System

Abstract

An object of a system according to an embodiment is to make a user confident in communication with the opposite gender and to propose matching of users having good compatibility.SOLUTION: A system includes a generation unit, a conversation unit, an analysis unit, and a matching unit. The generation unit generates a virtual lover based on the user's preference or desire. The conversation unit supports a message conversation between the virtual lover generated by the generation unit and the user. The analysis unit analyzes communication performed by the conversation unit and thinking of the user. The matching unit proposes matching of users having good compatibility on the basis of a result analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult for users to gain confidence in interacting with members of the opposite sex, and of not providing sufficient means to contribute to resolving the problem of unmarried people and declining birth rates.

[0005] The system according to the embodiment aims to give users confidence in interacting with members of the opposite sex and to suggest matches between users who are compatible with each other. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation unit, a conversation unit, an analysis unit, and a matching unit. The generation unit generates a virtual lover based on the user's preferences and wishes. The conversation unit supports conversations via messages between the user and the virtual lover generated by the generation unit. The analysis unit analyzes the exchanges carried out by the conversation unit and the user's thoughts. The matching unit suggests matching users who are compatible with each other based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to feel confident in interacting with members of the opposite sex and can suggest matches between users who are compatible with each other. [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 virtual lover generation system according to an embodiment of the present invention allows users to chat through messages and enjoy virtual dates. In this system, a generation AI creates a virtual lover based on the user's preferences and wishes, and analyzes the user's interactions and thoughts to suggest matches between users who are compatible. This allows the virtual lover generation system to increase users' confidence when interacting with members of the opposite sex in real life, contributing to the elimination of unmarried people and declining birth rates.

[0029] A virtual lover generation system according to an embodiment includes a generation unit, a conversation unit, an analysis unit, and a matching unit. The generation unit generates a virtual lover based on a user's preferences and wishes. For example, the generation unit uses a generation AI to create a virtual lover based on an ideal lover image input by the user. The generation unit can also learn the user's past romantic experiences and preferences to generate a more personalized virtual lover. The conversation unit supports messaging conversations between the user and the virtual lover generated by the generation unit. For example, the conversation unit analyzes messages sent by the user, and the generation AI generates appropriate replies. The conversation unit can also analyze the content of conversations between the user and the virtual lover and provide feedback to improve the user's communication skills. The analysis unit analyzes the interactions conducted by the conversation unit and the user's thoughts. For example, the analysis unit analyzes the content of the user's messages and responses to identify other users with common hobbies and values. The analysis unit can also provide positive feedback to increase the user's self-esteem. The matching unit suggests matching users who are compatible with each other based on the results of the analysis by the analysis unit. For example, the matching unit may consider the user's lifestyle and values ​​to perform more comprehensive matching. The matching unit may also implement a follow-up function after matching to periodically check whether the relationship is going well. This allows the virtual lover generation system according to the embodiment to increase users' confidence when interacting with members of the opposite sex in real life, contributing to the elimination of the trend toward unmarried people and declining birthrates. For example, users may improve their communication skills through interactions with their virtual lover. Furthermore, users may learn about the flow of dating and proper etiquette through virtual dates. This reduces users' anxiety about real-life relationships and allows them to interact with members of the opposite sex with confidence.

[0030] The generation unit can learn the user's past romantic experiences and preferences to generate a more personalized virtual lover. For example, the generation unit inputs details of a user's past romantic experiences, and the generation AI generates a personalized virtual lover based on that data. For example, the generation unit may reflect the past lover's personality and hobbies. The generation unit may also collect the user's preferences and past romantic experiences in the form of a questionnaire, and the generation AI may create the optimal virtual lover based on that information. For example, the generation unit may reflect the user's favorite movies and music preferences. The generation unit may also analyze the user's past message history and social media posts to learn their preferences and romantic tendencies and generate a virtual lover. For example, the generation unit may reflect specific language and topics. This allows the system to learn the user's past romantic experiences and preferences and generate a more personalized virtual lover, thereby increasing user satisfaction.

[0031] The generation unit can generate virtual lovers with settings of different cultures and nationalities, allowing the user to experience intercultural exchange. The generation unit generates a virtual lover based on, for example, a culture or nationality selected by the user, allowing the user to experience intercultural exchange. For example, for a user interested in French culture, the generation unit creates a French virtual lover. The generation unit also generates virtual lovers with settings of different cultures and nationalities, providing the user with an opportunity to learn about those cultures. For example, for a user interested in Japanese culture, the generation unit creates a Japanese virtual lover. The generation unit also generates virtual lovers with different nationalities and cultural backgrounds, allowing the user to enjoy intercultural exchange. For example, for a user interested in Italian culture, the generation unit creates an Italian virtual lover. In this way, the generation unit can generate virtual lovers with settings of different cultures and nationalities, allowing the user to experience intercultural exchange, thereby increasing user satisfaction.

[0032] The generation unit generates a virtual lover as a character specialized in the user's occupation or hobby, and can provide common topics of conversation. The generation unit generates a virtual lover based on the user's occupation or hobby, for example, and provides common topics of conversation. For example, if the user is a doctor, the generation unit creates a virtual lover that is knowledgeable about medicine. The generation unit also generates a virtual lover that is specialized in the user's hobbies and interests, allowing the user to enjoy conversations based on common topics. For example, if the user likes music, the generation unit creates a virtual lover that is knowledgeable about music. The generation unit also generates a virtual lover that matches the user's occupation or hobby, and livens up conversations based on common topics. For example, if the user likes sports, the generation unit creates a virtual lover that is knowledgeable about sports. In this way, the user's satisfaction can be increased by generating a virtual lover as a character specialized in the user's occupation or hobby, and providing common topics of conversation.

[0033] The conversation unit can analyze the content of the conversation between the user and the virtual lover and provide feedback to improve the user's communication skills. The conversation unit, for example, analyzes the content of the conversation between the user and the virtual lover and provides feedback to improve the communication skills. For example, it provides advice on appropriate language use and how to choose topics. The conversation unit also analyzes the content of the conversation and provides feedback to help the user communicate better. For example, it provides advice on the flow and timing of the conversation. The conversation unit also analyzes the content of the user's conversation and provides specific feedback to improve the communication skills. For example, it provides advice on how to ask questions and how to react. In this way, the content of the conversation between the user and the virtual lover can be analyzed and feedback to improve the user's communication skills can be provided, thereby increasing the user's confidence.

[0034] The conversation unit can record the user's actions and reactions during the virtual date and later provide a review and areas for improvement. The conversation unit, for example, builds a system that records the user's actions and reactions during the virtual date and later provides a review and areas for improvement. For example, it records the flow of the date and the content of the conversation. The conversation unit also records the user's actions and reactions and later provides a review and areas for improvement after the date. For example, it provides feedback on what went well during the date and areas for improvement. The conversation unit also develops a system that records the user's actions and reactions during the virtual date and later provides a review and areas for improvement. For example, it analyzes the reactions and content of the conversation during the date. This allows the user's dating skills to be improved by recording the user's actions and reactions during the virtual date and later providing a review and areas for improvement.

[0035] The conversation unit can use virtual reality (VR) technology to enable a more realistic experience of a virtual date. The conversation unit, for example, uses virtual reality (VR) technology to build a system that enables a more realistic experience of a virtual date. For example, a virtual date is experienced using a VR headset. The conversation unit also utilizes VR technology to enable a user to feel the virtual date as if it were real. For example, a virtual date spot is reproduced in 3D. The conversation unit also uses virtual reality technology to enable a user to experience the virtual date more realistically. For example, a date simulation in a VR environment is provided. This allows a user to experience a virtual date more realistically using virtual reality (VR) technology, thereby increasing user satisfaction.

[0036] The conversation unit uses voice recognition technology to enable conversations via messages to be conducted by voice, thereby realizing more natural communication. The conversation unit, for example, uses voice recognition technology to build a system in which conversations via messages can be conducted by voice. For example, a user sends a message by voice, and a virtual lover replies by voice. The conversation unit also utilizes voice recognition technology to enable a user to converse with a virtual lover by voice. For example, it provides a conversation system that combines voice input and voice output. The conversation unit also uses voice recognition technology to develop a system in which conversations via messages can be conducted by voice, thereby realizing more natural communication. For example, it supports real-time conversations by voice. This allows conversations via messages to be conducted by voice using voice recognition technology, thereby realizing more natural communication and increasing user satisfaction.

[0037] The analysis unit analyzes the user's conversations and actions, and can improve the user's confidence by providing feedback on specific areas for improvement and successful experiences. The analysis unit, for example, builds a system that analyzes the user's conversations and actions, and provides feedback on specific areas for improvement and successful experiences. For example, it highlights when the user successfully advances the conversation. The analysis unit also analyzes the user's actions and conversation content, and provides feedback on specific areas for improvement and successful experiences. For example, it praises the user when they give a good reaction. The analysis unit also develops a system that analyzes the user's conversations and actions, and provides feedback on specific areas for improvement and successful experiences to improve the user's confidence. For example, it provides a function that allows the user to look back on successful experiences. This allows the user's conversations and actions to be analyzed, and feedback on specific areas for improvement and successful experiences to improve the user's confidence.

[0038] The analysis unit can provide a program for encouraging users to interact with their virtual lover in a group session format, thereby increasing self-confidence together with other users. The analysis unit, for example, can provide a program for encouraging users to interact with their virtual lover in a group session format, thereby increasing self-confidence together with other users. For example, an online session can be held in which multiple users participate. The analysis unit can also provide a place where users can encourage each other. The analysis unit can also provide a system for encouraging users to interact with their virtual lover in a group session format, thereby increasing self-confidence together with other users. For example, the analysis unit can increase self-confidence through group discussions. This can improve the user's self-confidence by providing a program for encouraging users to interact with their virtual lover in a group session format, thereby increasing self-confidence together with other users.

[0039] The analysis unit introduces mini-games and quizzes to improve self-confidence, allowing users to improve their skills while having fun. The analysis unit, for example, introduces mini-games and quizzes to improve self-confidence and builds a system for improving skills while having fun. For example, it provides quizzes to improve communication skills. The analysis unit also allows users to improve their self-confidence while having fun through mini-games and quizzes. For example, it provides games that improve self-confidence by accumulating successful experiences. The analysis unit also develops a system for introducing mini-games and quizzes to improve self-confidence and improving skills while having fun. For example, it provides games that include scenarios that help users gain confidence. In this way, it is possible to improve a user's confidence by introducing mini-games and quizzes to improve self-confidence and improving skills while having fun.

[0040] The matching unit can analyze the user's message content and reactions in detail and develop a more accurate matching algorithm. The matching unit, for example, analyzes the user's message content and reactions in detail and builds a system that develops a more accurate matching algorithm. For example, it identifies users who share common hobbies and values. The matching unit also analyzes the message content and reactions and develops a more accurate matching algorithm. For example, it identifies users who are compatible with the user based on the user's conversation patterns and reactions. The matching unit also analyzes the user's message content and reactions in detail and develops a system that develops a more accurate matching algorithm. For example, it performs matching based on the user's emotional data. In this way, it is possible to analyze the user's message content and reactions in detail and develop a more accurate matching algorithm, thereby increasing user satisfaction.

[0041] The matching unit learns the user's thought patterns and predicts future behavior, thereby being able to propose more appropriate matches. The matching unit, for example, builds a system that proposes more appropriate matches by learning the user's thought patterns and predicting future behavior. For example, the prediction is made based on the user's past behavior data. The matching unit also learns the thought patterns and predicts future behavior to propose appropriate matches. For example, the matching unit analyzes the user's thought tendencies and identifies users who are compatible with the user. The matching unit also develops a system that proposes more appropriate matches by learning the user's thought patterns and predicting future behavior. For example, matching is performed based on the user's behavior prediction. In this way, the system can learn the user's thought patterns and predict future behavior to propose more appropriate matches, thereby increasing user satisfaction.

[0042] The matching unit takes into account the user's lifestyle and values ​​when matching, allowing for more comprehensive matching. The matching unit, for example, builds a system that takes into account the user's lifestyle and values ​​when matching, allowing for more comprehensive matching. For example, it identifies compatible users based on the user's lifestyle and values. The matching unit also analyzes the user's lifestyle and values ​​to perform comprehensive matching. For example, it identifies compatible users based on the user's hobbies and interests. The matching unit also develops a system that takes into account the user's lifestyle and values ​​when matching, allowing for more comprehensive matching. For example, it performs matching based on the user's values ​​and lifestyle. This allows for more comprehensive matching by taking into account the user's lifestyle and values ​​when matching, thereby increasing user satisfaction.

[0043] The matching unit can introduce a post-matching follow-up function and periodically check whether the relationship is going well. The matching unit, for example, introduces a post-matching follow-up function and builds a system that periodically checks whether the relationship is going well. For example, periodically sending a questionnaire to the user. The matching unit also uses the follow-up function to check whether the relationship is going well after matching. For example, it grasps the relationship status based on user feedback. The matching unit also introduces a post-matching follow-up function and develops a system that periodically checks whether the relationship is going well. For example, it analyzes the relationship status based on user emotional data. In this way, by introducing a post-matching follow-up function and periodically checking whether the relationship is going well, user satisfaction can be increased.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The generation unit can generate a virtual lover based on the user's health condition to support health management. For example, when the user inputs health-related data, the generation unit creates a virtual lover that suggests a healthy lifestyle based on that data. The generation unit can also monitor the user's health condition and generate a virtual lover that provides appropriate advice. For example, if the user is not getting enough exercise, the generation unit can create a virtual lover that encourages exercise. The generation unit can also generate a virtual lover that supports healthy lifestyle habits based on the user's diet and sleep data. In this way, the user's satisfaction can be increased by generating a virtual lover based on the user's health condition and supporting health management.

[0046] The generation unit can generate a virtual lover based on the user's learning goals to support their learning. For example, if the user wants to study a specific subject, the generation unit can create a virtual lover who is knowledgeable about that subject. The generation unit can also generate a virtual lover that monitors the user's learning progress and provides appropriate feedback. For example, the generation unit can create a virtual lover that supplements parts of the material the user is struggling to understand. The generation unit can also generate a virtual lover that matches the user's learning style and suggest effective learning methods. In this way, the generation unit can increase user satisfaction by generating a virtual lover based on the user's learning goals and supporting their learning.

[0047] The generation unit generates a virtual lover based on the user's hobbies and interests, allowing the user to enjoy a common hobby. For example, if the user likes music, the generation unit creates a virtual lover who is knowledgeable about music. The generation unit can also learn the user's hobbies and interests and generate a virtual lover who shares a common hobby. For example, if the user likes movies, the generation unit can create a virtual lover who is knowledgeable about movies. The generation unit can also generate a virtual lover based on the user's hobbies and interests, allowing the user to enjoy a common hobby. This increases the user's satisfaction by generating a virtual lover based on the user's hobbies and interests and enjoying a common hobby.

[0048] The generation unit generates a virtual lover as a character specialized in the user's occupation or hobby, and can provide common topics of conversation. For example, if the user is a doctor, the generation unit generates a virtual lover that is knowledgeable about medicine. The generation unit also generates a virtual lover that is specialized in the user's hobbies and interests, allowing the users to enjoy conversations based on common topics. For example, if the user likes music, the generation unit generates a virtual lover that is knowledgeable about music. The generation unit also generates a virtual lover that matches the user's occupation or hobby, and livens up conversations based on common topics. For example, if the user likes sports, the generation unit creates a virtual lover that is knowledgeable about sports. In this way, the user's satisfaction can be increased by generating a virtual lover as a character specialized in the user's occupation or hobby, and providing common topics of conversation.

[0049] The conversation unit can analyze the content of the conversation between the user and the virtual lover and provide feedback to improve the user's communication skills. The conversation unit, for example, analyzes the content of the conversation between the user and the virtual lover and provides feedback to improve the communication skills. For example, it provides advice on appropriate language use and how to choose topics. The conversation unit also analyzes the content of the conversation and provides feedback to help the user communicate better. For example, it provides advice on the flow and timing of the conversation. The conversation unit also analyzes the content of the user's conversation and provides specific feedback to improve the communication skills. For example, it provides advice on how to ask questions and how to react. In this way, the content of the conversation between the user and the virtual lover can be analyzed and feedback to improve the user's communication skills can be provided, thereby increasing the user's confidence.

[0050] The conversation unit can record the user's actions and reactions during the virtual date and later provide a review and areas for improvement. The conversation unit, for example, builds a system that records the user's actions and reactions during the virtual date and later provides a review and areas for improvement. For example, it records the flow of the date and the content of the conversation. The conversation unit also records the user's actions and reactions and later provides a review and areas for improvement after the date. For example, it provides feedback on what went well during the date and areas for improvement. The conversation unit also develops a system that records the user's actions and reactions during the virtual date and later provides a review and areas for improvement. For example, it analyzes the reactions and content of the conversation during the date. This allows the user's dating skills to be improved by recording the user's actions and reactions during the virtual date and later providing a review and areas for improvement.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The generator generates a virtual lover based on the user's preferences and wishes. For example, the generator uses AI to create a virtual lover based on the user's ideal lover image input. The generator can also learn about the user's past romantic experiences and preferences to create a more personalized virtual lover. Step 2: The conversation unit supports messaging conversations between the user and the virtual lover generated by the generation unit. For example, the conversation unit analyzes messages sent by the user, and the generation AI generates appropriate replies. The conversation unit can also analyze the content of conversations between the user and the virtual lover and provide feedback to improve the user's communication skills. Step 3: The analysis unit analyzes the interactions carried out by the conversation unit and the user's thoughts. For example, the analysis unit analyzes the content of the user's messages and responses to identify other users who share common interests and values. The analysis unit can also provide positive feedback to increase the user's self-esteem. Step 4: The matching department proposes matches between users who are compatible based on the results of the analysis by the analysis department. For example, the matching department may consider the user's lifestyle and values ​​to perform more comprehensive matching. The matching department may also implement a follow-up function after matching to periodically check whether the relationship is going well.

[0053] (Example 2) A virtual lover generation system according to an embodiment of the present invention allows users to chat through messages and enjoy virtual dates. In this system, a generation AI creates a virtual lover based on the user's preferences and wishes, and analyzes the user's interactions and thoughts to suggest matches between users who are compatible. This allows the virtual lover generation system to increase users' confidence when interacting with members of the opposite sex in real life, contributing to the elimination of unmarried people and declining birth rates.

[0054] A virtual lover generation system according to an embodiment includes a generation unit, a conversation unit, an analysis unit, and a matching unit. The generation unit generates a virtual lover based on a user's preferences and wishes. For example, the generation unit uses a generation AI to create a virtual lover based on an ideal lover image input by the user. The generation unit can also learn the user's past romantic experiences and preferences to generate a more personalized virtual lover. The conversation unit supports messaging conversations between the user and the virtual lover generated by the generation unit. For example, the conversation unit analyzes messages sent by the user, and the generation AI generates appropriate replies. The conversation unit can also analyze the content of conversations between the user and the virtual lover and provide feedback to improve the user's communication skills. The analysis unit analyzes the interactions conducted by the conversation unit and the user's thoughts. For example, the analysis unit analyzes the content of the user's messages and responses to identify other users with common hobbies and values. The analysis unit can also provide positive feedback to increase the user's self-esteem. The matching unit suggests matching users who are compatible with each other based on the results of the analysis by the analysis unit. For example, the matching unit may consider the user's lifestyle and values ​​to perform more comprehensive matching. The matching unit may also implement a follow-up function after matching to periodically check whether the relationship is going well. This allows the virtual lover generation system according to the embodiment to increase users' confidence when interacting with members of the opposite sex in real life, contributing to the elimination of the trend toward unmarried people and declining birthrates. For example, users may improve their communication skills through interactions with their virtual lover. Furthermore, users may learn about the flow of dating and proper etiquette through virtual dates. This reduces users' anxiety about real-life relationships and allows them to interact with members of the opposite sex with confidence.

[0055] The generation unit can learn the user's past romantic experiences and preferences to generate a more personalized virtual lover. For example, the generation unit inputs details of a user's past romantic experiences, and the generation AI generates a personalized virtual lover based on that data. For example, the generation unit may reflect the past lover's personality and hobbies. The generation unit may also collect the user's preferences and past romantic experiences in the form of a questionnaire, and the generation AI may create the optimal virtual lover based on that information. For example, the generation unit may reflect the user's favorite movies and music preferences. The generation unit may also analyze the user's past message history and social media posts to learn their preferences and romantic tendencies and generate a virtual lover. For example, the generation unit may reflect specific language and topics. This allows the system to learn the user's past romantic experiences and preferences and generate a more personalized virtual lover, thereby increasing user satisfaction.

[0056] The generation unit can dynamically change the personality and behavioral patterns of the virtual lover according to the user's real-time emotional state. For example, the generation unit analyzes the user's emotional state in real time and dynamically changes the personality and behavioral patterns of the virtual lover. For example, when the user is sad, the generation unit speaks comforting words. Furthermore, the generation unit causes the virtual lover to show an appropriate reaction according to the situation at that time based on the user's emotional data. For example, when the user is feeling stressed, the generation unit engages in conversation that relaxes the user. Furthermore, the generation unit uses an emotion estimation function to automatically adjust the behavior of the virtual lover according to the user's emotions. For example, when the user is excited, the generation unit suggests having fun together. In this way, the virtual lover's personality and behavioral patterns can be dynamically changed according to the user's real-time emotional state, thereby increasing user satisfaction.

[0057] The generation unit uses the emotion estimation function to generate a virtual lover that best suits the user's emotions, thereby keeping the user's emotions positive. The generation unit, for example, uses the emotion estimation function to analyze the user's emotional state and generate an optimal virtual lover based on the results. For example, the generation unit creates a lover with a personality that will encourage the user when they are feeling down. The generation unit also generates a virtual lover that elicits positive emotions based on the user's emotion data. For example, the generation unit creates a lover with a personality and topics that will help the user relax. The generation unit also uses the emotion estimation function to generate a virtual lover that will keep the user's emotions positive. For example, the generation unit creates a lover that suggests date plans that the user will find enjoyable. In this way, the emotion estimation function can be used to generate a virtual lover that best suits the user's emotions, keeping the user's emotions positive, thereby increasing the user's satisfaction.

[0058] The generation unit can generate virtual lovers with settings of different cultures and nationalities, allowing the user to experience intercultural exchange. The generation unit generates a virtual lover based on, for example, a culture or nationality selected by the user, allowing the user to experience intercultural exchange. For example, for a user interested in French culture, the generation unit creates a French virtual lover. The generation unit also generates virtual lovers with settings of different cultures and nationalities, providing the user with an opportunity to learn about those cultures. For example, for a user interested in Japanese culture, the generation unit creates a Japanese virtual lover. The generation unit also generates virtual lovers with different nationalities and cultural backgrounds, allowing the user to enjoy intercultural exchange. For example, for a user interested in Italian culture, the generation unit creates an Italian virtual lover. In this way, the generation unit can generate virtual lovers with settings of different cultures and nationalities, allowing the user to experience intercultural exchange, thereby increasing user satisfaction.

[0059] The generation unit generates a virtual lover as a character specialized in the user's occupation or hobby, and can provide common topics of conversation. The generation unit generates a virtual lover based on the user's occupation or hobby, for example, and provides common topics of conversation. For example, if the user is a doctor, the generation unit creates a virtual lover that is knowledgeable about medicine. The generation unit also generates a virtual lover that is specialized in the user's hobbies and interests, allowing the user to enjoy conversations based on common topics. For example, if the user likes music, the generation unit creates a virtual lover that is knowledgeable about music. The generation unit also generates a virtual lover that matches the user's occupation or hobby, and livens up conversations based on common topics. For example, if the user likes sports, the generation unit creates a virtual lover that is knowledgeable about sports. In this way, the user's satisfaction can be increased by generating a virtual lover as a character specialized in the user's occupation or hobby, and providing common topics of conversation.

[0060] The generation unit uses the emotion estimation function to generate a virtual lover that allows the user to relax most, thereby promoting stress relief. The generation unit, for example, uses the emotion estimation function to generate a virtual lover that allows the user to relax most. For example, it creates a lover with a personality that relaxes the user when the user is feeling stressed. The generation unit also generates a virtual lover that has a high relaxing effect based on the user's emotion data. For example, it creates a lover who has topics and hobbies that help the user relax. The generation unit also uses the emotion estimation function to generate a virtual lover that allows the user to relax, thereby promoting stress relief. For example, it creates a lover that suggests date plans that help the user relax. In this way, the emotion estimation function is used to generate a virtual lover that allows the user to relax most, thereby promoting stress relief, thereby increasing user satisfaction.

[0061] The conversation unit can analyze the content of the conversation between the user and the virtual lover and provide feedback to improve the user's communication skills. The conversation unit, for example, analyzes the content of the conversation between the user and the virtual lover and provides feedback to improve the communication skills. For example, it provides advice on appropriate language use and how to choose topics. The conversation unit also analyzes the content of the conversation and provides feedback to help the user communicate better. For example, it provides advice on the flow and timing of the conversation. The conversation unit also analyzes the content of the user's conversation and provides specific feedback to improve the communication skills. For example, it provides advice on how to ask questions and how to react. In this way, the content of the conversation between the user and the virtual lover can be analyzed and feedback to improve the user's communication skills can be provided, thereby increasing the user's confidence.

[0062] The conversation unit can record the user's actions and reactions during the virtual date and later provide a review and areas for improvement. The conversation unit, for example, builds a system that records the user's actions and reactions during the virtual date and later provides a review and areas for improvement. For example, it records the flow of the date and the content of the conversation. The conversation unit also records the user's actions and reactions and later provides a review and areas for improvement after the date. For example, it provides feedback on what went well during the date and areas for improvement. The conversation unit also develops a system that records the user's actions and reactions during the virtual date and later provides a review and areas for improvement. For example, it analyzes the reactions and content of the conversation during the date. This allows the user's dating skills to be improved by recording the user's actions and reactions during the virtual date and later providing a review and areas for improvement.

[0063] The conversation unit uses the emotion estimation function to suggest conversations and date plans that correspond to the user's emotions, thereby increasing user satisfaction. The conversation unit, for example, uses the emotion estimation function to build a system that suggests conversations and date plans that correspond to the user's emotions. For example, it suggests topics and date plans that the user finds enjoyable. The conversation unit also suggests conversations and date plans that correspond to the user's emotions based on the user's emotion data. For example, it suggests date plans that allow the user to relax. The conversation unit also uses the emotion estimation function to develop a system that suggests conversations and date plans that correspond to the user's emotions, thereby increasing satisfaction. For example, it suggests date plans that excite the user. In this way, the emotion estimation function is used to suggest conversations and date plans that correspond to the user's emotions, thereby increasing user satisfaction and increasing user confidence.

[0064] The conversation unit can use virtual reality (VR) technology to enable a more realistic experience of a virtual date. The conversation unit, for example, uses virtual reality (VR) technology to build a system that enables a more realistic experience of a virtual date. For example, a virtual date is experienced using a VR headset. The conversation unit also utilizes VR technology to enable a user to feel the virtual date as if it were real. For example, a virtual date spot is reproduced in 3D. The conversation unit also uses virtual reality technology to enable a user to experience the virtual date more realistically. For example, a date simulation in a VR environment is provided. This allows a user to experience a virtual date more realistically using virtual reality (VR) technology, thereby increasing user satisfaction.

[0065] The conversation unit uses voice recognition technology to enable conversations via messages to be conducted by voice, thereby realizing more natural communication. The conversation unit, for example, uses voice recognition technology to build a system in which conversations via messages can be conducted by voice. For example, a user sends a message by voice, and a virtual lover replies by voice. The conversation unit also utilizes voice recognition technology to enable a user to converse with a virtual lover by voice. For example, it provides a conversation system that combines voice input and voice output. The conversation unit also uses voice recognition technology to develop a system in which conversations via messages can be conducted by voice, thereby realizing more natural communication. For example, it supports real-time conversations by voice. This allows conversations via messages to be conducted by voice using voice recognition technology, thereby realizing more natural communication and increasing user satisfaction.

[0066] The conversation unit uses the emotion estimation function to suggest a date plan that the user will enjoy most, thereby improving the quality of the date. The conversation unit, for example, uses the emotion estimation function to build a system that suggests a date plan that the user will enjoy most. For example, it suggests an optimal date plan based on the user's emotion data. The conversation unit also suggests an enjoyable date plan based on the user's emotion data. For example, it suggests a date plan that includes activities that excite the user. The conversation unit also uses the emotion estimation function to develop a system that suggests a date plan that the user will enjoy most, thereby improving the quality of the date. For example, it suggests a date plan that allows the user to relax. In this way, it is possible to use the emotion estimation function to suggest a date plan that the user will enjoy most, improving the quality of the date and increasing user satisfaction.

[0067] The analysis unit can provide positive feedback to increase the user's self-esteem. The analysis unit provides positive feedback to increase the user's self-esteem, for example, through interactions with the virtual lover. For example, the analysis unit sends a message praising the user when the user behaves well. The analysis unit also provides positive feedback from the virtual lover in response to the user's actions or comments. For example, the analysis unit provides encouraging words that will help the user gain confidence. The analysis unit also builds a system in which the virtual lover provides positive feedback to increase the user's self-esteem. For example, the analysis unit sends a message that highlights the user's successful experiences. In this way, the user's confidence can be improved by providing positive feedback to increase the user's self-esteem.

[0068] The analysis unit analyzes the user's conversations and actions, and can improve the user's confidence by providing feedback on specific areas for improvement and successful experiences. The analysis unit, for example, builds a system that analyzes the user's conversations and actions, and provides feedback on specific areas for improvement and successful experiences. For example, it highlights when the user successfully advances the conversation. The analysis unit also analyzes the user's actions and conversation content, and provides feedback on specific areas for improvement and successful experiences. For example, it praises the user when they give a good reaction. The analysis unit also develops a system that analyzes the user's conversations and actions, and provides feedback on specific areas for improvement and successful experiences to improve the user's confidence. For example, it provides a function that allows the user to look back on successful experiences. This allows the user's conversations and actions to be analyzed, and feedback on specific areas for improvement and successful experiences to improve the user's confidence.

[0069] The analysis unit can use the emotion estimation function to send messages of encouragement and support to a user when their confidence is low. For example, the analysis unit uses the emotion estimation function to build a system that sends messages of encouragement and support to a user when their confidence is low. For example, sending an encouraging message when the user is feeling down. The analysis unit also sends messages of encouragement when their confidence is low based on the user's emotion data. For example, saying words of encouragement when the user is feeling anxious. The analysis unit also uses the emotion estimation function to develop a system that sends messages of encouragement and support to a user when their confidence is low. For example, sending a message that helps the user regain their confidence. In this way, the emotion estimation function can be used to send messages of encouragement and support to a user when their confidence is low, thereby improving the user's confidence.

[0070] The analysis unit can provide a program for encouraging users to interact with their virtual lover in a group session format, thereby increasing self-confidence together with other users. The analysis unit, for example, can provide a program for encouraging users to interact with their virtual lover in a group session format, thereby increasing self-confidence together with other users. For example, an online session can be held in which multiple users participate. The analysis unit can also provide a place where users can encourage each other. The analysis unit can also provide a system for encouraging users to interact with their virtual lover in a group session format, thereby increasing self-confidence together with other users. For example, the analysis unit can increase self-confidence through group discussions. This can improve the user's self-confidence by providing a program for encouraging users to interact with their virtual lover in a group session format, thereby increasing self-confidence together with other users.

[0071] The analysis unit introduces mini-games and quizzes to improve self-confidence, allowing users to improve their skills while having fun. The analysis unit, for example, introduces mini-games and quizzes to improve self-confidence and builds a system for improving skills while having fun. For example, it provides quizzes to improve communication skills. The analysis unit also allows users to improve their self-confidence while having fun through mini-games and quizzes. For example, it provides games that improve self-confidence by accumulating successful experiences. The analysis unit also develops a system for introducing mini-games and quizzes to improve self-confidence and improving skills while having fun. For example, it provides games that include scenarios that help users gain confidence. In this way, it is possible to improve a user's confidence by introducing mini-games and quizzes to improve self-confidence and improving skills while having fun.

[0072] The analysis unit can use the emotion estimation function to identify situations in which the user will feel most confident and recreate those situations. The analysis unit, for example, uses the emotion estimation function to identify situations in which the user will feel most confident and builds a system that recreates those situations. For example, it recreates a scene in which the user feels a sense of success. The analysis unit also identifies situations in which the user will feel confident based on the user's emotion data and recreates those situations. For example, it provides a scenario that will make the user feel confident. The analysis unit also uses the emotion estimation function to identify situations in which the user will feel most confident and develops a system that recreates those situations. For example, it suggests a date plan that will make the user feel confident. In this way, the user's confidence can be improved by using the emotion estimation function to identify situations in which the user will feel most confident and recreating those situations.

[0073] The matching unit can analyze the user's message content and reactions in detail and develop a more accurate matching algorithm. The matching unit, for example, analyzes the user's message content and reactions in detail and builds a system that develops a more accurate matching algorithm. For example, it identifies users who share common hobbies and values. The matching unit also analyzes the message content and reactions and develops a more accurate matching algorithm. For example, it identifies users who are compatible with the user based on the user's conversation patterns and reactions. The matching unit also analyzes the user's message content and reactions in detail and develops a system that develops a more accurate matching algorithm. For example, it performs matching based on the user's emotional data. In this way, it is possible to analyze the user's message content and reactions in detail and develop a more accurate matching algorithm, thereby increasing user satisfaction.

[0074] The matching unit learns the user's thought patterns and predicts future behavior, thereby being able to propose more appropriate matches. The matching unit, for example, builds a system that proposes more appropriate matches by learning the user's thought patterns and predicting future behavior. For example, the prediction is made based on the user's past behavior data. The matching unit also learns the thought patterns and predicts future behavior to propose appropriate matches. For example, the matching unit analyzes the user's thought tendencies and identifies users who are compatible with the user. The matching unit also develops a system that proposes more appropriate matches by learning the user's thought patterns and predicting future behavior. For example, matching is performed based on the user's behavior prediction. In this way, the system can learn the user's thought patterns and predict future behavior to propose more appropriate matches, thereby increasing user satisfaction.

[0075] The matching unit uses the emotion estimation function to perform matching based on the user's emotional state, thereby building an emotionally stable relationship. The matching unit, for example, uses the emotion estimation function to build a system that performs matching based on the user's emotional state. For example, it identifies users who are compatible with the user based on the user's emotional data. The matching unit also analyzes the user's emotional state and performs matching to build an emotionally stable relationship. For example, it identifies a partner with whom the user can relax. The matching unit also uses the emotion estimation function to perform matching based on the user's emotional state, thereby developing a system that builds an emotionally stable relationship. For example, it performs matching based on the user's emotional data. As a result, it is possible to increase user satisfaction by using the emotion estimation function to perform matching based on the user's emotional state and build an emotionally stable relationship.

[0076] The matching unit takes into account the user's lifestyle and values ​​when matching, allowing for more comprehensive matching. The matching unit, for example, builds a system that takes into account the user's lifestyle and values ​​when matching, allowing for more comprehensive matching. For example, it identifies compatible users based on the user's lifestyle and values. The matching unit also analyzes the user's lifestyle and values ​​to perform comprehensive matching. For example, it identifies compatible users based on the user's hobbies and interests. The matching unit also develops a system that takes into account the user's lifestyle and values ​​when matching, allowing for more comprehensive matching. For example, it performs matching based on the user's values ​​and lifestyle. This allows for more comprehensive matching by taking into account the user's lifestyle and values ​​when matching, thereby increasing user satisfaction.

[0077] The matching unit can introduce a post-matching follow-up function and periodically check whether the relationship is going well. The matching unit, for example, introduces a post-matching follow-up function and builds a system that periodically checks whether the relationship is going well. For example, periodically sending a questionnaire to the user. The matching unit also uses the follow-up function to check whether the relationship is going well after matching. For example, it grasps the relationship status based on user feedback. The matching unit also introduces a post-matching follow-up function and develops a system that periodically checks whether the relationship is going well. For example, it analyzes the relationship status based on user emotional data. In this way, by introducing a post-matching follow-up function and periodically checking whether the relationship is going well, user satisfaction can be increased.

[0078] The matching unit can use the emotion estimation function to identify a partner with whom the user can feel most relaxed and propose a match with less stress. The matching unit, for example, uses the emotion estimation function to build a system that identifies a partner with whom the user can feel most relaxed and proposes a match with less stress. For example, a partner with whom the user can feel relaxed is identified based on the user's emotion data. The matching unit also identifies a partner with whom the user can feel relaxed based on the user's emotion data and proposes a match with less stress. For example, a partner with whom the user can feel relaxed is identified. The matching unit also uses the emotion estimation function to develop a system that identifies a partner with whom the user can feel most relaxed and proposes a match with less stress. For example, matching is performed based on the user's emotion data. As a result, the emotion estimation function can be used to identify a partner with whom the user can feel most relaxed and propose a match with less stress, thereby increasing user satisfaction.

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

[0080] The generation unit can generate a virtual lover based on the user's health condition to support health management. For example, when the user inputs health-related data, the generation unit creates a virtual lover that suggests a healthy lifestyle based on that data. The generation unit can also monitor the user's health condition and generate a virtual lover that provides appropriate advice. For example, if the user is not getting enough exercise, the generation unit can create a virtual lover that encourages exercise. The generation unit can also generate a virtual lover that supports healthy lifestyle habits based on the user's diet and sleep data. In this way, the user's satisfaction can be increased by generating a virtual lover based on the user's health condition and supporting health management.

[0081] The generation unit can generate a virtual lover based on the user's learning goals to support their learning. For example, if the user wants to study a specific subject, the generation unit can create a virtual lover who is knowledgeable about that subject. The generation unit can also generate a virtual lover that monitors the user's learning progress and provides appropriate feedback. For example, the generation unit can create a virtual lover that supplements parts of the material the user is struggling to understand. The generation unit can also generate a virtual lover that matches the user's learning style and suggest effective learning methods. In this way, the generation unit can increase user satisfaction by generating a virtual lover based on the user's learning goals and supporting their learning.

[0082] The generation unit uses the user's emotion estimation function to generate a virtual lover that makes the user feel most relaxed, thereby promoting stress relief. For example, it creates a lover with a personality that relaxes the user when the user is feeling stressed. The generation unit also generates a virtual lover that has a high relaxing effect based on the user's emotion data. For example, it creates a lover who has topics and hobbies that help the user relax. The generation unit also uses the emotion estimation function to generate a virtual lover that makes the user feel most relaxed, thereby promoting stress relief. For example, it creates a lover who suggests date plans that help the user relax. In this way, the emotion estimation function can be used to generate a virtual lover that makes the user feel most relaxed, promoting stress relief, thereby increasing user satisfaction.

[0083] The generation unit generates a virtual lover based on the user's hobbies and interests, allowing the user to enjoy a common hobby. For example, if the user likes music, the generation unit creates a virtual lover who is knowledgeable about music. The generation unit can also learn the user's hobbies and interests and generate a virtual lover who shares a common hobby. For example, if the user likes movies, the generation unit can create a virtual lover who is knowledgeable about movies. The generation unit can also generate a virtual lover based on the user's hobbies and interests, allowing the user to enjoy a common hobby. This increases the user's satisfaction by generating a virtual lover based on the user's hobbies and interests and enjoying a common hobby.

[0084] The generation unit uses the user's emotion estimation function to generate a virtual lover that the user will enjoy the most and provide entertainment. For example, it creates a lover with a personality that the user finds enjoyable. The generation unit also generates a virtual lover with a high entertainment effect based on the user's emotion data. For example, it creates a lover who has topics and hobbies that the user will enjoy. The generation unit also uses the emotion estimation function to generate a virtual lover that the user will enjoy and provide entertainment. For example, it creates a lover who suggests date plans that the user will enjoy. In this way, the emotion estimation function can be used to generate a virtual lover that the user will enjoy the most and provide entertainment, thereby increasing user satisfaction.

[0085] The generation unit generates a virtual lover as a character specialized in the user's occupation or hobby, and can provide common topics of conversation. For example, if the user is a doctor, the generation unit generates a virtual lover that is knowledgeable about medicine. The generation unit also generates a virtual lover that is specialized in the user's hobbies and interests, allowing the users to enjoy conversations based on common topics. For example, if the user likes music, the generation unit generates a virtual lover that is knowledgeable about music. The generation unit also generates a virtual lover that matches the user's occupation or hobby, and livens up conversations based on common topics. For example, if the user likes sports, the generation unit creates a virtual lover that is knowledgeable about sports. In this way, the user's satisfaction can be increased by generating a virtual lover as a character specialized in the user's occupation or hobby, and providing common topics of conversation.

[0086] The generation unit uses the emotion estimation function to generate a virtual lover that allows the user to relax most, thereby promoting stress relief. The generation unit, for example, uses the emotion estimation function to generate a virtual lover that allows the user to relax most. For example, it creates a lover with a personality that relaxes the user when the user is feeling stressed. The generation unit also generates a virtual lover that has a high relaxing effect based on the user's emotion data. For example, it creates a lover who has topics and hobbies that help the user relax. The generation unit also uses the emotion estimation function to generate a virtual lover that allows the user to relax, thereby promoting stress relief. For example, it creates a lover that suggests date plans that help the user relax. In this way, the emotion estimation function is used to generate a virtual lover that allows the user to relax most, thereby promoting stress relief, thereby increasing user satisfaction.

[0087] The conversation unit can analyze the content of the conversation between the user and the virtual lover and provide feedback to improve the user's communication skills. The conversation unit, for example, analyzes the content of the conversation between the user and the virtual lover and provides feedback to improve the communication skills. For example, it provides advice on appropriate language use and how to choose topics. The conversation unit also analyzes the content of the conversation and provides feedback to help the user communicate better. For example, it provides advice on the flow and timing of the conversation. The conversation unit also analyzes the content of the user's conversation and provides specific feedback to improve the communication skills. For example, it provides advice on how to ask questions and how to react. In this way, the content of the conversation between the user and the virtual lover can be analyzed and feedback to improve the user's communication skills can be provided, thereby increasing the user's confidence.

[0088] The conversation unit can record the user's actions and reactions during the virtual date and later provide a review and areas for improvement. The conversation unit, for example, builds a system that records the user's actions and reactions during the virtual date and later provides a review and areas for improvement. For example, it records the flow of the date and the content of the conversation. The conversation unit also records the user's actions and reactions and later provides a review and areas for improvement after the date. For example, it provides feedback on what went well during the date and areas for improvement. The conversation unit also develops a system that records the user's actions and reactions during the virtual date and later provides a review and areas for improvement. For example, it analyzes the reactions and content of the conversation during the date. This allows the user's dating skills to be improved by recording the user's actions and reactions during the virtual date and later providing a review and areas for improvement.

[0089] The conversation unit uses the emotion estimation function to suggest conversations and date plans that correspond to the user's emotions, thereby increasing user satisfaction. The conversation unit, for example, uses the emotion estimation function to build a system that suggests conversations and date plans that correspond to the user's emotions. For example, it suggests topics and date plans that the user finds enjoyable. The conversation unit also suggests conversations and date plans that correspond to the user's emotions based on the user's emotion data. For example, it suggests date plans that allow the user to relax. The conversation unit also uses the emotion estimation function to develop a system that suggests conversations and date plans that correspond to the user's emotions, thereby increasing satisfaction. For example, it suggests date plans that excite the user. In this way, the emotion estimation function is used to suggest conversations and date plans that correspond to the user's emotions, thereby increasing user satisfaction and increasing user confidence.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The generator generates a virtual lover based on the user's preferences and wishes. For example, the generator uses AI to create a virtual lover based on the user's ideal lover image input. The generator can also learn about the user's past romantic experiences and preferences to create a more personalized virtual lover. Step 2: The conversation unit supports messaging conversations between the user and the virtual lover generated by the generation unit. For example, the conversation unit analyzes messages sent by the user, and the generation AI generates appropriate replies. The conversation unit can also analyze the content of conversations between the user and the virtual lover and provide feedback to improve the user's communication skills. Step 3: The analysis unit analyzes the interactions carried out by the conversation unit and the user's thoughts. For example, the analysis unit analyzes the content of the user's messages and responses to identify other users who share common interests and values. The analysis unit can also provide positive feedback to increase the user's self-esteem. Step 4: The matching department proposes matches between users who are compatible based on the results of the analysis by the analysis department. For example, the matching department may consider the user's lifestyle and values ​​to perform more comprehensive matching. The matching department may also implement a follow-up function after matching to periodically check whether the relationship is going well.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 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.

[0112] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] The 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.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0117] Fig. 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.

[0118] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] In the 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.

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] The data processing system 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.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the 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.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] The data processing system 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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."

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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]

[0159] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a generating unit that generates a virtual lover based on the user's preferences and wishes; a conversation unit that supports conversations between the user and the virtual lover generated by the generation unit through messages; an analysis unit that analyzes the exchanges made by the conversation unit and the user's thoughts; a matching unit that proposes matching between users who are compatible with each other based on the results of the analysis by the analysis unit; A system characterized by:

2. The generation unit Learn about the user's past romantic experiences and preferences to create a more personalized virtual lover 2. The system of claim 1.

3. The generation unit Dynamically changing the personality and behavior patterns of the virtual lover according to the user's real-time emotional state.

2. The system of claim 1.

4. The generation unit The virtual lover is generated to best suit the user's emotions, thereby maintaining the user's emotions positive.

2. The system of claim 1.

5. The generation unit The virtual lover is generated in a setting of a different culture or nationality, allowing the user to experience cross-cultural exchange.

2. The system of claim 1.

6. The generation unit The virtual lover is generated as a character specialized in the user's occupation or hobby, and common topics are provided.

2. The system of claim 1.

Images