system

Abstract

The system according to the embodiment aims to provide an integrated analysis of romantic situations, simulation, and counseling. [Solution] A system according to an embodiment includes an analysis unit, a simulation unit, and a consultation unit. The analysis unit analyzes the content of a conversation. The simulation unit performs a love simulation based on the results of the analysis by the analysis unit. The consultation unit provides counselor advice based on the results obtained by the simulation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately provide integrated analysis, simulation, and counseling of romantic situations, and there is room for improvement.

[0005] The system according to the embodiment aims to provide an integrated analysis of romantic situations, simulation, and counseling. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a simulation unit, and a consultation unit. The analysis unit analyzes the content of the conversation. The simulation unit performs a love simulation based on the results of the analysis by the analysis unit. The consultation unit provides counselor advice based on the results obtained by the simulation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide love situation analysis, simulation, and counseling in an integrated manner. [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 love support system according to an embodiment of the present invention analyzes the content of a user's conversation, performs a love simulation, and provides counselor advice. The love support system analyzes the content of the conversation entered by the user to grasp the current love situation. Next, it provides a love simulation function, allowing the user to practice confessing their love and simulate dates, and to simulate the other person's reactions in advance and develop countermeasures. Furthermore, the system also allows the user to receive love consultations from a counselor. For example, if a user inputs "My girlfriend has been cold lately," the love support system refers to similar past experiences and infers the background of her behavior. Next, if a user inputs "I want to practice confessing my love," the love support system generates a virtual partner and simulates a scenario in which the user confesses their love. Furthermore, it simulates the other person's reactions in advance and provides advice on how the user should develop countermeasures. Finally, if a user inputs "My relationship with my boyfriend is not going well," the love support system conveys the consultation content to a counselor and provides the user with advice from the counselor. In this way, the love support system can support the user's love life. This allows the love support system to accurately grasp the user's love situation and provide appropriate advice. For example, users can quickly and accurately resolve their romantic concerns, improving their chances of finding success in love. Users can also objectively understand their own romantic situations and take appropriate measures.

[0029] A love support system according to an embodiment includes an analysis unit, a simulation unit, and a consultation unit. The analysis unit analyzes conversation content input by a user. The conversation content may include, but is not limited to, text, audio, or video. The analysis unit may analyze the conversation content using, for example, natural language processing technology. The analysis unit may also analyze a user's emotions using sentiment analysis technology. For example, the analysis unit may analyze text data and estimate the user's emotions. The simulation unit performs a love simulation based on the results of the analysis by the analysis unit. The love simulation may include, but is not limited to, a scenario-based simulation or an interactive simulation. For example, the simulation unit may generate a virtual partner and simulate a scenario in which the user confesses their love. The simulation unit may also simulate the partner's reaction in advance and advise the user on how to deal with the situation. The consultation unit provides counselor advice based on the results obtained by the simulation unit. The counselor advice may include, but is not limited to, a text message or an audio guide. For example, when a user inputs "my relationship with my boyfriend is not going well," the consultation unit conveys the consultation content to a counselor and provides the user with advice from the counselor. In this way, the love support system according to the embodiment can support the user's love life.

[0030] When analyzing the content of a conversation, the analysis unit can improve the accuracy of the analysis by referring to the user's past conversation history. For example, the analysis unit can refer to conversation content previously input by the user to find similar patterns and improve the accuracy of the analysis. The analysis unit can also extract specific keywords from the user's past conversation history and reflect them in the analysis. The analysis unit can also perform a deeper analysis of the content of the current conversation based on the content of consultations the user has had in the past. In this way, by referring to the past conversation history, the accuracy of the analysis is improved. 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 past conversation history into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0031] When analyzing the content of the conversation, the analysis unit can filter the analysis based on the user's current living situation and areas of interest. For example, the analysis unit prioritizes analysis of relevant information taking into account the user's current living situation (work, studies, etc.). The analysis unit can also filter the content of the conversation based on the user's areas of interest (hobbies, interests, etc.) to improve the accuracy of the analysis. The analysis unit can also eliminate unnecessary information based on the user's current living situation and areas of interest to improve the efficiency of the analysis. In this way, analysis based on the user's living situation and areas of interest improves the accuracy of the analysis. 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 data on the user's living situation and areas of interest into the generation AI and have the generation AI perform analysis filtering.

[0032] When analyzing the content of a conversation, the analysis unit can select an appropriate analysis means depending on the user's input method. For example, if the user uses voice input, the analysis unit analyzes the content of the conversation using voice recognition technology. Furthermore, if the user uses text input, the analysis unit can also analyze the content of the conversation using natural language processing technology. Furthermore, if the user inputs an image, the analysis unit can also analyze the content of the conversation using image recognition technology. This improves the accuracy of the analysis by selecting the optimal analysis means depending on the user's input method. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's input data into a generation AI and have the generation AI select the optimal analysis means.

[0033] During the simulation, the simulation unit can improve the accuracy of the simulation by taking into account the attribute information of the virtual other person. The simulation unit can improve the accuracy of the simulation by taking into account, for example, the age and gender of the virtual other person. The simulation unit can also improve the accuracy of the simulation by taking into account the hobbies and interests of the virtual other person. The simulation unit can also improve the accuracy of the simulation by taking into account the virtual other person's past behavioral patterns. In this way, the accuracy of the simulation is improved by taking into account the attribute information of the virtual other person. Some or all of the above-described processing in the simulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the simulation unit can input the attribute information of the virtual other person into the generation AI and cause the generation AI to improve the accuracy of the simulation.

[0034] During a simulation, the simulation unit can improve the accuracy of the simulation by referring to the user's past simulation results. For example, the simulation unit can refer to the user's past simulation results and provide similar scenarios. The simulation unit can also extract specific patterns from the user's past simulation results and reflect them in the simulation. The simulation unit can also improve the accuracy of the simulation based on the user's past simulation results. In this way, the accuracy of the simulation is improved by referring to the past simulation results. Some or all of the above-mentioned processing in the simulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the simulation unit can input the user's past simulation results into a generation AI and cause the generation AI to improve the accuracy of the simulation.

[0035] During the simulation, the simulation unit can filter the simulation based on the user's current living situation and areas of interest. For example, the simulation unit provides relevant scenarios taking into account the user's current living situation (work, studies, etc.). The simulation unit can also filter the simulation based on the user's areas of interest (hobbies, interests, etc.). The simulation unit can also eliminate unnecessary scenarios based on the user's current living situation and areas of interest, thereby improving the efficiency of the simulation. By performing the simulation based on the user's living situation and areas of interest, the accuracy of the simulation is improved. Some or all of the above-described processing in the simulation unit can be performed using, for example, AI, or without AI. For example, the simulation unit can input data on the user's living situation and areas of interest into the generation AI and cause the generation AI to filter the simulation.

[0036] The consultation unit can improve the accuracy of advice by referring to the user's past consultation history during consultation. For example, the consultation unit can refer to the content of consultations the user has had in the past and provide advice for similar problems. The consultation unit can also extract specific patterns from the user's past consultation history and reflect them in the advice. The consultation unit can also improve the accuracy of advice based on the user's past consultation history. In this way, the accuracy of advice is improved by referring to the past consultation history. Some or all of the above-mentioned processing in the consultation unit may be performed using, for example, AI, or may be performed without using AI. For example, the consultation unit can input the user's past consultation history into a generation AI and cause the generation AI to improve the accuracy of advice.

[0037] During a consultation, the consultation unit can filter advice based on the user's current living situation and areas of interest. The consultation unit provides relevant advice, for example, taking into account the user's current living situation (work, studies, etc.). The consultation unit can also filter advice based on the user's areas of interest (hobbies, interests, etc.). The consultation unit can also eliminate unnecessary advice based on the user's current living situation and areas of interest, thereby improving the efficiency of advice. By providing advice based on the user's living situation and areas of interest, the accuracy of the advice is improved. Some or all of the above-described processing in the consultation unit may be performed, for example, using AI or without AI. For example, the consultation unit can input data on the user's living situation and areas of interest into a generation AI and cause the generation AI to filter the advice.

[0038] The consultation unit can select the optimal advice means depending on the user's input method during consultation. For example, if the user uses voice input, the consultation unit can provide advice using voice recognition technology. Furthermore, if the user uses text input, the consultation unit can also provide advice using natural language processing technology. Furthermore, if the user inputs an image, the consultation unit can also provide advice using image recognition technology. This improves the accuracy of advice by selecting the optimal advice means depending on the user's input method. Some or all of the above-mentioned processing in the consultation unit may be performed using, for example, AI, or may be performed without using AI. For example, the consultation unit can input the user's input data into a generation AI and cause the generation AI to select the optimal advice means.

[0039] During a consultation, the consultation unit can prioritize providing highly relevant advice by taking into account the user's geographical location information. For example, if the user is in a specific area, the consultation unit prioritizes providing advice related to that area. The consultation unit can also provide advice by taking into account local issues and situations based on the user's geographical location information. If the user is traveling, the consultation unit can also provide advice based on information about the user's travel destination. In this way, highly relevant advice can be provided by taking into account the geographical location information. Some or all of the above-described processing in the consultation unit may be performed using, for example, AI, or may be performed without using AI. For example, the consultation unit can input the user's geographical location information into a generation AI and cause the generation AI to provide highly relevant advice.

[0040] The consultation unit can analyze the user's social media activity during the consultation and provide relevant advice. For example, the consultation unit can analyze the content of the user's social media posts and provide relevant advice. The consultation unit can also provide relevant advice by referring to the activities of the user's friends on social media. The consultation unit can also provide relevant advice based on the user's social media check-in information. In this way, highly relevant advice can be provided by analyzing social media activity. Some or all of the above-described processing in the consultation unit may be performed using, for example, AI, or may be performed without using AI. For example, the consultation unit can input the user's social media activity data into a generation AI and cause the generation AI to provide relevant advice.

[0041] During a consultation, the consultation unit can customize the advice method by reflecting the user's past feedback. The consultation unit customizes the advice method based on, for example, feedback provided by the user in the past. The consultation unit can also preferentially use a specific advice method based on the user's past feedback. The consultation unit can also reflect the user's past feedback to improve the accuracy of the advice. In this way, the accuracy of the advice is improved by reflecting the past feedback. Some or all of the above-described processing in the consultation unit may be performed using, for example, AI, or may be performed without using AI. For example, the consultation unit can input the user's past feedback into a generation AI and cause the generation AI to customize the advice method.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The love support system may further include a health management unit that monitors the user's health condition. The health management unit may measure the user's heart rate and stress level and provide advice on the user's love situation based on this data. For example, if the user shows a high stress level, the health management unit may provide advice on how to relax. Also, if the user's heart rate rises sharply, the health management unit may provide advice to the user encouraging them to take deep breaths. Furthermore, the health management unit may monitor the user's sleep patterns and advise them to get enough rest. This makes it possible to provide love support that takes the user's health condition into consideration.

[0044] The love support system can further include a hobby analysis unit that analyzes the user's hobbies and interests. The hobby analysis unit can analyze information about the hobbies and interests entered by the user and customize the love simulation scenario based on this information. For example, if the user is interested in music, the hobby analysis unit can provide a music-related scenario. Also, if the user is interested in sports, the hobby analysis unit can provide a sports-related scenario. Furthermore, the hobby analysis unit can suggest date plans based on the user's hobbies. This makes it possible to provide love support that is tailored to the user's hobbies and interests.

[0045] The love support system may further include a social network analysis unit that analyzes the user's social network. The social network analysis unit analyzes data on the user's friendships and followers and can provide love advice based on this. For example, if the user frequently interacts with a particular friend, the social network analysis unit can provide advice on deepening the relationship with that friend. Also, if the user wants to make new friends, the social network analysis unit can introduce people who share common interests. Furthermore, the social network analysis unit can monitor changes in the user's friendships and provide advice at appropriate times. This enables love support that takes the user's social network into consideration.

[0046] The love support system can further include a behavior analysis unit that analyzes the user's behavioral patterns. The behavior analysis unit can collect data on the user's daily behavior and provide love advice based on this data. For example, if the user frequently visits a particular place, the behavior analysis unit can suggest date plans related to that place. Also, if the user is active during a particular time period, the behavior analysis unit can provide advice tailored to that time period. Furthermore, the behavior analysis unit can monitor changes in the user's behavioral patterns and provide advice at appropriate times. This makes it possible to provide love support that takes the user's behavioral patterns into consideration.

[0047] The love support system can further include a lifestyle analysis unit that analyzes the user's lifestyle. The lifestyle analysis unit can analyze the user's lifestyle habits and consumption behavior and provide love advice based on this. For example, if the user is health-conscious, the lifestyle analysis unit can suggest healthy date plans. Also, if the user is interested in fashion, the lifestyle analysis unit can provide fashion-related advice. Furthermore, the lifestyle analysis unit can monitor the user's consumption behavior and provide advice at appropriate times. This makes it possible to provide love support that takes the user's lifestyle into consideration.

[0048] The love support system can further include a cultural analysis unit that takes into account the user's cultural background. The cultural analysis unit can analyze the user's cultural background and values ​​and provide love advice based on this. For example, if the user belongs to a specific culture, the cultural analysis unit can provide advice appropriate to that culture. Also, if the user desires cross-cultural exchange, the cultural analysis unit can provide information about different cultures. Furthermore, the cultural analysis unit can take into account the user's values ​​and provide advice at an appropriate time. This makes it possible to provide love support that takes into account the user's cultural background.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The analysis unit analyzes the conversation content entered by the user. The conversation content may include, but is not limited to, text, audio, and video. The analysis unit uses natural language processing and sentiment analysis techniques to analyze the conversation content and the user's emotions. For example, the analysis unit analyzes text data and estimates the user's emotions. Step 2: The simulation unit performs a love simulation based on the results of the analysis by the analysis unit. Love simulations include scenario-based simulations and interactive simulations. For example, it generates a virtual partner and simulates a scenario in which the user confesses their feelings. It can also simulate the partner's reaction in advance and advise the user on how to respond. Step 3: The consultation unit provides counselor advice based on the results obtained by the simulation unit. The counselor's advice may include text messages, audio guides, etc. For example, if the user inputs "My relationship with my boyfriend is not going well," the consultation unit conveys the content of the consultation to the counselor and provides the counselor's advice to the user.

[0051] (Example 2) A love support system according to an embodiment of the present invention analyzes the content of a user's conversation, performs a love simulation, and provides counselor advice. The love support system analyzes the content of the conversation entered by the user to grasp the current love situation. Next, it provides a love simulation function, allowing the user to practice confessing their love and simulate dates, and to simulate the other person's reactions in advance and develop countermeasures. Furthermore, the system also allows the user to receive love consultations from a counselor. For example, if a user inputs "My girlfriend has been cold lately," the love support system refers to similar past experiences and infers the background of her behavior. Next, if a user inputs "I want to practice confessing my love," the love support system generates a virtual partner and simulates a scenario in which the user confesses their love. Furthermore, it simulates the other person's reactions in advance and provides advice on how the user should develop countermeasures. Finally, if a user inputs "My relationship with my boyfriend is not going well," the love support system conveys the consultation content to a counselor and provides the user with advice from the counselor. In this way, the love support system can support the user's love life. This allows the love support system to accurately grasp the user's love situation and provide appropriate advice. For example, users can quickly and accurately resolve their romantic concerns, improving their chances of finding success in love. Users can also objectively understand their own romantic situations and take appropriate measures.

[0052] A love support system according to an embodiment includes an analysis unit, a simulation unit, and a consultation unit. The analysis unit analyzes conversation content input by a user. The conversation content may include, but is not limited to, text, audio, or video. The analysis unit may analyze the conversation content using, for example, natural language processing technology. The analysis unit may also analyze a user's emotions using sentiment analysis technology. For example, the analysis unit may analyze text data and estimate the user's emotions. The simulation unit performs a love simulation based on the results of the analysis by the analysis unit. The love simulation may include, but is not limited to, a scenario-based simulation or an interactive simulation. For example, the simulation unit may generate a virtual partner and simulate a scenario in which the user confesses their love. The simulation unit may also simulate the partner's reaction in advance and advise the user on how to deal with the situation. The consultation unit provides counselor advice based on the results obtained by the simulation unit. The counselor advice may include, but is not limited to, a text message or an audio guide. For example, when a user inputs "my relationship with my boyfriend is not going well," the consultation unit conveys the consultation content to a counselor and provides the user with advice from the counselor. In this way, the love support system according to the embodiment can support the user's love life.

[0053] The analysis unit can estimate the user's emotions and adjust the analysis method for the conversation content based on the estimated user emotions. For example, if the user is sad, the analysis unit uses an emotion engine to estimate the user's emotions and provide analysis results in a gentle tone. Alternatively, if the user is excited, the analysis unit can use an emotion engine to estimate the user's emotions and provide analysis results that are quick and concise. Alternatively, if the user is anxious, the analysis unit can use an emotion engine to estimate the user's emotions and provide analysis results that provide a sense of security. This allows for more appropriate analysis results to be provided by adjusting the analysis method according to the user's emotions. 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 without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0054] When analyzing the content of a conversation, the analysis unit can improve the accuracy of the analysis by referring to the user's past conversation history. For example, the analysis unit can refer to conversation content previously input by the user to find similar patterns and improve the accuracy of the analysis. The analysis unit can also extract specific keywords from the user's past conversation history and reflect them in the analysis. The analysis unit can also perform a deeper analysis of the content of the current conversation based on the content of consultations the user has had in the past. In this way, by referring to the past conversation history, the accuracy of the analysis is improved. 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 past conversation history into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0055] When analyzing the content of the conversation, the analysis unit can filter the analysis based on the user's current living situation and areas of interest. For example, the analysis unit prioritizes analysis of relevant information taking into account the user's current living situation (work, studies, etc.). The analysis unit can also filter the content of the conversation based on the user's areas of interest (hobbies, interests, etc.) to improve the accuracy of the analysis. The analysis unit can also eliminate unnecessary information based on the user's current living situation and areas of interest to improve the efficiency of the analysis. In this way, analysis based on the user's living situation and areas of interest improves the accuracy of the analysis. 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 data on the user's living situation and areas of interest into the generation AI and have the generation AI perform analysis filtering.

[0056] When analyzing the content of a conversation, the analysis unit can select an appropriate analysis means depending on the user's input method. For example, if the user uses voice input, the analysis unit analyzes the content of the conversation using voice recognition technology. Furthermore, if the user uses text input, the analysis unit can also analyze the content of the conversation using natural language processing technology. Furthermore, if the user inputs an image, the analysis unit can also analyze the content of the conversation using image recognition technology. This improves the accuracy of the analysis by selecting the optimal analysis means depending on the user's input method. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's input data into a generation AI and have the generation AI select the optimal analysis means.

[0057] The simulation unit can estimate the user's emotions and adjust the simulation scenario based on the estimated user emotions. For example, if the user is nervous, the emotion engine can estimate the user's emotions and provide a relaxing scenario. Furthermore, if the user is excited, the emotion engine can estimate the user's emotions and provide a challenging scenario. Furthermore, if the user is anxious, the emotion engine can estimate the user's emotions and provide a scenario that provides a sense of security. This allows the simulation scenario to be adjusted according to the user's emotions, providing a more appropriate simulation. 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 simulation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the simulation unit can input the user's emotion data into the generation AI and have the generation AI adjust the scenario.

[0058] During the simulation, the simulation unit can improve the accuracy of the simulation by taking into account the attribute information of the virtual other person. The simulation unit can improve the accuracy of the simulation by taking into account, for example, the age and gender of the virtual other person. The simulation unit can also improve the accuracy of the simulation by taking into account the hobbies and interests of the virtual other person. The simulation unit can also improve the accuracy of the simulation by taking into account the virtual other person's past behavioral patterns. In this way, the accuracy of the simulation is improved by taking into account the attribute information of the virtual other person. Some or all of the above-described processing in the simulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the simulation unit can input the attribute information of the virtual other person into the generation AI and cause the generation AI to improve the accuracy of the simulation.

[0059] During a simulation, the simulation unit can improve the accuracy of the simulation by referring to the user's past simulation results. For example, the simulation unit can refer to the user's past simulation results and provide similar scenarios. The simulation unit can also extract specific patterns from the user's past simulation results and reflect them in the simulation. The simulation unit can also improve the accuracy of the simulation based on the user's past simulation results. In this way, the accuracy of the simulation is improved by referring to the past simulation results. Some or all of the above-mentioned processing in the simulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the simulation unit can input the user's past simulation results into a generation AI and cause the generation AI to improve the accuracy of the simulation.

[0060] During the simulation, the simulation unit can filter the simulation based on the user's current living situation and areas of interest. For example, the simulation unit provides relevant scenarios taking into account the user's current living situation (work, studies, etc.). The simulation unit can also filter the simulation based on the user's areas of interest (hobbies, interests, etc.). The simulation unit can also eliminate unnecessary scenarios based on the user's current living situation and areas of interest, thereby improving the efficiency of the simulation. By performing the simulation based on the user's living situation and areas of interest, the accuracy of the simulation is improved. Some or all of the above-described processing in the simulation unit can be performed using, for example, AI, or without AI. For example, the simulation unit can input data on the user's living situation and areas of interest into the generation AI and cause the generation AI to filter the simulation.

[0061] The consultation unit can estimate the user's emotions and adjust the way the counselor expresses advice based on the estimated user emotions. For example, if the user is sad, the consultation unit uses the emotion engine to estimate the user's emotions and provide advice in a gentle tone. Furthermore, if the user is excited, the consultation unit can use the emotion engine to estimate the user's emotions and provide advice quickly and concisely. Furthermore, if the user is anxious, the consultation unit can use the emotion engine to estimate the user's emotions and provide advice that gives the user a sense of security. This allows for more appropriate advice to be provided by adjusting the way advice is expressed 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 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 consultation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the consultation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way the advice is expressed.

[0062] The consultation unit can improve the accuracy of advice by referring to the user's past consultation history during consultation. For example, the consultation unit can refer to the content of consultations the user has had in the past and provide advice for similar problems. The consultation unit can also extract specific patterns from the user's past consultation history and reflect them in the advice. The consultation unit can also improve the accuracy of advice based on the user's past consultation history. In this way, the accuracy of advice is improved by referring to the past consultation history. Some or all of the above-mentioned processing in the consultation unit may be performed using, for example, AI, or may be performed without using AI. For example, the consultation unit can input the user's past consultation history into a generation AI and cause the generation AI to improve the accuracy of advice.

[0063] During a consultation, the consultation unit can filter advice based on the user's current living situation and areas of interest. The consultation unit provides relevant advice, for example, taking into account the user's current living situation (work, studies, etc.). The consultation unit can also filter advice based on the user's areas of interest (hobbies, interests, etc.). The consultation unit can also eliminate unnecessary advice based on the user's current living situation and areas of interest, thereby improving the efficiency of advice. By providing advice based on the user's living situation and areas of interest, the accuracy of the advice is improved. Some or all of the above-described processing in the consultation unit may be performed, for example, using AI or without AI. For example, the consultation unit can input data on the user's living situation and areas of interest into a generation AI and cause the generation AI to filter the advice.

[0064] The consultation unit can select the optimal advice means depending on the user's input method during consultation. For example, if the user uses voice input, the consultation unit can provide advice using voice recognition technology. Furthermore, if the user uses text input, the consultation unit can also provide advice using natural language processing technology. Furthermore, if the user inputs an image, the consultation unit can also provide advice using image recognition technology. This improves the accuracy of advice by selecting the optimal advice means depending on the user's input method. Some or all of the above-mentioned processing in the consultation unit may be performed using, for example, AI, or may be performed without using AI. For example, the consultation unit can input the user's input data into a generation AI and cause the generation AI to select the optimal advice means.

[0065] The consultation unit can estimate the user's emotions and determine the priority of advice based on the estimated user emotions. For example, if the user is sad, the consultation unit uses the emotion engine to estimate the user's emotions and prioritize providing comforting advice. Furthermore, if the user is excited, the consultation unit can also use the emotion engine to estimate the user's emotions and prioritize providing advice that requires a quick response. Furthermore, if the user is anxious, the consultation unit can also use the emotion engine to estimate the user's emotions and prioritize providing advice that provides a sense of security. This allows for more appropriate advice to be provided by determining the priority of advice based on 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 consultation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the consultation unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of advice.

[0066] During a consultation, the consultation unit can prioritize providing highly relevant advice by taking into account the user's geographical location information. For example, if the user is in a specific area, the consultation unit prioritizes providing advice related to that area. The consultation unit can also provide advice by taking into account local issues and situations based on the user's geographical location information. If the user is traveling, the consultation unit can also provide advice based on information about the user's travel destination. In this way, highly relevant advice can be provided by taking into account the geographical location information. Some or all of the above-described processing in the consultation unit may be performed using, for example, AI, or may be performed without using AI. For example, the consultation unit can input the user's geographical location information into a generation AI and cause the generation AI to provide highly relevant advice.

[0067] The consultation unit can analyze the user's social media activity during the consultation and provide relevant advice. For example, the consultation unit can analyze the content of the user's social media posts and provide relevant advice. The consultation unit can also provide relevant advice by referring to the activities of the user's friends on social media. The consultation unit can also provide relevant advice based on the user's social media check-in information. In this way, highly relevant advice can be provided by analyzing social media activity. Some or all of the above-described processing in the consultation unit may be performed using, for example, AI, or may be performed without using AI. For example, the consultation unit can input the user's social media activity data into a generation AI and cause the generation AI to provide relevant advice.

[0068] During a consultation, the consultation unit can customize the advice method by reflecting the user's past feedback. The consultation unit customizes the advice method based on, for example, feedback provided by the user in the past. The consultation unit can also preferentially use a specific advice method based on the user's past feedback. The consultation unit can also reflect the user's past feedback to improve the accuracy of the advice. In this way, the accuracy of the advice is improved by reflecting the past feedback. Some or all of the above-described processing in the consultation unit may be performed using, for example, AI, or may be performed without using AI. For example, the consultation unit can input the user's past feedback into a generation AI and cause the generation AI to customize the advice method. === Hard Collateral 1-1 === Each of the multiple elements including the above-described analysis unit, simulation unit, and counseling unit may be realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit may be realized by the processor 46 of the smart device 14 and analyze the conversation content entered by the user. The simulation unit may be realized by the specific processing unit 290 of the data processing device 12 and perform a love simulation. The counseling unit may be realized by the control unit 46A of the smart device 14 and provide advice from a counselor. The analysis unit, simulation unit, and counseling unit may be realized, for example, by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-described analysis unit, simulation unit, and counseling unit may be realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit may be realized by the processor 46 of the smart glasses 214 and analyze the conversation content entered by the user. The simulation unit may be realized, for example, by the specific processing unit 290 of the data processing device 12 and perform a love simulation. The counseling unit may be realized, for example, by the control unit 46A of the smart glasses 214 and provide advice from a counselor. The analysis unit, simulation unit, and counseling unit may be realized, for example, by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-described analysis unit, simulation unit, and counseling unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the headset type terminal 314 and analyzes the content of conversation entered by the user. The simulation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs a love simulation. The counseling unit is realized, for example, by the control unit 46A of the headset type terminal 314 and provides advice from a counselor. The analysis unit, simulation unit, and counseling unit may be realized, for example, 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 analysis unit, simulation unit, and counseling unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the robot 414 and analyzes the content of the conversation entered by the user. The simulation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs a love simulation. The counseling unit is realized, for example, by the control unit 46A of the robot 414 and provides advice from a counselor. The analysis unit, simulation unit, and counseling unit may be realized, for example, by the specific processing unit 290 of the data processing device 12.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The love support system may further include a health management unit that monitors the user's health condition. The health management unit may measure the user's heart rate and stress level and provide advice on the user's love situation based on this data. For example, if the user shows a high stress level, the health management unit may provide advice on how to relax. Also, if the user's heart rate rises sharply, the health management unit may provide advice to the user encouraging them to take deep breaths. Furthermore, the health management unit may monitor the user's sleep patterns and advise them to get enough rest. This makes it possible to provide love support that takes the user's health condition into consideration.

[0071] The love support system can further include a hobby analysis unit that analyzes the user's hobbies and interests. The hobby analysis unit can analyze information about the hobbies and interests entered by the user and customize the love simulation scenario based on this information. For example, if the user is interested in music, the hobby analysis unit can provide a music-related scenario. Also, if the user is interested in sports, the hobby analysis unit can provide a sports-related scenario. Furthermore, the hobby analysis unit can suggest date plans based on the user's hobbies. This makes it possible to provide love support that is tailored to the user's hobbies and interests.

[0072] The love support system may further include a social network analysis unit that analyzes the user's social network. The social network analysis unit analyzes data on the user's friendships and followers and can provide love advice based on this. For example, if the user frequently interacts with a particular friend, the social network analysis unit can provide advice on deepening the relationship with that friend. Also, if the user wants to make new friends, the social network analysis unit can introduce people who share common interests. Furthermore, the social network analysis unit can monitor changes in the user's friendships and provide advice at appropriate times. This enables love support that takes the user's social network into consideration.

[0073] The love support system may further include an emotion monitoring unit that monitors the user's emotions in real time. The emotion monitoring unit can analyze the user's facial expressions and tone of voice to estimate the user's emotions in real time. For example, if the user is smiling, the emotion monitoring unit may estimate that the user is happy and provide positive advice. Alternatively, if the user has a sad expression, the emotion monitoring unit may estimate that the user is sad and provide comforting advice. Furthermore, the emotion monitoring unit may analyze the user's tone of voice and provide advice to relax if the user is tense. This enables more appropriate love support through real-time emotion monitoring.

[0074] The love support system can further include a behavior analysis unit that analyzes the user's behavioral patterns. The behavior analysis unit can collect data on the user's daily behavior and provide love advice based on this data. For example, if the user frequently visits a particular place, the behavior analysis unit can suggest date plans related to that place. Also, if the user is active during a particular time period, the behavior analysis unit can provide advice tailored to that time period. Furthermore, the behavior analysis unit can monitor changes in the user's behavioral patterns and provide advice at appropriate times. This makes it possible to provide love support that takes the user's behavioral patterns into consideration.

[0075] The love support system can further include a psychological evaluation unit that evaluates the user's psychological state. The psychological evaluation unit can evaluate the user's psychological state based on information and behavioral data input by the user. For example, if the user is feeling stressed, the psychological evaluation unit can provide advice to relax. Also, if the user lacks confidence, the psychological evaluation unit can provide advice to increase confidence. Furthermore, the psychological evaluation unit can monitor changes in the user's psychological state and provide advice at an appropriate time. This makes it possible to provide love support that takes the user's psychological state into consideration.

[0076] The love support system can further include a lifestyle analysis unit that analyzes the user's lifestyle. The lifestyle analysis unit can analyze the user's lifestyle habits and consumption behavior and provide love advice based on this. For example, if the user is health-conscious, the lifestyle analysis unit can suggest healthy date plans. Also, if the user is interested in fashion, the lifestyle analysis unit can provide fashion-related advice. Furthermore, the lifestyle analysis unit can monitor the user's consumption behavior and provide advice at appropriate times. This makes it possible to provide love support that takes the user's lifestyle into consideration.

[0077] The love support system can further include a cultural analysis unit that takes into account the user's cultural background. The cultural analysis unit can analyze the user's cultural background and values ​​and provide love advice based on this. For example, if the user belongs to a specific culture, the cultural analysis unit can provide advice appropriate to that culture. Also, if the user desires cross-cultural exchange, the cultural analysis unit can provide information about different cultures. Furthermore, the cultural analysis unit can take into account the user's values ​​and provide advice at an appropriate time. This makes it possible to provide love support that takes into account the user's cultural background.

[0078] The love support system may further include an emotion sharing unit that shares the user's emotions. The emotion sharing unit allows the user to share the emotions he or she feels with other users and gain sympathy. For example, if the user is feeling joy, the emotion sharing unit can share that joy with other users and gain sympathy. Also, if the user is feeling sad, the emotion sharing unit can share that sadness with other users and gain comfort. Furthermore, the emotion sharing unit provides the user with a tool to express emotions, allowing emotions to be shared more effectively. This allows for richer love support by sharing the user's emotions.

[0079] The love support system can further include a goal setting unit that sets future goals for the user. The goal setting unit can set future goals that the user wants to achieve and provide love advice based on these. For example, if the user is aiming to get married, the goal setting unit can suggest steps toward marriage. Also, if the user is aiming for self-improvement, the goal setting unit can provide advice for self-improvement. Furthermore, the goal setting unit can monitor the user's progress toward achieving their goal and provide advice at an appropriate time. This makes it possible to provide love support that takes into account the user's future goals.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The analysis unit analyzes the conversation content entered by the user. The conversation content may include, but is not limited to, text, audio, and video. The analysis unit uses natural language processing and sentiment analysis techniques to analyze the conversation content and the user's emotions. For example, the analysis unit analyzes text data and estimates the user's emotions. Step 2: The simulation unit performs a love simulation based on the results of the analysis by the analysis unit. Love simulations include scenario-based simulations and interactive simulations. For example, it generates a virtual partner and simulates a scenario in which the user confesses their feelings. It can also simulate the partner's reaction in advance and advise the user on how to respond. Step 3: The consultation unit provides counselor advice based on the results obtained by the simulation unit. The counselor's advice may include text messages, audio guides, etc. For example, if the user inputs "My relationship with my boyfriend is not going well," the consultation unit conveys the content of the consultation to the counselor and provides the counselor's advice to the user.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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 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.

[0100] 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.

[0101] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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 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.

[0116] 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.

[0117] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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 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.

[0133] 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.

[0134] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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."

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0152] 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.

[0153] [Explanation of symbols]

[0154] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an analysis unit that analyzes the content of the conversation; a simulation unit that performs a love simulation based on the results of the analysis by the analysis unit; A counseling unit is provided that provides advice from a counselor based on the results obtained by the simulation unit. A system characterized by:

2. The analysis unit Estimate the user's emotions and adjust the analysis method of the conversation content based on the estimated user emotions.

2. The system of claim 1.

3. The analysis unit When analyzing conversation content, the accuracy of the analysis is improved by referring to the user's past conversation history.

2. The system of claim 1.

4. The analysis unit When analyzing conversation content, filtering analysis based on the user's current life situation and areas of interest 2. The system of claim 1.

5. The analysis unit When analyzing conversation content, select the appropriate analysis method depending on the user's input method.

2. The system of claim 1.

6. The simulation unit Estimate user emotions and adjust simulation scenarios based on the estimated user emotions 2. The system of claim 1.

7. The simulation unit During simulation, attribute information of the virtual opponent is taken into account to improve the accuracy of the simulation.

2. The system of claim 1.

8. The simulation unit During simulation, refer to the user's past simulation results to improve the accuracy of the simulation.

2. The system of claim 1.

Images