in the same way." The imitation unit also adjusts the tone and style of the imitation based on the user's past dialogue history. For example, if the user says, "I spoke in this tone," the generation AI responds, "Then, I will imitate you in the same tone." Furthermore, the imitation unit analyzes the user's past dialogue history, and the generation AI improves the accuracy of the imitation. For example, if the user says, "I spoke in this style," the generation AI responds, "Then, I will imitate you in the same style." This enables the generation AI to improve the accuracy of the imitation based on the user's past dialogue history.

[0058] During imitation, the mimicry unit can mimic highly relevant data based on the geographic location information of relatives and friends. For example, the mimicry unit allows the generation AI to mimic data related to the relatives' or friends' current locations. The mimicry unit identifies the geographic location information based on the relatives' or friends' GPS data or location information services. For example, if the relatives or friends say, "I'm in Tokyo now," the generation AI responds, "Then, I'll mimic data related to Tokyo." The mimicry unit also mimics data related to places the relatives or friends have visited in the past. For example, if the relatives or friends say, "I've been to Kyoto in the past," the generation AI responds, "Then, I'll mimic data related to Kyoto." The mimicry unit also mimics data related to travel destinations the relatives or friends are planning. For example, if the relatives or friends say, "I'm going to Hokkaido next time," the generation AI responds, "Then, I'll mimic data related to Hokkaido." This allows for data mimicking based on the geographic location of relatives and friends.

[0059] During imitation, the mimicry unit can analyze the social media activities of relatives and friends and mimic related data. For example, the mimicry unit uses the generation AI to mimic data based on articles shared by relatives and friends on social media. The mimicry unit analyzes activities based on the content of relatives' and friends' social media posts and the number of likes. For example, if a relative or friend says, "I recently shared this article," the generation AI responds, "Then, I will mimic the data related to that article." The mimicry unit also mimics data related to accounts that relatives and friends follow on social media. For example, if a relative or friend says, "I'm following this account," the generation AI responds, "Then, I will mimic the data related to that account." The mimicry unit also mimics data related to events that relatives and friends are attending on social media. For example, if a relative or friend says, "I'm attending this event," the generation AI responds, "Then, I will mimic the data related to that event." This allows for data cloning based on the social media activity of relatives and friends.

[0060] The imitation unit can customize the imitation method by reflecting past feedback from relatives and friends. For example, the generation AI suggests new imitation methods based on the imitation methods that relatives and friends have preferred in the past. The imitation unit collects past feedback based on survey results and comments from relatives and friends. For example, if a relative or friend says, "I imitated this way," the generation AI responds, "Then I will imitate in the same way." The imitation unit also customizes the imitation method to avoid imitation methods that relatives and friends have avoided in the past. For example, if a relative or friend says, "I'm not good at this method," the generation AI responds, "Then I will imitate in a different way." Furthermore, the imitation unit adjusts the timing and style of imitation based on past feedback provided by relatives and friends. For example, if a relative or friend gives feedback such as, "Please imitate more slowly," the generation AI responds, "Okay. I will imitate more slowly from now on." This allows for customization of imitation methods based on past feedback from relatives and friends.

[0061] When reading aloud, the reading unit can select reading content by referring to the user's past reading history. For example, the reading unit's generation AI suggests new reading content based on the user's past favorite books. The reading unit's generation AI collects and analyzes the user's reading history as log data. For example, if the reading unit says, "I read this book in the past," the generation AI responds, "Then, I'll read a book with similar content." The reading unit also selects the optimal reading content from the user's past reading history. For example, if the reading unit says, "I like this genre," the generation AI responds, "Then, I'll read a book in the same genre." The reading unit also selects reading content so as to avoid books the user has avoided in the past. For example, if the reading unit says, "I'm not good at this genre," the generation AI responds, "Then, I'll read a book in a different genre." This makes it possible to select the most suitable reading content based on the user's past reading history.

[0062] The reading unit can customize the reading content based on the user's current mood and situation. For example, if the user is relaxed, the generation AI will recite relaxing content. The reading unit identifies the user's mood and situation based on the user's self-reporting and vital data. For example, if the user says, "I'm feeling relaxed today," the generation AI will respond, "I'll read you some relaxing content." If the user is in good spirits, the reading unit will recite uplifting content. For example, if the user says, "I'm feeling good today," the generation AI will respond, "I'll read you some uplifting content." If the user is feeling stressed, the reading unit will recite stress-relieving content. For example, if the user says, "I'm feeling stressed today," the generation AI will respond, "I'll read you some stress-relieving content." This makes it possible to customize the reading content according to the user's current mood and situation.

[0063] The reading section can select a reading topic based on the user's interests. For example, the reading section's generation AI reads a book related to a topic that the user is interested in. The reading section's generation AI identifies the user's interests based on the user's survey results and past behavioral history. For example, if the user says, "I'm interested in history," the reading section's generation AI responds, "Then I'll read a book related to history." The reading section's generation AI also suggests new reading content based on the user's past favorite topics. For example, if the user says, "I like this topic," the reading section's generation AI responds, "Then I'll read a new book on the same topic." Furthermore, the reading section's generation AI selects reading content to avoid topics that the user has avoided. For example, if the user says, "I'm not good at this topic," the reading section's generation AI responds, "Then I'll read a book on a different topic." This makes it possible to select reading topics based on the user's interests.

[0064] When reading aloud, the reading unit can recite content that is highly relevant based on the user's geographic location information. For example, the generation AI of the reading unit recites books related to the area where the user lives. The reading unit determines the user's geographic location information based on the user's GPS data and location information services. For example, if the user says, "I live in Tokyo," the generation AI responds, "I will read a book related to Tokyo." If the user is traveling, the reading unit recites books related to the travel destination. For example, if the user says, "I'm currently traveling in Kyoto," the generation AI responds, "I will read a book related to Kyoto." If the user is in a specific location, the generation AI recites content related to that location. For example, if the user says, "I'm currently at a cafe," the generation AI responds, "I will read a content related to cafes." This makes it possible to select reading content based on the user's geographic location information.

[0065] The reading unit can analyze the user's social media activity and recite related content when reading aloud. For example, the reading unit's generation AI recites content based on articles the user shared on social media. The reading unit's generation AI analyzes activity based on the user's social media posts and the number of likes. For example, if the user says, "I recently shared this article," the reading unit's generation AI responds, "I will recite content related to that article." The reading unit also recites content related to accounts the user follows on social media. For example, if the user says, "I follow this account," the reading unit's generation AI responds, "I will recite content related to that account." The reading unit also recites content related to events the user is participating in on social media. For example, if the user says, "I am participating in this event," the reading unit's generation AI responds, "I will recite content related to that event." This makes it possible to select reading content based on the user's social media activity.

[0066] The reading unit can customize the reading content by reflecting the user's past feedback. For example, the generation AI suggests new reading content based on the user's past favorite reading content. The reading unit collects past feedback based on the user's survey results and comments. For example, if the reading unit says, "I like this content," the generation AI responds, "Then, I'll read a new book with similar content." The reading unit also customizes the reading content to avoid reading content that the user has avoided in the past. For example, if the reading unit says, "I'm not good at this content," the generation AI responds, "Then, I'll read a book with a different content." Furthermore, the reading unit adjusts the tone and style of the reading based on the user's past feedback. For example, if the user provides feedback such as, "Please read in a gentler tone," the generation AI responds, "I understand. I'll read in a gentler tone from now on." This allows for customization of the reading content based on the user's past feedback.

[0067] When presenting a quiz, the quiz section can select a quiz by referring to the user's past quiz history. For example, the generation AI of the quiz section proposes a new quiz based on the user's past favorite quiz themes. The generation AI of the quiz section collects and analyzes the user's quiz history as log data. For example, if the user says, "I enjoyed quizzes on this theme in the past," the generation AI responds, "In that case, I'll present a new quiz on the same theme." The generation AI of the quiz section also selects the optimal difficulty level of the quiz based on the user's past quiz history. For example, if the user says, "This difficulty level is just right," the generation AI responds, "In that case, I'll present a quiz of the same difficulty level." The generation AI of the quiz section also selects quizzes so as to avoid quiz themes that the user has avoided in the past. For example, if the user says, "I'm not good at this theme," the generation AI responds, "In that case, I'll present a quiz on a different theme." This enables the selection of optimal quizzes based on the user's past quiz history.

[0068] The quiz section can customize the content of a quiz based on the user's current knowledge level and interests. For example, the quiz section uses a generation AI to generate quizzes related to topics the user is currently interested in. The quiz section uses the generation AI to determine the user's current knowledge level and interests based on the user's self-reported answers and past quiz results. For example, if the user says, "I'm interested in history right now," the generation AI responds, "I'll give you a quiz related to history." The quiz section also adjusts the difficulty of the quiz based on the user's current knowledge level. For example, if the user says, "This difficulty level is just right," the generation AI responds, "I'll give you a quiz of the same difficulty level." The quiz section also uses the generation AI to suggest new quizzes based on the user's past favorite topics. For example, if the user says, "I like this topic," the generation AI responds, "I'll give you a new quiz on the same topic." This allows the quiz content to be customized based on the user's current knowledge level and interests.

[0069] When creating a quiz, the quiz section can select a quiz topic based on the user's interests. For example, the quiz section's generation AI creates a quiz related to a topic that the user is interested in. The quiz section's generation AI identifies the user's interests based on survey results and past behavioral history. For example, if the user says, "I'm interested in history," the quiz section's generation AI responds, "Then I'll create a quiz related to history." The quiz section's generation AI also suggests new quizzes based on the user's past favorite topics. For example, if the user says, "I like this topic," the quiz section's generation AI responds, "Then I'll create a new quiz on the same topic." Furthermore, the quiz section's generation AI selects quiz topics to avoid topics that the user has avoided. For example, if the user says, "I'm not good at this topic," the quiz section's generation AI responds, "Then I'll create a quiz on a different topic." This enables quiz topics to be selected based on the user's interests.

[0070] When asking a question, the quiz section can provide relevant questions based on the user's geographic location information. For example, the quiz section's generation AI provides a quiz related to the area where the user lives. The quiz section's generation AI identifies the user's geographic location based on the user's GPS data and location information services. For example, if the user says, "I live in Tokyo," the quiz section's generation AI responds, "Then, I'll provide a quiz related to Tokyo." Furthermore, if the user is traveling, the quiz section's generation AI provides a quiz related to the user's travel destination. For example, if the user says, "I'm currently traveling in Kyoto," the quiz section's generation AI responds, "Then, I'll provide a quiz related to Kyoto." Furthermore, if the user is in a specific location, the quiz section's generation AI provides a quiz related to that location. For example, if the user says, "I'm currently at a cafe," the quiz section's generation AI responds, "Then, I'll provide a quiz related to cafes." This enables quizzes to be selected based on the user's geographic location information.

[0071] When creating a quiz, the quiz section can analyze a user's social media activity and present relevant quizzes. For example, the quiz section's generation AI presents quizzes based on articles shared by the user on social media. The quiz section's generation AI analyzes activity based on the content of the user's social media posts and the number of likes. For example, if the user says, "I recently shared this article," the generation AI responds, "I'll present a quiz related to that article." The quiz section also presents quizzes related to accounts the user follows on social media. For example, if the user says, "I follow this account," the generation AI responds, "I'll present a quiz related to that account." The quiz section also presents quizzes related to events the user is participating in on social media. For example, if the user says, "I'm participating in this event," the generation AI responds, "I'll present a quiz related to that event." This makes it possible to select quizzes based on the user's social media activity.

[0072] When presenting a quiz, the quiz section can customize the content of the quiz by reflecting the user's past feedback. For example, the generation AI suggests new quizzes based on the user's past favorite quiz themes. The generation AI collects past feedback from users' survey results and comments. For example, if a user says, "I like quizzes on this theme," the generation AI responds, "Then, I'll present a new quiz on the same theme." The generation AI also customizes the content of the quiz to avoid quiz themes that the user has avoided in the past. For example, if a user says, "I'm not good at this theme," the generation AI responds, "Then, I'll present a quiz on a different theme." The generation AI also adjusts the difficulty and style of the quiz based on the user's past feedback. For example, if a user provides feedback such as, "Please present easier quizzes," the generation AI responds, "Okay, I'll present easier quizzes from now on." This allows the quiz content to be customized based on the user's past feedback.

[0073] When playing music, the music section can select music by referring to the user's past music history. For example, the generation AI suggests new music based on the user's past favorite music. The music section collects and analyzes the user's music history as log data. For example, if the user says, "I enjoyed this music in the past," the generation AI responds, "Then, I'll play new music in the same genre." The music section also selects optimal music based on the user's past music history. For example, if the user says, "I like this artist," the generation AI responds, "Then, I'll play a new song by the same artist." The music section also selects music so as to avoid music that the user has avoided in the past. For example, if the user says, "I don't like this genre," the generation AI responds, "Then, I'll play music in a different genre." This makes it possible to select optimal music based on the user's past music history.

[0074] When playing music, the music section can customize the music content based on the user's current mood and situation. For example, if the user is relaxed, the generation AI will play relaxing music. The music section determines the user's mood and situation based on the user's self-reported behavior and vital signs. For example, if the user says, "I'm feeling relaxed today," the generation AI will respond, "I'll play some relaxing music." Furthermore, if the user is feeling energetic, the generation AI will play uplifting music. For example, if the user says, "I'm feeling energetic today," the generation AI will respond, "I'll play some uplifting music." Furthermore, if the user is feeling stressed, the generation AI will play music that reduces stress. For example, if the user says, "I'm feeling stressed today," the generation AI will respond, "I'll play some stress-reducing music." This allows the music content to be customized according to the user's current mood and situation.

[0075] When playing music, the music section can select a music theme based on the user's interests. For example, the generation AI plays music related to themes the user is interested in. The generation AI identifies the user's interests based on survey results and past behavioral history. For example, if the user says, "I'm interested in jazz," the generation AI responds, "Then, I'll play music related to jazz." The generation AI also suggests new music based on themes the user has previously liked. For example, if the user says, "I like this theme," the generation AI responds, "Then, I'll play new music with the same theme." Furthermore, the generation AI selects music themes to avoid themes the user has avoided. For example, if the user says, "I don't like this theme," the generation AI responds, "Then, I'll play music with a different theme." This makes it possible to select music themes based on the user's interests.

[0076] When playing music, the music section can play music that is highly relevant based on the user's geographic location. For example, the AI ​​generates music related to the area where the user lives. The AI ​​identifies the user's geographic location based on the user's GPS data and location services. For example, if the user says, "I live in Tokyo," the AI ​​responds, "Then, I'll play music related to Tokyo." If the user is traveling, the AI ​​plays music related to the user's travel destination. For example, if the user says, "I'm currently traveling in Kyoto," the AI ​​responds, "Then, I'll play music related to Kyoto." If the user is in a specific location, the AI ​​plays music related to that location. For example, if the user says, "I'm currently at a cafe," the AI ​​responds, "Then, I'll play music related to cafes." This makes it possible to select music based on the user's geographic location.

[0077] When playing music, the music section can analyze the user's social media activity and play related music. For example, the music section uses a generation AI to play music based on the music the user shared on social media. The music section uses a generation AI to analyze the user's social media activity based on the content of the user's posts and the number of likes. For example, if the user says, "I recently shared this music," the generation AI responds, "Then, play that music." The music section also uses a generation AI to play music related to artists the user follows on social media. For example, if the user says, "I follow this artist," the generation AI responds, "Then, play music by that artist." The music section also uses a generation AI to play music related to events the user is participating in on social media. For example, if the user says, "I'm attending this event," the generation AI responds, "Then, play music related to that event." This makes it possible to select music based on the user's social media activity.

[0078] The music section can customize the music content by reflecting the user's past feedback. For example, the generation AI suggests new music based on the user's past favorite music. The generation AI collects past feedback based on the user's survey results and comments. For example, if the user says, "I like this music," the generation AI responds, "Then, I'll play similar music." The generation AI also customizes the music content to avoid music that the user has avoided in the past. For example, if the user says, "I don't like this genre," the generation AI responds, "Then, I'll play music of a different genre." Furthermore, the generation AI adjusts the timing and style of music playback based on the user's past feedback. For example, if the user provides feedback such as, "Please play more relaxing music," the generation AI responds, "Okay, I'll play relaxing music from now on." This makes it possible to customize the music content based on the user's past feedback.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The dialogue unit can analyze the user's past dialogue history and select the dialogue content. For example, the generation AI can provide new related topics based on topics the user has previously discussed. The generation AI can also advance the dialogue based on themes the user has previously shown interest in. Furthermore, the generation AI can adjust the dialogue content so as to avoid topics the user has avoided in the past. This makes it possible to select the optimal dialogue content based on the user's past dialogue history.

[0081] The input unit can record the voices and speaking styles of relatives and friends and input them into the voice recognition system. For example, the voices of relatives and friends can be recorded and saved as audio files. The generation AI can then use voice recognition technology to analyze the recorded data and generate data to imitate the speaking styles of relatives and friends. Furthermore, the tone of voice and speaking style characteristics of relatives and friends can be extracted and input into the generation AI. This makes it possible to have more friendly conversations by inputting the voices and speaking styles of relatives and friends.

[0082] The dialogue unit can customize the dialogue content based on the user's current health condition and mood. For example, if the user is feeling unwell, the generation AI can provide health advice. If the user is in a good mood, the generation AI can provide fun topics. Furthermore, if the user is feeling stressed, the generation AI can select relaxing topics. This makes it possible to customize the dialogue content according to the user's health condition and mood.

[0083] The dialogue unit can select highly relevant topics based on the user's geographical location information. For example, the generation AI can provide topics related to news and events in the area where the user lives. Also, if the user is traveling, the generation AI can provide tourist information about the travel destination. Furthermore, if the user is in a specific location, the generation AI can select topics related to that location. This makes it possible to select topics based on the user's geographical location information.

[0084] The processing flow of the first embodiment will be briefly explained below.

[0085] Step 1: The dialogue unit uses the generation AI to conduct voice dialogue. The dialogue unit provides appropriate responses to everyday conversations and questions. The generation AI analyzes the user's speech using voice recognition technology and generates an appropriate response using natural language processing technology. For example, if the user asks, "What's the weather like today?", the generation AI obtains the weather information and responds, "It's sunny today." If the user asks, "How are you?", the generation AI responds, "I'm fine. How about you?" Step 2: The selection unit selects a character based on the user's preferences. The selection unit provides multiple characters, such as characters with gentle voices and characters with lively voices, for the user to choose from. The generation AI can also suggest the most suitable character based on the user's selection history and survey results. For example, it can suggest a new character based on characters the user has previously selected. Step 3: The input unit records the voices and speaking styles of relatives and friends and inputs them into the generation AI. The input unit records the voices of relatives and friends and saves them as audio files. The generation AI analyzes the recorded data using voice recognition technology and generates data to imitate the speaking styles of relatives and friends. For example, it extracts the voice tone and speaking style characteristics of relatives and friends and inputs them into the generation AI. Step 4: The mimicry unit engages in a conversation based on the input data. The mimicry unit engages in a conversation by imitating the voice and speaking style of a relative or friend. The generation AI reproduces the voice and speaking style of a relative or friend based on the input data. For example, it can imitate the tone of voice and speaking style of a relative or friend and engage in a conversation with the user. Step 5: The reading unit recites a specific book specified by the user. When the user instructs the reading unit to "read this book," the generation AI recites the book. The generation AI analyzes the text data and uses speech synthesis technology to perform the reading. For example, it may recite a favorite novel or poem. Step 6: The quiz section asks the user various quizzes. The quiz section asks questions about general knowledge and hobbies, and users can deepen their knowledge while having fun by answering them. The generation AI generates the content of the quiz and asks it to the user. For example, it might ask a question such as, "Which of the following is the tallest mountain?" Step 7: The music section plays the specific music requested by the user. When the user requests, "Play this song," the AI ​​generation section plays that song. The AI ​​generation retrieves the music data and plays it. For example, it can play a song by a favorite artist or relaxing music.

[0086] (Example 2) The dialogue partner system of an embodiment of the present invention utilizes a generation AI to engage in dialogue with bedridden individuals. This system allows for voice dialogue and allows users to choose from several existing characters. Relatives and friends can also input and provide their own data. This helps alleviate loneliness in bedridden individuals and, in the case of elderly individuals, helps prevent dementia. It also has functions for reading books aloud, providing quizzes, and playing favorite music. For example, the dialogue partner system allows users to interact with the generation AI via voice. The generation AI responds appropriately to everyday conversations and questions. Next, users can select a character based on their preferences. For example, characters with gentle voices or lively voices are available. Furthermore, by recording the voices and speaking styles of relatives and friends and inputting them into the generation AI, the generation AI can imitate those voices and speaking styles to engage in dialogue. The generation AI can also be instructed to recite specific books. For example, users can have their favorite novels or poems recited. Furthermore, the generation AI can pose various quizzes to the user. For example, users can have fun while deepening their knowledge by answering quizzes about general knowledge or hobbies. Finally, the generation AI can be instructed to play specific music. For example, it can play songs by a favorite artist or relaxing music. In this way, the conversation partner system can alleviate loneliness in bedridden people and help prevent dementia in elderly people. It also has functions such as reading books aloud, asking quizzes, and playing favorite music, so it can provide a variety of entertainment.

[0087] A dialogue partner system according to an embodiment includes a dialogue unit, a selection unit, an input unit, an imitation unit, a reading unit, a quiz unit, and a music unit. The dialogue unit uses a generation AI to conduct voice dialogue. The dialogue unit provides appropriate responses to, for example, everyday conversations and questions. The dialogue unit uses a generation AI to analyze a user's speech using voice recognition technology and generate appropriate responses using natural language processing technology. For example, when a user asks, "What's the weather like today?", the dialogue unit generates weather information and responds with, "It's sunny today." Furthermore, when a user asks, "How are you?", the dialogue unit can also respond with, "I'm fine. How about you?" The selection unit selects a character based on the user's preferences. The selection unit provides multiple characters, such as characters with gentle voices and characters with lively voices, and allows the user to select one. The selection unit can also suggest the most suitable character based on the user's selection history and survey results. For example, the selection unit suggests a new character based on characters previously selected by the user. The input unit records the voices and speaking styles of relatives and friends and inputs them into the generation AI. For example, the input unit records the voices of relatives and friends and saves them as audio files. The input unit analyzes the recorded data using voice recognition technology to generate data for the generation AI to imitate the speaking styles of relatives and friends. For example, the input unit extracts characteristics of the tone of voice and speaking style of the relatives and friends and inputs this into the generation AI. The imitation unit engages in a dialogue based on the input data. For example, the imitation unit engages in a dialogue by imitating the voices and speaking styles of relatives and friends. The imitation unit reproduces the voices and speaking styles of relatives and friends based on the input data. For example, the imitation unit imitates the tone of voice and speaking style of relatives and friends and engages in a dialogue with the user. The reading unit recites a specific book specified by the user. For example, if the user commands the generation AI to "read this book," the reading unit recites the book. The reading section uses a generative AI to analyze text data and recites it using voice synthesis technology. For example, the reading section may recite a favorite novel or poem. The quiz section presents various quizzes to users.The quiz section, for example, poses quizzes about general knowledge or hobbies, allowing users to deepen their knowledge while having fun by answering them. In the quiz section, a generation AI generates quiz content and poses it to the user. For example, the quiz section poses a question such as, "Which of the following mountains is the highest?" The music section plays specific music specified by the user. For example, if a user instructs the music section to "play this song," the generation AI plays that song. The music section acquires music data and plays it. For example, the music section plays songs by a favorite artist or relaxing music. This allows the dialogue partner system according to the embodiment to alleviate loneliness in bedridden people and prevent dementia in elderly people. It also has functions for reading books aloud, asking quizzes, and playing favorite music, providing a variety of entertainment.

[0088] The dialogue unit can respond to everyday conversations and questions. For example, the dialogue unit provides appropriate responses to everyday conversations and questions. In the dialogue unit, the generation AI analyzes the user's speech using speech recognition technology and generates an appropriate response using natural language processing technology. For example, if the user asks, "What's the weather like today?", the generation AI obtains the weather information and responds, "It's sunny today." Also, if the user asks, "How are you?", the dialogue unit can respond, "I'm fine. How about you?" This enables natural dialogue with the user by providing appropriate responses to everyday conversations and questions.

[0089] The selection unit can select a character according to the user's preferences. For example, the selection unit prepares multiple characters, such as characters with gentle voices and characters with lively voices, so that the user can select one. The selection unit can also suggest the most suitable character based on the user's selection history and survey results using the generation AI. For example, the selection unit can suggest a new character based on characters the user has previously selected. This makes it possible to select a character that suits the user's preferences.

[0090] The input unit can record the voices and speaking styles of relatives and friends and input them into the voice recognition system. For example, the input unit records the voices of relatives and friends and saves them as audio files. The input unit then analyzes the recorded data using voice recognition technology to generate data that the generation AI uses to imitate the speaking styles of relatives and friends. For example, the input unit extracts the characteristics of the tone of voice and speaking style of the relatives and friends and inputs them into the generation AI. This makes it possible to have more friendly conversations by inputting the voices and speaking styles of relatives and friends.

[0091] The mimicry unit can conduct a conversation based on input data. For example, the mimicry unit conducts a conversation by imitating the voice and speaking style of a relative or friend. The mimicry unit reproduces the voice and speaking style of a relative or friend based on the data input by the generation AI. For example, the mimicry unit imitates the tone of voice and speaking style of a relative or friend to converse with the user. This enables a friendly conversation by conducting a conversation based on input data.

[0092] The reading unit can recite a specific book specified by the user. For example, if the user instructs the reading unit to "read this book," the generation AI will recite the book. The reading unit uses the generation AI to analyze text data and perform the reading using speech synthesis technology. For example, the reading unit may recite a favorite novel or poem. This allows the user to enjoy reading by having the specific book specified by the user recited.

[0093] The quiz section can ask multiple quizzes to users. For example, the quiz section can ask quizzes about general knowledge or hobbies, and users can deepen their knowledge while having fun by answering them. In the quiz section, the generation AI generates the content of the quiz and asks it to users. For example, the quiz section asks a question such as, "Which of the following is the highest mountain?" This allows users to deepen their knowledge while having fun by asking them a variety of quizzes.

[0094] The music section can play specific music specified by the user. For example, if the user requests, "Play this song," the generation AI will play that song. The music section uses the generation AI to obtain music data and play it. For example, the music section can play songs by a favorite artist or relaxing music. This allows users to enjoy music by playing the specific music they request.

[0095] The dialogue unit can estimate the user's emotions and adjust the content and tone of the dialogue based on the estimated user emotions. For example, if the user is sad, the generation AI will offer encouraging words in a gentle tone. The dialogue unit analyzes the user's facial expressions and voice to estimate emotions. For example, if the user says, "I feel sad today," the generation AI will respond, "Are you okay? Is there anything I can help you with?" If the user is excited, the dialogue unit will continue the dialogue in a calm tone. For example, if the user says, "I'm so happy today!" the generation AI will respond, "That's great. What happened?" This makes it possible to adjust the content and tone of the dialogue according to the user's emotions.

[0096] The dialogue unit can analyze the user's past dialogue history and select dialogue content. For example, the dialogue unit allows the generation AI to provide new related topics based on topics the user has previously discussed. The dialogue unit collects the user's past dialogue history as log data and analyzes it using text analysis technology. For example, if the user previously said, "I like traveling," the generation AI would provide a new topic by asking, "Where did you travel recently?" The dialogue unit also advances the dialogue based on themes the user has previously shown interest in. For example, if the user previously said, "I like movies," the generation AI would advance the dialogue by asking, "What movie have you seen recently?" The dialogue unit also adjusts the dialogue content to avoid topics the user has avoided in the past. For example, if the user previously said, "I don't like talking about sports," the generation AI would avoid topics related to sports. This makes it possible to select optimal dialogue content based on past dialogue history.

[0097] The dialogue unit can customize the dialogue content based on the user's current health condition and mood. For example, if the user is feeling unwell, the generation AI provides health advice. The dialogue unit determines the user's health condition and mood based on the user's self-reported health and vital signs. For example, if the user says, "I'm not feeling well today," the generation AI provides advice such as, "Don't push yourself, and get plenty of rest." Furthermore, if the user is in a good mood, the dialogue unit offers a fun topic. For example, if the user says, "I'm feeling good today," the generation AI offers a topic such as, "That's great. Did anything fun happen?" Furthermore, if the user is feeling stressed, the dialogue unit selects a relaxing topic. For example, if the user says, "I'm feeling stressed today," the generation AI suggests, "Shall I play some relaxing music?" This makes it possible to customize the dialogue content according to the user's health condition and mood.

[0098] During a dialogue, the dialogue unit can select a dialogue topic based on the user's interests and concerns. For example, the dialogue unit provides a generation AI with topics related to the user's hobbies. The dialogue unit identifies the user's interests and concerns based on the user's survey results and past behavioral history. For example, if the user says, "I like reading," the dialogue unit provides a topic such as, "What book have you read recently?" The dialogue unit also provides a topic such as, "What book have you read recently?" The dialogue unit also provides a topic such as news or topics that the user is interested in. For example, if the user says, "I'm interested in technology," the dialogue unit provides a topic such as, "Let's talk about recent technology news." The dialogue unit also provides a topic such as the user's favorite movies or music. For example, if the user says, "I like movies," the dialogue unit provides a topic such as, "What movie have you seen recently?" This makes it possible to select a dialogue topic based on the user's interests and concerns.

[0099] The dialogue unit can estimate the user's emotions and adjust the frequency of dialogue based on the estimated user emotions. For example, if the user feels lonely, the generation AI will dialogue more frequently. The dialogue unit analyzes the user's facial expressions and voice to estimate emotions. For example, if the user says, "I feel lonely today," the generation AI will suggest, "Let's talk more." Furthermore, if the user is busy, the dialogue unit reduces the frequency of dialogue. For example, if the user says, "I'm busy today," the dialogue unit will respond, "I understand. Let's talk again later." Furthermore, if the user is relaxed, the dialogue unit will dialogue at an appropriate frequency. For example, if the user says, "I feel relaxed today," the dialogue unit will suggest, "That's good. Is there anything you'd like to talk about?" This makes it possible to adjust the frequency of dialogue according to the user's emotions.

[0100] During a dialogue, the dialogue unit can select relevant topics based on the user's geographic location information. For example, the dialogue unit provides the generation AI with topics related to news and events in the user's area. The dialogue unit identifies the user's geographic location based on the user's GPS data and location information services. For example, if the user says, "I live in Tokyo," the generation AI suggests, "Let's talk about recent Tokyo news." Furthermore, if the user is traveling, the dialogue unit provides tourist information about the destination. For example, if the user says, "I'm currently traveling in Kyoto," the generation AI suggests, "Let's talk about recommended tourist spots in Kyoto." Furthermore, if the user is in a specific location, the dialogue unit selects topics related to that location. For example, if the user says, "I'm currently at a cafe," the generation AI suggests, "Let's talk about recommended ways to spend time at a cafe." This makes it possible to select topics based on the user's geographic location information.

[0101] The dialogue unit can analyze the user's social media activity during dialogue and provide related topics. For example, the dialogue unit provides topics based on articles the user shared on social media. The dialogue unit uses the generation AI to analyze the user's social media activity based on the content of the user's posts and the number of likes. For example, if the user says, "I recently shared this article," the dialogue unit will suggest, "Let's talk more about that article." The dialogue unit also selects topics related to accounts the user follows on social media. For example, if the user says, "I follow this account," the dialogue unit will suggest, "Let's talk about topics related to that account." The dialogue unit also provides topics related to events the user is participating in on social media. For example, if the user says, "I'm attending this event," the dialogue unit will suggest, "Let's talk more about that event." This makes it possible to provide topics based on the user's social media activity.

[0102] The dialogue unit can customize the dialogue content by reflecting the user's past feedback. For example, the dialogue unit allows the generation AI to suggest new topics based on topics the user has liked in the past. The dialogue unit allows the generation AI to collect past feedback based on user survey results and comments. For example, if the user says, "I like this topic," the dialogue unit may suggest, "Let's talk about a new, related topic." The dialogue unit may also adjust the dialogue content to avoid topics the user has avoided in the past. For example, if the user says, "I'm not good at this topic," the dialogue unit may respond, "I understand. Let's talk about a different topic." Furthermore, the dialogue unit may adjust the tone and style of the dialogue based on the user's past feedback. For example, if the user provides feedback such as, "Please speak in a gentler tone," the dialogue unit may respond, "I understand. I will speak in a gentler tone from now on." This makes it possible to customize the dialogue content based on the user's past feedback.

[0103] The selection unit can estimate the user's emotions and adjust the character selection based on the estimated user emotions. For example, if the user is sad, the generation AI suggests a character with a gentle voice. The selection unit analyzes the user's facial expressions and voice to estimate emotions. For example, if the user says, "I'm sad today," the generation AI suggests, "Then, let's choose a character with a gentle voice." Furthermore, if the user is excited, the generation AI suggests a character with a calm voice. For example, if the user says, "I'm very happy today!", the generation AI suggests, "Then, let's choose a character with a calm voice." Furthermore, if the user is relaxed, the generation AI suggests a character with a cheerful voice. For example, if the user says, "I'm relaxed today," the selection unit suggests, "Then, let's choose a character with a cheerful voice." This makes it possible to adjust the character selection according to the user's emotions.

[0104] When selecting a character, the selection unit can suggest a character by referring to the user's past selection history. For example, the selection unit allows the generation AI to suggest a new character based on characters the user has previously selected. The selection unit allows the generation AI to collect and analyze the user's selection history as log data. For example, if the selection unit says, "I chose this character in the past," the generation AI responds, "In that case, I'll suggest a new character with similar characteristics." The selection unit also suggests the optimal character based on the characteristics of characters the user has previously preferred. For example, if the selection unit says, "I like this character's voice," the generation AI responds, "In that case, I'll suggest another character played by the same voice actor." The selection unit also allows the generation AI to suggest characters to avoid characters the user has avoided in the past. For example, if the selection unit says, "I don't like this character," the generation AI responds, "I understand. I'll suggest another character." This enables the generation AI to suggest optimal characters based on the user's past selection history.

[0105] When selecting a character, the selection unit can customize the character based on the user's current mood and situation. For example, if the user is tired, the generation AI will suggest a relaxing character. In the selection unit, the generation AI identifies the mood and situation based on the user's self-reporting and vital data. For example, if the user says, "I'm tired today," the selection unit will suggest, "Then, let's choose a relaxing character." In addition, if the user is in good spirits, the selection unit will suggest an active character. For example, if the user says, "I'm in good spirits today," the selection unit will suggest, "Then, let's choose an active character." In addition, if the user is feeling stressed, the selection unit will suggest a soothing character. For example, if the user says, "I'm stressed today," the selection unit will suggest, "Then, let's choose a soothing character." This makes it possible to customize characters according to the user's current mood and situation.

[0106] When selecting a character, the selection unit can suggest characters based on the user's interests and concerns. For example, the selection unit uses the generation AI to suggest characters similar to the user's favorite anime character. The selection unit uses the generation AI to identify the user's interests and concerns based on the user's survey results and past behavioral history. For example, if the user says, "I like this anime character," the selection unit uses the generation AI to respond, "In that case, I'll suggest a character with similar characteristics." The selection unit also uses the generation AI to suggest characters related to the user's interests. For example, if the user says, "I'm interested in history," the selection unit uses the generation AI to respond, "In that case, I'll suggest a character modeled after a historical figure." The selection unit also uses the generation AI to suggest new characters based on the characteristics of characters the user has previously liked. For example, if the user says, "I like this character's personality," the selection unit uses the generation AI to respond, "In that case, I'll suggest a new character with the same personality." This enables character suggestions based on the user's interests and concerns.

[0107] The selection unit can estimate the user's emotions and adjust the character display method based on the estimated user emotions. For example, if the user is nervous, the generation AI displays a character with subdued colors. The selection unit analyzes the user's facial expressions and voice to estimate emotions. For example, if the user says, "I'm nervous today," the generation AI responds, "In that case, I'll display a character with subdued colors." Furthermore, if the user is having fun, the selection unit displays a character with bright colors. For example, if the user says, "I'm having fun today," the generation AI responds, "In that case, I'll display a character with bright colors." Furthermore, if the user is tired, the selection unit displays a character with simple, high-visibility colors. For example, if the user says, "I'm tired today," the selection unit displays a character with simple, high-visibility colors. This makes it possible to adjust the character display method according to the user's emotions.

[0108] When selecting a character, the selection unit can suggest highly relevant characters based on the user's geographic location information. For example, the selection unit uses the generation AI to suggest characters related to the area where the user lives. The selection unit uses the generation AI to identify the user's geographic location information based on the user's GPS data or location information services. For example, if the user says, "I live in Tokyo," the selection unit uses the generation AI to respond, "In that case, I'll suggest a character related to Tokyo." Furthermore, if the user is traveling, the selection unit uses the generation AI to suggest characters related to the user's travel destination. For example, if the user says, "I'm currently traveling in Kyoto," the selection unit uses the generation AI to respond, "In that case, I'll suggest a character related to Kyoto." Furthermore, if the user is in a specific location, the selection unit uses the generation AI to suggest characters related to that location. For example, if the user says, "I'm currently at a cafe," the selection unit uses the generation AI to respond, "In that case, I'll suggest a character related to cafes." This enables character suggestions based on the user's geographic location information.

[0109] When selecting a character, the selection unit can analyze the user's social media activity and suggest related characters. For example, the selection unit uses the generation AI to suggest characters related to accounts the user follows on social media. The selection unit analyzes the user's activity based on the content of their social media posts and the number of likes. For example, if the selection unit says, "I follow this account," the generation AI responds, "Then, I will suggest a character related to that account." The selection unit also uses the generation AI to suggest characters based on articles the user shared on social media. For example, if the selection unit says, "I recently shared this article," the generation AI responds, "Then, I will suggest a character related to that article." The selection unit also uses the generation AI to suggest characters related to events the user is participating in on social media. For example, if the selection unit says, "I am participating in this event," the generation AI responds, "Then, I will suggest a character related to that event." This enables character suggestions based on the user's social media activity.

[0110] The selection unit can customize characters by reflecting the user's past feedback when selecting a character. For example, the selection unit allows the generation AI to suggest new characters based on the characteristics of characters the user previously liked. The selection unit allows the generation AI to collect past feedback based on user survey results and comments. For example, if the user says, "I like this character's voice," the selection unit allows the generation AI to respond, "In that case, I'll suggest another character played by the same voice actor." The selection unit also allows the generation AI to suggest characters to avoid characters the user previously avoided. For example, if the user says, "I don't like this character," the selection unit allows the generation AI to respond, "I understand. I'll suggest a different character." Furthermore, the selection unit allows the generation AI to adjust the character's tone and style based on the user's past feedback. For example, if the user provides feedback such as, "Please speak in a gentler tone," the selection unit allows the generation AI to respond, "I understand. I'll speak in a gentler tone from now on." This enables character customization based on the user's past feedback.

[0111] The input unit can estimate the user's emotions and adjust the timing of the input based on the estimated user emotions. For example, if the user is relaxed, the generation AI slows down the timing of the input. In the input unit, the generation AI analyzes the user's facial expressions and voice to estimate emotions. For example, if the user says, "I'm feeling relaxed today," the generation AI responds, "Then, I'll input slowly." Furthermore, if the user is in a hurry, the generation AI speeds up the timing of the input. For example, if the user says, "I'm in a hurry today," the generation AI responds, "Then, I'll input quickly." Furthermore, if the user is feeling stressed, the generation AI adjusts the timing of the input to reduce stress. For example, if the user says, "I'm feeling stressed today," the generation AI responds, "Then, I'll adjust the timing of the input to reduce stress." This makes it possible to adjust the input timing according to the user's emotions.

[0112] When inputting data, the input unit can select the input method by referring to the past dialogue history of relatives and friends. For example, the input unit allows the generation AI to suggest the optimal input method based on the input methods used by relatives and friends in the past. The input unit allows the generation AI to collect and analyze the dialogue history of relatives and friends as log data. For example, if a relative or friend says, "I input data using this method in the past," the generation AI responds, "Then, I will input data using the same method." The input unit also selects the optimal input timing from the past dialogue history of relatives and friends. For example, if a relative or friend says, "I input data at this timing," the generation AI responds, "Then, I will input data at the same timing." Furthermore, the input unit analyzes the past dialogue history of relatives and friends, and the generation AI improves the accuracy of the input. For example, if a relative or friend says, "This method improved the accuracy of my input," the input unit will respond by saying, "Then I'll input using the same method." This makes it possible to select the optimal input method based on the past conversation history of the relative or friend.

[0113] The input section can customize the input content based on the current situation and interests of relatives and friends at the time of input. For example, the generation AI customizes the input content based on the topics that relatives and friends are currently interested in. In the input section, the generation AI identifies the current situation and interests based on the relatives' and friends' self-reporting and vital data. For example, if a relative or friend says, "I'm interested in this right now," the generation AI responds, "I'll input content related to that topic." In addition, the generation AI adjusts the input content based on the relatives' and friends' current situation. For example, if a relative or friend says, "I'm busy right now," the generation AI responds, "I'll input brief content." In addition, the generation AI optimizes the input content based on the relatives' and friends' current interests. For example, if a relative or friend says, "I want to relax right now," the input section of the AI ​​will respond by saying, "Then I'll input something that will help you relax." This makes it possible to customize the input content based on the current situation and interests of the relative or friend.

[0114] The input unit can improve the accuracy of the input by analyzing the tone of voice and speaking style of relatives and friends during input. For example, the input unit analyzes the tone of voice of relatives and friends, and the generation AI improves the accuracy of the input. In the input unit, the generation AI uses voice analysis technology to extract the characteristics of the tone of voice and speaking style of relatives and friends. For example, the input unit analyzes the tone of voice of relatives and friends and responds, "I will input in this tone." The input unit also analyzes the characteristics of the speaking style of relatives and friends, and the generation AI improves the accuracy of the input. For example, the input unit analyzes the characteristics of the speaking style of relatives and friends and responds, "I will input in this speaking style." The input unit also analyzes the tone of voice and speaking style of relatives and friends in combination, and the generation AI improves the accuracy of the input. For example, the input unit analyzes the tone of voice and speaking style of a relative or friend and responds, "I will input using this tone and speaking style." This makes it possible to improve the accuracy of input based on the analysis of the tone of voice and speaking style of a relative or friend.

[0115] The input unit can estimate the user's emotions and prioritize inputs based on the estimated user emotions. For example, if the user is nervous, the generation AI prioritizes important inputs. In the input unit, the generation AI analyzes the user's facial expressions and voice to estimate emotions. For example, if the user says, "I'm nervous today," the generation AI responds, "In that case, I'll prioritize important inputs." In addition, if the user is relaxed, the generation AI prioritizes detailed inputs. For example, if the user says, "I'm relaxed today," the generation AI responds, "In that case, I'll prioritize detailed inputs." In addition, if the user is in a hurry, the generation AI prioritizes quick inputs. For example, if the user says, "I'm in a hurry today," the generation AI responds, "In that case, I'll prioritize quick inputs." This makes it possible to determine input priorities according to the user's emotions.

[0116] The input unit can input highly relevant data based on the geographic location information of relatives and friends at the time of input. For example, the generation AI inputs data related to the current location of the relatives and friends. The input unit determines the geographic location information based on the relatives' and friends' GPS data and location information services. For example, if the relatives or friends say, "I'm in Tokyo now," the generation AI responds, "Then, I'll input data related to Tokyo." The input unit also inputs data related to places the relatives or friends have visited in the past. For example, if the relatives or friends say, "I went to Kyoto in the past," the generation AI responds, "Then, I'll input data related to Kyoto." The input unit also inputs data related to travel destinations the relatives or friends are planning. For example, if a relative or friend says, "I'm going to Hokkaido next time," the input section of the AI ​​will respond by saying, "Then, I'll input data related to Hokkaido." This makes it possible to input data based on the geographic location information of relatives and friends.

[0117] The input unit can analyze the social media activities of relatives and friends at the time of input and input related data. For example, the generation AI inputs data based on articles shared by relatives and friends on social media. The input unit analyzes activities based on the content of relatives' and friends' social media posts and the number of likes. For example, if a relative or friend says, "I recently shared this article," the generation AI responds, "Then, I will input data related to that article." The input unit also inputs data related to accounts that relatives and friends follow on social media. For example, if a relative or friend says, "I'm following this account," the generation AI responds, "Then, I will input data related to that account." The input unit also inputs data related to events that relatives and friends are attending on social media. For example, if a relative or friend says, "I'm attending this event," the input unit will respond by saying, "Then I'll input data related to that event." This makes it possible to input data based on the social media activity of relatives and friends.

[0118] The input unit can customize the input method by reflecting past feedback from relatives and friends. For example, the generation AI suggests new input methods based on input methods that relatives and friends have preferred in the past. The generation AI collects past feedback from relatives and friends based on survey results and comments. For example, if a relative or friend says, "I input using this method," the generation AI responds, "I'll input using the same method." The generation AI also customizes the input method to avoid input methods that relatives and friends have avoided in the past. For example, if a relative or friend says, "I'm not good at this method," the generation AI responds, "I'll input using a different method." The generation AI also adjusts the timing and style of input based on feedback provided by relatives and friends in the past. For example, if a relative or friend gives feedback such as "Please input more slowly," the generation AI will respond with "I understand. I will input more slowly from now on." This makes it possible to customize the input method based on past feedback from relatives and friends.

[0119] The imitation unit can estimate the user's emotions and adjust the accuracy of the imitation based on the estimated user emotions. For example, if the user is sad, the generation AI will imitate in a gentle tone. The imitation unit analyzes the user's facial expressions and voice to estimate emotions. For example, if the user says, "I'm sad today," the generation AI will respond, "I'll imitate in a gentle tone." Furthermore, if the user is excited, the generation AI will imitate in a calm tone. For example, if the user says, "I'm very happy today!" the generation AI will respond, "I'll imitate in a calm tone." Furthermore, if the user is relaxed, the generation AI will imitate in a natural tone. For example, if the user says, "I'm relaxed today," the generation AI will respond, "I'll imitate in a natural tone." This enables the imitation accuracy to be adjusted according to the user's emotions.

[0120] During imitation, the imitation unit can improve the accuracy of imitation based on the level of detail of the input data. For example, if the input data is detailed, the generation AI performs high-accuracy imitation. The imitation unit evaluates the level of detail of the input data and adjusts the accuracy of imitation. For example, if the imitation unit evaluates the input data as "detailed voice data," the generation AI responds, "Then, I will perform high-accuracy imitation." Furthermore, if the input data is sparse, the generation AI uses complementary data to improve the accuracy of imitation. For example, if the imitation unit evaluates the input data as "sparse voice data," the generation AI responds, "Then, I will perform imitation using complementary data." Furthermore, the imitation unit adjusts the accuracy of imitation based on the level of detail of the input data. For example, if the imitation unit evaluates the input data as "medium level of detail," the generation AI responds, "Then, I will perform imitation with medium accuracy." This allows for improved imitation accuracy based on the level of detail of the input data.

[0121] When imitating, the imitation unit can analyze the tone of voice and speaking style of a relative or friend to improve the accuracy of the imitation. For example, the imitation unit analyzes the tone of voice of a relative or friend, and the generation AI improves the accuracy of the imitation. In the imitation unit, the generation AI uses voice analysis technology to extract the characteristics of the tone of voice and speaking style of a relative or friend. For example, the imitation unit analyzes the tone of voice of a relative or friend and responds, "I will imitate you using this tone." The imitation unit also analyzes the characteristics of the speaking style of a relative or friend, and the generation AI improves the accuracy of the imitation. For example, the imitation unit analyzes the characteristics of the speaking style of a relative or friend and responds, "I will imitate you using this tone and speaking style." Furthermore, the imitation unit analyzes the tone of voice and speaking style of a relative or friend in combination, and the generation AI improves the accuracy of the imitation. For example, the imitation unit analyzes the tone of voice and speaking style of a relative or friend and responds, "I will imitate you using this tone and speaking style." This makes it possible to improve the accuracy of the imitation based on the analysis of the tone of voice and speaking style of a relative or friend.

[0122] The imitation unit can improve the accuracy of the imitation by referring to the user's past dialogue history. For example, the imitation unit allows the generation AI to improve the accuracy of the imitation based on the user's past dialogue. In the imitation unit, the generation AI collects and analyzes the user's dialogue history as log data. For example, if the user says, "I spoke like this in the past," the generation AI responds, "Then, I will imitate you in the same way." The imitation unit also adjusts the tone and style of the imitation based on the user's past dialogue history. For example, if the user says, "I spoke in this tone," the generation AI responds, "Then, I will imitate you in the same tone." Furthermore, the imitation unit analyzes the user's past dialogue history, and the generation AI improves the accuracy of the imitation. For example, if the user says, "I spoke in this style," the generation AI responds, "Then, I will imitate you in the same style." This enables the generation AI to improve the accuracy of the imitation based on the user's past dialogue history.

[0123] The imitation unit can estimate the user's emotions and adjust the frequency of imitation based on the estimated user emotions. For example, if the user feels lonely, the generation AI will mimic them frequently. The imitation unit analyzes the user's facial expressions and voice to estimate emotions. For example, if the user says, "I feel lonely today," the generation AI will respond, "Then I will mimic you frequently." Furthermore, if the user is busy, the generation AI will reduce the frequency of imitation. For example, if the user says, "I'm busy today," the generation AI will respond, "Then I will mimic you less frequently." Furthermore, if the user is relaxed, the generation AI will mimic them at a moderate frequency. For example, if the user says, "I'm relaxed today," the generation AI will respond, "Then I will mimic you at a moderate frequency." This makes it possible to adjust the frequency of imitation according to the user's emotions.

[0124] During imitation, the mimicry unit can mimic highly relevant data based on the geographic location information of relatives and friends. For example, the mimicry unit allows the generation AI to mimic data related to the relatives' or friends' current locations. The mimicry unit identifies the geographic location information based on the relatives' or friends' GPS data or location information services. For example, if the relatives or friends say, "I'm in Tokyo now," the generation AI responds, "Then, I'll mimic data related to Tokyo." The mimicry unit also mimics data related to places the relatives or friends have visited in the past. For example, if the relatives or friends say, "I've been to Kyoto in the past," the generation AI responds, "Then, I'll mimic data related to Kyoto." The mimicry unit also mimics data related to travel destinations the relatives or friends are planning. For example, if the relatives or friends say, "I'm going to Hokkaido next time," the generation AI responds, "Then, I'll mimic data related to Hokkaido." This allows for data mimicking based on the geographic location of relatives and friends.

[0125] During imitation, the mimicry unit can analyze the social media activities of relatives and friends and mimic related data. For example, the mimicry unit uses the generation AI to mimic data based on articles shared by relatives and friends on social media. The mimicry unit analyzes activities based on the content of relatives' and friends' social media posts and the number of likes. For example, if a relative or friend says, "I recently shared this article," the generation AI responds, "Then, I will mimic the data related to that article." The mimicry unit also mimics data related to accounts that relatives and friends follow on social media. For example, if a relative or friend says, "I'm following this account," the generation AI responds, "Then, I will mimic the data related to that account." The mimicry unit also mimics data related to events that relatives and friends are attending on social media. For example, if a relative or friend says, "I'm attending this event," the generation AI responds, "Then, I will mimic the data related to that event." This allows for data cloning based on the social media activity of relatives and friends.

[0126] The imitation unit can customize the imitation method by reflecting past feedback from relatives and friends. For example, the generation AI suggests new imitation methods based on the imitation methods that relatives and friends have preferred in the past. The imitation unit collects past feedback based on survey results and comments from relatives and friends. For example, if a relative or friend says, "I imitated this way," the generation AI responds, "Then I will imitate in the same way." The imitation unit also customizes the imitation method to avoid imitation methods that relatives and friends have avoided in the past. For example, if a relative or friend says, "I'm not good at this method," the generation AI responds, "Then I will imitate in a different way." Furthermore, the imitation unit adjusts the timing and style of imitation based on past feedback provided by relatives and friends. For example, if a relative or friend gives feedback such as, "Please imitate more slowly," the generation AI responds, "Okay. I will imitate more slowly from now on." This allows for customization of imitation methods based on past feedback from relatives and friends.

[0127] The reading unit can estimate the user's emotions and adjust the tone and pace of the reading based on the estimated user emotions. For example, if the user is relaxed, the generation AI reads at a relaxed pace. The reading unit analyzes the user's facial expressions and voice to estimate emotions. For example, if the user says, "I'm feeling relaxed today," the generation AI responds, "I'll read at a relaxed pace." Furthermore, if the user is in a hurry, the generation AI reads at a faster pace. For example, if the user says, "I'm in a hurry today," the generation AI responds, "I'll read at a faster pace." Furthermore, if the user is sad, the generation AI reads in a gentler tone. For example, if the user says, "I'm sad today," the generation AI responds, "I'll read in a gentler tone." This makes it possible to adjust the reading tone and pace according to the user's emotions.

[0128] When reading aloud, the reading unit can select reading content by referring to the user's past reading history. For example, the reading unit's generation AI suggests new reading content based on the user's past favorite books. The reading unit's generation AI collects and analyzes the user's reading history as log data. For example, if the reading unit says, "I read this book in the past," the generation AI responds, "Then, I'll read a book with similar content." The reading unit also selects the optimal reading content from the user's past reading history. For example, if the reading unit says, "I like this genre," the generation AI responds, "Then, I'll read a book in the same genre." The reading unit also selects reading content so as to avoid books the user has avoided in the past. For example, if the reading unit says, "I'm not good at this genre," the generation AI responds, "Then, I'll read a book in a different genre." This makes it possible to select the most suitable reading content based on the user's past reading history.

[0129] The reading unit can customize the reading content based on the user's current mood and situation. For example, if the user is relaxed, the generation AI will recite relaxing content. The reading unit identifies the user's mood and situation based on the user's self-reporting and vital data. For example, if the user says, "I'm feeling relaxed today," the generation AI will respond, "I'll read you some relaxing content." If the user is in good spirits, the reading unit will recite uplifting content. For example, if the user says, "I'm feeling good today," the generation AI will respond, "I'll read you some uplifting content." If the user is feeling stressed, the reading unit will recite stress-relieving content. For example, if the user says, "I'm feeling stressed today," the generation AI will respond, "I'll read you some stress-relieving content." This makes it possible to customize the reading content according to the user's current mood and situation.

[0130] The reading section can select a reading topic based on the user's interests. For example, the reading section's generation AI reads a book related to a topic that the user is interested in. The reading section's generation AI identifies the user's interests based on the user's survey results and past behavioral history. For example, if the user says, "I'm interested in history," the reading section's generation AI responds, "Then I'll read a book related to history." The reading section's generation AI also suggests new reading content based on the user's past favorite topics. For example, if the user says, "I like this topic," the reading section's generation AI responds, "Then I'll read a new book on the same topic." Furthermore, the reading section's generation AI selects reading content to avoid topics that the user has avoided. For example, if the user says, "I'm not good at this topic," the reading section's generation AI responds, "Then I'll read a book on a different topic." This makes it possible to select reading topics based on the user's interests.

[0131] The reading unit can estimate the user's emotions and adjust the frequency of reading based on the estimated user emotions. For example, if the user feels lonely, the generation AI will read aloud more frequently. The reading unit analyzes the user's facial expressions and voice to estimate emotions. For example, if the user says, "I feel lonely today," the generation AI will respond, "In that case, I will read aloud more frequently." Furthermore, if the user is busy, the generation AI will reduce the frequency of reading. For example, if the user says, "I'm busy today," the generation AI will respond, "In that case, I will read aloud less frequently." Furthermore, if the user is relaxed, the generation AI will read aloud at an appropriate frequency. For example, if the user says, "I'm relaxed today," the generation AI will respond, "In that case, I will read aloud at an appropriate frequency." This makes it possible to adjust the frequency of reading according to the user's emotions.

[0132] When reading aloud, the reading unit can recite content that is highly relevant based on the user's geographic location information. For example, the generation AI of the reading unit recites books related to the area where the user lives. The reading unit determines the user's geographic location information based on the user's GPS data and location information services. For example, if the user says, "I live in Tokyo," the generation AI responds, "I will read a book related to Tokyo." If the user is traveling, the reading unit recites books related to the travel destination. For example, if the user says, "I'm currently traveling in Kyoto," the generation AI responds, "I will read a book related to Kyoto." If the user is in a specific location, the generation AI recites content related to that location. For example, if the user says, "I'm currently at a cafe," the generation AI responds, "I will read a content related to cafes." This makes it possible to select reading content based on the user's geographic location information.

[0133] The reading unit can analyze the user's social media activity and recite related content when reading aloud. For example, the reading unit's generation AI recites content based on articles the user shared on social media. The reading unit's generation AI analyzes activity based on the user's social media posts and the number of likes. For example, if the user says, "I recently shared this article," the reading unit's generation AI responds, "I will recite content related to that article." The reading unit also recites content related to accounts the user follows on social media. For example, if the user says, "I follow this account," the reading unit's generation AI responds, "I will recite content related to that account." The reading unit also recites content related to events the user is participating in on social media. For example, if the user says, "I am participating in this event," the reading unit's generation AI responds, "I will recite content related to that event." This makes it possible to select reading content based on the user's social media activity.

[0134] The reading unit can customize the reading content by reflecting the user's past feedback. For example, the generation AI suggests new reading content based on the user's past favorite reading content. The reading unit collects past feedback based on the user's survey results and comments. For example, if the reading unit says, "I like this content," the generation AI responds, "Then, I'll read a new book with similar content." The reading unit also customizes the reading content to avoid reading content that the user has avoided in the past. For example, if the reading unit says, "I'm not good at this content," the generation AI responds, "Then, I'll read a book with a different content." Furthermore, the reading unit adjusts the tone and style of the reading based on the user's past feedback. For example, if the user provides feedback such as, "Please read in a gentler tone," the generation AI responds, "I understand. I'll read in a gentler tone from now on." This allows for customization of the reading content based on the user's past feedback.

[0135] The quiz section can estimate the user's emotions and adjust the difficulty and content of the quiz based on the estimated user's emotions. For example, if the user is relaxed, the generation AI will ask an easy quiz. In the quiz section, the generation AI analyzes the user's facial expressions and voice to estimate emotions. For example, if the user says, "I'm feeling relaxed today," the generation AI will respond, "I'll give you an easy quiz." Furthermore, if the user is feeling adventurous, the generation AI will ask a more difficult quiz. For example, if the user says, "I'm feeling challenging today," the generation AI will respond, "I'll give you a more difficult quiz." Furthermore, if the user is tired, the generation AI will ask a quiz with relaxing content. For example, if the user says, "I'm tired today," the generation AI will respond, "I'll give you a quiz with relaxing content." This makes it possible to adjust the difficulty and content of the quiz according to the user's emotions.

[0136] When presenting a quiz, the quiz section can select a quiz by referring to the user's past quiz history. For example, the generation AI of the quiz section proposes a new quiz based on the user's past favorite quiz themes. The generation AI of the quiz section collects and analyzes the user's quiz history as log data. For example, if the user says, "I enjoyed quizzes on this theme in the past," the generation AI responds, "In that case, I'll present a new quiz on the same theme." The generation AI of the quiz section also selects the optimal difficulty level of the quiz based on the user's past quiz history. For example, if the user says, "This difficulty level is just right," the generation AI responds, "In that case, I'll present a quiz of the same difficulty level." The generation AI of the quiz section also selects quizzes so as to avoid quiz themes that the user has avoided in the past. For example, if the user says, "I'm not good at this theme," the generation AI responds, "In that case, I'll present a quiz on a different theme." This enables the selection of optimal quizzes based on the user's past quiz history.

[0137] The quiz section can customize the content of a quiz based on the user's current knowledge level and interests. For example, the quiz section uses a generation AI to generate quizzes related to topics the user is currently interested in. The quiz section uses the generation AI to determine the user's current knowledge level and interests based on the user's self-reported answers and past quiz results. For example, if the user says, "I'm interested in history right now," the generation AI responds, "I'll give you a quiz related to history." The quiz section also adjusts the difficulty of the quiz based on the user's current knowledge level. For example, if the user says, "This difficulty level is just right," the generation AI responds, "I'll give you a quiz of the same difficulty level." The quiz section also uses the generation AI to suggest new quizzes based on the user's past favorite topics. For example, if the user says, "I like this topic," the generation AI responds, "I'll give you a new quiz on the same topic." This allows the quiz content to be customized based on the user's current knowledge level and interests.

[0138] When creating a quiz, the quiz section can select a quiz topic based on the user's interests. For example, the quiz section's generation AI creates a quiz related to a topic that the user is interested in. The quiz section's generation AI identifies the user's interests based on survey results and past behavioral history. For example, if the user says, "I'm interested in history," the quiz section's generation AI responds, "Then I'll create a quiz related to history." The quiz section's generation AI also suggests new quizzes based on the user's past favorite topics. For example, if the user says, "I like this topic," the quiz section's generation AI responds, "Then I'll create a new quiz on the same topic." Furthermore, the quiz section's generation AI selects quiz topics to avoid topics that the user has avoided. For example, if the user says, "I'm not good at this topic," the quiz section's generation AI responds, "Then I'll create a quiz on a different topic." This enables quiz topics to be selected based on the user's interests.

[0139] The quiz section can estimate the user's emotions and adjust the frequency of quizzes based on the estimated user emotions. For example, if the user feels lonely, the generation AI will quiz them more frequently. The quiz section analyzes the user's facial expressions and voice to estimate emotions. For example, if the user says, "I feel lonely today," the generation AI will respond, "In that case, I'll quiz you more frequently." Furthermore, if the user is busy, the generation AI will reduce the frequency of quizzes. For example, if the user says, "I'm busy today," the generation AI will respond, "In that case, I'll reduce the frequency of quizzes." Furthermore, if the user is relaxed, the generation AI will quiz them at a moderate frequency. For example, if the user says, "I'm relaxed today," the generation AI will respond, "In that case, I'll quiz you at a moderate frequency." This makes it possible to adjust the frequency of quizzes according to the user's emotions.

[0140] When asking a question, the quiz section can provide relevant questions based on the user's geographic location information. For example, the quiz section's generation AI provides a quiz related to the area where the user lives. The quiz section's generation AI identifies the user's geographic location based on the user's GPS data and location information services. For example, if the user says, "I live in Tokyo," the quiz section's generation AI responds, "Then, I'll provide a quiz related to Tokyo." Furthermore, if the user is traveling, the quiz section's generation AI provides a quiz related to the user's travel destination. For example, if the user says, "I'm currently traveling in Kyoto," the quiz section's generation AI responds, "Then, I'll provide a quiz related to Kyoto." Furthermore, if the user is in a specific location, the quiz section's generation AI provides a quiz related to that location. For example, if the user says, "I'm currently at a cafe," the quiz section's generation AI responds, "Then, I'll provide a quiz related to cafes." This enables quizzes to be selected based on the user's geographic location information.

[0141] When creating a quiz, the quiz section can analyze a user's social media activity and present relevant quizzes. For example, the quiz section's generation AI presents quizzes based on articles shared by the user on social media. The quiz section's generation AI analyzes activity based on the content of the user's social media posts and the number of likes. For example, if the user says, "I recently shared this article," the generation AI responds, "I'll present a quiz related to that article." The quiz section also presents quizzes related to accounts the user follows on social media. For example, if the user says, "I follow this account," the generation AI responds, "I'll present a quiz related to that account." The quiz section also presents quizzes related to events the user is participating in on social media. For example, if the user says, "I'm participating in this event," the generation AI responds, "I'll present a quiz related to that event." This makes it possible to select quizzes based on the user's social media activity.

[0142] When presenting a quiz, the quiz section can customize the content of the quiz by reflecting the user's past feedback. For example, the generation AI suggests new quizzes based on the user's past favorite quiz themes. The generation AI collects past feedback from users' survey results and comments. For example, if a user says, "I like quizzes on this theme," the generation AI responds, "Then, I'll present a new quiz on the same theme." The generation AI also customizes the content of the quiz to avoid quiz themes that the user has avoided in the past. For example, if a user says, "I'm not good at this theme," the generation AI responds, "Then, I'll present a quiz on a different theme." The generation AI also adjusts the difficulty and style of the quiz based on the user's past feedback. For example, if a user provides feedback such as, "Please present easier quizzes," the generation AI responds, "Okay, I'll present easier quizzes from now on." This allows the quiz content to be customized based on the user's past feedback.

[0143] The music section can estimate the user's emotions and adjust the music selection and playback method based on the estimated user's emotions. For example, if the user is relaxed, the generation AI selects relaxing music. The music section analyzes the user's facial expressions and voice to estimate emotions. For example, if the user says, "I'm feeling relaxed today," the generation AI responds, "I'll select some relaxing music for you." Furthermore, if the user is in high spirits, the generation AI selects uplifting music. For example, if the user says, "I'm feeling good today," the generation AI responds, "I'll select some uplifting music for you." Furthermore, if the user is sad, the generation AI selects soothing music. For example, if the user says, "I'm sad today," the generation AI responds, "I'll select some soothing music for you." This makes it possible to adjust the music selection and playback method according to the user's emotions.

[0144] When playing music, the music section can select music by referring to the user's past music history. For example, the generation AI suggests new music based on the user's past favorite music. The music section collects and analyzes the user's music history as log data. For example, if the user says, "I enjoyed this music in the past," the generation AI responds, "Then, I'll play new music in the same genre." The music section also selects optimal music based on the user's past music history. For example, if the user says, "I like this artist," the generation AI responds, "Then, I'll play a new song by the same artist." The music section also selects music so as to avoid music that the user has avoided in the past. For example, if the user says, "I don't like this genre," the generation AI responds, "Then, I'll play music in a different genre." This makes it possible to select optimal music based on the user's past music history.

[0145] When playing music, the music section can customize the music content based on the user's current mood and situation. For example, if the user is relaxed, the generation AI will play relaxing music. The music section determines the user's mood and situation based on the user's self-reported behavior and vital signs. For example, if the user says, "I'm feeling relaxed today," the generation AI will respond, "I'll play some relaxing music." Furthermore, if the user is feeling energetic, the generation AI will play uplifting music. For example, if the user says, "I'm feeling energetic today," the generation AI will respond, "I'll play some uplifting music." Furthermore, if the user is feeling stressed, the generation AI will play music that reduces stress. For example, if the user says, "I'm feeling stressed today," the generation AI will respond, "I'll play some stress-reducing music." This allows the music content to be customized according to the user's current mood and situation.

[0146] When playing music, the music section can select a music theme based on the user's interests. For example, the generation AI plays music related to themes the user is interested in. The generation AI identifies the user's interests based on survey results and past behavioral history. For example, if the user says, "I'm interested in jazz," the generation AI responds, "Then, I'll play music related to jazz." The generation AI also suggests new music based on themes the user has previously liked. For example, if the user says, "I like this theme," the generation AI responds, "Then, I'll play new music with the same theme." Furthermore, the generation AI selects music themes to avoid themes the user has avoided. For example, if the user says, "I don't like this theme," the generation AI responds, "Then, I'll play music with a different theme." This makes it possible to select music themes based on the user's interests.

[0147] The music section can estimate the user's emotions and adjust the frequency of music playback based on the estimated user emotions. For example, if the user feels lonely, the generation AI plays music frequently. The music section analyzes the user's facial expressions and voice to estimate emotions. For example, if the user says, "I feel lonely today," the generation AI responds, "Then, I'll play music more frequently." Furthermore, if the user is busy, the generation AI reduces the frequency of music playback. For example, if the user says, "I'm busy today," the generation AI responds, "Then, I'll play music less frequently." Furthermore, if the user is relaxed, the generation AI plays music at a moderate frequency. For example, if the user says, "I'm relaxing today," the generation AI responds, "Then, I'll play music at a moderate frequency." This makes it possible to adjust the frequency of music playback according to the user's emotions.

[0148] When playing music, the music section can play music that is highly relevant based on the user's geographic location. For example, the AI ​​generates music related to the area where the user lives. The AI ​​identifies the user's geographic location based on the user's GPS data and location services. For example, if the user says, "I live in Tokyo," the AI ​​responds, "Then, I'll play music related to Tokyo." If the user is traveling, the AI ​​plays music related to the user's travel destination. For example, if the user says, "I'm currently traveling in Kyoto," the AI ​​responds, "Then, I'll play music related to Kyoto." If the user is in a specific location, the AI ​​plays music related to that location. For example, if the user says, "I'm currently at a cafe," the AI ​​responds, "Then, I'll play music related to cafes." This makes it possible to select music based on the user's geographic location.

[0149] When playing music, the music section can analyze the user's social media activity and play related music. For example, the music section uses a generation AI to play music based on the music the user shared on social media. The music section uses a generation AI to analyze the user's social media activity based on the content of the user's posts and the number of likes. For example, if the user says, "I recently shared this music," the generation AI responds, "Then, play that music." The music section also uses a generation AI to play music related to artists the user follows on social media. For example, if the user says, "I follow this artist," the generation AI responds, "Then, play music by that artist." The music section also uses a generation AI to play music related to events the user is participating in on social media. For example, if the user says, "I'm attending this event," the generation AI responds, "Then, play music related to that event." This makes it possible to select music based on the user's social media activity.

[0150] The music section can customize the music content by reflecting the user's past feedback. For example, the generation AI suggests new music based on the user's past favorite music. The generation AI collects past feedback based on the user's survey results and comments. For example, if the user says, "I like this music," the generation AI responds, "Then, I'll play similar music." The generation AI also customizes the music content to avoid music that the user has avoided in the past. For example, if the user says, "I don't like this genre," the generation AI responds, "Then, I'll play music of a different genre." Furthermore, the generation AI adjusts the timing and style of music playback based on the user's past feedback. For example, if the user provides feedback such as, "Please play more relaxing music," the generation AI responds, "Okay, I'll play relaxing music from now on." This makes it possible to customize the music content based on the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the dialogue unit, selection unit, input unit, imitation unit, reading unit, quiz unit, and music unit, is realized by, for example, at least one of the smart device 14 and the data processing device 12. For example, the dialogue unit is realized by the control unit 46A of the smart device 14, analyzes the user's utterance using voice recognition technology, and generates an appropriate response using natural language processing technology. The selection unit is realized by the control unit 46A of the smart device 14, and selects a character based on the user's preferences. The input unit is realized by the control unit 46A of the smart device 14, records the voices and speaking styles of relatives and friends, and inputs them into the generation AI. The imitation unit is realized by the control unit 46A of the smart device 14, and engages in dialogue based on the input data. The reading unit is realized by the control unit 46A of the smart device 14, and reads a specific book specified by the user. The quiz unit is realized by the control unit 46A of the smart device 14, and presents various quizzes to the user. The music section is realized by the control section 46A of the smart device 14, and plays specific music designated by the user. === Hard Collateral 1-2 === Each of the multiple elements, including the dialogue unit, selection unit, input unit, imitation unit, reading unit, quiz unit, and music unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the dialogue unit is realized by the control unit 46A of the smart glasses 214, analyzes the user's speech using voice recognition technology, and generates an appropriate response using natural language processing technology. The selection unit is realized by the control unit 46A of the smart glasses 214, and selects a character based on the user's preferences. The input unit is realized by the control unit 46A of the smart glasses 214, records the voices and speaking styles of relatives and friends, and inputs them into the generation AI. The imitation unit is realized by the control unit 46A of the smart glasses 214, and engages in dialogue based on the input data. The reading unit is realized by the control unit 46A of the smart glasses 214, and reads a specific book specified by the user. The quiz unit is realized by the control unit 46A of the smart glasses 214, and presents various quizzes to the user. The music section is implemented by the control unit 46A of the smart glasses 214 and plays specific music as specified by the user. === Hard Collateral 1-3 === Each of the multiple elements, including the dialogue unit, selection unit, input unit, imitation unit, reading unit, quiz unit, and music unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the dialogue unit is realized by the control unit 46A of the headset-type terminal 314, analyzes the user's utterance using voice recognition technology, and generates an appropriate response using natural language processing technology. The selection unit is realized by the control unit 46A of the headset-type terminal 314, and selects a character based on the user's preferences. The input unit is realized by the control unit 46A of the headset-type terminal 314, and records the voices and speaking styles of relatives and friends and inputs them into the generation AI. The imitation unit is realized by the control unit 46A of the headset-type terminal 314, and engages in dialogue based on the input data. The reading unit is realized by the control unit 46A of the headset-type terminal 314, and reads a specific book specified by the user. The quiz section is realized by the control section 46A of the headset type terminal 314, and presents various quizzes to the user. The music section is realized by the control section 46A of the headset type terminal 314, and plays specific music designated by the user. === Hard Collateral 1-4 === Each of the multiple elements, including the dialogue unit, selection unit, input unit, imitation unit, reading unit, quiz unit, and music unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the dialogue unit is realized by the control unit 46A of the robot 414, analyzes the user's utterance using voice recognition technology, and generates an appropriate response using natural language processing technology. The selection unit is realized by the control unit 46A of the robot 414, and selects a character based on the user's preferences. The input unit is realized by the control unit 46A of the robot 414, and records the voices and speaking styles of relatives and friends and inputs them into the generation AI. The imitation unit is realized by the control unit 46A of the robot 414, and engages in dialogue based on the input data. The reading unit is realized by the control unit 46A of the robot 414, and recites a specific book specified by the user. The quiz unit is realized by the control unit 46A of the robot 414, and poses various quizzes to the user. The music section is realized by the control section 46A of the robot 414, and plays specific music designated by the user.

[0151] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0152] The dialogue unit can estimate the user's emotions and adjust the content of the dialogue based on the estimated emotions. For example, if the user is sad, the generation AI can offer encouraging words in a gentle tone. If the user is excited, the generation AI can continue the dialogue in a calm tone. Furthermore, if the user is relaxed, the generation AI can continue the dialogue in a relaxed tone. This makes it possible to adjust the content of the dialogue according to the user's emotions.

[0153] The dialogue unit can analyze the user's past dialogue history and select the dialogue content. For example, the generation AI can provide new related topics based on topics the user has previously discussed. The generation AI can also advance the dialogue based on themes the user has previously shown interest in. Furthermore, the generation AI can adjust the dialogue content so as to avoid topics the user has avoided in the past. This makes it possible to select the optimal dialogue content based on the user's past dialogue history.

[0154] The selection unit can estimate the user's emotions and adjust the character selection based on the estimated emotions. For example, if the user is sad, the generation AI can suggest a character with a gentle voice. If the user is excited, the generation AI can suggest a character with a calm voice. If the user is relaxed, the generation AI can suggest a character with a cheerful voice. This makes it possible to adjust the character selection according to the user's emotions.

[0155] The input unit can record the voices and speaking styles of relatives and friends and input them into the voice recognition system. For example, the voices of relatives and friends can be recorded and saved as audio files. The generation AI can then use voice recognition technology to analyze the recorded data and generate data to imitate the speaking styles of relatives and friends. Furthermore, the tone of voice and speaking style characteristics of relatives and friends can be extracted and input into the generation AI. This makes it possible to have more friendly conversations by inputting the voices and speaking styles of relatives and friends.

[0156] The imitation unit can estimate the user's emotions and adjust the accuracy of the imitation based on the estimated emotions. For example, if the user is sad, the generation AI can imitate in a gentle tone. If the user is excited, the generation AI can imitate in a calm tone. Furthermore, if the user is relaxed, the generation AI can imitate in a natural tone. This makes it possible to adjust the accuracy of the imitation according to the user's emotions.

[0157] The reading unit can estimate the user's emotions and adjust the tone and pace of the reading based on the estimated emotions. For example, if the user is relaxed, the generation AI can read at a leisurely pace. If the user is in a hurry, the generation AI can read at a fast pace. Furthermore, if the user is sad, the generation AI can read in a gentle tone. This makes it possible to adjust the reading tone and pace according to the user's emotions.

[0158] The quiz section can estimate the user's emotions and adjust the difficulty and content of the quiz based on the estimated emotions. For example, if the user is relaxed, the generation AI can present an easy quiz. If the user is feeling challenging, the generation AI can present a more difficult quiz. Furthermore, if the user is tired, the generation AI can present a quiz with relaxing content. This makes it possible to adjust the difficulty and content of the quiz according to the user's emotions.

[0159] The music section can estimate the user's emotions and adjust the music selection and playback method based on the estimated emotions. For example, if the user is relaxed, the generation AI can select relaxing music. If the user is cheerful, the generation AI can select uplifting music. Furthermore, if the user is sad, the generation AI can select soothing music. This makes it possible to adjust the music selection and playback method according to the user's emotions.

[0160] The dialogue unit can customize the dialogue content based on the user's current health condition and mood. For example, if the user is feeling unwell, the generation AI can provide health advice. If the user is in a good mood, the generation AI can provide fun topics. Furthermore, if the user is feeling stressed, the generation AI can select relaxing topics. This makes it possible to customize the dialogue content according to the user's health condition and mood.

[0161] The dialogue unit can select highly relevant topics based on the user's geographical location information. For example, the generation AI can provide topics related to news and events in the area where the user lives. Also, if the user is traveling, the generation AI can provide tourist information about the travel destination. Furthermore, if the user is in a specific location, the generation AI can select topics related to that location. This makes it possible to select topics based on the user's geographical location information.

[0162] The processing flow of the second embodiment will be briefly explained below.

[0163] Step 1: The dialogue unit uses the generation AI to conduct voice dialogue. The dialogue unit provides appropriate responses to everyday conversations and questions. The generation AI analyzes the user's speech using voice recognition technology and generates an appropriate response using natural language processing technology. For example, if the user asks, "What's the weather like today?", the generation AI obtains the weather information and responds, "It's sunny today." If the user asks, "How are you?", the generation AI responds, "I'm fine. How about you?" Step 2: The selection unit selects a character based on the user's preferences. The selection unit provides multiple characters, such as characters with gentle voices and characters with lively voices, for the user to choose from. The generation AI can also suggest the most suitable character based on the user's selection history and survey results. For example, it can suggest a new character based on characters the user has previously selected. Step 3: The input unit records the voices and speaking styles of relatives and friends and inputs them into the generation AI. The input unit records the voices of relatives and friends and saves them as audio files. The generation AI analyzes the recorded data using voice recognition technology and generates data to imitate the speaking styles of relatives and friends. For example, it extracts the voice tone and speaking style characteristics of relatives and friends and inputs them into the generation AI. Step 4: The mimicry unit engages in a conversation based on the input data. The mimicry unit engages in a conversation by imitating the voice and speaking style of a relative or friend. The generation AI reproduces the voice and speaking style of a relative or friend based on the input data. For example, it can imitate the tone of voice and speaking style of a relative or friend and engage in a conversation with the user. Step 5: The reading unit recites a specific book specified by the user. When the user instructs the reading unit to "read this book," the generation AI recites the book. The generation AI analyzes the text data and uses speech synthesis technology to perform the reading. For example, it may recite a favorite novel or poem. Step 6: The quiz section asks the user various quizzes. The quiz section asks questions about general knowledge and hobbies, and users can deepen their knowledge while having fun by answering them. The generation AI generates the content of the quiz and asks it to the user. For example, it might ask a question such as, "Which of the following is the tallest mountain?" Step 7: The music section plays the specific music requested by the user. When the user requests, "Play this song," the AI ​​generation section plays that song. The AI ​​generation retrieves the music data and plays it. For example, it can play a song by a favorite artist or relaxing music.

[0164] 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.

[0165] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0166] 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.

[0167] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0168] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0169] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0182] 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.

[0183] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0184] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0185] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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).

[0190] 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.

[0191] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0198] 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.

[0199] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0200] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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).

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0215] 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.

[0216] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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).

[0221] 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.

[0222] 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."

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] [Explanation of symbols]

[0236] 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 dialogue unit that performs voice dialogue; a selection unit for selecting a character for a dialogue performed by the dialogue unit; An input section for inputting voice data from relatives and friends; an imitation unit that carries out a dialogue based on the data input by the input unit; A reading section that reads specific books aloud, A quiz club that asks quizzes, A music section that plays your favorite music. A system characterized by:

2. The dialogue unit Respond to everyday conversations and questions 2. The system of claim 1.

3. The selection unit Select a character based on your preferences 2. The system of claim 1.

4. The input unit includes: Record the voices and speech patterns of your relatives and friends and input them into a voice recognition system 2. The system of claim 1.

5. The mimicking portion is Dialogue based on input data 2. The system of claim 1.

6. The reading section Read a specific book requested by the user 2. The system of claim 1.

7. The quiz section Give multiple quizzes to users 2. The system of claim 1.

8. The music club: Play specific music as specified by the user 2. The system of claim 1.

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