System

Abstract

An object of the system according to the embodiment is to identify a user's emotion and provide an empathetic response and expert advice.SOLUTION: A system includes a feeling identification part, an empathy response generation part, an advice provision part, and an expert connection part. The emotion identification unit analyzes the input content of the user and identifies the emotion. The sympathetic response generation unit generates a sympathetic response based on the emotion identified by the emotion identification unit. The advice providing portion provides expert advice based on the response generated by the sympathetic response generating portion. The expert connector supports connection to an expert as needed based on the advice provided by the advice provider.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 of making it difficult to accurately identify a user's emotions and provide appropriate empathetic responses or expert advice.

[0005] The system according to the embodiment aims to identify the user's emotions and provide empathetic responses and expert advice. [Means for solving the problem]

[0006] The system according to the embodiment includes an emotion identification unit, an empathetic response generation unit, an advice providing unit, and an expert connection unit. The emotion identification unit analyzes user input content and identifies emotions. The empathetic response generation unit generates an empathetic response based on the emotion identified by the emotion identification unit. The advice providing unit provides expert advice based on the response generated by the empathetic response generation unit. The expert connection unit supports connection to an expert as needed based on the advice provided by the advice providing unit. [Effects of the Invention]

[0007] The system according to the embodiment can identify the user's emotions and provide empathetic responses and expert advice. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 AI-assisted service according to the embodiment of the present invention is a system in which a generative AI resonates with users when they share their feelings or experiences and provides them with expert advice. This allows the AI-assisted service to understand the user's emotions and provide appropriate support and guidance.

[0029] An AI-assisted service according to an embodiment includes an emotion identification unit, an empathic response generation unit, an advice providing unit, and an expert connection unit. The emotion identification unit analyzes user input content and identifies emotions. For example, the emotion identification unit analyzes text input using natural language processing technology and identifies emotions. The emotion identification unit can also analyze voice input using voice analysis technology and identify emotions. The emotion identification unit can also analyze image input using facial expression recognition technology and identify emotions. The empathic response generation unit generates an empathic response based on the emotion identified by the emotion identification unit. For example, the empathic response generation unit generates an empathic response using a template. The empathic response generation unit can also generate an empathic response using a response pattern according to the emotion. The empathic response generation unit can also dynamically generate an empathic response using a generation AI. The advice providing unit provides expert advice based on the response generated by the empathic response generation unit. For example, the advice providing unit can provide expert advice based on knowledge of psychology. The advice providing unit can also provide medical advice. The advice providing unit can also provide legal advice. The expert connection unit supports connection to an expert as needed based on the advice provided by the advice providing unit. For example, the expert connection unit supports connection to an expert through online chat. The expert connection unit may also support connection to an expert through video calls. The expert connection unit may also support connection to an expert through face-to-face consultations. As a result, the AI-assisted service according to the embodiment can support the user's mental health by identifying the user's emotions, providing empathetic responses and expert advice, and supporting connection to an expert as needed.

[0030] The advice providing unit can collect user feedback on the advice provided by the generation AI and continuously improve the accuracy of the advice based on that feedback. The advice providing unit, for example, builds a system that collects user feedback on the advice provided by the generation AI. For example, a user may rate the advice as "helpful." The advice providing unit can also analyze the user feedback and improve the accuracy of the advice based on the results. For example, if a user rates the advice as "not very helpful," the generation AI can improve the content of the advice based on that feedback. The advice providing unit can also optimize advice patterns based on user feedback. For example, if a user rates the advice as "very helpful," the generation AI can apply that pattern to other users. This allows the accuracy of advice to be continuously improved based on user feedback, thereby providing more effective support.

[0031] The advice providing unit can have the generation AI present multiple advice options based on the user's input, allowing the user to select the most appropriate advice. The advice providing unit, for example, analyzes the user's input and generates multiple advice options. For example, if the user inputs "I'm stressed," the generation AI presents three options: "deep breathing," "meditation," and "exercise." The advice providing unit can also provide more detailed advice based on the advice option selected by the user. For example, if the user selects "deep breathing," the generation AI responds, "I'll explain how to take deep breaths." The advice providing unit can also save the user's selection history and reflect it in future advice. For example, if the user previously selected "meditation," the generation AI responds, "How was your last meditation?" This allows the user to select the most appropriate advice, thereby providing more effective support.

[0032] The advice providing unit can customize the advice provided by the generation AI based on the user's lifestyle and preferences, providing more practical advice. For example, the advice providing unit registers the user's lifestyle and preferences in a database and customizes the advice based on that information. For example, if a user inputs, "I'm a night owl," the generation AI might suggest, "Try some ways to relax at night." The advice providing unit can also adjust the content of the advice based on the user's preferences. For example, if a user inputs, "I like music," the generation AI might suggest, "Try listening to relaxing music." The advice providing unit can also adjust the frequency of advice based on the user's lifestyle. For example, if a user inputs, "I exercise every day," the generation AI might suggest, "Try some ways to relax after exercising." This allows the advice to be customized based on the user's lifestyle and preferences, providing more practical advice.

[0033] The advice providing unit can adjust the content of the advice based on the language and cultural background selected by the user, making it possible to accommodate international users. For example, the advice providing unit translates and provides the content of the advice based on the language selected by the user. For example, if the user selects "English," the generation AI advises, "Take a deep breath and relax." The advice providing unit can also adjust the content of the advice based on the user's cultural background. For example, if the user inputs, "I'm knowledgeable about Japanese culture," the generation AI suggests, "Try traditional Japanese relaxation techniques." The advice providing unit can also adjust the tone and expression of the advice based on the user's language and cultural background. For example, if the user inputs, "I'm knowledgeable about French culture," the generation AI suggests, "Try French relaxation techniques." This allows the advice to be adjusted based on the language and cultural background selected by the user, making it possible to accommodate international users.

[0034] The expert connection unit can develop an algorithm that analyzes a user's needs in detail and automatically matches them with the most appropriate experts and support groups. For example, the expert connection unit analyzes user input and develops an algorithm that automatically matches them with the most appropriate experts and support groups based on their needs. For example, if a user inputs, "I'm suffering from work stress," the generation AI would suggest, "I'll introduce you to a stress management expert." The expert connection unit can also evaluate the qualifications and experience of experts based on the user's needs and suggest the most appropriate expert. For example, if a user inputs, "I'd like to receive psychological counseling," the generation AI would suggest, "I'll introduce you to a psychological counseling expert." The expert connection unit can also evaluate the activities of support groups based on the user's needs and suggest the most appropriate support group. For example, if a user inputs, "I'd like to receive support for depression," the generation AI would suggest, "I'll introduce you to a support group that provides support for depression." This allows for a detailed analysis of the user's needs and automatically matches them with the most appropriate experts and support groups, thereby providing more effective support.

[0035] The expert connection unit allows the generation AI to share the user's situation with the expert in advance when the user requests to be connected to a specialist, thereby enabling smooth support. The expert connection unit builds a system in which the generation AI shares the user's situation with the expert in advance when the user requests to be connected to a specialist. For example, if the user inputs "I would like to receive counseling," the generation AI suggests, "I will share the user's situation with the expert." The expert connection unit can also take measures to protect privacy when sharing the user's situation with the expert. For example, it may share only necessary information with the user's consent. The expert connection unit can also take measures to ensure the accuracy of the information when sharing the user's situation with the expert. For example, it may check the user's input and share accurate information. This allows smooth support by sharing the situation in advance when the user requests to be connected to a specialist.

[0036] The expert connection unit can optimize the expert connection service provided by the generation AI based on the user's geographic location information, thereby providing community-based support. For example, the expert connection unit analyzes the user's geographic location information and introduces the most appropriate experts and support organizations based on that information. For example, if a user inputs, "I'm looking for a counseling service nearby," the generation AI may suggest, "These counseling services are nearby." The expert connection unit can also adjust the content of the expert connection based on the user's geographic location information. For example, if a user inputs, "I'm looking for a medical institution nearby," the generation AI may suggest, "These medical institutions are nearby." The expert connection unit can also adjust the content of the support organization connection based on the user's geographic location information. For example, if a user inputs, "I'm looking for a support organization nearby," the generation AI may suggest, "These support organizations are nearby." This allows the expert connection service to be optimized based on the user's geographic location information, thereby providing community-based support.

[0037] The expert connection unit can allow the user to select the communication method they prefer when connecting with an expert. For example, the expert connection unit builds a system that allows the user to select the communication method they prefer. For example, if a user inputs, "I would like to consult via video call," the generation AI suggests, "I will send you a video call link." The expert connection unit can also adjust the content of the connection to the expert based on the user's preferred communication method. For example, if a user inputs, "I would like to consult via chat," the generation AI suggests, "I will send you a chat link." The expert connection unit can also adjust the content of the connection to the support organization based on the user's preferred communication method. For example, if a user inputs, "I would like to consult via phone," the generation AI suggests, "I will send you a phone link." This allows the user to select their preferred communication method, thereby providing more effective support.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The advice providing unit can customize the advice provided by the generation AI based on the user's input by associating it with the user's hobbies and interests. For example, if the user inputs "I like reading," the generation AI will suggest, "Try reading your favorite book to relax." The advice providing unit can also adjust the content of the advice based on the user's hobbies. For example, if the user inputs "I like music," the generation AI will suggest, "Try listening to relaxing music." The advice providing unit can also adjust the frequency of advice based on the user's interests. For example, if the user inputs "I like exercise," the generation AI will suggest, "Try ways to relax after exercising." This allows the advice to be customized by associating it with the user's hobbies and interests, making it possible to provide more practical advice.

[0040] The advice providing unit can customize the advice provided by the generation AI based on the user's input to suit the user's lifestyle. For example, if the user inputs, "I'm a night owl," the generation AI will suggest, "Try some ways to relax at night." The advice providing unit can also adjust the content of the advice based on the user's lifestyle. For example, if the user inputs, "I exercise every day," the generation AI will suggest, "Try some ways to relax after exercising." The advice providing unit can also adjust the frequency of advice based on the user's lifestyle. For example, if the user inputs, "I'm busy at work," the generation AI will suggest, "Try some ways to relax in a short amount of time." This allows the advice to be customized to suit the user's lifestyle, making it possible to provide more practical advice.

[0041] The advice providing unit can customize the advice provided by the generation AI based on the user's input to suit the user's health condition. For example, if the user inputs, "I haven't been feeling well lately," the generation AI will suggest, "Try some relaxation techniques to improve your health." The advice providing unit can also adjust the content of the advice based on the user's health condition. For example, if the user inputs, "I'm feeling stressed," the generation AI will suggest, "Try some relaxation techniques to reduce stress." Furthermore, the advice providing unit can adjust the frequency of advice based on the user's health condition. For example, if the user inputs, "I feel chronically fatigued," the generation AI will suggest, "Try some relaxation techniques regularly." This allows the advice to be customized to suit the user's health condition, providing more practical advice.

[0042] The advice providing unit can customize the advice provided by the generation AI based on the user's input to match the user's goals. For example, if a user inputs "I'm on a diet," the generation AI might suggest, "Try some relaxation techniques that will help with your diet." The advice providing unit can also adjust the content of the advice based on the user's goals. For example, if a user inputs, "I want to improve my work efficiency," the generation AI might suggest, "Try some relaxation techniques to improve your work efficiency." Furthermore, the advice providing unit can adjust the frequency of advice based on the user's goals. For example, if a user inputs, "I want to continue exercising every day," the generation AI might suggest, "Try some relaxation techniques after exercising." This allows the advice to be customized to match the user's goals, providing more practical advice.

[0043] The advice providing unit can customize the advice provided by the generation AI based on the user's input to suit the user's schedule. For example, if the user inputs, "I'm busy every day," the generation AI will suggest, "Try some ways to relax for a short time." The advice providing unit can also adjust the content of the advice based on the user's schedule. For example, if the user inputs, "I want to relax on the weekend," the generation AI will suggest, "Try some ways to relax on the weekend." The advice providing unit can also adjust the frequency of advice based on the user's schedule. For example, if the user inputs, "I'm busy every day," the generation AI will suggest, "Try some ways to relax for a short time every day." This allows the advice to be customized to suit the user's schedule, making it possible to provide more practical advice.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The emotion identification unit analyzes the user's input content and identifies emotions. For example, the emotion identification unit may analyze text input using natural language processing technology and identify emotions. The emotion identification unit may also analyze voice input using voice analysis technology and identify emotions. Furthermore, the emotion identification unit may analyze image input using facial expression recognition technology and identify emotions. Step 2: The empathic response generation unit generates an empathic response based on the emotion identified by the emotion identification unit. For example, the empathic response generation unit generates an empathic response using a template. The empathic response generation unit can also generate an empathic response using a response pattern according to the emotion. Furthermore, the empathic response generation unit can dynamically generate an empathic response using a generation AI. Step 3: The advice providing unit provides professional advice based on the response generated by the empathic response generating unit. For example, the advice providing unit provides professional advice based on psychological knowledge. The advice providing unit can also provide medical advice. Furthermore, the advice providing unit can also provide legal advice. Step 4: The expert connection unit supports connection to an expert as needed based on the advice provided by the advice providing unit. For example, the expert connection unit supports connection to an expert through online chat. The expert connection unit can also support connection to an expert through video call. Furthermore, the expert connection unit can support connection to an expert through face-to-face consultation.

[0046] (Example 2) The AI-assisted service according to the embodiment of the present invention is a system in which a generative AI resonates with users when they share their feelings or experiences and provides them with expert advice. This allows the AI-assisted service to understand the user's emotions and provide appropriate support and guidance.

[0047] An AI-assisted service according to an embodiment includes an emotion identification unit, an empathic response generation unit, an advice providing unit, and an expert connection unit. The emotion identification unit analyzes user input content and identifies emotions. For example, the emotion identification unit analyzes text input using natural language processing technology and identifies emotions. The emotion identification unit can also analyze voice input using voice analysis technology and identify emotions. The emotion identification unit can also analyze image input using facial expression recognition technology and identify emotions. The empathic response generation unit generates an empathic response based on the emotion identified by the emotion identification unit. For example, the empathic response generation unit generates an empathic response using a template. The empathic response generation unit can also generate an empathic response using a response pattern according to the emotion. The empathic response generation unit can also dynamically generate an empathic response using a generation AI. The advice providing unit provides expert advice based on the response generated by the empathic response generation unit. For example, the advice providing unit can provide expert advice based on knowledge of psychology. The advice providing unit can also provide medical advice. The advice providing unit can also provide legal advice. The expert connection unit supports connection to an expert as needed based on the advice provided by the advice providing unit. For example, the expert connection unit supports connection to an expert through online chat. The expert connection unit may also support connection to an expert through video calls. The expert connection unit may also support connection to an expert through face-to-face consultations. As a result, the AI-assisted service according to the embodiment can support the user's mental health by identifying the user's emotions, providing empathetic responses and expert advice, and supporting connection to an expert as needed.

[0048] The emotion recognition unit can track emotional changes in real time based on the user's input and generate responses based on those emotional fluctuations. For example, the emotion recognition unit analyzes the text entered by the user in real time to track emotional changes. For example, if the user enters, "I'm feeling a little better today," the generation AI responds, "That's good. Did anything special happen?" The emotion recognition unit can also analyze voice input in real time to track emotional changes. For example, if the user's voice tone becomes brighter, the generation AI responds, "You look good today." The emotion recognition unit can also analyze image input in real time to track emotional changes. For example, if the user's facial expression changes to a smile, the generation AI responds, "Your smile is lovely." This allows the system to track the user's emotional changes in real time and generate appropriate responses, thereby providing more effective support.

[0049] The emotion recognition unit analyzes a user's past input history to understand long-term emotional trends and provide more personalized, empathetic conversations. For example, the emotion recognition unit stores the user's past input history in a database and analyzes long-term emotional trends. For example, if a user previously inputs, "I've been feeling sad lately," the generation AI generates a response based on past trends, such as, "How are you feeling these days?" The emotion recognition unit can also analyze past voice logs to understand long-term emotional trends. For example, if a user's voice tone was previously analyzed as being low, the generation AI would respond with, "How is your voice tone these days?" The emotion recognition unit can also analyze past image logs to understand long-term emotional trends. For example, if a user's facial expression was previously analyzed as "sad," the generation AI would respond with, "How is your facial expression these days?" This allows the system to analyze a user's past input history and understand long-term emotional trends, providing more personalized, empathetic conversations.

[0050] The emotion identification unit can use the emotion estimation function to estimate the user's emotion and suggest appropriate music or videos based on that emotion. The emotion identification unit, for example, analyzes the user's input and estimates the emotion. For example, if the user inputs, "I feel very sad," the generation AI will suggest, "Why don't you listen to some relaxing music?" The emotion identification unit can also analyze voice input and estimate the emotion. For example, if the user's voice is analyzed as having a low tone, the generation AI will suggest, "Why don't you watch some uplifting videos?" The emotion identification unit can also analyze image input and estimate the emotion. For example, if the user's facial expression is analyzed as "sad," the generation AI will suggest, "Why don't you watch some relaxing videos?" This allows for emotional stabilization by suggesting appropriate music or videos based on the user's emotion.

[0051] The emotion recognition unit can not only recognize emotions in response to user input, but also analyze physical responses to provide comprehensive support. For example, the emotion recognition unit uses a device that monitors changes in heart rate and breathing in conjunction with the user's input. For example, if a user inputs "I'm very nervous," the generation AI might suggest, "Try taking a deep breath." The emotion recognition unit can also use a device that monitors electrodermal activity to analyze physical responses. For example, if a user inputs "My hands are shaking," the generation AI might suggest, "Try warming your hands to relax." The emotion recognition unit can also monitor changes in heart rate and analyze physical responses. For example, if a user inputs "My heart is pounding," the generation AI might suggest, "Try taking a slow, deep breath." This allows for a comprehensive analysis of the user's emotions and physical responses to provide more effective support.

[0052] The emotion identification unit reflects the emotion identification result in an avatar selected by the user, allowing the user to visually express empathy. The emotion identification unit, for example, identifies the user's emotion and reflects the result in the avatar's facial expression and behavior. For example, if the user inputs "sad," the avatar will make a sad expression. The emotion identification unit can also identify the user's emotion and reflect the result in the avatar's gestures. For example, if the user inputs "happy," the avatar will make a gesture of joy. The emotion identification unit can also identify the user's emotion and reflect the result in the avatar's behavior. For example, if the user inputs "angry," the avatar will make an angry gesture. In this way, by reflecting the emotion identification result in the avatar, the user can feel a sense of familiarity.

[0053] The advice providing unit can collect user feedback on the advice provided by the generation AI and continuously improve the accuracy of the advice based on that feedback. The advice providing unit, for example, builds a system that collects user feedback on the advice provided by the generation AI. For example, a user may rate the advice as "helpful." The advice providing unit can also analyze the user feedback and improve the accuracy of the advice based on the results. For example, if a user rates the advice as "not very helpful," the generation AI can improve the content of the advice based on that feedback. The advice providing unit can also optimize advice patterns based on user feedback. For example, if a user rates the advice as "very helpful," the generation AI can apply that pattern to other users. This allows the accuracy of advice to be continuously improved based on user feedback, thereby providing more effective support.

[0054] The advice providing unit can have the generation AI present multiple advice options based on the user's input, allowing the user to select the most appropriate advice. The advice providing unit, for example, analyzes the user's input and generates multiple advice options. For example, if the user inputs "I'm stressed," the generation AI presents three options: "deep breathing," "meditation," and "exercise." The advice providing unit can also provide more detailed advice based on the advice option selected by the user. For example, if the user selects "deep breathing," the generation AI responds, "I'll explain how to take deep breaths." The advice providing unit can also save the user's selection history and reflect it in future advice. For example, if the user previously selected "meditation," the generation AI responds, "How was your last meditation?" This allows the user to select the most appropriate advice, thereby providing more effective support.

[0055] The advice providing unit can customize the advice provided by the generation AI based on the user's lifestyle and preferences, providing more practical advice. For example, the advice providing unit registers the user's lifestyle and preferences in a database and customizes the advice based on that information. For example, if a user inputs, "I'm a night owl," the generation AI might suggest, "Try some ways to relax at night." The advice providing unit can also adjust the content of the advice based on the user's preferences. For example, if a user inputs, "I like music," the generation AI might suggest, "Try listening to relaxing music." The advice providing unit can also adjust the frequency of advice based on the user's lifestyle. For example, if a user inputs, "I exercise every day," the generation AI might suggest, "Try some ways to relax after exercising." This allows the advice to be customized based on the user's lifestyle and preferences, providing more practical advice.

[0056] The advice providing unit can adjust the content of the advice based on the language and cultural background selected by the user, making it possible to accommodate international users. For example, the advice providing unit translates and provides the content of the advice based on the language selected by the user. For example, if the user selects "English," the generation AI advises, "Take a deep breath and relax." The advice providing unit can also adjust the content of the advice based on the user's cultural background. For example, if the user inputs, "I'm knowledgeable about Japanese culture," the generation AI suggests, "Try traditional Japanese relaxation techniques." The advice providing unit can also adjust the tone and expression of the advice based on the user's language and cultural background. For example, if the user inputs, "I'm knowledgeable about French culture," the generation AI suggests, "Try French relaxation techniques." This allows the advice to be adjusted based on the language and cultural background selected by the user, making it possible to accommodate international users.

[0057] The advice providing unit uses the emotion estimation function to adjust the frequency and timing of advice according to the user's emotions, allowing it to provide support at the appropriate time. For example, the advice providing unit analyzes the user's emotional state in real time and adjusts the frequency of advice based on the results. For example, if a user inputs "I'm very anxious," the generation AI might suggest, "Try relaxing frequently." The advice providing unit can also adjust the timing of advice based on the user's emotional state. For example, if a user inputs "I'm very nervous," the generation AI might suggest, "Try taking a deep breath right now." The advice providing unit can also adjust the content of advice based on the user's emotional state. For example, if a user inputs "I'm very sad," the generation AI might suggest, "Try listening to relaxing music." This allows the frequency and timing of advice to be adjusted according to the user's emotions, allowing support to be provided at the appropriate time.

[0058] The expert connection unit can develop an algorithm that analyzes a user's needs in detail and automatically matches them with the most appropriate experts and support groups. For example, the expert connection unit analyzes user input and develops an algorithm that automatically matches them with the most appropriate experts and support groups based on their needs. For example, if a user inputs, "I'm suffering from work stress," the generation AI would suggest, "I'll introduce you to a stress management expert." The expert connection unit can also evaluate the qualifications and experience of experts based on the user's needs and suggest the most appropriate expert. For example, if a user inputs, "I'd like to receive psychological counseling," the generation AI would suggest, "I'll introduce you to a psychological counseling expert." The expert connection unit can also evaluate the activities of support groups based on the user's needs and suggest the most appropriate support group. For example, if a user inputs, "I'd like to receive support for depression," the generation AI would suggest, "I'll introduce you to a support group that provides support for depression." This allows for a detailed analysis of the user's needs and automatically matches them with the most appropriate experts and support groups, thereby providing more effective support.

[0059] The expert connection unit allows the generation AI to share the user's situation with the expert in advance when the user requests to be connected to a specialist, thereby enabling smooth support. The expert connection unit builds a system in which the generation AI shares the user's situation with the expert in advance when the user requests to be connected to a specialist. For example, if the user inputs "I would like to receive counseling," the generation AI suggests, "I will share the user's situation with the expert." The expert connection unit can also take measures to protect privacy when sharing the user's situation with the expert. For example, it may share only necessary information with the user's consent. The expert connection unit can also take measures to ensure the accuracy of the information when sharing the user's situation with the expert. For example, it may check the user's input and share accurate information. This allows smooth support by sharing the situation in advance when the user requests to be connected to a specialist.

[0060] The expert connection unit uses the emotion estimation function to understand the user's emotional state when they request a connection to an expert and can make suggestions at the appropriate time. For example, the expert connection unit analyzes the user's emotional state in real time and suggests connecting them to an expert based on the results. For example, if a user inputs "I'm very anxious," the generation AI may suggest, "Why don't you consult with an expert?" The expert connection unit can also adjust the timing of connecting them to an expert based on the user's emotional state. For example, if a user inputs "I'm very nervous," the generation AI may suggest, "Let's consult with an expert right now." The expert connection unit can also adjust the content of the expert connection based on the user's emotional state. For example, if a user inputs "I'm very sad," the generation AI may suggest, "Let's consult with an expert." This allows the system to understand the user's emotional state and suggest connecting them to an expert at the appropriate time, thereby providing more effective support.

[0061] The expert connection unit can optimize the expert connection service provided by the generation AI based on the user's geographic location information, thereby providing community-based support. For example, the expert connection unit analyzes the user's geographic location information and introduces the most appropriate experts and support organizations based on that information. For example, if a user inputs, "I'm looking for a counseling service nearby," the generation AI may suggest, "These counseling services are nearby." The expert connection unit can also adjust the content of the expert connection based on the user's geographic location information. For example, if a user inputs, "I'm looking for a medical institution nearby," the generation AI may suggest, "These medical institutions are nearby." The expert connection unit can also adjust the content of the support organization connection based on the user's geographic location information. For example, if a user inputs, "I'm looking for a support organization nearby," the generation AI may suggest, "These support organizations are nearby." This allows the expert connection service to be optimized based on the user's geographic location information, thereby providing community-based support.

[0062] The expert connection unit can allow the user to select the communication method they prefer when connecting with an expert. For example, the expert connection unit builds a system that allows the user to select the communication method they prefer. For example, if a user inputs, "I would like to consult via video call," the generation AI suggests, "I will send you a video call link." The expert connection unit can also adjust the content of the connection to the expert based on the user's preferred communication method. For example, if a user inputs, "I would like to consult via chat," the generation AI suggests, "I will send you a chat link." The expert connection unit can also adjust the content of the connection to the support organization based on the user's preferred communication method. For example, if a user inputs, "I would like to consult via phone," the generation AI suggests, "I will send you a phone link." This allows the user to select their preferred communication method, thereby providing more effective support.

[0063] The expert connection unit uses its emotion estimation function to analyze the user's emotional state when requesting a connection to an expert and can suggest the most suitable expert. For example, the expert connection unit analyzes the user's emotional state in real time and suggests the most suitable expert based on the results. For example, if a user inputs "I'm very anxious," the generation AI might suggest, "We'll introduce you to an expert who specializes in anxiety." The expert connection unit can also adjust the content of the connection to an expert based on the user's emotional state. For example, if a user inputs, "I'm very nervous," the generation AI might suggest, "We'll introduce you to an expert who can help relieve tension." The expert connection unit can also adjust the timing of the connection to an expert based on the user's emotional state. For example, if a user inputs, "I'm very sad," the generation AI might suggest, "Let's consult with an expert right away." This allows for more effective support by analyzing the user's emotional state and suggesting the most suitable expert.

[0064] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0065] The emotion identification unit not only identifies emotions based on the user's input, but also analyzes the user's behavioral patterns and daily activity data to predict emotional fluctuations. For example, if a user performs a specific activity at the same time every day, the emotion identification unit can analyze the user's sleep patterns and meal records to predict the impact of these factors on emotions. Furthermore, the emotion identification unit can analyze the user's social media activity to predict emotional fluctuations. This makes it possible to predict emotional fluctuations and provide more effective support by analyzing the user's behavioral patterns and daily activity data.

[0066] The emotion identification unit not only identifies emotions based on the user's input, but can also analyze environmental data surrounding the user to predict emotional fluctuations. For example, the emotion identification unit can analyze the sound environment surrounding the user to predict the likelihood of stress when the noise level is high. The emotion identification unit can also analyze the lighting environment surrounding the user to predict the impact of brightness on emotions. Furthermore, the emotion identification unit can analyze the temperature and humidity surrounding the user to predict the impact of these factors on emotions. In this way, by analyzing environmental data surrounding the user, it is possible to predict emotional fluctuations and provide more effective support.

[0067] The emotion identification unit can not only identify emotions based on the user's input, but also analyze the user's physiological data to predict emotional fluctuations. For example, it can monitor the user's heart rate and blood pressure and predict the impact of these data on emotions. The emotion identification unit can also monitor the user's electrodermal activity and predict stress levels. Furthermore, the emotion identification unit can analyze the user's breathing patterns and predict the impact of these patterns on emotions. Thus, by analyzing the user's physiological data, it is possible to predict emotional fluctuations and provide more effective support.

[0068] The emotion identification unit not only identifies emotions based on the user's input, but can also analyze the user's past emotion data and predict emotional fluctuations. For example, it stores in a database the emotions the user felt in specific situations in the past and predicts emotional fluctuations if a similar situation occurs again. The emotion identification unit can also analyze the user's past emotion data and find specific patterns. Furthermore, the emotion identification unit can predict future emotional fluctuations based on the user's past emotion data. In this way, by analyzing the user's past emotion data, it is possible to predict emotional fluctuations and provide more effective support.

[0069] The emotion identification unit not only identifies emotions based on the user's input, but can also analyze the user's social relationship data and predict emotional fluctuations. For example, it can analyze the user's communication patterns with friends and family and predict the impact this will have on emotions. The emotion identification unit can also analyze the user's relationships at work or school and predict the impact this will have on emotions. Furthermore, the emotion identification unit can analyze the user's activities in online communities and predict the impact this will have on emotions. In this way, by analyzing the user's social relationship data, it is possible to predict emotional fluctuations and provide more effective support.

[0070] The advice providing unit can customize the advice provided by the generation AI based on the user's input by associating it with the user's hobbies and interests. For example, if the user inputs "I like reading," the generation AI will suggest, "Try reading your favorite book to relax." The advice providing unit can also adjust the content of the advice based on the user's hobbies. For example, if the user inputs "I like music," the generation AI will suggest, "Try listening to relaxing music." The advice providing unit can also adjust the frequency of advice based on the user's interests. For example, if the user inputs "I like exercise," the generation AI will suggest, "Try ways to relax after exercising." This allows the advice to be customized by associating it with the user's hobbies and interests, making it possible to provide more practical advice.

[0071] The advice providing unit can customize the advice provided by the generation AI based on the user's input to suit the user's lifestyle. For example, if the user inputs, "I'm a night owl," the generation AI will suggest, "Try some ways to relax at night." The advice providing unit can also adjust the content of the advice based on the user's lifestyle. For example, if the user inputs, "I exercise every day," the generation AI will suggest, "Try some ways to relax after exercising." The advice providing unit can also adjust the frequency of advice based on the user's lifestyle. For example, if the user inputs, "I'm busy at work," the generation AI will suggest, "Try some ways to relax in a short amount of time." This allows the advice to be customized to suit the user's lifestyle, making it possible to provide more practical advice.

[0072] The advice providing unit can customize the advice provided by the generation AI based on the user's input to suit the user's health condition. For example, if the user inputs, "I haven't been feeling well lately," the generation AI will suggest, "Try some relaxation techniques to improve your health." The advice providing unit can also adjust the content of the advice based on the user's health condition. For example, if the user inputs, "I'm feeling stressed," the generation AI will suggest, "Try some relaxation techniques to reduce stress." Furthermore, the advice providing unit can adjust the frequency of advice based on the user's health condition. For example, if the user inputs, "I feel chronically fatigued," the generation AI will suggest, "Try some relaxation techniques regularly." This allows the advice to be customized to suit the user's health condition, providing more practical advice.

[0073] The advice providing unit can customize the advice provided by the generation AI based on the user's input to match the user's goals. For example, if a user inputs "I'm on a diet," the generation AI might suggest, "Try some relaxation techniques that will help with your diet." The advice providing unit can also adjust the content of the advice based on the user's goals. For example, if a user inputs, "I want to improve my work efficiency," the generation AI might suggest, "Try some relaxation techniques to improve your work efficiency." Furthermore, the advice providing unit can adjust the frequency of advice based on the user's goals. For example, if a user inputs, "I want to continue exercising every day," the generation AI might suggest, "Try some relaxation techniques after exercising." This allows the advice to be customized to match the user's goals, providing more practical advice.

[0074] The advice providing unit can customize the advice provided by the generation AI based on the user's input to suit the user's schedule. For example, if the user inputs, "I'm busy every day," the generation AI will suggest, "Try some ways to relax for a short time." The advice providing unit can also adjust the content of the advice based on the user's schedule. For example, if the user inputs, "I want to relax on the weekend," the generation AI will suggest, "Try some ways to relax on the weekend." The advice providing unit can also adjust the frequency of advice based on the user's schedule. For example, if the user inputs, "I'm busy every day," the generation AI will suggest, "Try some ways to relax for a short time every day." This allows the advice to be customized to suit the user's schedule, making it possible to provide more practical advice.

[0075] The processing flow of the second embodiment will be briefly explained below.

[0076] Step 1: The emotion identification unit analyzes the user's input content and identifies emotions. For example, the emotion identification unit may analyze text input using natural language processing technology and identify emotions. The emotion identification unit may also analyze voice input using voice analysis technology and identify emotions. Furthermore, the emotion identification unit may analyze image input using facial expression recognition technology and identify emotions. Step 2: The empathic response generation unit generates an empathic response based on the emotion identified by the emotion identification unit. For example, the empathic response generation unit generates an empathic response using a template. The empathic response generation unit can also generate an empathic response using a response pattern according to the emotion. Furthermore, the empathic response generation unit can dynamically generate an empathic response using a generation AI. Step 3: The advice providing unit provides professional advice based on the response generated by the empathic response generating unit. For example, the advice providing unit provides professional advice based on psychological knowledge. The advice providing unit can also provide medical advice. Furthermore, the advice providing unit can also provide legal advice. Step 4: The expert connection unit supports connection to an expert as needed based on the advice provided by the advice providing unit. For example, the expert connection unit supports connection to an expert through online chat. The expert connection unit can also support connection to an expert through video call. Furthermore, the expert connection unit can support connection to an expert through face-to-face consultation.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0081] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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).

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0090] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0091] 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.

[0092] 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.

[0093] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0094] 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.

[0095] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0096] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0097] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0098] The 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.

[0099] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0100] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0101] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0102] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0103] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0104] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0105] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0106] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0107] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0108] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0109] 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.

[0110] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0111] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0113] The 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.

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

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

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

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0121] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0122] 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.

[0123] 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.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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."

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0143] 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]

[0144] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an emotion identification unit that analyzes the content of a user's input and identifies the emotion; an empathetic response generation unit that generates an empathetic response based on the emotion identified by the emotion identification unit; an advice providing unit that provides professional advice based on the response generated by the empathetic response generating unit; an expert connection unit that supports connection to an expert as needed based on the advice provided by the advice providing unit; A system characterized by:

2. The emotion identification unit Tracking emotional changes in response to the user's input in real time and generating a response in accordance with the emotional changes.

2. The system of claim 1.

3. The emotion identification unit The system analyzes not only the user's emotions but also their physical reactions to the input, providing comprehensive support.

2. The system of claim 1.

4. The advice providing unit Collecting feedback from the user regarding the advice provided by the generating AI, and continuously improving the accuracy of the advice based on the feedback.

2. The system of claim 1.

5. The expert connection section Develop an algorithm that analyzes the needs of users in detail and automatically matches them with the most suitable experts and support organizations.

2. The system of claim 1.

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