System

Abstract

An object of a system according to an embodiment is to perform a dialogue corresponding to an emotional state of a user in real time.SOLUTION: A system according to an embodiment includes an emotion estimation unit, a dialogue start unit, and a dialogue generation unit. The emotion estimation unit estimates an emotional state of the user in real time. The dialogue starter starts a dialogue based on the emotional state estimated by the emotion estimator. The dialogue generation unit generates dialogue content started by the dialogue start unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to conduct dialogue in real time according to the user's emotional state.

[0005] The system according to the embodiment aims to conduct a dialogue in real time according to the emotional state of the user. [Means for solving the problem]

[0006] A system according to an embodiment includes a feeling estimation unit, a dialogue initiation unit, and a dialogue generation unit. The feeling estimation unit estimates an emotional state of a user in real time. The dialogue initiation unit starts a dialogue based on the emotional state estimated by the feeling estimation unit. The dialogue generation unit generates content of the dialogue initiated by the dialogue initiation unit. [Effects of the Invention]

[0007] The system according to the embodiment can conduct a dialogue in real time according to the emotional state of the user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention is a system that allows people to listen to their own opinions without feeling lonely. This system is a technology that allows people to have a conversation with themselves using the concept of "F (F) in their mind." This allows people to listen to their own opinions and have a conversation with themselves as their best partner without feeling lonely.

[0029] The dialogue generation unit can analyze the dialogue history and automatically generate optimal dialogue content for reducing feelings of loneliness. The dialogue generation unit, for example, analyzes the user's past dialogue history and extracts dialogue patterns for reducing feelings of loneliness. For example, it automatically generates a new dialogue based on dialogue content that was effective in the past. The dialogue generation unit also identifies keywords and phrases for reducing feelings of loneliness based on the user's dialogue history and automatically generates a dialogue using them. For example, it includes topics that the user likes or content that attracts their interest. The dialogue generation unit also analyzes the past dialogue history and automatically generates a dialogue that provides emotional support for reducing feelings of loneliness. For example, it reproduces dialogue content that the user found reassuring in the past. In this way, optimal dialogue content can be generated based on the past dialogue history, thereby reducing feelings of loneliness.

[0030] The dialogue generation unit can suggest dialogue themes based on hobbies and interests. For example, the dialogue generation unit creates a database of the user's hobbies and interests and suggests dialogue themes based on them. For example, the dialogue generation unit starts a dialogue about the user's favorite movies or music. The dialogue generation unit also analyzes the user's past behavioral history and search history to suggest dialogue themes that pique the user's interest. For example, it holds a dialogue related to recently searched topics or purchased products. The dialogue generation unit also analyzes the content posted on the user's social media or blog to suggest dialogue themes based on the user's hobbies and interests. For example, it holds a dialogue related to content frequently posted by the user. In this way, dialogue themes based on the user's hobbies and interests can be suggested, reducing feelings of loneliness.

[0031] The system has a function to simulate conversations with pets and plants. For example, the system has a function to simulate the behavior and sounds of the user's pet and engage in conversations. For example, the system can call out the pet's name and reproduce the pet's behavior. The system also has a function to simulate the growth of plants the user is growing and engage in conversations. For example, the system can share advice and observations about plant growth. The system also has a function to create a database of information about the user's pets and plants and engage in conversations based on that information. For example, the system can engage in conversations about the pet's health and plant cultivation methods. This simulates conversations with pets and plants, making it possible to reduce feelings of loneliness.

[0032] The system has a function to anonymously match users and promote dialogue on common topics. For example, the system may anonymously match users who feel lonely with each other and promote dialogue based on common hobbies and interests. For example, it may match users who share the same hobbies. The system may also anonymously match users who feel lonely with each other and promote dialogue about common worries and problems. For example, it may be possible to match users who share the same worries. The system may also anonymously match users who feel lonely with each other and promote dialogue based on common experiences and backgrounds. For example, it may be possible to match users who have the same occupation or living environment. This makes it possible to anonymously match users who feel lonely with each other and promote dialogue on common topics.

[0033] The system creates a database of the user's past decisions and their outcomes, and suggests the optimal decision in similar situations. For example, the system creates a database of the user's past decisions and their outcomes, and builds a system that suggests the optimal decision in similar situations. For example, suggestions are made based on past successes and failures. The system also analyzes past decisions and their outcomes, and develops an algorithm that suggests the optimal decision. For example, it can automatically generate the optimal decision based on past data. The system also engages in dialogue to support decisions in similar situations based on the user's past decision history. For example, it engages in dialogue that refers to past successes. This makes it possible to suggest the optimal decision based on past decisions and their outcomes.

[0034] The system sets judgment criteria based on the user's values ​​and beliefs, and provides advice in line with them. For example, the system creates a database of the user's values ​​and beliefs and sets judgment criteria based on them. For example, it provides advice based on the values ​​that the user holds dear. The system also analyzes the user's values ​​and beliefs and develops an algorithm to provide advice in line with them. For example, it can also suggest optimal decisions based on the user's beliefs. The system also sets judgment criteria based on the user's values ​​and beliefs, and engages in dialogue in line with them. For example, it provides advice that matches the user's values. This makes it possible to set judgment criteria based on the user's values ​​and beliefs, and provide advice in line with them.

[0035] The system expands the self-decision support function to advice specialized in specific fields such as business and academics. For example, the system develops a self-decision support function specialized in the business field to support important business decisions. For example, it provides advice on management strategies and marketing strategies. The system also develops a self-decision support function specialized in the academic field to support important academic decisions. For example, it can provide advice on career path selection and study plans. The system also develops a self-decision support function specialized in a specific field to suggest optimal decisions in that field. For example, it provides advice in the medical field or technical field. This makes it possible to provide advice specialized in a specific field.

[0036] The system has a function to provide advice based on success stories of other users. The system has a function to, for example, create a database of success stories of other users and provide advice based on that. For example, the system suggests optimal decisions based on success stories. The system also analyzes success stories of other users and develops an algorithm that suggests optimal decisions in similar situations. For example, it can automatically generate advice based on success stories. The system also has a function to hold dialogues to support decisions based on success stories of other users. For example, it holds dialogues based on success stories. This makes it possible to provide advice based on success stories of other users.

[0037] The system records the user's past successful experiences and engages in dialogue to remind the user of those experiences when the user is in a difficult situation. For example, the system creates a database of the user's past successful experiences and engages in dialogue to remind the user of those experiences when the user is in a difficult situation. For example, it engages in encouraging dialogue based on past success stories. The system also records the user's successful experiences and automatically generates dialogue content to remind the user of those experiences when the user is in a difficult situation. For example, it can engage in positive dialogue based on successful experiences. The system also develops an algorithm to engage in dialogue to increase self-esteem in difficult situations based on the user's past successful experiences. For example, it engages in dialogue to remind the user of successful experiences. This makes it possible to engage in dialogue to remind the user of past successful experiences.

[0038] The system provides dialogue to maintain motivation based on the user's long-term goals. For example, the system creates a database of the user's long-term goals and provides dialogue to maintain motivation based on those goals. For example, a dialogue is held to check progress toward achieving the goal. The system also automatically generates dialogue content to maintain motivation based on the user's goals. For example, it can also provide dialogue to encourage the user toward goal achievement. The system also develops an algorithm to perform dialogue to maintain motivation based on the user's long-term goals. For example, it provides specific advice toward goal achievement. This makes it possible to provide dialogue to maintain motivation based on the user's long-term goals.

[0039] The system has a function of simulating conversations with the user's friends and family and holding conversations to increase self-esteem. The system has a function of simulating conversations with the user's friends and family and holding conversations to increase self-esteem, for example, by reproducing the words of friends and family. The system also has a function of creating a database of information about the user's friends and family and holding conversations based on that information. For example, it can hold conversations based on the names and relationships of friends and family. The system also simulates conversations with the user's friends and family and automatically generates conversation content to increase self-esteem. For example, it can reproduce encouraging words from friends and family. This allows the system to simulate conversations with friends and family and increase self-esteem.

[0040] The system has a function to visualize and display a user's past successful experiences when the user is faced with a difficult situation. The system has a function to visualize the user's past successful experiences and display them in difficult situations. For example, it displays photos or videos of the successful experiences. The system also has a function to visualize the user's successful experiences and engage in dialogue to remind the user of them in difficult situations. For example, it can display graphs or charts of the successful experiences. The system also has a function to visualize the user's past successful experiences and engage in dialogue to increase self-esteem in difficult situations. For example, it displays a slideshow of the successful experiences. This makes it possible to visualize and display past successful experiences in difficult situations.

[0041] The system creates a database of the user's past opinions and thoughts and conducts a dialogue based on that. For example, the system creates a database of the user's past opinions and thoughts and builds a system that conducts a dialogue based on that. For example, a dialogue is conducted with reference to the past opinions. The system also analyzes the user's past opinions and thoughts and automatically generates dialogue content based on that. For example, it is possible to conduct an optimal dialogue based on the past opinions. The system also develops an algorithm that conducts a dialogue based on the user's past opinions and thoughts. For example, a dialogue is conducted with reference to the past opinions. This makes it possible to conduct a dialogue based on the past opinions and thoughts.

[0042] The system has a function to visualize user opinions and make them easier to understand visually. For example, the system has a function to visualize user opinions and make them easier to understand visually. For example, the opinions are displayed in graphs or charts. The system also develops an algorithm to visualize user opinions and make them easier to understand visually. For example, the opinions can be displayed in diagrams or icons. The system also has a function to visualize user opinions and make them easier to understand visually. For example, the opinions are displayed in a slideshow or infographic. This makes it possible to visualize opinions and make them easier to understand visually.

[0043] The system analyzes the user's past dialogue history and engages in dialogue to increase the depth of introspection. For example, the system creates a database of the user's past dialogue history and engages in dialogue to increase the depth of introspection. For example, a dialogue is engaged in to encourage deep introspection based on the content of the past dialogue. The system also analyzes the user's past dialogue history and automatically generates dialogue content to increase the depth of introspection. For example, a dialogue can be engaged in to encourage deep introspection based on the content of the past dialogue. The system also develops an algorithm to engage in dialogue to increase the depth of introspection based on the user's past dialogue history. For example, a dialogue is engaged in to encourage deep introspection based on the content of the past dialogue. This makes it possible to analyze the past dialogue history and engage in dialogue to increase the depth of introspection.

[0044] The system analyzes the content of a dialogue from multiple angles and provides feedback to gain a deeper understanding of the user's inner thoughts. For example, the system analyzes the content of a dialogue from multiple angles and provides feedback to gain a deeper understanding of the user's inner thoughts. For example, the system analyzes the content of the dialogue from perspectives such as emotions, values, and beliefs. The system also analyzes the content of a dialogue from multiple angles and automatically generates feedback to gain a deeper understanding of the user's inner thoughts. For example, it can provide specific feedback based on the content of the dialogue. The system also develops an algorithm that analyzes the content of a dialogue from multiple angles and provides feedback to gain a deeper understanding of the user's inner thoughts. For example, it can provide specific feedback based on the content of the dialogue. This makes it possible to analyze the content of a dialogue from multiple angles and provide feedback to gain a deeper understanding of the user's inner thoughts.

[0045] The system extends the function of simulating a dialogue with oneself into a dialogue with another user. The system, for example, has a function of simulating a dialogue with oneself into a dialogue with another user. For example, a dialogue is held that incorporates the opinions and thoughts of other users. The system also develops an algorithm for simulating a dialogue with oneself into a dialogue with another user. For example, a dialogue can be held that is based on the opinions and thoughts of other users. The system also has a function of simulating a dialogue with oneself into a dialogue with another user, and holds a dialogue. For example, a dialogue is held that takes into account the opinions and thoughts of other users. This allows the system to extend the function of simulating a dialogue with oneself into a dialogue with other users.

[0046] The system has a function of visualizing the user's introspection time to make it easier to understand visually. The system, for example, has a function of visualizing the user's introspection time to make it easier to understand visually. For example, the system displays the introspection time in a graph or chart. The system also develops an algorithm for visualizing the user's introspection time to make it easier to understand visually. For example, the system can display the introspection time in a diagram or icon. The system also has a function of visualizing the user's introspection time to make it easier to understand visually. For example, the system displays the introspection time in a slideshow or infographic. This makes it possible to visualize the introspection time to make it easier to understand visually.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The system creates a database of the user's past decisions and their outcomes, and builds a system that suggests optimal decisions for similar situations. For example, it makes suggestions based on past successes and failures. The system also analyzes past decisions and their outcomes, and develops algorithms that suggest optimal decisions. For example, it can automatically generate optimal decisions based on past data. The system also uses the user's past decision history to hold dialogues that support decisions in similar situations. For example, it holds dialogues that refer to past successes. This makes it possible to suggest optimal decisions based on past decisions and their outcomes.

[0049] The system creates a database of the user's values ​​and beliefs and sets decision-making criteria based on them. For example, it provides advice based on the values ​​that the user holds dear. The system also analyzes the user's values ​​and beliefs and develops an algorithm to provide advice in line with them. For example, it can suggest optimal decisions based on the user's beliefs. The system also sets decision-making criteria based on the user's values ​​and beliefs and engages in dialogue in line with these criteria. For example, it provides advice that matches the user's values. This makes it possible to set decision-making criteria based on the user's values ​​and beliefs and provide advice in line with them.

[0050] The system develops self-decision support functions specialized for the business field to support important business decisions. For example, it provides advice on management strategies and marketing strategies. The system also develops self-decision support functions specialized for the academic field to support important academic decisions. For example, it can provide advice on career path selection and study plans. The system also develops self-decision support functions specialized for a specific field to suggest optimal decisions in that field. For example, it provides advice in the medical field or technology field. This makes it possible to provide advice specialized for a specific field.

[0051] The system has the function of compiling a database of success stories of other users and providing advice based on that. For example, it can suggest optimal decisions based on success stories. The system also analyzes the success stories of other users and develops algorithms that suggest optimal decisions in similar situations. For example, it can automatically generate advice based on success stories. The system also has the function of holding dialogues to support decisions based on the success stories of other users. For example, it can hold dialogues based on success stories. This makes it possible to provide advice based on the success stories of other users.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The emotion estimation unit estimates the user's emotional state in real time. For example, the emotion estimation unit uses facial expression recognition technology to analyze the user's facial expression and estimate the emotional state. It can also use voice analysis technology to analyze the tone and speed of the user's voice and estimate the emotional state. Furthermore, it can use biosensors to measure heart rate and electrodermal activity and estimate the emotional state. Step 2: The dialogue initiation unit initiates a dialogue based on the emotional state estimated by the emotion estimation unit. For example, the dialogue is initiated when the user is estimated to feel lonely, anxious, or happy. Step 3: The dialogue generation unit generates the dialogue content initiated by the dialogue initiation unit. For example, the dialogue content is generated using a generation AI (text generation AI or multimodal generation AI). The dialogue content can also be generated based on the user's emotional state or the user's past dialogue history.

[0054] (Example 2) A system according to an embodiment of the present invention is a system that allows people to listen to their own opinions without feeling lonely. This system is a technology that allows people to have a conversation with themselves using the concept of "F (F) in their mind." This allows people to listen to their own opinions and have a conversation with themselves as their best partner without feeling lonely.

[0055] A system according to an embodiment includes an emotion estimation unit, a dialogue initiation unit, and a dialogue generation unit. The emotion estimation unit estimates the emotional state of a user in real time. For example, the emotion estimation unit estimates the emotional state by analyzing the user's facial expression using facial expression recognition technology. The emotion estimation unit can also estimate the emotional state by analyzing the tone and speed of the user's voice using voice analysis technology. The emotion estimation unit can also estimate the emotional state by measuring the heart rate and electrodermal activity using a biosensor. The dialogue initiation unit starts a dialogue based on the emotional state estimated by the emotion estimation unit. For example, the dialogue initiation unit starts a dialogue when it is estimated that the user is feeling lonely. The dialogue initiation unit can also start a dialogue when it is estimated that the user is feeling anxious. The dialogue initiation unit can also start a dialogue when it is estimated that the user is feeling happy. The dialogue generation unit generates content of the dialogue started by the dialogue initiation unit. For example, the dialogue generation unit generates content of the dialogue using a generation AI (e.g., a text generation AI or a multimodal generation AI). The dialogue generation unit can also generate content of the dialogue according to the user's emotional state. The dialogue generation unit can also generate dialogue content based on the user's past dialogue history, which allows the system according to the embodiment to start a dialogue based on the user's emotional state and reduce feelings of loneliness.

[0056] The dialogue generation unit can analyze the dialogue history and automatically generate optimal dialogue content for reducing feelings of loneliness. The dialogue generation unit, for example, analyzes the user's past dialogue history and extracts dialogue patterns for reducing feelings of loneliness. For example, it automatically generates a new dialogue based on dialogue content that was effective in the past. The dialogue generation unit also identifies keywords and phrases for reducing feelings of loneliness based on the user's dialogue history and automatically generates a dialogue using them. For example, it includes topics that the user likes or content that attracts their interest. The dialogue generation unit also analyzes the past dialogue history and automatically generates a dialogue that provides emotional support for reducing feelings of loneliness. For example, it reproduces dialogue content that the user found reassuring in the past. In this way, optimal dialogue content can be generated based on the past dialogue history, thereby reducing feelings of loneliness.

[0057] The dialogue generation unit can suggest dialogue themes based on hobbies and interests. For example, the dialogue generation unit creates a database of the user's hobbies and interests and suggests dialogue themes based on them. For example, the dialogue generation unit starts a dialogue about the user's favorite movies or music. The dialogue generation unit also analyzes the user's past behavioral history and search history to suggest dialogue themes that pique the user's interest. For example, it holds a dialogue related to recently searched topics or purchased products. The dialogue generation unit also analyzes the content posted on the user's social media or blog to suggest dialogue themes based on the user's hobbies and interests. For example, it holds a dialogue related to content frequently posted by the user. In this way, dialogue themes based on the user's hobbies and interests can be suggested, reducing feelings of loneliness.

[0058] The system has a function to simulate conversations with pets and plants. For example, the system has a function to simulate the behavior and sounds of the user's pet and engage in conversations. For example, the system can call out the pet's name and reproduce the pet's behavior. The system also has a function to simulate the growth of plants the user is growing and engage in conversations. For example, the system can share advice and observations about plant growth. The system also has a function to create a database of information about the user's pets and plants and engage in conversations based on that information. For example, the system can engage in conversations about the pet's health and plant cultivation methods. This simulates conversations with pets and plants, making it possible to reduce feelings of loneliness.

[0059] The system has a function to anonymously match users and promote dialogue on common topics. For example, the system may anonymously match users who feel lonely with each other and promote dialogue based on common hobbies and interests. For example, it may match users who share the same hobbies. The system may also anonymously match users who feel lonely with each other and promote dialogue about common worries and problems. For example, it may be possible to match users who share the same worries. The system may also anonymously match users who feel lonely with each other and promote dialogue based on common experiences and backgrounds. For example, it may be possible to match users who have the same occupation or living environment. This makes it possible to anonymously match users who feel lonely with each other and promote dialogue on common topics.

[0060] The system uses an emotion estimation function to detect signs that a user is feeling lonely and initiate a dialogue proactively. For example, the system monitors the user's emotional state in real time to detect signs of feeling lonely. For example, it analyzes changes in facial expressions and voice to initiate a dialogue before the user's sense of loneliness increases. The system also analyzes the user's behavioral patterns to predict signs of feeling lonely. For example, it can make predictions based on behavioral data from specific times of day or places and initiate a dialogue. The system also analyzes the user's usage of social media and messaging apps to identify signs of feeling lonely. For example, it can detect long periods of unread messages or delayed replies and initiate a dialogue. This makes it possible to detect signs of feeling lonely and initiate a dialogue proactively.

[0061] The system uses an emotion estimation function to analyze the emotional state of the user when making an important decision and engages in dialogue to encourage calm judgment. For example, the system monitors the user's emotional state in real time and analyzes the emotional state when making an important decision. For example, it analyzes changes in facial expressions and voice and engages in dialogue to encourage calm judgment. The system also creates a database of the user's emotional state when making past decisions and analyzes the emotional state in similar situations. For example, it can engage in dialogue to encourage calm judgment based on past data. The system also automatically generates dialogue content to encourage calm judgment based on the user's emotional state. For example, if the user is emotionally charged, it engages in dialogue to help the user relax. This can encourage calm judgment when making important decisions.

[0062] The system creates a database of the user's past decisions and their outcomes, and suggests the optimal decision in similar situations. For example, the system creates a database of the user's past decisions and their outcomes, and builds a system that suggests the optimal decision in similar situations. For example, suggestions are made based on past successes and failures. The system also analyzes past decisions and their outcomes, and develops an algorithm that suggests the optimal decision. For example, it can automatically generate the optimal decision based on past data. The system also engages in dialogue to support decisions in similar situations based on the user's past decision history. For example, it engages in dialogue that refers to past successes. This makes it possible to suggest the optimal decision based on past decisions and their outcomes.

[0063] The system sets judgment criteria based on the user's values ​​and beliefs, and provides advice in line with them. For example, the system creates a database of the user's values ​​and beliefs and sets judgment criteria based on them. For example, it provides advice based on the values ​​that the user holds dear. The system also analyzes the user's values ​​and beliefs and develops an algorithm to provide advice in line with them. For example, it can also suggest optimal decisions based on the user's beliefs. The system also sets judgment criteria based on the user's values ​​and beliefs, and engages in dialogue in line with them. For example, it provides advice that matches the user's values. This makes it possible to set judgment criteria based on the user's values ​​and beliefs, and provide advice in line with them.

[0064] The system expands the self-decision support function to advice specialized in specific fields such as business and academics. For example, the system develops a self-decision support function specialized in the business field to support important business decisions. For example, it provides advice on management strategies and marketing strategies. The system also develops a self-decision support function specialized in the academic field to support important academic decisions. For example, it can provide advice on career path selection and study plans. The system also develops a self-decision support function specialized in a specific field to suggest optimal decisions in that field. For example, it provides advice in the medical field or technical field. This makes it possible to provide advice specialized in a specific field.

[0065] The system has a function to provide advice based on success stories of other users. The system has a function to, for example, create a database of success stories of other users and provide advice based on that. For example, the system suggests optimal decisions based on success stories. The system also analyzes success stories of other users and develops an algorithm that suggests optimal decisions in similar situations. For example, it can automatically generate advice based on success stories. The system also has a function to hold dialogues to support decisions based on success stories of other users. For example, it holds dialogues based on success stories. This makes it possible to provide advice based on success stories of other users.

[0066] The system uses its emotion estimation function to engage in dialogue to support users in making decisions they will not regret. For example, the system monitors the user's emotional state in real time and engages in dialogue to support decisions they will not regret. For example, it analyzes changes in facial expressions and voice and engages in dialogue to encourage calm decisions. The system also creates a database of the user's emotional state when making past decisions and analyzes their emotional state in similar situations. For example, it can engage in dialogue to support decisions they will not regret based on past data. The system also automatically generates dialogue content to support decisions they will not regret based on the user's emotional state. For example, if the user is emotionally charged, it engages in dialogue to help the user relax. This can support decisions they will not regret.

[0067] The system uses an emotion estimation function to automatically generate dialogue content to help users increase their self-esteem. For example, the system monitors the user's emotional state in real time and automatically generates dialogue content to increase self-esteem. For example, it analyzes changes in facial expressions and voice and engages in positive dialogue. The system also creates a database of the user's past emotional states and extracts dialogue patterns to increase self-esteem. For example, it can automatically generate new dialogue based on dialogue content that was effective in the past. The system also develops an algorithm to automatically generate dialogue content to increase self-esteem based on the user's emotional state. For example, if the user's emotions are low, it will engage in encouraging dialogue. This makes it possible to automatically generate dialogue content to increase self-esteem.

[0068] The system records the user's past successful experiences and engages in dialogue to remind the user of those experiences when the user is in a difficult situation. For example, the system creates a database of the user's past successful experiences and engages in dialogue to remind the user of those experiences when the user is in a difficult situation. For example, it engages in encouraging dialogue based on past success stories. The system also records the user's successful experiences and automatically generates dialogue content to remind the user of those experiences when the user is in a difficult situation. For example, it can engage in positive dialogue based on successful experiences. The system also develops an algorithm to engage in dialogue to increase self-esteem in difficult situations based on the user's past successful experiences. For example, it engages in dialogue to remind the user of successful experiences. This makes it possible to engage in dialogue to remind the user of past successful experiences.

[0069] The system provides dialogue to maintain motivation based on the user's long-term goals. For example, the system creates a database of the user's long-term goals and provides dialogue to maintain motivation based on those goals. For example, a dialogue is held to check progress toward achieving the goal. The system also automatically generates dialogue content to maintain motivation based on the user's goals. For example, it can also provide dialogue to encourage the user toward goal achievement. The system also develops an algorithm to perform dialogue to maintain motivation based on the user's long-term goals. For example, it provides specific advice toward goal achievement. This makes it possible to provide dialogue to maintain motivation based on the user's long-term goals.

[0070] The system has a function of simulating conversations with the user's friends and family and holding conversations to increase self-esteem. The system has a function of simulating conversations with the user's friends and family and holding conversations to increase self-esteem, for example, by reproducing the words of friends and family. The system also has a function of creating a database of information about the user's friends and family and holding conversations based on that information. For example, it can hold conversations based on the names and relationships of friends and family. The system also simulates conversations with the user's friends and family and automatically generates conversation content to increase self-esteem. For example, it can reproduce encouraging words from friends and family. This allows the system to simulate conversations with friends and family and increase self-esteem.

[0071] The system has a function to visualize and display a user's past successful experiences when the user is faced with a difficult situation. The system has a function to visualize the user's past successful experiences and display them in difficult situations. For example, it displays photos or videos of the successful experiences. The system also has a function to visualize the user's successful experiences and engage in dialogue to remind the user of them in difficult situations. For example, it can display graphs or charts of the successful experiences. The system also has a function to visualize the user's past successful experiences and engage in dialogue to increase self-esteem in difficult situations. For example, it displays a slideshow of the successful experiences. This makes it possible to visualize and display past successful experiences in difficult situations.

[0072] The system uses an emotion estimation function to initiate a dialogue at the optimal timing for the user to increase self-esteem. For example, the system monitors the user's emotional state in real time and initiates a dialogue at the optimal timing for increasing self-esteem. For example, the dialogue may be initiated when the user's emotions are low. The system also creates a database of the user's past emotional states and identifies the optimal timing for increasing self-esteem. For example, the dialogue may be initiated based on past data. The system also develops an algorithm for starting a dialogue at the optimal timing for increasing self-esteem based on the user's emotional state. For example, the system may initiate an encouraging dialogue when the user's emotions are low. This allows the dialogue to be initiated at the optimal timing for increasing self-esteem.

[0073] The system uses an emotion estimation function to analyze the emotional state of the user when listening to their own opinion and provides optimal dialogue content. The system, for example, monitors the user's emotional state in real time and analyzes the emotional state when listening to their own opinion. For example, it analyzes changes in facial expressions and voice and provides optimal dialogue. The system also creates a database of the user's past emotional states and analyzes the emotional state when listening to their own opinion. For example, it can also provide optimal dialogue based on past data. The system also automatically generates optimal dialogue content when listening to their own opinion based on the user's emotional state. For example, if the user is emotionally charged, it will provide dialogue to help them relax. This makes it possible to analyze the user's emotional state when listening to their own opinion and provide optimal dialogue content.

[0074] The system creates a database of the user's past opinions and thoughts and conducts a dialogue based on that. For example, the system creates a database of the user's past opinions and thoughts and builds a system that conducts a dialogue based on that. For example, a dialogue is conducted with reference to the past opinions. The system also analyzes the user's past opinions and thoughts and automatically generates dialogue content based on that. For example, it is possible to conduct an optimal dialogue based on the past opinions. The system also develops an algorithm that conducts a dialogue based on the user's past opinions and thoughts. For example, a dialogue is conducted with reference to the past opinions. This makes it possible to conduct a dialogue based on the past opinions and thoughts.

[0075] The system has a function of recording a user's opinions and tracking changes in emotions when the user rereads them as "inner self F." For example, the system has a function of recording a user's opinions and tracking changes in emotions when the user rereads them as "inner self F." For example, the system monitors the user's emotional state when the user rereads the opinions. The system also develops an algorithm for recording a user's opinions and tracking changes in emotions when the user rereads them. For example, the system can analyze the user's emotional state when the user rereads the opinions. The system also has a function of recording a user's opinions and tracking changes in emotions when the user rereads them. For example, the system creates a database of the user's emotional state when the user rereads the opinions. This makes it possible to track changes in emotions when the user rereads the opinions.

[0076] The system has a function to visualize user opinions and make them easier to understand visually. For example, the system has a function to visualize user opinions and make them easier to understand visually. For example, the opinions are displayed in graphs or charts. The system also develops an algorithm to visualize user opinions and make them easier to understand visually. For example, the opinions can be displayed in diagrams or icons. The system also has a function to visualize user opinions and make them easier to understand visually. For example, the opinions are displayed in a slideshow or infographic. This makes it possible to visualize opinions and make them easier to understand visually.

[0077] The system uses an emotion estimation function to analyze the emotional reactions of a user when listening to their own opinion and provide optimal dialogue. For example, the system monitors the user's emotional state in real time and analyzes the emotional reactions when listening to their own opinion. For example, it analyzes changes in facial expressions and voice to provide optimal dialogue. The system also creates a database of the user's past emotional states and analyzes the emotional reactions when listening to their own opinion. For example, it can also provide optimal dialogue based on past data. The system also automatically generates optimal dialogue content for when listening to the user's own opinion based on the user's emotional state. For example, if the user is emotionally charged, it provides dialogue to help the user relax. This makes it possible to analyze the emotional reactions when listening to their own opinion and provide optimal dialogue.

[0078] The system uses an emotion estimation function to analyze the emotional state of the user when interacting with themselves and provide optimal dialogue content. The system, for example, monitors the user's emotional state in real time and analyzes the emotional state when interacting with themselves. For example, it analyzes changes in facial expressions and voice to provide optimal dialogue. The system also creates a database of the user's past emotional states and analyzes the emotional state when interacting with themselves. For example, it can also provide optimal dialogue based on past data. The system also automatically generates optimal dialogue content for interacting with themselves based on the user's emotional state. For example, if the user is emotionally charged, it will provide dialogue to help them relax. This makes it possible to analyze the user's emotional state when interacting with themselves and provide optimal dialogue content.

[0079] The system analyzes the user's past dialogue history and engages in dialogue to increase the depth of introspection. For example, the system creates a database of the user's past dialogue history and engages in dialogue to increase the depth of introspection. For example, a dialogue is engaged in to encourage deep introspection based on the content of the past dialogue. The system also analyzes the user's past dialogue history and automatically generates dialogue content to increase the depth of introspection. For example, a dialogue can be engaged in to encourage deep introspection based on the content of the past dialogue. The system also develops an algorithm to engage in dialogue to increase the depth of introspection based on the user's past dialogue history. For example, a dialogue is engaged in to encourage deep introspection based on the content of the past dialogue. This makes it possible to analyze the past dialogue history and engage in dialogue to increase the depth of introspection.

[0080] The system analyzes the content of a dialogue from multiple angles and provides feedback to gain a deeper understanding of the user's inner thoughts. For example, the system analyzes the content of a dialogue from multiple angles and provides feedback to gain a deeper understanding of the user's inner thoughts. For example, the system analyzes the content of the dialogue from perspectives such as emotions, values, and beliefs. The system also analyzes the content of a dialogue from multiple angles and automatically generates feedback to gain a deeper understanding of the user's inner thoughts. For example, it can provide specific feedback based on the content of the dialogue. The system also develops an algorithm that analyzes the content of a dialogue from multiple angles and provides feedback to gain a deeper understanding of the user's inner thoughts. For example, it can provide specific feedback based on the content of the dialogue. This makes it possible to analyze the content of a dialogue from multiple angles and provide feedback to gain a deeper understanding of the user's inner thoughts.

[0081] The system extends the function of simulating a dialogue with oneself into a dialogue with another user. The system, for example, has a function of simulating a dialogue with oneself into a dialogue with another user. For example, a dialogue is held that incorporates the opinions and thoughts of other users. The system also develops an algorithm for simulating a dialogue with oneself into a dialogue with another user. For example, a dialogue can be held that is based on the opinions and thoughts of other users. The system also has a function of simulating a dialogue with oneself into a dialogue with another user, and holds a dialogue. For example, a dialogue is held that takes into account the opinions and thoughts of other users. This allows the system to extend the function of simulating a dialogue with oneself into a dialogue with other users.

[0082] The system has a function of visualizing the user's introspection time to make it easier to understand visually. The system, for example, has a function of visualizing the user's introspection time to make it easier to understand visually. For example, the system displays the introspection time in a graph or chart. The system also develops an algorithm for visualizing the user's introspection time to make it easier to understand visually. For example, the system can display the introspection time in a diagram or icon. The system also has a function of visualizing the user's introspection time to make it easier to understand visually. For example, the system displays the introspection time in a slideshow or infographic. This makes it possible to visualize the introspection time to make it easier to understand visually.

[0083] The system uses an emotion estimation function to analyze the emotional reactions of a user when conversing with themselves and provide optimal dialogue. For example, the system monitors the user's emotional state in real time and analyzes the emotional reactions when conversing with themselves. For example, it analyzes changes in facial expressions and voice to provide optimal dialogue. The system also creates a database of the user's past emotional states and analyzes the emotional reactions when conversing with themselves. For example, it can also provide optimal dialogue based on past data. The system also automatically generates optimal dialogue content for when conversing with oneself based on the user's emotional state. For example, if the user is emotionally charged, it will provide dialogue to help the user relax. In this way, it is possible to analyze the emotional reactions when conversing with oneself and provide optimal dialogue.

[0084] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0085] The system monitors a user's emotional state in real time and detects signs of loneliness. For example, it analyzes changes in facial expressions and voice and initiates a dialogue before feelings of loneliness increase. The system also analyzes a user's behavioral patterns and predicts signs of loneliness. For example, it can predict and initiate a dialogue based on behavioral data from specific times of day or locations. The system also analyzes a user's usage of social media and messaging apps to identify signs of loneliness. For example, it can detect long periods of unread messages or delayed replies and initiate a dialogue. This makes it possible to detect signs of loneliness and initiate a dialogue preventatively.

[0086] The system creates a database of the user's past decisions and their outcomes, and builds a system that suggests optimal decisions for similar situations. For example, it makes suggestions based on past successes and failures. The system also analyzes past decisions and their outcomes, and develops algorithms that suggest optimal decisions. For example, it can automatically generate optimal decisions based on past data. The system also uses the user's past decision history to hold dialogues that support decisions in similar situations. For example, it holds dialogues that refer to past successes. This makes it possible to suggest optimal decisions based on past decisions and their outcomes.

[0087] The system creates a database of the user's values ​​and beliefs and sets decision-making criteria based on them. For example, it provides advice based on the values ​​that the user holds dear. The system also analyzes the user's values ​​and beliefs and develops an algorithm to provide advice in line with them. For example, it can suggest optimal decisions based on the user's beliefs. The system also sets decision-making criteria based on the user's values ​​and beliefs and engages in dialogue in line with these criteria. For example, it provides advice that matches the user's values. This makes it possible to set decision-making criteria based on the user's values ​​and beliefs and provide advice in line with them.

[0088] The system develops self-decision support functions specialized for the business field to support important business decisions. For example, it provides advice on management strategies and marketing strategies. The system also develops self-decision support functions specialized for the academic field to support important academic decisions. For example, it can provide advice on career path selection and study plans. The system also develops self-decision support functions specialized for a specific field to suggest optimal decisions in that field. For example, it provides advice in the medical field or technology field. This makes it possible to provide advice specialized for a specific field.

[0089] The system has the function of compiling a database of success stories of other users and providing advice based on that. For example, it can suggest optimal decisions based on success stories. The system also analyzes the success stories of other users and develops algorithms that suggest optimal decisions in similar situations. For example, it can automatically generate advice based on success stories. The system also has the function of holding dialogues to support decisions based on the success stories of other users. For example, it can hold dialogues based on success stories. This makes it possible to provide advice based on the success stories of other users.

[0090] The system monitors the user's emotional state in real time and analyzes it when making important decisions. For example, it analyzes changes in facial expressions and voice and provides dialogue to encourage calm judgment. The system also creates a database of the user's emotional state when making past decisions and analyzes their emotional state in similar situations. For example, it can provide dialogue to encourage calm judgment based on past data. The system also automatically generates dialogue content to encourage calm judgment based on the user's emotional state. For example, if the user is feeling emotional, it provides dialogue to help the user relax. This can encourage calm judgment when making important decisions.

[0091] The system monitors the user's emotional state in real time and engages in dialogue to support decisions that will not cause regret. For example, it analyzes changes in facial expressions and voice and engages in dialogue to encourage calm decisions. The system also creates a database of the user's emotional state at the time of past decisions and analyzes their emotional state in similar situations. For example, it can engage in dialogue to support decisions that will not cause regret based on past data. The system also automatically generates dialogue content to support decisions that will not cause regret based on the user's emotional state. For example, if the user is emotionally charged, it engages in dialogue to help the user relax. This can support decisions that will not cause regret.

[0092] The system monitors the user's emotional state in real time and automatically generates dialogue content to increase self-esteem. For example, it analyzes changes in facial expressions and voice and engages in positive dialogue. The system also creates a database of the user's past emotional states and extracts dialogue patterns to increase self-esteem. For example, it can automatically generate new dialogue based on dialogue content that has been effective in the past. The system also develops an algorithm to automatically generate dialogue content to increase self-esteem based on the user's emotional state. For example, if emotions are low, it will engage in encouraging dialogue. This makes it possible to automatically generate dialogue content to increase self-esteem.

[0093] The system monitors the user's emotional state in real time and analyzes the emotional state when listening to one's own opinion. For example, it analyzes changes in facial expressions and voice and provides the most appropriate dialogue. The system also creates a database of the user's past emotional states and analyzes the emotional state when listening to one's own opinion. For example, it can provide the most appropriate dialogue based on past data. The system also automatically generates the most appropriate dialogue content when listening to one's own opinion based on the user's emotional state. For example, if the user is emotionally charged, it will provide dialogue to help the user relax. This makes it possible to analyze the user's emotional state when listening to one's own opinion and provide the most appropriate dialogue content.

[0094] The system monitors the user's emotional state in real time and analyzes their emotional reactions when they converse with themselves. For example, it analyzes changes in facial expressions and voice to provide the most appropriate dialogue. The system also creates a database of the user's past emotional states and analyzes their emotional reactions when they converse with themselves. For example, it can provide the most appropriate dialogue based on past data. The system also automatically generates the most appropriate dialogue content for when they converse with themselves, based on the user's emotional state. For example, if the user is emotionally charged, it will provide dialogue to help them relax. This makes it possible to analyze their emotional reactions when they converse with themselves and provide the most appropriate dialogue.

[0095] The processing flow of the second embodiment will be briefly explained below.

[0096] Step 1: The emotion estimation unit estimates the user's emotional state in real time. For example, the emotion estimation unit uses facial expression recognition technology to analyze the user's facial expression and estimate the emotional state. It can also use voice analysis technology to analyze the tone and speed of the user's voice and estimate the emotional state. Furthermore, it can use biosensors to measure heart rate and electrodermal activity and estimate the emotional state. Step 2: The dialogue initiation unit initiates a dialogue based on the emotional state estimated by the emotion estimation unit. For example, the dialogue is initiated when the user is estimated to feel lonely, anxious, or happy. Step 3: The dialogue generation unit generates the dialogue content initiated by the dialogue initiation unit. For example, the dialogue content is generated using a generation AI (text generation AI or multimodal generation AI). The dialogue content can also be generated based on the user's emotional state or the user's past dialogue history.

[0097] 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.

[0098] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0099] 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.

[0100] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0101] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0116] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The 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.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0122] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0123] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0125] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0126] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0127] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0129] 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.

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 7, the 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0141] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

[0142] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0143] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0163] 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. [Explanation of symbols]

[0164] 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. an emotion estimation unit that estimates an emotional state of a user in real time; a dialogue initiation unit that initiates a dialogue based on the emotional state estimated by the emotion estimation unit; a dialogue generation unit that generates the dialogue content started by the dialogue initiation unit; A system characterized by:

2. The dialogue generation unit Analyzes conversation history and automatically generates optimal conversation content to reduce feelings of loneliness 2. The system of claim 1.

3. The dialogue generation unit Suggest conversation topics based on hobbies and interests 2. The system of claim 1.

4. Features include the ability to simulate interactions with pets and plants 2. The system of claim 1.

5. It has a function to anonymously match users and encourage conversations on common topics.

2. The system of claim 1.

Images