System

Abstract

An object of the system according to the embodiment is to allow a user to easily talk about a consultation or a complaint and obtain an appropriate solution or information.SOLUTION: A system according to an embodiment includes a consultation handling unit, a solution providing unit, and an audience unit. The consultation handling unit uses the generated AI to listen to the user and provides a necessary solution or information. The solution providing unit provides an appropriate solution or information to the user based on the solution or information provided by the consultation handling unit. The listening part listens to the user's complaint or consultation by the consultation corresponding part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that if a user does not have anyone to whom they can easily talk about their concerns or complaints, it is difficult for them to obtain appropriate solutions or information.

[0005] The system according to the embodiment aims to enable users to easily ask for advice or express their complaints and obtain appropriate solutions and information. [Means for solving the problem]

[0006] The system according to the embodiment includes a consultation response unit, a solution providing unit, and a listener unit. The consultation response unit uses a generation AI to listen to the user and provide the necessary solutions and information. The solution providing unit provides the user with appropriate solutions and information based on the solutions and information provided by the consultation response unit. The listener unit listens to the user's complaints and inquiries via the consultation response unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to easily ask for advice or express their complaints, and obtain appropriate solutions and information. [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 Aibou Chat system according to an embodiment of the present invention allows users to easily discuss their concerns or vent their frustrations even when they cannot meet with their family or friends immediately. This system uses a generative AI to listen to the user's story and provide necessary solutions and information. This allows the Aibou Chat system to easily discuss their concerns or vent their frustrations even when they cannot meet with their family or friends immediately. For example, talking to Aibou Chat about work stress or everyday worries can lighten the mood. Furthermore, obtaining necessary solutions and information can help solve problems.

[0029] The Aibou chat system according to the embodiment includes a consultation unit, a solution provider, and a listener. The consultation unit uses a generation AI to listen to the user's story and provide necessary solutions and information. For example, if a user says, "Work was tough today," the generation AI responds with, "That sounds tough. What happened?" The solution provider provides appropriate solutions and information to the user based on the solutions and information provided by the consultation unit. For example, if a user asks, "Tell me how to relieve stress," the generation AI provides advice such as, "Deep breathing, light exercise, and immersing yourself in a hobby are effective." The listener listens to the user's complaints and concerns using the consultation unit. For example, if a user says, "Work hasn't been going well lately," the generation AI responds with, "That's tough. What exactly is going wrong?" to draw out the user's story. This allows the Aibou chat system to easily talk about problems and concerns with family and friends even when they cannot meet immediately.

[0030] The consultation response unit can refer to the user's past consultation history and generate responses optimized for individual users. In the consultation response unit, for example, the generation AI refers to the user's past consultation history and generates consistent responses to similar consultation content. For example, if a user previously consulted about work stress, the generation AI will again suggest stress relief methods. In addition, in the consultation response unit, the generation AI analyzes the user's past consultation history and generates responses based on the user's preferences and tendencies. For example, if a user previously talked about their hobbies, advice related to those hobbies will be provided. In addition, in the consultation response unit, the generation AI predicts the user's emotional state based on the user's past consultation history and generates responses accordingly. For example, if a user previously consulted about sadness, words of encouragement will be provided. This makes it possible to provide consistent responses to users.

[0031] The consultation response unit can have a function to accept user consultation content not only via voice but also via text and images. In the consultation response unit, for example, the generation AI has a function to accept not only voice input but also text input. For example, when a user inputs the consultation content in text, the generation AI generates a response accordingly. In addition, the consultation response unit has a function to accept image input and perform image analysis. For example, when a user uploads an image, the generation AI generates a response based on that image. In addition, the consultation response unit has a function to accept multimodal input of voice, text, and images and perform comprehensive analysis. For example, when a user sends text or images while speaking, the generation AI combines them to generate a response. This makes it possible to accept user consultation content in a variety of formats.

[0032] The solution providing unit can present multiple solutions in response to a user's question and explain the advantages and disadvantages of each. For example, the generation AI presents multiple solutions in response to a user's question and explains the advantages and disadvantages of each. For example, the generation AI presents three stress relief methods: deep breathing, exercise, and hobbies, and explains the effects and precautions of each. The solution providing unit also presents multiple solutions in response to a user's question and explains how to implement each. For example, the generation AI presents three time management methods: using a time management app, using a handwritten scheduler, and the Pomodoro technique, and explains how to use each. The solution providing unit also presents multiple solutions in response to a user's question and explains application scenarios for each. For example, the generation AI presents three methods for improving leadership: taking leadership training, reading self-help books, and practical projects, and explains application scenarios for each. This allows the user to compare multiple solutions.

[0033] The solution providing unit can generate answers to user questions by citing relevant expert opinions and research results. For example, the generation AI generates answers to user questions by citing relevant expert opinions. For example, advice on stress relief methods is provided by citing the opinions of psychologists. The solution providing unit also generates answers to user questions by citing relevant research results. For example, advice on health management is provided by citing the latest medical research results. The solution providing unit also generates answers to user questions by citing a combination of expert opinions and research results. For example, advice on career advancement is provided by citing the opinions of career consultants and labor market research results. This allows users to obtain reliable information.

[0034] The solution providing unit can provide visual information such as videos and infographics in response to a user's question. For example, the generation AI in the solution providing unit provides an explanatory video in response to a user's question. For example, when it comes to stress relief, a video showing deep breathing techniques is provided. The generation AI in the solution providing unit also provides an infographic in response to a user's question. For example, when it comes to time management methods, an infographic showing time management tips is provided. The generation AI in the solution providing unit also provides a combination of visual information in response to a user's question. For example, when it comes to health management, an infographic showing a balanced diet and a video showing exercise methods are provided. This allows the user to obtain information that is visually easy to understand.

[0035] The solution providing unit can generate answers to user questions by citing success stories and personal experiences of other users. For example, the generation AI generates answers to user questions by citing success stories of other users. For example, when it comes to stress relief methods, it introduces methods that other users have successfully tried. The solution providing unit also generates answers to user questions by citing personal experiences of other users. For example, when it comes to career advancement, it introduces the success stories of other users. The solution providing unit also generates answers to user questions by citing a combination of success stories and personal experiences. For example, when it comes to health management, it introduces the success stories and personal experiences of other users. This allows users to refer to the experiences of other users.

[0036] The listener can summarize the main points of a user's complaints or concerns and provide feedback. For example, if a user says, "I'm so busy at work, I'm stressed," the generator AI can summarize the main points by saying, "So, being busy at work is the cause of your stress." The listener also organizes the main points of a user's complaints or concerns and provides feedback. For example, if a user says, "Recently, things haven't been going well at work, and I've had a lot of problems in my personal life," the generator AI can summarize the main points by saying, "You're having problems both at work and in your personal life." The listener also extracts the main points of a user's complaints or concerns and provides feedback. For example, if a user says, "My relationship with my boss isn't going well, and I feel depressed every day," the generator AI can extract the main points by saying, "Your relationship with your boss is the main cause of your problems." This makes it easier for users to understand the main points of their own complaints.

[0037] The listener unit can provide a function that allows the generation AI to record a user's complaints or concerns and play them back later when listening to the user's complaints or concerns. For example, the listener unit provides a function that allows the generation AI to record a user's complaints or concerns and play them back later. For example, it records what the user said and plays it back to look back on later. The listener unit also provides a function that allows the generation AI to record a user's complaints or concerns and play back specific parts. For example, if the user wants to play back only the important parts, they can select and play back those parts. The listener unit also provides a function that allows the generation AI to record a user's complaints or concerns and emphasize the key points when playing them back. For example, it can play back what the user said with the key points emphasized. This allows the user to look back on what they said later.

[0038] The listener unit, when listening to a user's complaints or concerns, can convert what the user says into text and provide a function that allows the user to read it back later. For example, the listener unit provides a function whereby the generation AI converts the user's complaints or concerns into text and allows the user to read it back later. For example, it converts what the user has said into text so that it can be checked later. The listener unit also provides a function whereby the generation AI converts the user's complaints or concerns into text and allows the user to search for specific parts. For example, the user can search for specific keywords and read back those parts. The listener unit also provides a function whereby the generation AI converts the user's complaints or concerns into text and displays the main points in a summary. For example, it extracts and displays the main points of what the user said. This allows the user to check what they said later.

[0039] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0040] The consultation response unit can analyze the content of the user's consultation and provide related news articles and blog articles. For example, if the user says, "I want to know about the current economic situation," the generation AI will provide the latest economic news articles. If the user says, "I want to find a new hobby," the generation AI will provide blog articles about hobbies. Furthermore, if the user says, "I want information about health," the generation AI will provide articles from reliable health information sites. This allows the user to obtain the latest information related to the content of their consultation.

[0041] The consultation response unit can introduce relevant online communities and forums based on the content of the user's consultation. For example, if a user says, "I want to share my child-rearing concerns," the generation AI will introduce online forums related to child-rearing. If a user says, "I want to gather information about a specific illness," the generation AI will introduce support groups for that illness. Furthermore, if a user says, "I want to find friends who share my hobbies," the generation AI will introduce online communities related to hobbies. This allows users to connect with people who share the same concerns and interests.

[0042] The consultation response unit can recommend related books and movies based on the content of the user's consultation. For example, if the user says, "I want to know how to relieve stress," the generation AI will recommend books on stress management. If the user says, "I want to increase my motivation," the generation AI will recommend movies that will help improve motivation. Furthermore, if the user says, "I want to relax," the generation AI will recommend relaxing novels and movies. This allows the user to enjoy entertainment related to the content of their consultation.

[0043] The consultation response unit can introduce relevant online courses and workshops based on the content of the user's consultation. For example, if a user says, "I want to learn a new skill," the generation AI will introduce online courses. If a user says, "I want to learn how to manage stress," the generation AI will introduce workshops on stress management. Furthermore, if a user says, "I want to acquire skills to advance my career," the generation AI will introduce online courses that will help with career advancement. This allows users to obtain learning opportunities related to the content of their consultation.

[0044] The consultation response unit can recommend related apps and tools based on the content of the user's consultation. For example, if a user says, "I want to know how to manage my time," the generation AI will recommend a time management app. If a user says, "I want to know how to relax," the generation AI will recommend a meditation app that helps with relaxation. Furthermore, if a user says, "I want to know how to manage my health," the generation AI will recommend a health management tool. This allows users to use useful apps and tools related to the content of their consultation.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The consultation response unit uses the generation AI to listen to the user and provide the necessary solutions and information. For example, if the user says, "I had a hard time at work today," the generation AI will respond with something like, "That's tough. What happened?" Step 2: The solution provider provides the user with appropriate solutions and information based on the solutions and information provided by the consultation provider. For example, if a user asks, "Tell me how to relieve stress," the AI ​​generator will provide advice such as, "Deep breathing, light exercise, and immersing yourself in a hobby are effective." Step 3: The listener listens to the user's complaints and concerns through the consultation section. For example, if the user says, "Work hasn't been going well lately," the generating AI will respond by saying, "That's tough. What exactly is going wrong?", drawing out the user's story.

[0047] (Example 2) The Aibou Chat system according to an embodiment of the present invention allows users to easily discuss their concerns or vent their frustrations even when they cannot meet with their family or friends immediately. This system uses a generative AI to listen to the user's story and provide necessary solutions and information. This allows the Aibou Chat system to easily discuss their concerns or vent their frustrations even when they cannot meet with their family or friends immediately. For example, talking to Aibou Chat about work stress or everyday worries can lighten the mood. Furthermore, obtaining necessary solutions and information can help solve problems.

[0048] The Aibou chat system according to the embodiment includes a consultation unit, a solution provider, and a listener. The consultation unit uses a generation AI to listen to the user's story and provide necessary solutions and information. For example, if a user says, "Work was tough today," the generation AI responds with, "That sounds tough. What happened?" The solution provider provides appropriate solutions and information to the user based on the solutions and information provided by the consultation unit. For example, if a user asks, "Tell me how to relieve stress," the generation AI provides advice such as, "Deep breathing, light exercise, and immersing yourself in a hobby are effective." The listener listens to the user's complaints and concerns using the consultation unit. For example, if a user says, "Work hasn't been going well lately," the generation AI responds with, "That's tough. What exactly is going wrong?" to draw out the user's story. This allows the Aibou chat system to easily talk about problems and concerns with family and friends even when they cannot meet immediately.

[0049] The consultation response unit can analyze the user's tone of voice and speaking speed, detect changes in emotion in real time, and generate an appropriate response. In the consultation response unit, for example, the generation AI analyzes the user's tone of voice and detects changes in emotion in real time. For example, if the user speaks in an angry tone, the generation AI generates a calm response. In the consultation response unit, the generation AI analyzes the user's speaking speed and detects changes in emotion in real time. For example, if the user speaks quickly, the generation AI generates a calm response. In the consultation response unit, the generation AI analyzes the user's tone of voice and speaking speed in combination to detect changes in emotion in real time. For example, if the user speaks slowly in a sad tone, the generation AI generates an empathetic response. This makes it possible to provide an appropriate response according to the user's emotions.

[0050] The consultation response unit can refer to the user's past consultation history and generate responses optimized for individual users. In the consultation response unit, for example, the generation AI refers to the user's past consultation history and generates consistent responses to similar consultation content. For example, if a user previously consulted about work stress, the generation AI will again suggest stress relief methods. In addition, in the consultation response unit, the generation AI analyzes the user's past consultation history and generates responses based on the user's preferences and tendencies. For example, if a user previously talked about their hobbies, advice related to those hobbies will be provided. In addition, in the consultation response unit, the generation AI predicts the user's emotional state based on the user's past consultation history and generates responses accordingly. For example, if a user previously consulted about sadness, words of encouragement will be provided. This makes it possible to provide consistent responses to users.

[0051] The consultation response unit can use the emotion estimation function to estimate the user's emotions and generate a response that helps the user relax. The consultation response unit, for example, uses the emotion estimation function to analyze the user's emotions in real time and generate a response that helps the user relax. For example, if the user is tense, the generation AI suggests taking deep breaths to relax. The consultation response unit also uses the emotion estimation function to estimate the user's emotions and switch to a relaxing topic. For example, if the user is feeling stressed, the generation AI suggests a topic about hobbies that will help them relax. The consultation response unit also uses the emotion estimation function to estimate the user's emotions and suggest relaxing music or videos. For example, if the user is tired, the generation AI plays relaxing music. This makes it possible to generate a response that helps the user relax.

[0052] The consultation response unit can analyze the user's non-verbal communication and generate a response based on that. In the consultation response unit, for example, the generation AI analyzes the user's facial expression and generates a response based on their emotion. For example, if the user is smiling, the generation AI generates a positive response. In the consultation response unit, the generation AI analyzes the user's gestures and generates a response based on their emotion. For example, if the user shrugs, the generation AI generates a response that shows understanding. In the consultation response unit, the generation AI analyzes the user's facial expression and gestures in combination and generates a response based on their emotion. For example, if the user clasps their hands with a sad expression, the generation AI generates an empathetic response. This makes it possible to respond based on the user's non-verbal communication.

[0053] The consultation response unit can have a function to accept user consultation content not only via voice but also via text and images. In the consultation response unit, for example, the generation AI has a function to accept not only voice input but also text input. For example, when a user inputs the consultation content in text, the generation AI generates a response accordingly. In addition, the consultation response unit has a function to accept image input and perform image analysis. For example, when a user uploads an image, the generation AI generates a response based on that image. In addition, the consultation response unit has a function to accept multimodal input of voice, text, and images and perform comprehensive analysis. For example, when a user sends text or images while speaking, the generation AI combines them to generate a response. This makes it possible to accept user consultation content in a variety of formats.

[0054] The consultation response unit can use the emotion estimation function to suggest music and videos that will help the user relax before starting a consultation. For example, the consultation response unit uses the emotion estimation function to suggest music that will help the user relax before starting a consultation. For example, if the user is nervous, the generation AI plays relaxing classical music. The consultation response unit also uses the emotion estimation function to suggest videos that will help the user relax before starting a consultation. For example, if the user is feeling stressed, the generation AI plays relaxing videos of natural scenery. The consultation response unit also uses the emotion estimation function to suggest a combination of music and videos that will help the user relax before starting a consultation. For example, if the user is tired, the generation AI plays relaxing music and videos simultaneously. This makes it possible to provide an environment in which the user can relax before starting a consultation.

[0055] The solution providing unit can present multiple solutions in response to a user's question and explain the advantages and disadvantages of each. For example, the generation AI presents multiple solutions in response to a user's question and explains the advantages and disadvantages of each. For example, the generation AI presents three stress relief methods: deep breathing, exercise, and hobbies, and explains the effects and precautions of each. The solution providing unit also presents multiple solutions in response to a user's question and explains how to implement each. For example, the generation AI presents three time management methods: using a time management app, using a handwritten scheduler, and the Pomodoro technique, and explains how to use each. The solution providing unit also presents multiple solutions in response to a user's question and explains application scenarios for each. For example, the generation AI presents three methods for improving leadership: taking leadership training, reading self-help books, and practical projects, and explains application scenarios for each. This allows the user to compare multiple solutions.

[0056] The solution providing unit can generate answers to user questions by citing relevant expert opinions and research results. For example, the generation AI generates answers to user questions by citing relevant expert opinions. For example, advice on stress relief methods is provided by citing the opinions of psychologists. The solution providing unit also generates answers to user questions by citing relevant research results. For example, advice on health management is provided by citing the latest medical research results. The solution providing unit also generates answers to user questions by citing a combination of expert opinions and research results. For example, advice on career advancement is provided by citing the opinions of career consultants and labor market research results. This allows users to obtain reliable information.

[0057] The solution providing unit can use the emotion estimation function to prioritize solutions that will give the user the most peace of mind. The solution providing unit, for example, uses the emotion estimation function to prioritize solutions that will give the user the most peace of mind. For example, if the user is feeling anxious, the generation AI prioritizes solutions that will help the user relax. The solution providing unit also uses the emotion estimation function to analyze the user's emotional state and present solutions that will give the user peace of mind. For example, if the user is nervous, the generation AI prioritizes solutions that are simple and easy to implement. The solution providing unit also uses the emotion estimation function to present solutions that are in tune with the user's emotions. For example, if the user is sad, the generation AI prioritizes solutions that include emotional support. This allows the user to select a solution with peace of mind.

[0058] The solution providing unit can provide visual information such as videos and infographics in response to a user's question. For example, the generation AI in the solution providing unit provides an explanatory video in response to a user's question. For example, when it comes to stress relief, a video showing deep breathing techniques is provided. The generation AI in the solution providing unit also provides an infographic in response to a user's question. For example, when it comes to time management methods, an infographic showing time management tips is provided. The generation AI in the solution providing unit also provides a combination of visual information in response to a user's question. For example, when it comes to health management, an infographic showing a balanced diet and a video showing exercise methods are provided. This allows the user to obtain information that is visually easy to understand.

[0059] The solution providing unit can generate answers to user questions by citing success stories and personal experiences of other users. For example, the generation AI generates answers to user questions by citing success stories of other users. For example, when it comes to stress relief methods, it introduces methods that other users have successfully tried. The solution providing unit also generates answers to user questions by citing personal experiences of other users. For example, when it comes to career advancement, it introduces the success stories of other users. The solution providing unit also generates answers to user questions by citing a combination of success stories and personal experiences. For example, when it comes to health management, it introduces the success stories and personal experiences of other users. This allows users to refer to the experiences of other users.

[0060] The solution providing unit can use the emotion estimation function to analyze the emotion a user has when inputting a question and present a solution at the optimal timing. The solution providing unit, for example, uses the emotion estimation function to analyze the emotion a user has when inputting a question and presents a solution at the optimal timing. For example, if the user is anxious, the generation AI presents a solution at a time when the user is calm. The solution providing unit also uses the emotion estimation function to analyze the user's emotional state and present a solution at the optimal timing. For example, if the user is relaxed, the generation AI presents a solution at that time. The solution providing unit also uses the emotion estimation function to present a solution at a time according to the user's emotion. For example, if the user is feeling anxious, the generation AI presents a solution at a time when the user feels reassured. This allows the user to obtain a solution at the optimal time.

[0061] The listener can appropriately insert words of empathy and encouragement when listening to a user's complaints or concerns. For example, when the generation AI listens to a user's complaints or concerns, the listener appropriately inserts words of empathy. For example, if the user says, "Work is tough," the generation AI inserts a word of empathy, such as, "That must be really tough." The listener also appropriately inserts words of encouragement when the generation AI listens to a user's complaints or concerns. For example, if the user says, "Things haven't been going well lately," the generation AI inserts an encouraging word, such as, "It will get better." The listener also inserts a combination of empathy and encouragement when the generation AI listens to a user's complaints or concerns. For example, if the user says, "I'm tired," the generation AI responds, "That's tough. But I'm sure you'll get through it." This allows the user to feel empathy and encouragement.

[0062] The listener can summarize the main points of a user's complaints or concerns and provide feedback. For example, if a user says, "I'm so busy at work, I'm stressed," the generator AI can summarize the main points by saying, "So, being busy at work is the cause of your stress." The listener also organizes the main points of a user's complaints or concerns and provides feedback. For example, if a user says, "Recently, things haven't been going well at work, and I've had a lot of problems in my personal life," the generator AI can summarize the main points by saying, "You're having problems both at work and in your personal life." The listener also extracts the main points of a user's complaints or concerns and provides feedback. For example, if a user says, "My relationship with my boss isn't going well, and I feel depressed every day," the generator AI can extract the main points by saying, "Your relationship with your boss is the main cause of your problems." This makes it easier for users to understand the main points of their own complaints.

[0063] The listener unit can use the emotion estimation function to empathize with the user's emotions and insert words of empathy at the appropriate time. For example, the listener unit uses the emotion estimation function to analyze the user's emotions in real time and insert words of empathy at the appropriate time. For example, if the user speaks in a sad tone, the generation AI will insert words of empathy such as, "That must be really painful." The listener unit also uses the emotion estimation function to estimate the user's emotions and insert words of empathy at the appropriate time. For example, if the user is feeling angry, the generation AI will insert words of empathy such as, "I understand how you feel." The listener unit also uses the emotion estimation function to empathize with the user's emotions and insert words of empathy at the appropriate time. For example, if the user is feeling anxious, the generation AI will insert words of empathy such as, "I understand your anxiety." This provides emotional support to the user.

[0064] The listener unit can provide a function that allows the generation AI to record a user's complaints or concerns and play them back later when listening to the user's complaints or concerns. For example, the listener unit provides a function that allows the generation AI to record a user's complaints or concerns and play them back later. For example, it records what the user said and plays it back to look back on later. The listener unit also provides a function that allows the generation AI to record a user's complaints or concerns and play back specific parts. For example, if the user wants to play back only the important parts, they can select and play back those parts. The listener unit also provides a function that allows the generation AI to record a user's complaints or concerns and emphasize the key points when playing them back. For example, it can play back what the user said with the key points emphasized. This allows the user to look back on what they said later.

[0065] The listener unit, when listening to a user's complaints or concerns, can convert what the user says into text and provide a function that allows the user to read it back later. For example, the listener unit provides a function whereby the generation AI converts the user's complaints or concerns into text and allows the user to read it back later. For example, it converts what the user has said into text so that it can be checked later. The listener unit also provides a function whereby the generation AI converts the user's complaints or concerns into text and allows the user to search for specific parts. For example, the user can search for specific keywords and read back those parts. The listener unit also provides a function whereby the generation AI converts the user's complaints or concerns into text and displays the main points in a summary. For example, it extracts and displays the main points of what the user said. This allows the user to check what they said later.

[0066] The listener unit can use the emotion estimation function to suggest a relaxing environment for the user when he or she wants to complain or ask for advice. For example, the listener unit can use the emotion estimation function to suggest music that will help the user relax when he or she wants to complain or ask for advice. For example, if the user is nervous, the generation AI will play relaxing music. The listener unit can also use the emotion estimation function to suggest an environment that will help the user relax when he or she wants to complain or ask for advice. For example, if the user is feeling stressed, the generation AI will suggest relaxing lighting or fragrance. The listener unit can also use the emotion estimation function to suggest a video that will help the user relax when he or she wants to complain or ask for advice. For example, if the user is tired, the generation AI will play a video of a relaxing natural landscape. This allows the user to complain or ask for advice in a relaxed state.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The consultation response unit can analyze the content of the user's consultation and provide related news articles and blog articles. For example, if the user says, "I want to know about the current economic situation," the generation AI will provide the latest economic news articles. If the user says, "I want to find a new hobby," the generation AI will provide blog articles about hobbies. Furthermore, if the user says, "I want information about health," the generation AI will provide articles from reliable health information sites. This allows the user to obtain the latest information related to the content of their consultation.

[0069] The consultation response unit can introduce relevant online communities and forums based on the content of the user's consultation. For example, if a user says, "I want to share my child-rearing concerns," the generation AI will introduce online forums related to child-rearing. If a user says, "I want to gather information about a specific illness," the generation AI will introduce support groups for that illness. Furthermore, if a user says, "I want to find friends who share my hobbies," the generation AI will introduce online communities related to hobbies. This allows users to connect with people who share the same concerns and interests.

[0070] The consultation response unit can recommend related books and movies based on the content of the user's consultation. For example, if the user says, "I want to know how to relieve stress," the generation AI will recommend books on stress management. If the user says, "I want to increase my motivation," the generation AI will recommend movies that will help improve motivation. Furthermore, if the user says, "I want to relax," the generation AI will recommend relaxing novels and movies. This allows the user to enjoy entertainment related to the content of their consultation.

[0071] The consultation response unit can introduce relevant online courses and workshops based on the content of the user's consultation. For example, if a user says, "I want to learn a new skill," the generation AI will introduce online courses. If a user says, "I want to learn how to manage stress," the generation AI will introduce workshops on stress management. Furthermore, if a user says, "I want to acquire skills to advance my career," the generation AI will introduce online courses that will help with career advancement. This allows users to obtain learning opportunities related to the content of their consultation.

[0072] The consultation response unit can recommend related apps and tools based on the content of the user's consultation. For example, if a user says, "I want to know how to manage my time," the generation AI will recommend a time management app. If a user says, "I want to know how to relax," the generation AI will recommend a meditation app that helps with relaxation. Furthermore, if a user says, "I want to know how to manage my health," the generation AI will recommend a health management tool. This allows users to use useful apps and tools related to the content of their consultation.

[0073] The consultation response unit can use the emotion estimation function to suggest relaxation techniques according to the user's emotions. For example, if the user is tense, the generation AI will suggest deep breathing or meditation. If the user is sad, the generation AI will suggest relaxation music or aromatherapy. Furthermore, if the user is feeling angry, the generation AI will suggest stretching or light exercise. This allows the user to practice relaxation techniques according to their emotions.

[0074] The consultation response unit can use the emotion estimation function to provide positive affirmations that correspond to the user's emotions. For example, if the user is feeling unsure, the AI ​​generation system will provide affirmations such as "You are a valuable person." If the user is feeling anxious, the AI ​​generation system will provide affirmations such as "Everything will be fine." If the user is feeling down, the AI ​​generation system will provide affirmations such as "You are a strong person." This allows the user to feel positive.

[0075] The consultation response unit can use the emotion estimation function to provide reflection questions that correspond to the user's emotions. For example, if the user is feeling stressed, the generation AI will provide a question such as, "What has been making you feel stressed recently?" If the user is feeling happy, the generation AI will provide a question such as, "Try to recall a moment when you felt that joy." Furthermore, if the user is feeling anxious, the generation AI will provide a question such as, "Try to put your anxiety into concrete words." This allows the user to gain a deeper understanding of their own emotions.

[0076] The consultation response unit can use the emotion estimation function to make self-care suggestions based on the user's emotions. For example, if the user is tired, the generation AI will make a self-care suggestion such as "Try to get some rest early today." If the user is irritated, the generation AI will make a self-care suggestion such as "Try taking a short walk." Furthermore, if the user is sad, the generation AI will make a self-care suggestion such as "Take time to take care of yourself." This allows the user to practice self-care based on their emotions.

[0077] The consultation response unit can use the emotion estimation function to suggest relaxation exercises that correspond to the user's emotions. For example, if the user is nervous, the generation AI will suggest a relaxation exercise such as "Try taking a deep breath." If the user is sad, the generation AI will suggest a relaxation exercise such as "Try some light stretching." Furthermore, if the user is feeling angry, the generation AI will suggest a relaxation exercise such as "Try meditation." This allows the user to practice relaxation exercises that correspond to their emotions.

[0078] The processing flow of the second embodiment will be briefly explained below.

[0079] Step 1: The consultation response unit uses the generation AI to listen to the user and provide the necessary solutions and information. For example, if the user says, "I had a hard time at work today," the generation AI will respond with something like, "That's tough. What happened?" Step 2: The solution provider provides the user with appropriate solutions and information based on the solutions and information provided by the consultation provider. For example, if a user asks, "Tell me how to relieve stress," the AI ​​generator will provide advice such as, "Deep breathing, light exercise, and immersing yourself in a hobby are effective." Step 3: The listener listens to the user's complaints and concerns through the consultation section. For example, if the user says, "Work hasn't been going well lately," the generating AI will respond by saying, "That's tough. What exactly is going wrong?", drawing out the user's story.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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).

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0097] 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.

[0098] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0112] 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.

[0113] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] 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.

[0126] 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.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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."

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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]

[0147] 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. The consultation department uses generative AI to listen to users and provide necessary solutions and information. a solution providing unit that provides appropriate solutions and information to a user based on the solutions and information provided by the consultation response unit; a listening unit that listens to the complaints and consultations of the user by the consultation response unit, A system characterized by:

2. The consultation department Analyzing the user's tone of voice and speaking rate to detect emotional changes in real time and generate responses accordingly 2. The system of claim 1.

3. The consultation department Refer to the user's past consultation history and generate a response optimized for each individual user.

2. The system of claim 1.

4. The consultation department Estimating the user's emotions and generating a response that allows the user to relax 2. The system of claim 1.

5. The consultation department Analyzing the user's non-verbal communication and generating a response based thereon.

2. The system of claim 1.

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