System

Abstract

An object of the system according to the embodiment is to provide confidence in communication with a person of the opposite sex and to solve the problems of marriage and low birthrate.SOLUTION: A system includes a registration unit, a conversation unit, an analysis unit, and a matching unit. The registration unit allows a user to register with the service and create a virtual lover. The conversation unit has a conversation with the virtual lover generated by the registration unit using a message. The analysis unit analyzes exchanges performed by the conversation unit and proposes matching between users. The matching unit matches the users based on the matching proposed 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 that many users lack confidence in interacting with members of the opposite sex, and the problems of unmarried people and declining birth rates have not been resolved.

[0005] The system according to the embodiment aims to give people confidence in interacting with the opposite sex and to solve the problems of the increasing number of unmarried people and declining birthrates. [Means for solving the problem]

[0006] The system according to the embodiment includes a registration unit, a conversation unit, an analysis unit, and a matching unit. The registration unit allows a user to register for the service and create a virtual lover. The conversation unit converses with the virtual lover created by the registration unit via messages. The analysis unit analyzes the exchanges made by the conversation unit and proposes matches between users. The matching unit matches users based on the matches proposed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can give people confidence in interacting with the opposite sex and solve the problems of unmarried people and declining birth rates. [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 creation system according to an embodiment of the present invention allows users to create a virtual lover, converse with them via messages, and enjoy virtual dates. In the virtual lover creation system, users register for a service and create a virtual lover. This virtual lover is generated by AI and customized to the user's preferences and personality. The user then converses with the virtual lover via messages and enjoys virtual dates. The AI ​​analyzes the user's interactions and thoughts and suggests matching users with compatible partners. This allows users to gain confidence when interacting with members of the opposite sex in real life, contributing to the elimination of unmarried people and declining birth rates. For example, in a virtual lover creation system, a user registers for a service and creates a virtual lover. The user inputs their preferences and personality, and the AI ​​generates a virtual lover based on that information. For example, if a user inputs, "I want a lover with a kind personality who enjoys watching movies," the AI ​​generates a virtual lover that meets those criteria. The virtual lover creation system then allows users to converse with the virtual lover via messages and enjoy virtual dates. The AI ​​generates appropriate responses to the user's messages, enabling natural conversations. For example, if a user sends a message asking, "What movie do you want to see today?", the AI ​​will respond with, "I want to see an action movie today." In addition, during virtual dates, users can watch a movie or enjoy a meal at a restaurant with their virtual lover. Furthermore, in the virtual lover generation system, the AI ​​analyzes users' interactions and thoughts to suggest matching compatible users. For example, if user A's hobby is watching movies and user B's hobby is the same, the AI ​​will suggest matching the two. This allows users to gain confidence when interacting with members of the opposite sex in real life. This contributes to alleviating the problem of unmarried people and declining birth rates. Through interactions with virtual lovers, users can gain confidence in interacting with members of the opposite sex, increasing their desire for real-life romance and marriage. For example, by dating a virtual lover, users can learn how to date and conversation tips, which will help them act with confidence on real dates.This allows the virtual lover creation system to allow users to enjoy conversations and dates with their virtual lover, and to match users who are compatible with each other. For example, by conversing with a virtual lover, users can gain confidence in interacting with the opposite sex, increasing their desire for real-life romance and marriage. This will contribute to alleviating the problem of unmarried people and declining birth rates.

[0029] A virtual lover generation system according to an embodiment includes a registration unit, a conversation unit, an analysis unit, and a matching unit. The registration unit allows a user to register for the service and create a virtual lover. The user inputs their preferences and personality, and the AI ​​generates a virtual lover based on the input. For example, if a user inputs, "I want a lover with a kind personality and a hobby of watching movies," the AI ​​generates a virtual lover that meets those criteria. The conversation unit uses the generation AI to generate appropriate responses to the user's messages. For example, if a user sends a message such as, "What kind of movie do you want to see today?", the generation AI replies, "I'd like to see an action movie today." The conversation unit can also generate virtual date scenarios. For example, it generates a scenario in which a user watches a movie with their virtual lover or enjoys a meal at a restaurant. The analysis unit uses the generation AI to analyze users' interactions and thoughts and propose matches between compatible users. For example, if user A enjoys watching movies and user B also has the same hobby, the generation AI proposes a match between the two. The matching unit matches users based on the proposed matches. For example, user A and user B are matched based on the proposed match. As a result, the virtual lover generation system according to the embodiment allows users to enjoy conversations and dates with their virtual lovers, and matches users who are compatible with each other. For example, through conversations with a virtual lover, users can gain confidence in interacting with members of the opposite sex, increasing their desire for real-life romance and marriage. This contributes to alleviating the problem of unmarried people and declining birth rates.

[0030] The registration unit can input the user's preferences and personality and generate a virtual lover. For example, if the user inputs, "I want a lover with a kind personality and a hobby of watching movies," the AI ​​generates a virtual lover that meets those criteria. The registration unit can also provide a questionnaire for the user to enter details about the user's personality and preferences. For example, when the user answers the questionnaire, the AI ​​generates a virtual lover based on that information. Furthermore, the registration unit can collect user behavioral data and generate a virtual lover based on that data. For example, the AI ​​generates a virtual lover based on the content the user has previously viewed and their purchase history. This provides a more personalized experience by generating a virtual lover based on the user's preferences and personality. Some or all of the above-described processing in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input data about the user's preferences and personality into the generation AI and have the generation AI generate a virtual lover.

[0031] The conversation unit can generate an appropriate response to a user's message. For example, if a user sends a message such as "What movie do you want to see today?", the generation AI will respond with "I want to see an action movie today." The conversation unit can also generate an appropriate response based on the context of the user's message. For example, if a user sends a message such as "I'm tired today," the generation AI will respond with "Then let's watch a relaxing movie." The conversation unit can also generate an appropriate response based on the user's emotions. For example, if a user sends a message such as "I'm sad today," the generation AI will generate an empathetic response such as "Did something happen?" This allows for a natural conversation with the user. Some or all of the above-described processing in the conversation unit may be performed using, or without, AI. For example, the conversation unit can input a user's message into the generation AI and have the generation AI generate an appropriate response.

[0032] The conversation unit can generate a virtual date scenario. For example, the conversation unit generates a scenario in which the user watches a movie or enjoys a meal at a restaurant with a virtual lover. The conversation unit can also customize the virtual date scenario based on the user's preferences. For example, if the user inputs, "I want to go to an Italian restaurant today," the generation AI generates that scenario. Furthermore, the conversation unit can generate an optimal date scenario by referring to the user's past dating history. For example, the generation AI generates a new date scenario based on date scenarios that the user enjoyed in the past. This allows the user to enjoy a virtual date. Some or all of the above-mentioned processing in the conversation unit may be performed, for example, using AI, or may be performed without using AI. For example, the conversation unit can input the user's preference data into the generation AI and cause the generation AI to generate a virtual date scenario.

[0033] The analysis unit can analyze user interactions and suggest matching between users. For example, if user A has a hobby of watching movies and user B has the same hobby, the analysis unit can suggest matching the two. The analysis unit can also analyze the content and frequency of user interactions to match users who are compatible with each other. For example, if user A and user B frequently exchange messages, the generation AI can suggest matching the two. The analysis unit can also analyze user emotions to match users who are compatible with each other. For example, if user A likes fun conversations and user B also likes fun conversations, the generation AI can suggest matching the two. This suggests a match between users who are compatible with each other. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input user interaction data into the generation AI and have the generation AI execute a matching suggestion.

[0034] The matching unit can match users based on the proposed match. For example, the matching unit matches user A and user B based on the proposed match. The matching unit can also adjust the match based on user preferences and conditions. For example, if user A requests to be matched with someone who enjoys watching movies, the generation AI matches users who meet those conditions. Furthermore, the matching unit can perform optimal matching by referring to the user's past matching history. For example, the generation AI proposes a new match based on the characteristics of user A's past successful matches. This allows matching between users. Some or all of the above-described processing in the matching unit may be performed using AI, for example, or may be performed without using AI. For example, the matching unit can input the proposed match data into the generation AI and have the generation AI perform matching between users.

[0035] The registration unit can analyze the user's past romantic experiences and generate a virtual lover. For example, the registration unit can analyze the characteristics of lovers that the user has liked in the past and generate a virtual lover based on the analysis. The registration unit can also eliminate lovers with characteristics that the user has avoided in the past and generate an optimal virtual lover. The registration unit can also analyze the causes of the user's past romantic failures and generate a virtual lover that avoids those causes. In this way, an optimal virtual lover is generated based on the user's past romantic experiences. Some or all of the above-described processing in the registration unit may be performed using AI, for example, or may be performed without using AI. For example, the registration unit can input data on the user's past romantic experiences into the generation AI and cause the generation AI to generate a virtual lover.

[0036] The registration unit can customize the virtual lover based on the user's lifestyle and hobbies. For example, if the user likes the outdoors, the registration unit can generate a virtual lover with the same hobbies. Furthermore, if the user likes reading, the registration unit can generate a virtual lover who is interested in reading. Furthermore, if the user has a night-owl lifestyle, the registration unit can generate a virtual lover who is a night-owl. In this way, a virtual lover that suits the user's lifestyle and hobbies is generated. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can input data about the user's lifestyle and hobbies into the generation AI and cause the generation AI to customize the virtual lover.

[0037] The registration unit can generate a virtual lover according to the user's input method. For example, if the user inputs their preferences by voice, the registration unit can generate a virtual lover using voice analysis. Also, if the user inputs detailed preferences in text, the registration unit can generate a virtual lover using text analysis. Also, if the user uploads an image, the registration unit can generate a virtual lover using image analysis. In this way, a virtual lover is generated according to the user's input method. Some or all of the above-described processing in the registration unit may be performed using AI, for example, or may be performed without using AI. For example, the registration unit can input the user's input data into a generation AI and cause the generation AI to generate a virtual lover.

[0038] The registration unit can generate a virtual lover taking into account the user's geographical location information. For example, if the user lives in an urban area, the registration unit can generate a virtual lover suited to urban life. Furthermore, if the user lives in the countryside, the registration unit can generate a virtual lover who loves nature. Furthermore, if the user lives overseas, the registration unit can generate a virtual lover who is familiar with the local culture. In this way, a virtual lover is generated based on the user's geographical location information. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can input the user's geographical location information into the generation AI and cause the generation AI to generate a virtual lover.

[0039] The registration unit can analyze the user's social media activity and generate a related virtual lover. For example, the registration unit can analyze the content the user frequently posts on social media and generate a virtual lover based on the content. The registration unit can also analyze the user's social media friendships and generate a virtual lover with common interests. The registration unit can also analyze the user's social media activity time and generate a virtual lover based on the time spent on social media. In this way, a virtual lover based on the user's social media activity is generated. Some or all of the above-described processing in the registration unit can be performed using, for example, AI, or can be performed without using AI. For example, the registration unit can input the user's social media activity data into a generation AI and cause the generation AI to generate a virtual lover.

[0040] The registration unit can customize the virtual lover by reflecting the user's past feedback. For example, the registration unit can adjust the virtual lover's personality based on feedback provided by the user in the past. The registration unit can also adjust the virtual lover's appearance based on feedback provided by the user in the past. The registration unit can also adjust the virtual lover's hobbies and interests based on feedback provided by the user in the past. In this way, a virtual lover based on the user's past feedback is generated. Some or all of the above-described processing in the registration unit may be performed using AI, for example, or may be performed without using AI. For example, the registration unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the virtual lover.

[0041] The conversation unit can generate a reply by referring to the user's past conversation history. The conversation unit can, for example, suggest related topics based on content that the user has previously spoken about. The conversation unit can also generate an optimal reply based on topics in which the user has previously shown interest. The conversation unit can also generate a reply to a specific topic from the user's past conversation history. This generates an optimal reply based on the user's past conversation history. Some or all of the above-mentioned processing in the conversation unit can be performed, for example, using AI or without AI. For example, the conversation unit can input the user's past conversation history data into a generation AI and have the generation AI generate a reply.

[0042] The conversation unit can customize the conversation content according to the user's current mood and situation. For example, if the user is tired, the conversation unit can provide a relaxing topic. Furthermore, if the user is excited, the conversation unit can provide a stimulating topic. Furthermore, if the user is depressed, the conversation unit can provide words of encouragement. In this way, conversation content is provided according to the user's mood and situation. Some or all of the above-mentioned processing in the conversation unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversation unit can input the user's mood and situation data into the generation AI and have the generation AI customize the conversation content.

[0043] The conversation unit can generate an optimal response depending on the user's input method. For example, when the user asks a question by voice, the conversation unit generates an optimal response using voice analysis. Furthermore, when the user asks a question by text, the conversation unit can also generate an optimal response using text analysis. Furthermore, when the user sends an image, the conversation unit can also generate an optimal response using image analysis. In this way, an optimal response depending on the user's input method is generated. Some or all of the above-described processing in the conversation unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversation unit can input the user's input data into a generation AI and have the generation AI generate a response.

[0044] The conversation unit can customize the conversation content taking into account the user's geographical location information. For example, if the user is in an urban area, the conversation unit can provide topics related to the city. Furthermore, if the user is in a rural area, the conversation unit can provide topics related to nature. Furthermore, if the user is overseas, the conversation unit can provide topics related to the local culture. In this way, conversation content based on the user's geographical location information is provided. Some or all of the above-described processing in the conversation unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversation unit can input the user's geographical location information into the generation AI and cause the generation AI to customize the conversation content.

[0045] The conversation unit can analyze the user's social media activity and generate related conversation content. The conversation unit can provide related topics based on, for example, content that the user frequently posts on social media. The conversation unit can also analyze the user's friendships on social media and provide common topics. The conversation unit can also analyze the user's social media activity time and provide conversation content tailored to that time. In this way, conversation content based on the user's social media activity is provided. Some or all of the above-mentioned processing in the conversation unit can be performed using, for example, AI, or can be performed without using AI. For example, the conversation unit can input the user's social media activity data into a generation AI and cause the generation AI to generate conversation content.

[0046] The conversation unit can customize the conversation content by reflecting the user's past feedback. The conversation unit, for example, adjusts the tone of the conversation based on feedback provided by the user in the past. The conversation unit can also adjust the conversation content based on feedback provided by the user in the past. The conversation unit can also adjust the frequency of the conversation based on feedback provided by the user in the past. In this way, the conversation content is provided based on the user's past feedback. Some or all of the above-mentioned processing in the conversation unit may be performed using AI, for example, or may be performed without using AI. For example, the conversation unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the conversation content.

[0047] The analysis unit can apply an analysis method by referring to the user's past interaction history. The analysis unit can apply a relevant analysis method based on, for example, the user's past interactions. The analysis unit can also extract and analyze specific patterns from the user's past interaction history. The analysis unit can also select an optimal analysis method based on the user's past interaction history. This allows for optimal analysis based on the user's past interaction history. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's past interaction history data into the generation AI and have the generation AI apply the analysis method.

[0048] The analysis unit can customize the analysis content according to the user's current mood and situation. For example, if the user is tired, the analysis unit can analyze the user by focusing on interactions that help them relax. Furthermore, if the user is excited, the analysis unit can analyze the user by focusing on stimulating interactions. Furthermore, if the user is depressed, the analysis unit can analyze the user by focusing on encouraging interactions. This allows the analysis content to be provided according to the user's mood and situation. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's mood and situation data into the generation AI and have the generation AI customize the analysis content.

[0049] The analysis unit can select the optimal analysis method depending on the user's input method. For example, if the user communicates via voice, the analysis unit performs analysis using voice analysis. Furthermore, if the user communicates via text, the analysis unit can also perform analysis using text analysis. Furthermore, if the user sends an image, the analysis unit can also perform analysis using image analysis. This allows for the optimal analysis to be performed depending on the user's input method. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's input data into a generation AI and have the generation AI select an analysis method.

[0050] The analysis unit can customize the analysis content taking into account the user's geographical location information. For example, if the user is in an urban area, the analysis unit can emphasize city-related interactions in the analysis. Furthermore, if the user is in a rural area, the analysis unit can emphasize nature-related interactions in the analysis. Furthermore, if the user is overseas, the analysis unit can emphasize local culture-related interactions in the analysis. This provides analysis content based on the user's geographical location information. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the user's geographical location information into the generation AI and cause the generation AI to customize the analysis content.

[0051] The analysis unit can analyze the user's social media activities and generate related analysis results. The analysis unit can generate related analysis results based on, for example, the content the user frequently posts on social media. The analysis unit can also analyze the user's social media friendships and suggest users with common interests. The analysis unit can also analyze the user's social media activity time and generate analysis results tailored to the time spent on social media. This provides analysis results based on the user's social media activities. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's social media activity data into a generation AI and cause the generation AI to generate analysis results.

[0052] The analysis unit can customize the analysis method by reflecting the user's past feedback. The analysis unit can adjust the analysis method based on, for example, feedback provided by the user in the past. The analysis unit can also adjust the focus of the analysis based on feedback provided by the user in the past. The analysis unit can also adjust the display method of the analysis results based on feedback provided by the user in the past. This provides an analysis method based on the user's past feedback. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the analysis method.

[0053] The matching unit can apply a matching method by referring to the user's past matching history. The matching unit, for example, can suggest an optimal partner based on the characteristics of successful matches made by the user in the past. The matching unit can also eliminate the characteristics of matches that the user has previously failed to make and suggest an optimal partner. The matching unit can also analyze the user's past matching history and suggest a partner with the best compatibility. This allows optimal matching to be performed based on the user's past matching history. Some or all of the above-described processing in the matching unit can be performed using, for example, AI, or can be performed without using AI. For example, the matching unit can input the user's past matching history data into the generation AI and cause the generation AI to apply a matching method.

[0054] The matching unit can customize the matching content according to the user's current mood and situation. For example, if the user is tired, the matching unit can suggest a partner who can relax the user. Furthermore, if the user is excited, the matching unit can suggest a stimulating partner. Furthermore, if the user is depressed, the matching unit can suggest a partner who will offer words of encouragement. In this way, matching content is provided according to the user's mood and situation. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input the user's mood and situation data into the generation AI and cause the generation AI to customize the matching content.

[0055] The matching unit can select the optimal matching method depending on the user's input method. For example, if the user inputs their preferences by voice, the matching unit can use voice analysis to suggest the optimal partner. Also, if the user inputs detailed preferences in text, the matching unit can use text analysis to suggest the optimal partner. Also, if the user uploads an image, the matching unit can use image analysis to suggest the optimal partner. This allows optimal matching to be performed depending on the user's input method. Some or all of the above-mentioned processing in the matching unit may be performed using AI, for example, or may be performed without using AI. For example, the matching unit can input the user's input data into a generation AI and have the generation AI select a matching method.

[0056] The matching unit can customize the matching content taking into account the user's geographical location information. For example, if the user is in an urban area, the matching unit can prioritize suggesting partners related to the city. Furthermore, if the user is in a rural area, the matching unit can prioritize suggesting partners related to nature. Furthermore, if the user is overseas, the matching unit can prioritize suggesting partners who are familiar with the culture of that region. This provides matching content based on the user's geographical location information. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input the user's geographical location information into the generation AI and cause the generation AI to customize the matching content.

[0057] The matching unit can analyze the user's social media activity and generate relevant matching results. The matching unit can, for example, suggest related partners based on the content the user frequently posts on social media. The matching unit can also analyze the user's social media friendships and suggest partners with common interests. The matching unit can also analyze the user's social media activity time and suggest partners that match that. This provides matching results based on the user's social media activity. Some or all of the above-mentioned processing in the matching unit can be performed, for example, using AI or without AI. For example, the matching unit can input the user's social media activity data into a generation AI and cause the generation AI to generate matching results.

[0058] The matching unit can customize the matching method by reflecting the user's past feedback. The matching unit can adjust the matching method based on, for example, feedback provided by the user in the past. The matching unit can also adjust the focus of matching based on feedback provided by the user in the past. The matching unit can also adjust the display method of matching results based on feedback provided by the user in the past. This provides a matching method based on the user's past feedback. Some or all of the above-described processing in the matching unit can be performed using AI, for example, or can be performed without using AI. For example, the matching unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the matching method.

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

[0060] The virtual lover generation system can further analyze the user's musical preferences and customize the virtual lover's hobbies. For example, if the user likes rock music, a virtual lover who also likes rock music can be generated. If the user likes classical music, a virtual lover who is knowledgeable about classical music can be generated. Furthermore, if the user frequently goes to live concerts, a virtual lover who enjoys live concerts can be generated. In this way, a virtual lover based on the user's musical preferences can be generated.

[0061] The virtual lover generation system can further analyze the user's travel history and customize the virtual lover's travel hobbies. For example, based on countries and cities the user has visited in the past, a virtual lover who is interested in the same places can be generated. If the user likes adventurous travel, a virtual lover with a strong sense of adventure can be generated. Furthermore, if the user likes resort destinations, a virtual lover who is familiar with relaxing resort destinations can be generated. In this way, a virtual lover based on the user's travel history can be generated.

[0062] The virtual lover generation system can further analyze the user's reading history and customize the virtual lover's reading preferences. For example, if the user likes mystery novels, a virtual lover who also likes mystery novels can be generated. If the user likes science books, a virtual lover who is knowledgeable about science can be generated. If the user likes poetry collections, a virtual lover who is interested in poetry can be generated. In this way, a virtual lover is generated based on the user's reading history.

[0063] The virtual lover generation system can also obtain the user's exercise data and customize the virtual lover's exercise habits. For example, if the user likes running, it can generate a virtual lover who also enjoys running. If the user likes yoga, it can generate a virtual lover who is knowledgeable about yoga. Furthermore, if the user has a habit of going to the gym, it can generate a virtual lover who enjoys training at the gym. In this way, a virtual lover is generated based on the user's exercise data.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: In the registration section, the user registers for the service and creates a virtual lover. The user inputs their preferences and personality, and the AI ​​generates a virtual lover based on that. For example, if the user inputs, "I want a lover who has a kind personality and whose hobby is watching movies," the AI ​​will generate a virtual lover that meets those criteria. Step 2: The conversation unit uses the generation AI to generate an appropriate response to the user's message. For example, if the user sends a message asking, "What movie do you want to see today?", the generation AI will respond with, "I'd like to see an action movie today." The conversation unit can also generate virtual date scenarios. For example, it can generate scenarios in which the user watches a movie with a virtual lover or enjoys a meal at a restaurant. Step 3: The analysis unit uses the generation AI to analyze the user's interactions and thoughts and propose matches between users who are compatible. For example, if user A enjoys watching movies and user B also has the same hobby, the generation AI will propose a match between the two. Step 4: The matching unit matches users together based on the proposed matching. For example, it matches user A with user B based on the proposed matching.

[0066] (Example 2) A virtual lover creation system according to an embodiment of the present invention allows users to create a virtual lover, converse with them via messages, and enjoy virtual dates. In the virtual lover creation system, users register for a service and create a virtual lover. This virtual lover is generated by AI and customized to the user's preferences and personality. The user then converses with the virtual lover via messages and enjoys virtual dates. The AI ​​analyzes the user's interactions and thoughts and suggests matching users with compatible partners. This allows users to gain confidence when interacting with members of the opposite sex in real life, contributing to the elimination of unmarried people and declining birth rates. For example, in a virtual lover creation system, a user registers for a service and creates a virtual lover. The user inputs their preferences and personality, and the AI ​​generates a virtual lover based on that information. For example, if a user inputs, "I want a lover with a kind personality who enjoys watching movies," the AI ​​generates a virtual lover that meets those criteria. The virtual lover creation system then allows users to converse with the virtual lover via messages and enjoy virtual dates. The AI ​​generates appropriate responses to the user's messages, enabling natural conversations. For example, if a user sends a message asking, "What movie do you want to see today?", the AI ​​will respond with, "I want to see an action movie today." In addition, during virtual dates, users can watch a movie or enjoy a meal at a restaurant with their virtual lover. Furthermore, in the virtual lover generation system, the AI ​​analyzes users' interactions and thoughts to suggest matching compatible users. For example, if user A's hobby is watching movies and user B's hobby is the same, the AI ​​will suggest matching the two. This allows users to gain confidence when interacting with members of the opposite sex in real life. This contributes to alleviating the problem of unmarried people and declining birth rates. Through interactions with virtual lovers, users can gain confidence in interacting with members of the opposite sex, increasing their desire for real-life romance and marriage. For example, by dating a virtual lover, users can learn how to date and conversation tips, which will help them act with confidence on real dates.This allows the virtual lover creation system to allow users to enjoy conversations and dates with their virtual lover, and to match users who are compatible with each other. For example, by conversing with a virtual lover, users can gain confidence in interacting with the opposite sex, increasing their desire for real-life romance and marriage. This will contribute to alleviating the problem of unmarried people and declining birth rates.

[0067] A virtual lover generation system according to an embodiment includes a registration unit, a conversation unit, an analysis unit, and a matching unit. The registration unit allows a user to register for the service and create a virtual lover. The user inputs their preferences and personality, and the AI ​​generates a virtual lover based on the input. For example, if a user inputs, "I want a lover with a kind personality and a hobby of watching movies," the AI ​​generates a virtual lover that meets those criteria. The conversation unit uses the generation AI to generate appropriate responses to the user's messages. For example, if a user sends a message such as, "What kind of movie do you want to see today?", the generation AI replies, "I'd like to see an action movie today." The conversation unit can also generate virtual date scenarios. For example, it generates a scenario in which a user watches a movie with their virtual lover or enjoys a meal at a restaurant. The analysis unit uses the generation AI to analyze users' interactions and thoughts and propose matches between compatible users. For example, if user A enjoys watching movies and user B also has the same hobby, the generation AI proposes a match between the two. The matching unit matches users based on the proposed matches. For example, user A and user B are matched based on the proposed match. As a result, the virtual lover generation system according to the embodiment allows users to enjoy conversations and dates with their virtual lovers, and matches users who are compatible with each other. For example, through conversations with a virtual lover, users can gain confidence in interacting with members of the opposite sex, increasing their desire for real-life romance and marriage. This contributes to alleviating the problem of unmarried people and declining birth rates.

[0068] The registration unit can input the user's preferences and personality and generate a virtual lover. For example, if the user inputs, "I want a lover with a kind personality and a hobby of watching movies," the AI ​​generates a virtual lover that meets those criteria. The registration unit can also provide a questionnaire for the user to enter details about the user's personality and preferences. For example, when the user answers the questionnaire, the AI ​​generates a virtual lover based on that information. Furthermore, the registration unit can collect user behavioral data and generate a virtual lover based on that data. For example, the AI ​​generates a virtual lover based on the content the user has previously viewed and their purchase history. This provides a more personalized experience by generating a virtual lover based on the user's preferences and personality. Some or all of the above-described processing in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input data about the user's preferences and personality into the generation AI and have the generation AI generate a virtual lover.

[0069] The conversation unit can generate an appropriate response to a user's message. For example, if a user sends a message such as "What movie do you want to see today?", the generation AI will respond with "I want to see an action movie today." The conversation unit can also generate an appropriate response based on the context of the user's message. For example, if a user sends a message such as "I'm tired today," the generation AI will respond with "Then let's watch a relaxing movie." The conversation unit can also generate an appropriate response based on the user's emotions. For example, if a user sends a message such as "I'm sad today," the generation AI will generate an empathetic response such as "Did something happen?" This allows for a natural conversation with the user. Some or all of the above-described processing in the conversation unit may be performed using, or without, AI. For example, the conversation unit can input a user's message into the generation AI and have the generation AI generate an appropriate response.

[0070] The conversation unit can generate a virtual date scenario. For example, the conversation unit generates a scenario in which the user watches a movie or enjoys a meal at a restaurant with a virtual lover. The conversation unit can also customize the virtual date scenario based on the user's preferences. For example, if the user inputs, "I want to go to an Italian restaurant today," the generation AI generates that scenario. Furthermore, the conversation unit can generate an optimal date scenario by referring to the user's past dating history. For example, the generation AI generates a new date scenario based on date scenarios that the user enjoyed in the past. This allows the user to enjoy a virtual date. Some or all of the above-mentioned processing in the conversation unit may be performed, for example, using AI, or may be performed without using AI. For example, the conversation unit can input the user's preference data into the generation AI and cause the generation AI to generate a virtual date scenario.

[0071] The analysis unit can analyze user interactions and suggest matching between users. For example, if user A has a hobby of watching movies and user B has the same hobby, the analysis unit can suggest matching the two. The analysis unit can also analyze the content and frequency of user interactions to match users who are compatible with each other. For example, if user A and user B frequently exchange messages, the generation AI can suggest matching the two. The analysis unit can also analyze user emotions to match users who are compatible with each other. For example, if user A likes fun conversations and user B also likes fun conversations, the generation AI can suggest matching the two. This suggests a match between users who are compatible with each other. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input user interaction data into the generation AI and have the generation AI execute a matching suggestion.

[0072] The matching unit can match users based on the proposed match. For example, the matching unit matches user A and user B based on the proposed match. The matching unit can also adjust the match based on user preferences and conditions. For example, if user A requests to be matched with someone who enjoys watching movies, the generation AI matches users who meet those conditions. Furthermore, the matching unit can perform optimal matching by referring to the user's past matching history. For example, the generation AI proposes a new match based on the characteristics of user A's past successful matches. This allows matching between users. Some or all of the above-described processing in the matching unit may be performed using AI, for example, or may be performed without using AI. For example, the matching unit can input the proposed match data into the generation AI and have the generation AI perform matching between users.

[0073] The registration unit can estimate the user's emotions and adjust the personality and appearance of the virtual lover based on the estimated user emotions. For example, if the user is feeling stressed, the registration unit can generate a virtual lover with a relaxed personality. Furthermore, if the user is feeling happy, the registration unit can generate a virtual lover with a cheerful and happy personality. Furthermore, if the user is feeling lonely, the registration unit can generate a virtual lover with a friendly and empathetic personality. In this way, a virtual lover is generated according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the registration unit can be performed using, for example, an AI, or without an AI. For example, the registration unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the personality and appearance of the virtual lover.

[0074] The registration unit can analyze the user's past romantic experiences and generate a virtual lover. For example, the registration unit can analyze the characteristics of lovers that the user has liked in the past and generate a virtual lover based on the analysis. The registration unit can also eliminate lovers with characteristics that the user has avoided in the past and generate an optimal virtual lover. The registration unit can also analyze the causes of the user's past romantic failures and generate a virtual lover that avoids those causes. In this way, an optimal virtual lover is generated based on the user's past romantic experiences. Some or all of the above-described processing in the registration unit may be performed using AI, for example, or may be performed without using AI. For example, the registration unit can input data on the user's past romantic experiences into the generation AI and cause the generation AI to generate a virtual lover.

[0075] The registration unit can customize the virtual lover based on the user's lifestyle and hobbies. For example, if the user likes the outdoors, the registration unit can generate a virtual lover with the same hobbies. Furthermore, if the user likes reading, the registration unit can generate a virtual lover who is interested in reading. Furthermore, if the user has a night-owl lifestyle, the registration unit can generate a virtual lover who is a night-owl. In this way, a virtual lover that suits the user's lifestyle and hobbies is generated. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can input data about the user's lifestyle and hobbies into the generation AI and cause the generation AI to customize the virtual lover.

[0076] The registration unit can generate a virtual lover according to the user's input method. For example, if the user inputs their preferences by voice, the registration unit can generate a virtual lover using voice analysis. Also, if the user inputs detailed preferences in text, the registration unit can generate a virtual lover using text analysis. Also, if the user uploads an image, the registration unit can generate a virtual lover using image analysis. In this way, a virtual lover is generated according to the user's input method. Some or all of the above-described processing in the registration unit may be performed using AI, for example, or may be performed without using AI. For example, the registration unit can input the user's input data into a generation AI and cause the generation AI to generate a virtual lover.

[0077] The registration unit can estimate the user's emotions and adjust the hobbies and interests of the virtual lover based on the estimated user emotions. For example, if the user is feeling stressed, the registration unit can generate a virtual lover with a relaxing hobby. Furthermore, if the user is feeling happy, the registration unit can generate a virtual lover with an active hobby. Furthermore, if the user is feeling lonely, the registration unit can generate a virtual lover with a common hobby. This generates a virtual lover with hobbies and interests that correspond to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the registration unit can be performed using, for example, an AI, or without an AI. For example, the registration unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the virtual lover's hobbies and interests.

[0078] The registration unit can generate a virtual lover taking into account the user's geographical location information. For example, if the user lives in an urban area, the registration unit can generate a virtual lover suited to urban life. Furthermore, if the user lives in the countryside, the registration unit can generate a virtual lover who loves nature. Furthermore, if the user lives overseas, the registration unit can generate a virtual lover who is familiar with the local culture. In this way, a virtual lover is generated based on the user's geographical location information. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can input the user's geographical location information into the generation AI and cause the generation AI to generate a virtual lover.

[0079] The registration unit can analyze the user's social media activity and generate a related virtual lover. For example, the registration unit can analyze the content the user frequently posts on social media and generate a virtual lover based on the content. The registration unit can also analyze the user's social media friendships and generate a virtual lover with common interests. The registration unit can also analyze the user's social media activity time and generate a virtual lover based on the time spent on social media. In this way, a virtual lover based on the user's social media activity is generated. Some or all of the above-described processing in the registration unit can be performed using, for example, AI, or can be performed without using AI. For example, the registration unit can input the user's social media activity data into a generation AI and cause the generation AI to generate a virtual lover.

[0080] The registration unit can customize the virtual lover by reflecting the user's past feedback. For example, the registration unit can adjust the virtual lover's personality based on feedback provided by the user in the past. The registration unit can also adjust the virtual lover's appearance based on feedback provided by the user in the past. The registration unit can also adjust the virtual lover's hobbies and interests based on feedback provided by the user in the past. In this way, a virtual lover based on the user's past feedback is generated. Some or all of the above-described processing in the registration unit may be performed using AI, for example, or may be performed without using AI. For example, the registration unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the virtual lover.

[0081] The conversation unit can estimate the user's emotions and adjust the tone and content of the conversation based on the estimated user's emotions. For example, if the user is feeling stressed, the conversation unit can use a relaxing tone when speaking. Furthermore, if the user is feeling happy, the conversation unit can use a bright and cheerful tone when speaking. Furthermore, if the user is feeling lonely, the conversation unit can use a friendly and empathetic tone when speaking. This allows for a conversation that matches the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the conversation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the conversation unit can input the user's emotion data into the generation AI and have the generation AI adjust the tone and content of the conversation.

[0082] The conversation unit can generate a reply by referring to the user's past conversation history. The conversation unit can, for example, suggest related topics based on content that the user has previously spoken about. The conversation unit can also generate an optimal reply based on topics in which the user has previously shown interest. The conversation unit can also generate a reply to a specific topic from the user's past conversation history. This generates an optimal reply based on the user's past conversation history. Some or all of the above-mentioned processing in the conversation unit can be performed, for example, using AI or without AI. For example, the conversation unit can input the user's past conversation history data into a generation AI and have the generation AI generate a reply.

[0083] The conversation unit can customize the conversation content according to the user's current mood and situation. For example, if the user is tired, the conversation unit can provide a relaxing topic. Furthermore, if the user is excited, the conversation unit can provide a stimulating topic. Furthermore, if the user is depressed, the conversation unit can provide words of encouragement. In this way, conversation content is provided according to the user's mood and situation. Some or all of the above-mentioned processing in the conversation unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversation unit can input the user's mood and situation data into the generation AI and have the generation AI customize the conversation content.

[0084] The conversation unit can generate an optimal response depending on the user's input method. For example, when the user asks a question by voice, the conversation unit generates an optimal response using voice analysis. Furthermore, when the user asks a question by text, the conversation unit can also generate an optimal response using text analysis. Furthermore, when the user sends an image, the conversation unit can also generate an optimal response using image analysis. In this way, an optimal response depending on the user's input method is generated. Some or all of the above-described processing in the conversation unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversation unit can input the user's input data into a generation AI and have the generation AI generate a response.

[0085] The conversation unit can estimate the user's emotions and adjust the length and frequency of conversations based on the estimated user emotions. For example, if the user is feeling stressed, the conversation unit can suggest short conversations. Furthermore, if the user is feeling happy, the conversation unit can also suggest long conversations. Furthermore, if the user is feeling lonely, the conversation unit can also suggest frequent conversations. This adjusts the length and frequency of conversations according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the conversation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the conversation unit can input the user's emotion data into the generation AI and have the generation AI adjust the length and frequency of conversations.

[0086] The conversation unit can customize the conversation content taking into account the user's geographical location information. For example, if the user is in an urban area, the conversation unit can provide topics related to the city. Furthermore, if the user is in a rural area, the conversation unit can provide topics related to nature. Furthermore, if the user is overseas, the conversation unit can provide topics related to the local culture. In this way, conversation content based on the user's geographical location information is provided. Some or all of the above-described processing in the conversation unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversation unit can input the user's geographical location information into the generation AI and cause the generation AI to customize the conversation content.

[0087] The conversation unit can analyze the user's social media activity and generate related conversation content. The conversation unit can provide related topics based on, for example, content that the user frequently posts on social media. The conversation unit can also analyze the user's friendships on social media and provide common topics. The conversation unit can also analyze the user's social media activity time and provide conversation content tailored to that time. In this way, conversation content based on the user's social media activity is provided. Some or all of the above-mentioned processing in the conversation unit can be performed using, for example, AI, or can be performed without using AI. For example, the conversation unit can input the user's social media activity data into a generation AI and cause the generation AI to generate conversation content.

[0088] The conversation unit can customize the conversation content by reflecting the user's past feedback. The conversation unit, for example, adjusts the tone of the conversation based on feedback provided by the user in the past. The conversation unit can also adjust the conversation content based on feedback provided by the user in the past. The conversation unit can also adjust the frequency of the conversation based on feedback provided by the user in the past. In this way, the conversation content is provided based on the user's past feedback. Some or all of the above-mentioned processing in the conversation unit may be performed using AI, for example, or may be performed without using AI. For example, the conversation unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the conversation content.

[0089] The analysis unit can estimate the user's emotions and adjust the interaction analysis method based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can prioritize relaxing interactions. Furthermore, if the user is feeling happy, the analysis unit can prioritize enjoyable interactions. Furthermore, if the user is feeling lonely, the analysis unit can prioritize empathetic interactions. This allows interactions to be analyzed according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the interaction analysis method.

[0090] The analysis unit can apply an analysis method by referring to the user's past interaction history. The analysis unit can apply a relevant analysis method based on, for example, the user's past interactions. The analysis unit can also extract and analyze specific patterns from the user's past interaction history. The analysis unit can also select an optimal analysis method based on the user's past interaction history. This allows for optimal analysis based on the user's past interaction history. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's past interaction history data into the generation AI and have the generation AI apply the analysis method.

[0091] The analysis unit can customize the analysis content according to the user's current mood and situation. For example, if the user is tired, the analysis unit can analyze the user by focusing on interactions that help them relax. Furthermore, if the user is excited, the analysis unit can analyze the user by focusing on stimulating interactions. Furthermore, if the user is depressed, the analysis unit can analyze the user by focusing on encouraging interactions. This allows the analysis content to be provided according to the user's mood and situation. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's mood and situation data into the generation AI and have the generation AI customize the analysis content.

[0092] The analysis unit can select the optimal analysis method depending on the user's input method. For example, if the user communicates via voice, the analysis unit performs analysis using voice analysis. Furthermore, if the user communicates via text, the analysis unit can also perform analysis using text analysis. Furthermore, if the user sends an image, the analysis unit can also perform analysis using image analysis. This allows for the optimal analysis to be performed depending on the user's input method. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's input data into a generation AI and have the generation AI select an analysis method.

[0093] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is feeling happy, the analysis unit can provide a display method including detailed information. Furthermore, if the user is feeling lonely, the analysis unit can provide a friendly display method. This allows the analysis results to be displayed according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method of the analysis results.

[0094] The analysis unit can customize the analysis content taking into account the user's geographical location information. For example, if the user is in an urban area, the analysis unit can emphasize city-related interactions in the analysis. Furthermore, if the user is in a rural area, the analysis unit can emphasize nature-related interactions in the analysis. Furthermore, if the user is overseas, the analysis unit can emphasize local culture-related interactions in the analysis. This provides analysis content based on the user's geographical location information. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the user's geographical location information into the generation AI and cause the generation AI to customize the analysis content.

[0095] The analysis unit can analyze the user's social media activities and generate related analysis results. The analysis unit can generate related analysis results based on, for example, the content the user frequently posts on social media. The analysis unit can also analyze the user's social media friendships and suggest users with common interests. The analysis unit can also analyze the user's social media activity time and generate analysis results tailored to the time spent on social media. This provides analysis results based on the user's social media activities. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's social media activity data into a generation AI and cause the generation AI to generate analysis results.

[0096] The analysis unit can customize the analysis method by reflecting the user's past feedback. The analysis unit can adjust the analysis method based on, for example, feedback provided by the user in the past. The analysis unit can also adjust the focus of the analysis based on feedback provided by the user in the past. The analysis unit can also adjust the display method of the analysis results based on feedback provided by the user in the past. This provides an analysis method based on the user's past feedback. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the analysis method.

[0097] The matching unit can estimate the user's emotions and adjust matching criteria based on the estimated user emotions. For example, if the user is feeling stressed, the matching unit can prioritize matching with partners who can relax them. Furthermore, if the user is feeling happy, the matching unit can prioritize matching with partners who have a fun personality. Furthermore, if the user is feeling lonely, the matching unit can prioritize matching with partners who are highly empathetic. This provides matching criteria according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the matching unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the matching unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the matching criteria.

[0098] The matching unit can apply a matching method by referring to the user's past matching history. The matching unit, for example, can suggest an optimal partner based on the characteristics of successful matches made by the user in the past. The matching unit can also eliminate the characteristics of matches that the user has previously failed to make and suggest an optimal partner. The matching unit can also analyze the user's past matching history and suggest a partner with the best compatibility. This allows optimal matching to be performed based on the user's past matching history. Some or all of the above-described processing in the matching unit can be performed using, for example, AI, or can be performed without using AI. For example, the matching unit can input the user's past matching history data into the generation AI and cause the generation AI to apply a matching method.

[0099] The matching unit can customize the matching content according to the user's current mood and situation. For example, if the user is tired, the matching unit can suggest a partner who can relax the user. Furthermore, if the user is excited, the matching unit can suggest a stimulating partner. Furthermore, if the user is depressed, the matching unit can suggest a partner who will offer words of encouragement. In this way, matching content is provided according to the user's mood and situation. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input the user's mood and situation data into the generation AI and cause the generation AI to customize the matching content.

[0100] The matching unit can select the optimal matching method depending on the user's input method. For example, if the user inputs their preferences by voice, the matching unit can use voice analysis to suggest the optimal partner. Also, if the user inputs detailed preferences in text, the matching unit can use text analysis to suggest the optimal partner. Also, if the user uploads an image, the matching unit can use image analysis to suggest the optimal partner. This allows optimal matching to be performed depending on the user's input method. Some or all of the above-mentioned processing in the matching unit may be performed using AI, for example, or may be performed without using AI. For example, the matching unit can input the user's input data into a generation AI and have the generation AI select a matching method.

[0101] The matching unit can estimate the user's emotions and adjust the display method of the matching results based on the estimated user emotions. For example, if the user is feeling stressed, the matching unit can provide a simple, highly visible display method. Furthermore, if the user is feeling happy, the matching unit can also provide a display method including detailed information. Furthermore, if the user is feeling lonely, the matching unit can also provide a friendly display method. This allows the display of matching results according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the matching unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the matching unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the display method of the matching results.

[0102] The matching unit can customize the matching content taking into account the user's geographical location information. For example, if the user is in an urban area, the matching unit can prioritize suggesting partners related to the city. Furthermore, if the user is in a rural area, the matching unit can prioritize suggesting partners related to nature. Furthermore, if the user is overseas, the matching unit can prioritize suggesting partners who are familiar with the culture of that region. This provides matching content based on the user's geographical location information. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input the user's geographical location information into the generation AI and cause the generation AI to customize the matching content.

[0103] The matching unit can analyze the user's social media activity and generate relevant matching results. The matching unit can, for example, suggest related partners based on the content the user frequently posts on social media. The matching unit can also analyze the user's social media friendships and suggest partners with common interests. The matching unit can also analyze the user's social media activity time and suggest partners that match that. This provides matching results based on the user's social media activity. Some or all of the above-mentioned processing in the matching unit can be performed, for example, using AI or without AI. For example, the matching unit can input the user's social media activity data into a generation AI and cause the generation AI to generate matching results.

[0104] The matching unit can customize the matching method by reflecting the user's past feedback. The matching unit can adjust the matching method based on, for example, feedback provided by the user in the past. The matching unit can also adjust the focus of matching based on feedback provided by the user in the past. The matching unit can also adjust the display method of matching results based on feedback provided by the user in the past. This provides a matching method based on the user's past feedback. Some or all of the above-described processing in the matching unit can be performed using AI, for example, or can be performed without using AI. For example, the matching unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the matching method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned registration unit, conversation unit, analysis unit, and matching unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the conversation unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the matching unit is realized by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned registration unit, conversation unit, analysis unit, and matching unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the conversation unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the matching unit is realized by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned registration unit, conversation unit, analysis unit, and matching unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the conversation unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the matching unit is realized by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned registration unit, conversation unit, analysis unit, and matching unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the conversation unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the matching unit is realized by the specific processing unit 290 of the data processing device 12.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The virtual lover generation system can also obtain the user's health data and suggest a virtual lover. For example, by analyzing the user's heart rate and sleep data, if the user is under a lot of stress, the system can suggest a virtual lover who can help them relax. If the user is not getting enough exercise, the system can also suggest a virtual lover who has active hobbies. Furthermore, the system can generate a virtual lover who suggests healthy meals based on the user's dietary data. This allows the system to generate a virtual lover that matches the user's health condition, providing a more personalized experience.

[0107] The virtual lover generation system can further analyze the user's musical preferences and customize the virtual lover's hobbies. For example, if the user likes rock music, a virtual lover who also likes rock music can be generated. If the user likes classical music, a virtual lover who is knowledgeable about classical music can be generated. Furthermore, if the user frequently goes to live concerts, a virtual lover who enjoys live concerts can be generated. In this way, a virtual lover based on the user's musical preferences can be generated.

[0108] The virtual lover generation system can further analyze the user's travel history and customize the virtual lover's travel hobbies. For example, based on countries and cities the user has visited in the past, a virtual lover who is interested in the same places can be generated. If the user likes adventurous travel, a virtual lover with a strong sense of adventure can be generated. Furthermore, if the user likes resort destinations, a virtual lover who is familiar with relaxing resort destinations can be generated. In this way, a virtual lover based on the user's travel history can be generated.

[0109] The virtual lover generation system can further analyze the user's reading history and customize the virtual lover's reading preferences. For example, if the user likes mystery novels, a virtual lover who also likes mystery novels can be generated. If the user likes science books, a virtual lover who is knowledgeable about science can be generated. If the user likes poetry collections, a virtual lover who is interested in poetry can be generated. In this way, a virtual lover is generated based on the user's reading history.

[0110] The virtual lover generation system can also obtain the user's exercise data and customize the virtual lover's exercise habits. For example, if the user likes running, it can generate a virtual lover who also enjoys running. If the user likes yoga, it can generate a virtual lover who is knowledgeable about yoga. Furthermore, if the user has a habit of going to the gym, it can generate a virtual lover who enjoys training at the gym. In this way, a virtual lover is generated based on the user's exercise data.

[0111] The virtual lover generation system can further estimate the user's emotions and adjust the virtual lover's conversation style based on the estimated user's emotions. For example, if the user is feeling stressed, a virtual lover with a relaxing conversation style can be generated. Alternatively, if the user is feeling happy, a virtual lover with a bright and cheerful conversation style can be generated. Furthermore, if the user is feeling lonely, a virtual lover with a friendly and empathetic conversation style can be generated. In this way, a virtual lover with a conversation style that corresponds to the user's emotions can be generated.

[0112] The virtual lover generation system can further estimate the user's emotions and adjust the appearance of the virtual lover based on the estimated user's emotions. For example, if the user is feeling stressed, a virtual lover with a relaxing appearance can be generated. Also, if the user is feeling happy, a virtual lover with a bright and cheerful appearance can be generated. Furthermore, if the user is feeling lonely, a virtual lover with a friendly appearance can be generated. In this way, a virtual lover with an appearance that corresponds to the user's emotions can be generated.

[0113] The virtual lover generation system can further estimate the user's emotions and adjust the virtual lover's hobbies based on the estimated user's emotions. For example, if the user is feeling stressed, a virtual lover with a relaxing hobby can be generated. Also, if the user is feeling happy, a virtual lover with an active hobby can be generated. Furthermore, if the user is feeling lonely, a virtual lover with a common hobby can be generated. In this way, a virtual lover with a hobby corresponding to the user's emotions can be generated.

[0114] The virtual lover generation system can further estimate the user's emotions and adjust the virtual lover's date plan based on the estimated user's emotions. For example, if the user is feeling stressed, a relaxing date plan can be suggested. If the user is feeling happy, an active date plan can be suggested. Furthermore, if the user is feeling lonely, a friendly date plan can be suggested. In this way, a date plan that matches the user's emotions can be provided.

[0115] The virtual lover generation system can further estimate the user's emotions and adjust the virtual lover's hobbies and interests based on the estimated user's emotions. For example, if the user is feeling stressed, a virtual lover with a relaxing hobby can be generated. Alternatively, if the user is feeling happy, a virtual lover with an active hobby can be generated. Alternatively, if the user is feeling lonely, a virtual lover with a common hobby can be generated. In this way, a virtual lover with hobbies and interests that correspond to the user's emotions can be generated.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: In the registration section, the user registers for the service and creates a virtual lover. The user inputs their preferences and personality, and the AI ​​generates a virtual lover based on that. For example, if the user inputs, "I want a lover who has a kind personality and whose hobby is watching movies," the AI ​​will generate a virtual lover that meets those criteria. Step 2: The conversation unit uses the generation AI to generate an appropriate response to the user's message. For example, if the user sends a message asking, "What movie do you want to see today?", the generation AI will respond with, "I'd like to see an action movie today." The conversation unit can also generate virtual date scenarios. For example, it can generate scenarios in which the user watches a movie with a virtual lover or enjoys a meal at a restaurant. Step 3: The analysis unit uses the generation AI to analyze the user's interactions and thoughts and propose matches between users who are compatible. For example, if user A enjoys watching movies and user B also has the same hobby, the generation AI will propose a match between the two. Step 4: The matching unit matches users together based on the proposed matching. For example, it matches user A with user B based on the proposed matching.

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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.

[0121] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] 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.

[0147] 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.

[0148] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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."

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0188] 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.

[0189] [Explanation of symbols]

[0190] 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 registration unit where a user registers with the service and creates a virtual lover; a conversation unit that converses with the virtual lover created by the registration unit through messages; an analysis unit that analyzes the exchanges made by the conversation unit and proposes matching between users; a matching unit that matches users based on the matching proposed by the analysis unit. A system characterized by:

2. The registration unit Input the user's preferences and personality to generate a virtual lover 2. The system of claim 1.

3. The conversation unit is Generate an appropriate reply to the user's message 2. The system of claim 1.

4. The conversation unit is Generate hypothetical dating scenarios 2. The system of claim 1.

5. The analysis unit Analyze user interactions and suggest matching between users 2. The system of claim 1.

6. The matching unit Matching users based on suggested matches 2. The system of claim 1.

7. The registration unit Estimate the user's emotions and adjust the personality and appearance of the virtual lover based on the estimated user emotions.

2. The system of claim 1.

8. The registration unit Analyzes the user's past romantic experiences and generates a virtual lover 2. The system of claim 1.

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