System

Abstract

An object of the system according to the embodiment is to reduce loneliness and stress of a single person and enhance a feeling of happiness.SOLUTION: A system includes a conversation response unit, a knowledge providing unit, and a customization unit. The conversation response unit responds to an input from the user at any time in 24 hours. The knowledge provider corresponds to any topic of the user. The customization unit customizes the appearance, voice, and personality in response to a request from the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately provide effective means to alleviate the loneliness and stress felt by single people, and there is room for improvement.

[0005] The system according to the embodiment aims to reduce loneliness and stress among single people and increase their sense of happiness. [Means for solving the problem]

[0006] The system according to the embodiment includes a conversation response unit, a knowledge provision unit, and a customization unit. The conversation response unit responds to user input 24 hours a day. The knowledge provision unit responds to any topic the user may have. The customization unit customizes the user's appearance, voice, and personality according to the user's requests. [Effects of the Invention]

[0007] The system according to the embodiment can reduce loneliness and stress among single people and increase their sense of happiness. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The virtual lover system according to an embodiment of the present invention provides a virtual lover that can converse using a generation AI. This system responds to users 24 hours a day, provides knowledge, and provides a customizable virtual lover. This allows the virtual lover system to reduce users' feelings of loneliness and improve their happiness.

[0029] The virtual lover system according to the embodiment includes a conversation response unit, a knowledge providing unit, and a customization unit. The conversation response unit responds to user input 24 hours a day. For example, if a user says, "I'm tired from work today," the conversation response unit responds, "Thank you for your hard work. Shall we do something fun to relax?" If a user says, "Tell me about the latest news," the conversation response unit can provide the latest news. If a user says, "I'm feeling a little down today," the conversation response unit can respond, "Are you okay? I'd love to talk to you about anything." The knowledge providing unit responds to any topic a user may have. For example, if a user says, "Tell me about the latest news," the knowledge providing unit can provide the latest news. If a user says, "Tell me about the latest movies," the knowledge providing unit can provide the latest movie information. If a user says, "Tell me about the latest sports," the knowledge providing unit can provide the latest sports news. The customization unit customizes the appearance, voice, and personality according to the user's requests. For example, if a user requests a "cheerer voice," the customization unit can change the tone of the voice in response to the request. Also, if a user requests a "change in hair color," the customization unit can change the appearance in response to the request. Furthermore, if a user requests a "more humorous personality," the customization unit can adjust the personality in response to the request. In this way, the virtual lover system according to the embodiment can provide a user with a customizable virtual lover, providing knowledge and responding to the user 24 hours a day.

[0030] The conversation response unit stores the user's past conversation history in a database, and the generation AI analyzes that data to generate responses optimized for each individual user. For example, the conversation response unit stores the user's past conversation history in a database, and the generation AI analyzes that data. For example, it learns the user's frequently discussed topics and preferences and optimizes responses based on that. The conversation response unit also analyzes the conversation history to identify the user's preferences and interests. For example, if the user often talks about movies, the generation AI can provide the latest movie information and movie recommendations. Furthermore, the conversation response unit uses the user's past conversation history to predict the user's emotions and mood and generate responses accordingly. For example, it can suggest relaxation methods based on conversations the user had when they were stressed in the past. This allows the generation of optimized responses based on the user's past conversation history.

[0031] The customization unit manages the user's schedule and can suggest conversations at appropriate times. The customization unit, for example, manages the user's schedule and builds a system that suggests conversations at appropriate times. For example, the customization unit suggests conversations to help the user relax when they return home from work. The customization unit also allows the virtual lover to refer to the user's calendar and reminders and suggest conversations to coincide with important events and appointments. For example, the customization unit can send a congratulatory message on the user's birthday. The customization unit also manages the user's schedule and suggests conversations to help the user relax during times when stress tends to build up. For example, the customization unit provides advice on how to relax after a meeting. This makes it possible to suggest conversations at appropriate times based on the user's schedule.

[0032] The customization unit can monitor the user's health condition and provide health-related advice. The customization unit, for example, builds a system that monitors the user's health condition and provides health-related advice. For example, the customization unit analyzes the user's sleep patterns and provides advice on how to get good quality sleep. The customization unit also allows a virtual lover to collect the user's health data and provide advice according to the user's health condition. For example, the customization unit can analyze the user's exercise volume and suggest an appropriate exercise plan. Furthermore, the customization unit monitors the user's health condition in real time and provides health-related advice. For example, the customization unit can analyze the user's diet and suggest a balanced diet. This makes it possible to monitor the user's health condition and provide health-related advice.

[0033] The knowledge providing unit can learn the user's interests and provide knowledge based on them. For example, the knowledge providing unit builds a system that learns the user's interests and provides knowledge based on them. For example, if the user is interested in history, it can provide information about history. The knowledge providing unit can also analyze the content of the user's past conversations to identify their interests and concerns. For example, if the user often talks about sports, it can provide the latest sports news. Furthermore, the knowledge providing unit can learn the user's interests and provide knowledge based on them. For example, if the user is interested in cooking, it can suggest new recipes. This makes it possible to provide knowledge based on the user's interests and concerns.

[0034] The knowledge provision unit can analyze the user's past questions and conversation content to provide deeper knowledge. For example, the knowledge provision unit stores the user's past questions and conversation content in a database, and the generation AI analyzes that data. For example, it can provide deeper knowledge related to the questions the user has asked in the past. The knowledge provision unit also analyzes the conversation content to provide detailed information on topics that interest the user. For example, if the user is interested in science, it can introduce the latest scientific research. Furthermore, the knowledge provision unit builds a system in which the generation AI provides deeper knowledge based on the user's past questions and conversation content. For example, if the user wants to know more about a particular topic, it can provide detailed information on that topic. This makes it possible to provide deeper knowledge based on the user's past questions and conversation content.

[0035] The knowledge providing unit can suggest events and activities based on the user's hobbies and interests. The knowledge providing unit, for example, learns the user's hobbies and interests and builds a system that suggests events and activities based on them. For example, if the user is interested in music, concert information is provided. The knowledge providing unit also analyzes the user's hobbies and interests, and suggests events and activities based on them. For example, if the user is interested in the outdoors, hiking information can be provided. Furthermore, the knowledge providing unit develops a system in which the virtual lover suggests events and activities based on the user's hobbies and interests. For example, if the user is interested in art, information on art exhibitions is provided. This makes it possible to suggest events and activities based on the user's hobbies and interests.

[0036] The knowledge providing unit supports the user's learning and can provide quizzes and tests related to the learning content. For example, the knowledge providing unit builds a system that analyzes the user's learning content and provides quizzes and tests based on that. For example, if the user is studying history, it can provide quizzes related to history. The knowledge providing unit also supports the user's learning through a virtual lover and provides quizzes and tests related to the learning content. For example, if the user is studying mathematics, it can pose mathematics questions. Furthermore, the knowledge providing unit develops a system in which the virtual lover provides quizzes and tests based on the user's learning content. For example, if the user is studying science, it can provide tests related to science. In this way, it is possible to support the user's learning and provide quizzes and tests.

[0037] The customization unit can make fine adjustments to the appearance and voice based on the user's preferences. For example, the customization unit builds a system that makes fine adjustments to the appearance and voice based on the user's preferences. For example, if the user wants to change the tone of their voice, the tone of their voice is adjusted in response to that request. The customization unit also makes fine adjustments to the appearance and voice of the virtual lover to suit the user's preferences. For example, if the user wants to change their hair color, the hair color can be changed in response to that request. Furthermore, the customization unit develops a system that makes fine adjustments to the appearance and voice based on the user's preferences. For example, if the user wants to change their eye color, the eye color is changed in response to that request. This makes it possible to make fine adjustments to the appearance and voice based on the user's preferences.

[0038] The customization unit can collect user feedback and continuously improve the appearance, voice, and personality based on the feedback. For example, the customization unit collects user feedback and builds a system that continuously improves the appearance, voice, and personality based on the feedback. For example, if a user provides feedback on the tone of voice, the voice is adjusted based on the feedback. The customization unit also collects user feedback on the appearance, voice, and personality of the virtual lover and makes improvements based on the feedback. For example, if a user provides feedback on appearance, the appearance can be adjusted based on the feedback. Furthermore, the customization unit develops a system that continuously improves the appearance, voice, and personality based on the user feedback. For example, if a user provides feedback on personality, the personality is adjusted based on the feedback. In this way, the appearance, voice, and personality can be continuously improved based on the user feedback.

[0039] The customization unit can provide fashion and style advice to the user. For example, the customization unit builds a system in which a virtual lover provides fashion and style advice to the user. For example, if a user asks for advice on clothing, advice is provided based on the latest fashion trends. The customization unit also provides fashion advice to the virtual lover based on the user's preferences and style. For example, the customization unit can suggest outfits for the user when attending a specific event. Furthermore, the customization unit develops a system in which a virtual lover provides fashion and style advice to the user. For example, if a user wants to try a new hairstyle, advice that matches that style is provided. In this way, fashion and style advice can be provided to the user.

[0040] The customization unit can adjust the appearance, voice, and personality based on the preferences of the user's family and friends. For example, the customization unit builds a system in which a virtual lover adjusts the appearance, voice, and personality based on the preferences of the user's family and friends. For example, the customization unit changes the tone of voice to match the preferences of the user's family. The customization unit also learns the preferences of the user's family and friends and adjusts the appearance, voice, and personality based on the preferences. For example, the customization unit can change the appearance to match the preferences of the user's friends. Furthermore, the customization unit develops a system in which a virtual lover adjusts the appearance, voice, and personality based on the preferences of the user's family and friends. For example, the customization unit adjusts the personality to match the preferences of the user's family. This makes it possible to adjust the appearance, voice, and personality based on the preferences of the user's family and friends.

[0041] The knowledge providing unit can monitor the user's stress level and suggest appropriate relaxation methods. The knowledge providing unit, for example, builds a system that monitors the user's stress level and suggests appropriate relaxation methods. For example, the knowledge providing unit analyzes the user's heart rate and breathing patterns and suggests deep breathing to relax. The knowledge providing unit also monitors the user's stress level in real time using a virtual lover and suggests relaxation methods. For example, if the user is feeling stressed, it can suggest meditation or yoga methods. Furthermore, the knowledge providing unit develops a system that monitors the user's stress level and suggests appropriate relaxation methods. For example, if the user is feeling stressed, it plays relaxing music. This makes it possible to monitor the user's stress level and suggest appropriate relaxation methods.

[0042] The knowledge providing unit can support the user's mental health and suggest collaboration with experts. For example, the knowledge providing unit builds a system in which a virtual lover supports the user's mental health and suggests collaboration with experts as needed. For example, if the user is feeling severe stress, the knowledge providing unit suggests consulting with a counselor. The knowledge providing unit also monitors the user's mental health and suggests collaboration with experts. For example, if the user has been depressed for a long period of time, the knowledge providing unit can suggest consulting with a psychotherapist. Furthermore, the knowledge providing unit develops a system in which a virtual lover supports the user's mental health and suggests collaboration with experts. For example, if the user is feeling anxious, the knowledge providing unit suggests consulting with a mental health expert. This makes it possible to support the user's mental health and suggest collaboration with experts.

[0043] The knowledge providing unit can suggest new activities based on the user's hobbies and interests. For example, the knowledge providing unit builds a system that learns the user's hobbies and interests and suggests new activities based on them. For example, if the user is interested in cooking, it suggests new recipes. The knowledge providing unit also analyzes the user's hobbies and interests and suggests new activities based on them. For example, if the user is interested in sports, it can suggest trying a new sport. Furthermore, the knowledge providing unit develops a system in which the virtual lover suggests new activities based on the user's hobbies and interests. For example, if the user is interested in art, it can suggest a new art project. This makes it possible to suggest new activities based on the user's hobbies and interests.

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

[0045] The virtual lover system can also suggest related events and activities based on the user's hobbies and interests. For example, if the user is interested in music, it can provide information about nearby concerts. If the user likes outdoor activities, it can suggest information about hiking and camping. Furthermore, if the user is interested in art, it can provide information about art exhibitions and workshops. This allows the system to provide new experiences based on the user's hobbies and interests.

[0046] The virtual lover system can monitor the user's health and provide health advice. For example, it can analyze the user's sleep patterns and provide advice on how to get a good night's sleep. It can also analyze the user's exercise volume and suggest an appropriate exercise plan. It can also analyze the user's diet and suggest balanced meals. This can support the user's health and promote a healthy lifestyle.

[0047] The virtual lover system can manage the user's schedule and suggest conversations at appropriate times. For example, it can suggest conversations to help the user relax when they get home from work. It can also refer to the user's calendar and reminders to suggest conversations to coincide with important events or appointments. It can also suggest conversations to help the user relax during times when stress tends to build up. This allows it to suggest conversations at appropriate times based on the user's schedule.

[0048] The virtual lover system can support the user's learning and provide quizzes and tests related to the learning content. For example, if the user is studying history, a quiz related to history can be provided. If the user is studying mathematics, mathematics questions can be provided. Furthermore, if the user is studying science, tests related to science can be provided. In this way, the virtual lover system can support the user's learning and provide quizzes and tests.

[0049] The virtual lover system can support the user's mental health and suggest collaboration with experts. For example, if the user is experiencing severe stress, it can suggest consulting with a counselor. Also, if the user has been depressed for a long period of time, it can suggest consulting with a psychotherapist. Furthermore, if the user is experiencing anxiety, it can suggest consulting with a mental health expert. This allows the system to support the user's mental health and suggest collaboration with experts.

[0050] The virtual lover system can suggest new activities based on the user's hobbies and interests. For example, if the user is interested in cooking, it can suggest new recipes. If the user is interested in sports, it can suggest trying new sports. Furthermore, if the user is interested in art, it can suggest new art projects. In this way, it is possible to suggest new activities based on the user's hobbies and interests.

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

[0052] Step 1: The conversation response unit responds to user input 24 hours a day. For example, if the user says, "I'm tired from work today," the conversation response unit responds, "Thank you for your hard work. Would you like to do something fun to relax?" If the user says, "Tell me about the latest news," the conversation response unit can provide the latest news. Furthermore, if the user says, "I'm feeling a little down today," the conversation response unit can respond, "Are you okay? I'd love to talk to you if there's anything you'd like to talk about." Step 2: The knowledge provider responds to any topic of the user. For example, if the user says, "Tell me about the latest news," the knowledge provider will provide the latest news. Also, if the user says, "Tell me about the latest movies," the knowledge provider can provide the latest movie information. Furthermore, if the user says, "Tell me about the latest sports," the knowledge provider can provide the latest sports news. Step 3: The customization unit customizes the appearance, voice, and personality according to the user's request. For example, if the user requests a "cheerer voice," the customization unit changes the tone of the voice in response to the request. Also, if the user requests a "change in hair color," the customization unit can change the appearance in response to the request. Furthermore, if the user requests a "more humorous personality," the customization unit can adjust the personality in response to the request.

[0053] (Example 2) The virtual lover system according to an embodiment of the present invention provides a virtual lover that can converse using a generation AI. This system responds to users 24 hours a day, provides knowledge, and provides a customizable virtual lover. This allows the virtual lover system to reduce users' feelings of loneliness and improve their happiness.

[0054] The virtual lover system according to the embodiment includes a conversation response unit, a knowledge providing unit, and a customization unit. The conversation response unit responds to user input 24 hours a day. For example, if a user says, "I'm tired from work today," the conversation response unit responds, "Thank you for your hard work. Shall we do something fun to relax?" If a user says, "Tell me about the latest news," the conversation response unit can provide the latest news. If a user says, "I'm feeling a little down today," the conversation response unit can respond, "Are you okay? I'd love to talk to you about anything." The knowledge providing unit responds to any topic a user may have. For example, if a user says, "Tell me about the latest news," the knowledge providing unit can provide the latest news. If a user says, "Tell me about the latest movies," the knowledge providing unit can provide the latest movie information. If a user says, "Tell me about the latest sports," the knowledge providing unit can provide the latest sports news. The customization unit customizes the appearance, voice, and personality according to the user's requests. For example, if a user requests a "cheerer voice," the customization unit can change the tone of the voice in response to the request. Also, if a user requests a "change in hair color," the customization unit can change the appearance in response to the request. Furthermore, if a user requests a "more humorous personality," the customization unit can adjust the personality in response to the request. In this way, the virtual lover system according to the embodiment can provide a user with a customizable virtual lover, providing knowledge and responding to the user 24 hours a day.

[0055] The conversation response unit stores the user's past conversation history in a database, and the generation AI analyzes that data to generate responses optimized for each individual user. For example, the conversation response unit stores the user's past conversation history in a database, and the generation AI analyzes that data. For example, it learns the user's frequently discussed topics and preferences and optimizes responses based on that. The conversation response unit also analyzes the conversation history to identify the user's preferences and interests. For example, if the user often talks about movies, the generation AI can provide the latest movie information and movie recommendations. Furthermore, the conversation response unit uses the user's past conversation history to predict the user's emotions and mood and generate responses accordingly. For example, it can suggest relaxation methods based on conversations the user had when they were stressed in the past. This allows the generation of optimized responses based on the user's past conversation history.

[0056] The conversation response unit can analyze the user's tone of voice and speaking style in real time and generate a response that corresponds to their emotional state at that time. For example, the conversation response unit analyzes the user's tone of voice and speaking style in real time to estimate their emotional state. For example, if the user speaks low and slowly, the generation AI determines that the user is tired and suggests ways to relax. The conversation response unit also uses voice analysis technology to estimate emotions from the user's tone of voice and speaking style and generate a response accordingly. For example, if the user speaks high and quickly, the generation AI determines that the user is excited and can provide an interesting topic to talk about. Furthermore, the conversation response unit builds a system that analyzes the user's tone of voice and speaking style and generates a response that corresponds to their emotional state. For example, if the user's voice is trembling, the generation AI determines that the user is nervous and provides advice on how to relax. This makes it possible to analyze the user's tone of voice and speaking style and generate a response that corresponds to their emotional state.

[0057] The conversation response unit can use the emotion estimation function to estimate the user's emotions and generate a response according to the emotions. The conversation response unit, for example, uses the emotion estimation function to analyze the user's emotions in real time and generate a response according to the emotions. For example, if the user is sad, the generation AI will offer words of encouragement. The conversation response unit also builds a system that estimates the user's emotions and generates a response according to the emotions. For example, if the user is angry, the generation AI can offer advice on how to stay calm. Furthermore, the conversation response unit uses the emotion estimation function to analyze the user's emotional state and generate a response according to the emotion. For example, if the user is happy, the generation AI will offer words of empathy. In this way, the emotion estimation function can be used to generate a response according to the user's emotions.

[0058] The customization unit manages the user's schedule and can suggest conversations at appropriate times. The customization unit, for example, manages the user's schedule and builds a system that suggests conversations at appropriate times. For example, the customization unit suggests conversations to help the user relax when they return home from work. The customization unit also allows the virtual lover to refer to the user's calendar and reminders and suggest conversations to coincide with important events and appointments. For example, the customization unit can send a congratulatory message on the user's birthday. The customization unit also manages the user's schedule and suggests conversations to help the user relax during times when stress tends to build up. For example, the customization unit provides advice on how to relax after a meeting. This makes it possible to suggest conversations at appropriate times based on the user's schedule.

[0059] The customization unit can monitor the user's health condition and provide health-related advice. The customization unit, for example, builds a system that monitors the user's health condition and provides health-related advice. For example, the customization unit analyzes the user's sleep patterns and provides advice on how to get good quality sleep. The customization unit also allows a virtual lover to collect the user's health data and provide advice according to the user's health condition. For example, the customization unit can analyze the user's exercise volume and suggest an appropriate exercise plan. Furthermore, the customization unit monitors the user's health condition in real time and provides health-related advice. For example, the customization unit can analyze the user's diet and suggest a balanced diet. This makes it possible to monitor the user's health condition and provide health-related advice.

[0060] The customization unit can use the emotion estimation function to suggest appropriate music or video when the user is in a specific emotional state. For example, the customization unit can use the emotion estimation function to suggest relaxing music when the user is in a specific emotional state. For example, if the user is feeling stressed, relaxing music can be played. The customization unit can also analyze the user's emotional state and suggest video corresponding to that emotion. For example, if the user is sad, an uplifting video can be played. Furthermore, the customization unit can use the emotion estimation function to build a system that suggests appropriate music or video when the user is in a specific emotional state. For example, if the user is happy, a fun video can be played. In this way, appropriate music or video can be suggested according to the user's emotional state.

[0061] The knowledge providing unit can learn the user's interests and provide knowledge based on them. For example, the knowledge providing unit builds a system that learns the user's interests and provides knowledge based on them. For example, if the user is interested in history, it can provide information about history. The knowledge providing unit can also analyze the content of the user's past conversations to identify their interests and concerns. For example, if the user often talks about sports, it can provide the latest sports news. Furthermore, the knowledge providing unit can learn the user's interests and provide knowledge based on them. For example, if the user is interested in cooking, it can suggest new recipes. This makes it possible to provide knowledge based on the user's interests and concerns.

[0062] The knowledge provision unit can analyze the user's past questions and conversation content to provide deeper knowledge. For example, the knowledge provision unit stores the user's past questions and conversation content in a database, and the generation AI analyzes that data. For example, it can provide deeper knowledge related to the questions the user has asked in the past. The knowledge provision unit also analyzes the conversation content to provide detailed information on topics that interest the user. For example, if the user is interested in science, it can introduce the latest scientific research. Furthermore, the knowledge provision unit builds a system in which the generation AI provides deeper knowledge based on the user's past questions and conversation content. For example, if the user wants to know more about a particular topic, it can provide detailed information on that topic. This makes it possible to provide deeper knowledge based on the user's past questions and conversation content.

[0063] The knowledge providing unit can suggest events and activities based on the user's hobbies and interests. The knowledge providing unit, for example, learns the user's hobbies and interests and builds a system that suggests events and activities based on them. For example, if the user is interested in music, concert information is provided. The knowledge providing unit also analyzes the user's hobbies and interests, and suggests events and activities based on them. For example, if the user is interested in the outdoors, hiking information can be provided. Furthermore, the knowledge providing unit develops a system in which the virtual lover suggests events and activities based on the user's hobbies and interests. For example, if the user is interested in art, information on art exhibitions is provided. This makes it possible to suggest events and activities based on the user's hobbies and interests.

[0064] The knowledge providing unit supports the user's learning and can provide quizzes and tests related to the learning content. For example, the knowledge providing unit builds a system that analyzes the user's learning content and provides quizzes and tests based on that. For example, if the user is studying history, it can provide quizzes related to history. The knowledge providing unit also supports the user's learning through a virtual lover and provides quizzes and tests related to the learning content. For example, if the user is studying mathematics, it can pose mathematics questions. Furthermore, the knowledge providing unit develops a system in which the virtual lover provides quizzes and tests based on the user's learning content. For example, if the user is studying science, it can provide tests related to science. In this way, it is possible to support the user's learning and provide quizzes and tests.

[0065] The knowledge providing unit can use the emotion estimation function to suggest new topics that the user is likely to be interested in. For example, the knowledge providing unit uses the emotion estimation function to analyze the user's emotional state and suggest new topics according to that emotion. For example, if the user is excited, a new topic to further increase that excitement is provided. The knowledge providing unit also builds a system that estimates the user's emotion and suggests new topics according to that emotion. For example, if the user is relaxed, a new topic related to relaxation can be provided. Furthermore, the knowledge providing unit uses the emotion estimation function to analyze the user's emotional state and suggest new topics according to that. For example, if the user is depressed, a new topic to cheer them up is provided. In this way, the emotion estimation function can be used to suggest new topics that the user is likely to be interested in.

[0066] The customization unit can make fine adjustments to the appearance and voice based on the user's preferences. For example, the customization unit builds a system that makes fine adjustments to the appearance and voice based on the user's preferences. For example, if the user wants to change the tone of their voice, the tone of their voice is adjusted in response to that request. The customization unit also makes fine adjustments to the appearance and voice of the virtual lover to suit the user's preferences. For example, if the user wants to change their hair color, the hair color can be changed in response to that request. Furthermore, the customization unit develops a system that makes fine adjustments to the appearance and voice based on the user's preferences. For example, if the user wants to change their eye color, the eye color is changed in response to that request. This makes it possible to make fine adjustments to the appearance and voice based on the user's preferences.

[0067] The customization unit can collect user feedback and continuously improve the appearance, voice, and personality based on the feedback. For example, the customization unit collects user feedback and builds a system that continuously improves the appearance, voice, and personality based on the feedback. For example, if a user provides feedback on the tone of voice, the voice is adjusted based on the feedback. The customization unit also collects user feedback on the appearance, voice, and personality of the virtual lover and makes improvements based on the feedback. For example, if a user provides feedback on appearance, the appearance can be adjusted based on the feedback. Furthermore, the customization unit develops a system that continuously improves the appearance, voice, and personality based on the user feedback. For example, if a user provides feedback on personality, the personality is adjusted based on the feedback. In this way, the appearance, voice, and personality can be continuously improved based on the user feedback.

[0068] The customization unit can use the emotion estimation function to adjust the appearance, voice, and personality according to the user's emotional state. For example, the customization unit uses the emotion estimation function to build a system that analyzes the user's emotional state and adjusts the appearance, voice, and personality accordingly. For example, if the user is depressed, the voice of the virtual lover can be changed to a gentler tone. The customization unit can also estimate the user's emotions and adjust the appearance, voice, and personality according to the emotions. For example, if the user is excited, the appearance of the virtual lover can be changed to a brighter color. Furthermore, the customization unit can use the emotion estimation function to develop a system that analyzes the user's emotional state and adjusts the appearance, voice, and personality accordingly. For example, if the user is relaxed, the personality of the virtual lover can be adjusted to a gentler tone. This makes it possible to use the emotion estimation function to adjust the appearance, voice, and personality according to the user's emotional state.

[0069] The customization unit can provide fashion and style advice to the user. For example, the customization unit builds a system in which a virtual lover provides fashion and style advice to the user. For example, if a user asks for advice on clothing, advice is provided based on the latest fashion trends. The customization unit also provides fashion advice to the virtual lover based on the user's preferences and style. For example, the customization unit can suggest outfits for the user when attending a specific event. Furthermore, the customization unit develops a system in which a virtual lover provides fashion and style advice to the user. For example, if a user wants to try a new hairstyle, advice that matches that style is provided. In this way, fashion and style advice can be provided to the user.

[0070] The customization unit can adjust the appearance, voice, and personality based on the preferences of the user's family and friends. For example, the customization unit builds a system in which a virtual lover adjusts the appearance, voice, and personality based on the preferences of the user's family and friends. For example, the customization unit changes the tone of voice to match the preferences of the user's family. The customization unit also learns the preferences of the user's family and friends and adjusts the appearance, voice, and personality based on the preferences. For example, the customization unit can change the appearance to match the preferences of the user's friends. Furthermore, the customization unit develops a system in which a virtual lover adjusts the appearance, voice, and personality based on the preferences of the user's family and friends. For example, the customization unit adjusts the personality to match the preferences of the user's family. This makes it possible to adjust the appearance, voice, and personality based on the preferences of the user's family and friends.

[0071] The customization unit can use the emotion estimation function to adjust the appearance, voice, and personality of the user when the user is in a specific emotional state. For example, the customization unit uses the emotion estimation function to build a system that adjusts the appearance, voice, and personality of the user when the user is in a specific emotional state. For example, if the user is sad, the voice of the virtual lover is changed to a gentle tone. The customization unit also estimates the user's emotion and adjusts the appearance, voice, and personality according to the emotion. For example, if the user is excited, the appearance of the virtual lover can be changed to a bright color. Furthermore, the customization unit uses the emotion estimation function to develop a system that analyzes the user's emotional state and adjusts the appearance, voice, and personality accordingly. For example, if the user is relaxed, the personality of the virtual lover is adjusted to be gentle. In this way, the emotion estimation function can be used to adjust the appearance, voice, and personality of the user when the user is in a specific emotional state.

[0072] The knowledge providing unit can monitor the user's stress level and suggest appropriate relaxation methods. The knowledge providing unit, for example, builds a system that monitors the user's stress level and suggests appropriate relaxation methods. For example, the knowledge providing unit analyzes the user's heart rate and breathing patterns and suggests deep breathing to relax. The knowledge providing unit also monitors the user's stress level in real time using a virtual lover and suggests relaxation methods. For example, if the user is feeling stressed, it can suggest meditation or yoga methods. Furthermore, the knowledge providing unit develops a system that monitors the user's stress level and suggests appropriate relaxation methods. For example, if the user is feeling stressed, it plays relaxing music. This makes it possible to monitor the user's stress level and suggest appropriate relaxation methods.

[0073] The knowledge providing unit can use the emotion estimation function to suggest a relaxation method according to the user's emotional state. For example, the knowledge providing unit uses the emotion estimation function to build a system that analyzes the user's emotional state and suggests a relaxation method according to that state. For example, if the user is depressed, the knowledge providing unit suggests a meditation method to help them relax. The knowledge providing unit also estimates the user's emotion and suggests a relaxation method according to that emotion. For example, if the user is excited, the knowledge providing unit can suggest deep breathing to help them relax. Furthermore, the knowledge providing unit uses the emotion estimation function to develop a system that analyzes the user's emotional state and suggests a relaxation method according to that state. For example, if the user is relaxed, the knowledge providing unit suggests music to further relax them. In this way, the emotion estimation function can be used to suggest a relaxation method according to the user's emotional state.

[0074] The knowledge providing unit can support the user's mental health and suggest collaboration with experts. For example, the knowledge providing unit builds a system in which a virtual lover supports the user's mental health and suggests collaboration with experts as needed. For example, if the user is feeling severe stress, the knowledge providing unit suggests consulting with a counselor. The knowledge providing unit also monitors the user's mental health and suggests collaboration with experts. For example, if the user has been depressed for a long period of time, the knowledge providing unit can suggest consulting with a psychotherapist. Furthermore, the knowledge providing unit develops a system in which a virtual lover supports the user's mental health and suggests collaboration with experts. For example, if the user is feeling anxious, the knowledge providing unit suggests consulting with a mental health expert. This makes it possible to support the user's mental health and suggest collaboration with experts.

[0075] The knowledge providing unit can suggest new activities based on the user's hobbies and interests. For example, the knowledge providing unit builds a system that learns the user's hobbies and interests and suggests new activities based on them. For example, if the user is interested in cooking, it suggests new recipes. The knowledge providing unit also analyzes the user's hobbies and interests and suggests new activities based on them. For example, if the user is interested in sports, it can suggest trying a new sport. Furthermore, the knowledge providing unit develops a system in which the virtual lover suggests new activities based on the user's hobbies and interests. For example, if the user is interested in art, it can suggest a new art project. This makes it possible to suggest new activities based on the user's hobbies and interests.

[0076] The knowledge providing unit can use the emotion estimation function to suggest an appropriate relaxation method when the user is in a specific emotional state. The knowledge providing unit, for example, uses the emotion estimation function to build a system that suggests a relaxation method when the user is in a specific emotional state. For example, if the user is feeling stressed, the knowledge providing unit suggests deep breathing to relax. The knowledge providing unit also estimates the user's emotion and suggests a relaxation method according to that emotion. For example, if the user is feeling depressed, the knowledge providing unit can suggest a meditation method to relax. Furthermore, the knowledge providing unit uses the emotion estimation function to develop a system that analyzes the user's emotional state and suggests a relaxation method according to that emotion. For example, if the user is relaxed, the knowledge providing unit suggests music to further relax the user. In this way, the emotion estimation function can be used to suggest an appropriate relaxation method when the user is in a specific emotional state.

[0077] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0078] The virtual lover system can also suggest related events and activities based on the user's hobbies and interests. For example, if the user is interested in music, it can provide information about nearby concerts. If the user likes outdoor activities, it can suggest information about hiking and camping. Furthermore, if the user is interested in art, it can provide information about art exhibitions and workshops. This allows the system to provide new experiences based on the user's hobbies and interests.

[0079] The virtual lover system can monitor the user's health and provide health advice. For example, it can analyze the user's sleep patterns and provide advice on how to get a good night's sleep. It can also analyze the user's exercise volume and suggest an appropriate exercise plan. It can also analyze the user's diet and suggest balanced meals. This can support the user's health and promote a healthy lifestyle.

[0080] The virtual lover system can analyze the user's tone of voice and speaking style in real time and generate responses according to the user's emotional state at that time. For example, if the user speaks in a low, slow voice, it can determine that the user is tired and suggest ways to relax. If the user speaks in a high, fast voice, it can determine that the user is excited and offer interesting topics to talk about. Furthermore, if the user's voice is trembling, it can determine that the user is nervous and offer advice on how to relax. This makes it possible to generate responses according to the user's emotional state.

[0081] The virtual lover system can manage the user's schedule and suggest conversations at appropriate times. For example, it can suggest conversations to help the user relax when they get home from work. It can also refer to the user's calendar and reminders to suggest conversations to coincide with important events or appointments. It can also suggest conversations to help the user relax during times when stress tends to build up. This allows it to suggest conversations at appropriate times based on the user's schedule.

[0082] The virtual lover system can suggest music and images according to the user's emotional state. For example, if the user is feeling stressed, relaxing music can be played. If the user is sad, an uplifting image can be played. Furthermore, if the user is happy, a fun image can be played. In this way, appropriate music and images can be suggested according to the user's emotional state.

[0083] The virtual lover system can support the user's learning and provide quizzes and tests related to the learning content. For example, if the user is studying history, a quiz related to history can be provided. If the user is studying mathematics, mathematics questions can be provided. Furthermore, if the user is studying science, tests related to science can be provided. In this way, the virtual lover system can support the user's learning and provide quizzes and tests.

[0084] The virtual lover system can support the user's mental health and suggest collaboration with experts. For example, if the user is experiencing severe stress, it can suggest consulting with a counselor. Also, if the user has been depressed for a long period of time, it can suggest consulting with a psychotherapist. Furthermore, if the user is experiencing anxiety, it can suggest consulting with a mental health expert. This allows the system to support the user's mental health and suggest collaboration with experts.

[0085] The virtual lover system can suggest relaxation methods according to the user's emotional state. For example, if the user is depressed, it can suggest meditation methods to help them relax. If the user is excited, it can also suggest deep breathing to help them relax. Furthermore, if the user is relaxed, it can suggest music to help them relax even more. In this way, it is possible to suggest relaxation methods according to the user's emotional state.

[0086] The virtual lover system can suggest new activities based on the user's hobbies and interests. For example, if the user is interested in cooking, it can suggest new recipes. If the user is interested in sports, it can suggest trying new sports. Furthermore, if the user is interested in art, it can suggest new art projects. In this way, it is possible to suggest new activities based on the user's hobbies and interests.

[0087] The virtual lover system can suggest new topics according to the user's emotional state. For example, if the user is excited, a new topic to further increase the user's excitement can be provided. If the user is relaxed, a new topic related to relaxation can be provided. Furthermore, if the user is depressed, a new topic to cheer the user up can be provided. In this way, new topics can be suggested according to the user's emotional state.

[0088] The processing flow of the second embodiment will be briefly explained below.

[0089] Step 1: The conversation response unit responds to user input 24 hours a day. For example, if the user says, "I'm tired from work today," the conversation response unit responds, "Thank you for your hard work. Would you like to do something fun to relax?" If the user says, "Tell me about the latest news," the conversation response unit can provide the latest news. Furthermore, if the user says, "I'm feeling a little down today," the conversation response unit can respond, "Are you okay? I'd love to talk to you if there's anything you'd like to talk about." Step 2: The knowledge provider responds to any topic of the user. For example, if the user says, "Tell me about the latest news," the knowledge provider will provide the latest news. Also, if the user says, "Tell me about the latest movies," the knowledge provider can provide the latest movie information. Furthermore, if the user says, "Tell me about the latest sports," the knowledge provider can provide the latest sports news. Step 3: The customization unit customizes the appearance, voice, and personality according to the user's request. For example, if the user requests a "cheerer voice," the customization unit changes the tone of the voice in response to the request. Also, if the user requests a "change in hair color," the customization unit can change the appearance in response to the request. Furthermore, if the user requests a "more humorous personality," the customization unit can adjust the personality in response to the request.

[0090] 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.

[0091] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0092] 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.

[0093] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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).

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0109] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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."

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0157] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A system for developing a virtual lover that can converse using a generation AI, The generated AI is A conversation response unit that responds to user input 24 hours a day; a knowledge provider that responds to all topics of the user; A customization unit that customizes the appearance, voice, and personality according to the user's request. A system characterized by:

2. The conversation response unit The user's past conversation history is stored in a database, and the generation AI analyzes the data to generate responses optimized for each individual user.

2. The system of claim 1.

3. The conversation response unit Analyze the user's tone of voice and speaking style in real time to generate a response that matches their emotional state at that time.

2. The system of claim 1.

4. The conversation response unit Estimating the user's emotion and generating a response according to the emotion 2. The system of claim 1.

5. The customization unit Manage the user's schedule and suggest conversations at appropriate times 2. The system of claim 1.

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