System

Abstract

An object of a system according to an embodiment is to analyze physiological data collected from a wearable terminal, diagnose a mental state, and make a positive proposal.SOLUTION: A system includes a data collection part, a diagnosis part, a proposal part, and a voice output part. The data collection unit collects physiological data from the wearable terminal. The diagnosis unit analyzes the physiological data collected by the data collection unit to diagnose a mental state. The suggestion unit generates a positive suggestion based on a result of the diagnosis by the diagnosis unit. The voice output unit transmits the positive proposal generated by the proposal unit to the user by voice.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of not being able to fully utilize physiological data collected from wearable devices to diagnose mental state and provide appropriate suggestions.

[0005] The system according to the embodiment aims to analyze physiological data collected from a wearable device, diagnose mental state, and make positive suggestions. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, a diagnosis unit, a suggestion unit, and an audio output unit. The data collection unit collects physiological data from a wearable device. The diagnosis unit analyzes the physiological data collected by the data collection unit to diagnose the mental state. The suggestion unit generates positive suggestions based on the results of the diagnosis by the diagnosis unit. The audio output unit conveys the positive suggestions generated by the suggestion unit to the user by audio. [Effects of the Invention]

[0007] The system according to the embodiment can analyze physiological data collected from a wearable device, diagnose mental state, and make positive suggestions. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The mental care system according to an embodiment of the present invention uses a generative AI to diagnose symptoms based on data collected from a wearable device and provide positive voice suggestions. This enables the early detection and prevention of mental illnesses and improves people's well-being.

[0029] A mental care system according to an embodiment includes a data collection unit, a diagnosis unit, a suggestion unit, and an audio output unit. The data collection unit collects physiological data from a wearable device. For example, the data collection unit collects physiological data such as heart rate, body temperature, and electrodermal activity. The data collection unit can also transmit data to an application in real time via an emotion sensor. The diagnosis unit analyzes the physiological data collected by the data collection unit to diagnose the user's mental state. For example, the diagnosis unit detects a sudden increase in heart rate or a change in electrodermal activity to recognize signs of stress or anxiety. The diagnosis unit can also use a generative AI to diagnose the user's mental state based on the collected data. The suggestion unit generates positive suggestions based on the diagnosis results. For example, the suggestion unit provides specific advice to the user, such as, "After the ●● meeting, your central nervous system is feeling unsettled. Taking a short rest will double your productivity." or "Did something bother you? I'd be happy to listen." The suggestion unit can also use the generative AI to make positive suggestions to the user based on the diagnosis results. The voice output unit communicates the positive suggestions generated by the suggestion unit to the user by voice. For example, the voice output unit communicates the suggestions generated by the generation AI to the user by voice. The voice output unit can also reduce stress and anxiety through dialogue with the user using a voice robot. This enables the mental care system according to the embodiment to realize early detection and prevention of mental illness and improve people's well-being. For example, the user can understand their own mental state in real time through the application and take appropriate measures. Furthermore, stress and anxiety can be reduced through dialogue with the voice robot.

[0030] The diagnostic unit can detect sudden increases in heart rate and changes in electrodermal activity and recognize signs of stress and anxiety. In the diagnostic unit, the generative AI analyzes the user's past behavioral patterns and predicts their future mental state based on data such as heart rate, body temperature, and electrodermal activity collected from the wearable device. For example, it analyzes data from the past month and predicts the impact of specific behaviors on their mental state. The diagnostic unit also analyzes the user's past behavioral patterns and predicts their future mental state. For example, it identifies periods of increased stress based on past data and suggests taking measures at those times. The diagnostic unit also analyzes the user's behavioral patterns and predicts their future mental state based on data from the wearable device. For example, it predicts that specific behaviors cause stress based on past data and suggests avoiding those behaviors. This allows for early recognition of signs of stress and anxiety.

[0031] The suggestion unit learns the user's past reaction history and can make more personalized positive suggestions. In the suggestion unit, for example, the generation AI learns the user's past reaction history and makes personalized positive suggestions. For example, it suggests relaxation methods that have been effective in the past. In addition, the suggestion unit makes more personalized positive suggestions based on the user's past reaction history. For example, it identifies and suggests relaxation methods that the user prefers from past data. In addition, the suggestion unit makes more personalized positive suggestions based on the user's past reaction history. For example, it suggests relaxation methods that the user has found effective from past data. This makes it possible to make more personalized suggestions to the user.

[0032] The data collection unit also collects the user's diet and exercise data, allowing it to diagnose the user's overall health condition. The data collection unit, for example, adds the user's diet and exercise data to data collected from a wearable device. For example, it records the contents of meals and frequency of exercise and diagnoses the user's overall health condition. The data collection unit also allows the generation AI to analyze the user's diet and exercise data in addition to the data from the wearable device and diagnose the user's overall health condition. For example, it evaluates the nutritional balance of meals and the effectiveness of exercise. The data collection unit also adds the user's diet and exercise data to the data collected from the wearable device, allowing the generation AI to diagnose the user's overall health condition. For example, it analyzes the calories in meals and the calories burned through exercise and evaluates the user's health condition. This allows it to diagnose the user's overall health condition.

[0033] The data collection unit integrates data from different types of wearable devices, allowing for more multifaceted diagnoses. For example, the data collection unit integrates data collected from different types of wearable devices, allowing the generation AI to perform a more multifaceted diagnosis. For example, it combines heart rate data from a smartwatch and gaze data from smartglasses for analysis. The data collection unit also integrates data from different types of wearable devices, allowing the generation AI to improve the accuracy of mental state diagnosis. For example, it analyzes body temperature data from a smartwatch and gaze data from smartglasses to evaluate stress levels. The data collection unit also integrates data from different types of wearable devices, allowing the generation AI to perform a more multifaceted diagnosis. For example, it combines electrodermal activity data from a smartwatch and gaze data from smartglasses for analysis. This enables a more multifaceted diagnosis.

[0034] The suggestion unit can suggest relaxation methods and activities taking into consideration the user's hobbies and interests. In the suggestion unit, for example, the generation AI suggests relaxation methods and activities taking into consideration the user's hobbies and interests. For example, it suggests listening to music that the user likes. In addition, the suggestion unit can suggest relaxation methods and activities based on the user's hobbies and interests. For example, it suggests activities that the user is interested in. In addition, the suggestion unit can suggest relaxation methods and activities taking into consideration the user's hobbies and interests. For example, it suggests watching a movie that the user likes. In this way, it is possible to suggest relaxation methods and activities based on the user's hobbies and interests.

[0035] The suggestion unit can analyze the user's communication history with friends and family and make suggestions that promote positive dialogue. In the suggestion unit, for example, the generation AI analyzes the user's communication history with friends and family and makes suggestions that promote positive dialogue. For example, suggestions are made based on the content of enjoyable conversations in the past. In addition, the suggestion unit can use the generation AI to make suggestions that promote positive dialogue based on the user's communication history. For example, suggestions are made based on enjoyable memories with friends and family. In addition, the suggestion unit can use the generation AI to analyze the user's communication history with friends and family and make suggestions that promote positive dialogue. For example, fun topics are suggested based on the content of past conversations. This makes it possible to make suggestions that promote positive dialogue with the user's friends and family.

[0036] The suggestion unit can make specific work improvement suggestions to reduce stress, taking into consideration the user's work environment and work content. In the suggestion unit, for example, the generation AI makes work improvement suggestions to reduce stress, taking into consideration the user's work environment and work content. For example, it makes suggestions on how to improve work efficiency and how to take breaks. In addition, the suggestion unit makes specific work improvement suggestions to reduce stress, based on the user's work environment. For example, it makes suggestions on adjusting desk arrangements and lighting. In addition, the suggestion unit makes work improvement suggestions to reduce stress, taking into consideration the user's work content. For example, it makes suggestions to review task priorities. This makes it possible to make work improvement suggestions to reduce stress based on the user's work environment and work content.

[0037] The voice output unit can learn the user's dialogue history and realize a more natural and friendly dialogue. The voice output unit, for example, a voice robot, learns the user's dialogue history and realizes a more natural and friendly dialogue. For example, the voice robot may conduct a dialogue that reflects the user's preferences and interests based on the content of past dialogue. The voice output unit can also learn the user's dialogue history and realize a more natural and friendly dialogue. For example, the voice robot may learn words and phrases that the user frequently uses and incorporate them into the dialogue. The voice output unit can also learn the user's dialogue history and realize a more natural and friendly dialogue. For example, the voice robot may provide an appropriate response based on the user's past utterances. This makes the dialogue with the user more natural and friendly.

[0038] The voice output unit can imitate the voice of a user's favorite character or celebrity to provide a more familiar dialogue. For example, the voice output unit can have a voice robot imitate the voice of a user's favorite character to provide a familiar dialogue. For example, it can speak in the voice of an anime character. The voice output unit can also have a voice robot imitate the voice of a celebrity to provide a familiar dialogue. For example, it can speak in the voice of an actor the user likes. The voice output unit can also have a voice robot imitate the voice of a user's favorite character or celebrity to provide a familiar dialogue. For example, it can speak in the voice of an athlete the user admires. This allows the user to be provided with a familiar dialogue.

[0039] The audio output unit can provide reminders and advice at appropriate times in accordance with the user's lifestyle rhythm. The audio output unit, for example, a voice robot, provides reminders at appropriate times in accordance with the user's lifestyle rhythm. For example, it may remind the user about meal times or exercise times. The audio output unit can also provide advice at appropriate times in accordance with the user's lifestyle rhythm. For example, it may suggest relaxation methods during times of high stress. The audio output unit can also provide reminders and advice at appropriate times in accordance with the user's lifestyle rhythm. For example, it may remind the user to sleep and encourage good quality sleep. This allows the user to receive reminders and advice that are in line with the user's lifestyle rhythm.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The mental care system can also collect the user's sleep data and evaluate the quality of their sleep. For example, the data collection unit records the user's heart rate and body movements while they sleep, and analyzes the depth of their sleep and the frequency of interruptions. The diagnosis unit can also evaluate the user's sleep quality based on the collected sleep data and suggest areas for improvement. For example, if there are frequent interruptions to their sleep, the diagnosis unit can suggest improvements to the bedroom environment or relaxation techniques. This allows the user's overall health to be understood in more detail and appropriate measures to be taken.

[0042] The mental care system can further collect data on the user's social activities and evaluate the user's social health. For example, the data collection unit records the user's frequency of social networking site use and message exchanges, and analyzes the user's social activity patterns. The diagnosis unit can evaluate the user's social health based on the collected social activity data and suggest areas for improvement. For example, if the user is not socially active, the diagnosis unit can suggest taking up a new hobby or joining a new community. This can improve the user's social health.

[0043] The mental care system can also suggest relaxation methods and activities based on the user's hobbies and interests. For example, the suggestion unit can suggest relaxing content based on the user's favorite music or movies. The system can also suggest activities that interest the user, helping to reduce stress. For example, if the user likes outdoor activities, the system can suggest hiking or camping. This makes it possible to provide relaxation methods and activities based on the user's hobbies and interests.

[0044] The mental care system can also make specific work improvement suggestions to reduce stress, taking into account the user's work environment and work content. For example, the suggestion unit can suggest ways to improve the user's work efficiency and how to take breaks. It can also suggest improvements to the work environment, such as desk arrangement and lighting adjustments. This makes it possible to make work improvement suggestions to reduce stress based on the user's work environment and work content.

[0045] The mental care system can also collect the user's dietary and exercise data to diagnose their overall health condition. For example, the data collection unit records the user's dietary habits and exercise frequency to evaluate their overall health condition. The diagnosis unit can also evaluate the user's health condition based on the collected data and suggest areas for improvement. For example, it can evaluate the nutritional balance of meals and the effectiveness of exercise, and provide specific advice for improving their health condition. This allows the user's overall health condition to be diagnosed and appropriate measures to be taken.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The data collection unit collects physiological data from the wearable device, such as heart rate, body temperature, and electrodermal activity, and can also transmit the data to the application in real time via the emotion sensor. Step 2: The diagnosis unit analyzes the physiological data collected by the data collection unit to diagnose the user's mental state. For example, it detects sudden increases in heart rate and changes in electrodermal activity to recognize signs of stress or anxiety. Generative AI can also be used to diagnose the user's mental state based on the collected data. Step 3: The suggestion unit generates positive suggestions based on the results of the diagnosis by the diagnosis unit. For example, it provides specific advice to the user in the form of, "After the ●● meeting, your central nervous system is disrupted. Taking a short rest will double your productivity," or "Was there anything unpleasant that happened? I'd be happy to listen if you'd like." Generative AI can also be used to make positive suggestions to users based on the diagnosis results. Step 4: The voice output unit communicates the positive suggestions generated by the suggestion unit to the user by voice. For example, the suggestions generated by the generation AI are communicated to the user by voice. In addition, a voice robot can be used to reduce stress and anxiety through dialogue with the user.

[0048] (Example 2) The mental care system according to an embodiment of the present invention uses a generative AI to diagnose symptoms based on data collected from a wearable device and provide positive voice suggestions. This enables the early detection and prevention of mental illnesses and improves people's well-being.

[0049] A mental care system according to an embodiment includes a data collection unit, a diagnosis unit, a suggestion unit, and an audio output unit. The data collection unit collects physiological data from a wearable device. For example, the data collection unit collects physiological data such as heart rate, body temperature, and electrodermal activity. The data collection unit can also transmit data to an application in real time via an emotion sensor. The diagnosis unit analyzes the physiological data collected by the data collection unit to diagnose the user's mental state. For example, the diagnosis unit detects a sudden increase in heart rate or a change in electrodermal activity to recognize signs of stress or anxiety. The diagnosis unit can also use a generative AI to diagnose the user's mental state based on the collected data. The suggestion unit generates positive suggestions based on the diagnosis results. For example, the suggestion unit provides specific advice to the user, such as, "After the ●● meeting, your central nervous system is feeling unsettled. Taking a short rest will double your productivity." or "Did something bother you? I'd be happy to listen." The suggestion unit can also use the generative AI to make positive suggestions to the user based on the diagnosis results. The voice output unit communicates the positive suggestions generated by the suggestion unit to the user by voice. For example, the voice output unit communicates the suggestions generated by the generation AI to the user by voice. The voice output unit can also reduce stress and anxiety through dialogue with the user using a voice robot. This enables the mental care system according to the embodiment to realize early detection and prevention of mental illness and improve people's well-being. For example, the user can understand their own mental state in real time through the application and take appropriate measures. Furthermore, stress and anxiety can be reduced through dialogue with the voice robot.

[0050] The diagnostic unit can detect sudden increases in heart rate and changes in electrodermal activity and recognize signs of stress and anxiety. In the diagnostic unit, the generative AI analyzes the user's past behavioral patterns and predicts their future mental state based on data such as heart rate, body temperature, and electrodermal activity collected from the wearable device. For example, it analyzes data from the past month and predicts the impact of specific behaviors on their mental state. The diagnostic unit also analyzes the user's past behavioral patterns and predicts their future mental state. For example, it identifies periods of increased stress based on past data and suggests taking measures at those times. The diagnostic unit also analyzes the user's behavioral patterns and predicts their future mental state based on data from the wearable device. For example, it predicts that specific behaviors cause stress based on past data and suggests avoiding those behaviors. This allows for early recognition of signs of stress and anxiety.

[0051] The suggestion unit learns the user's past reaction history and can make more personalized positive suggestions. In the suggestion unit, for example, the generation AI learns the user's past reaction history and makes personalized positive suggestions. For example, it suggests relaxation methods that have been effective in the past. In addition, the suggestion unit makes more personalized positive suggestions based on the user's past reaction history. For example, it identifies and suggests relaxation methods that the user prefers from past data. In addition, the suggestion unit makes more personalized positive suggestions based on the user's past reaction history. For example, it suggests relaxation methods that the user has found effective from past data. This makes it possible to make more personalized suggestions to the user.

[0052] The audio output unit can analyze the user's voice tone and speaking style, and provide feedback on the emotional state in real time. The audio output unit, for example, a voice robot, analyzes the user's voice tone and speaking style, and provides feedback on the emotional state in real time. For example, if the voice tone is low, the voice robot suggests relaxation techniques. The audio output unit also analyzes the user's voice tone and speaking style, and provides feedback on the emotional state in real time. For example, if the voice tone is fast, the voice robot suggests taking deep breaths. The audio output unit also analyzes the user's voice tone and speaking style, and provides feedback on the emotional state in real time. For example, if the voice tone is high, the voice robot suggests relaxing music. This makes it possible to grasp the user's emotional state in real time and provide appropriate feedback.

[0053] The data collection unit also collects the user's diet and exercise data, allowing it to diagnose the user's overall health condition. The data collection unit, for example, adds the user's diet and exercise data to data collected from a wearable device. For example, it records the contents of meals and frequency of exercise and diagnoses the user's overall health condition. The data collection unit also allows the generation AI to analyze the user's diet and exercise data in addition to the data from the wearable device and diagnose the user's overall health condition. For example, it evaluates the nutritional balance of meals and the effectiveness of exercise. The data collection unit also adds the user's diet and exercise data to the data collected from the wearable device, allowing the generation AI to diagnose the user's overall health condition. For example, it analyzes the calories in meals and the calories burned through exercise and evaluates the user's health condition. This allows it to diagnose the user's overall health condition.

[0054] The data collection unit integrates data from different types of wearable devices, allowing for more multifaceted diagnoses. For example, the data collection unit integrates data collected from different types of wearable devices, allowing the generation AI to perform a more multifaceted diagnosis. For example, it combines heart rate data from a smartwatch and gaze data from smartglasses for analysis. The data collection unit also integrates data from different types of wearable devices, allowing the generation AI to improve the accuracy of mental state diagnosis. For example, it analyzes body temperature data from a smartwatch and gaze data from smartglasses to evaluate stress levels. The data collection unit also integrates data from different types of wearable devices, allowing the generation AI to perform a more multifaceted diagnosis. For example, it combines electrodermal activity data from a smartwatch and gaze data from smartglasses for analysis. This enables a more multifaceted diagnosis.

[0055] The data collection unit can use the emotion estimation function to analyze emotional fluctuations when the user is in a specific location and identify stress factors for each location. The data collection unit, for example, uses the emotion estimation function to analyze emotional fluctuations when the user is in a specific location. For example, the data collection unit analyzes stress levels at work and identifies areas for improvement in the work environment. The data collection unit also uses the emotion estimation function to analyze emotional fluctuations when the user is at home and suggests ways to relax. For example, the data collection unit analyzes stress levels at home and suggests an environment where the user can relax. The data collection unit also uses the emotion estimation function to analyze emotional fluctuations when the user is in a specific location and identify stress factors for each location. For example, the data collection unit analyzes emotional fluctuations at work and at home and identifies stress factors. This makes it possible to identify stress factors for each location.

[0056] The suggestion unit can suggest relaxation methods and activities taking into consideration the user's hobbies and interests. In the suggestion unit, for example, the generation AI suggests relaxation methods and activities taking into consideration the user's hobbies and interests. For example, it suggests listening to music that the user likes. In addition, the suggestion unit can suggest relaxation methods and activities based on the user's hobbies and interests. For example, it suggests activities that the user is interested in. In addition, the suggestion unit can suggest relaxation methods and activities taking into consideration the user's hobbies and interests. For example, it suggests watching a movie that the user likes. In this way, it is possible to suggest relaxation methods and activities based on the user's hobbies and interests.

[0057] The suggestion unit uses the emotion estimation function to suggest music and video content that matches the user's emotional state, thereby stabilizing the user's emotions. The suggestion unit, for example, uses the emotion estimation function to suggest music that matches the user's emotional state. For example, it suggests music that is relaxing when the user is under high stress. The suggestion unit also uses the emotion estimation function to suggest video content that matches the user's emotional state. For example, it suggests videos that are relaxing when the user is under high anxiety. The suggestion unit also uses the emotion estimation function to suggest music and video content that matches the user's emotional state, thereby stabilizing the user's emotions. For example, it suggests music and videos that are relaxing when the user's emotions are fluctuating greatly. In this way, it is possible to suggest music and video content that matches the user's emotional state, thereby stabilizing the user's emotions.

[0058] The suggestion unit can analyze the user's communication history with friends and family and make suggestions that promote positive dialogue. In the suggestion unit, for example, the generation AI analyzes the user's communication history with friends and family and makes suggestions that promote positive dialogue. For example, suggestions are made based on the content of enjoyable conversations in the past. In addition, the suggestion unit can use the generation AI to make suggestions that promote positive dialogue based on the user's communication history. For example, suggestions are made based on enjoyable memories with friends and family. In addition, the suggestion unit can use the generation AI to analyze the user's communication history with friends and family and make suggestions that promote positive dialogue. For example, fun topics are suggested based on the content of past conversations. This makes it possible to make suggestions that promote positive dialogue with the user's friends and family.

[0059] The suggestion unit can make specific work improvement suggestions to reduce stress, taking into consideration the user's work environment and work content. In the suggestion unit, for example, the generation AI makes work improvement suggestions to reduce stress, taking into consideration the user's work environment and work content. For example, it makes suggestions on how to improve work efficiency and how to take breaks. In addition, the suggestion unit makes specific work improvement suggestions to reduce stress, based on the user's work environment. For example, it makes suggestions on adjusting desk arrangements and lighting. In addition, the suggestion unit makes work improvement suggestions to reduce stress, taking into consideration the user's work content. For example, it makes suggestions to review task priorities. This makes it possible to make work improvement suggestions to reduce stress based on the user's work environment and work content.

[0060] The suggestion unit can use the emotion estimation function to analyze the stress the user feels during a specific time period and suggest a relaxation method suitable for that time period. For example, the suggestion unit can use the emotion estimation function to analyze the stress the user feels during a specific time period and suggest a relaxation method suitable for that time period. For example, it can suggest a relaxation method after work. The suggestion unit can also use the emotion estimation function to analyze the stress the user feels during a specific time period and suggest a relaxation method suitable for that time period. For example, it can suggest a method for reducing stress in the morning. The suggestion unit can also use the emotion estimation function to analyze the stress the user feels during a specific time period and suggest a relaxation method suitable for that time period. For example, it can suggest a relaxation method for lunch break. This makes it possible to suggest a relaxation method suitable for the stress the user feels during a specific time period.

[0061] The voice output unit can learn the user's dialogue history and realize a more natural and friendly dialogue. The voice output unit, for example, a voice robot, learns the user's dialogue history and realizes a more natural and friendly dialogue. For example, the voice robot may conduct a dialogue that reflects the user's preferences and interests based on the content of past dialogue. The voice output unit can also learn the user's dialogue history and realize a more natural and friendly dialogue. For example, the voice robot may learn words and phrases that the user frequently uses and incorporate them into the dialogue. The voice output unit can also learn the user's dialogue history and realize a more natural and friendly dialogue. For example, the voice robot may provide an appropriate response based on the user's past utterances. This makes the dialogue with the user more natural and friendly.

[0062] The voice output unit can use the emotion estimation function to speak to the user in a tone and tempo that corresponds to the user's emotion. The voice output unit, for example, uses the emotion estimation function to cause the voice robot to speak to the user in a tone and tempo that corresponds to the user's emotion. For example, if the user is feeling stressed, the voice robot speaks to the user in a calm tone. The voice output unit also uses the emotion estimation function to cause the voice robot to speak to the user in a tone and tempo that corresponds to the user's emotion. For example, if the user is feeling relaxed, the voice robot speaks to the user in a bright tone. The voice output unit also uses the emotion estimation function to cause the voice robot to speak to the user in a tone and tempo that corresponds to the user's emotion. For example, if the user is feeling anxious, the voice robot speaks to the user in a slow tempo. This makes it possible to speak to the user in a tone and tempo that corresponds to the user's emotion.

[0063] The voice output unit can imitate the voice of a user's favorite character or celebrity to provide a more familiar dialogue. For example, the voice output unit can have a voice robot imitate the voice of a user's favorite character to provide a familiar dialogue. For example, it can speak in the voice of an anime character. The voice output unit can also have a voice robot imitate the voice of a celebrity to provide a familiar dialogue. For example, it can speak in the voice of an actor the user likes. The voice output unit can also have a voice robot imitate the voice of a user's favorite character or celebrity to provide a familiar dialogue. For example, it can speak in the voice of an athlete the user admires. This allows the user to be provided with a familiar dialogue.

[0064] The audio output unit can provide reminders and advice at appropriate times in accordance with the user's lifestyle rhythm. The audio output unit, for example, a voice robot, provides reminders at appropriate times in accordance with the user's lifestyle rhythm. For example, it may remind the user about meal times or exercise times. The audio output unit can also provide advice at appropriate times in accordance with the user's lifestyle rhythm. For example, it may suggest relaxation methods during times of high stress. The audio output unit can also provide reminders and advice at appropriate times in accordance with the user's lifestyle rhythm. For example, it may remind the user to sleep and encourage good quality sleep. This allows the user to receive reminders and advice that are in line with the user's lifestyle rhythm.

[0065] The voice output unit uses the emotion estimation function to provide jokes or light topics that correspond to the user's emotional state, thereby encouraging relaxation. The voice output unit, for example, uses the emotion estimation function to cause a voice robot to provide jokes that correspond to the user's emotional state. For example, if the user is feeling stressed, the voice output unit tells a joke that will help the user relax. The voice output unit also uses the emotion estimation function to cause the voice robot to provide light topics that correspond to the user's emotional state. For example, if the user is feeling anxious, the voice output unit provides a topic that will help the user relax. The voice output unit also uses the emotion estimation function to cause the voice robot to provide jokes or light topics that correspond to the user's emotional state, thereby encouraging relaxation. For example, if the user is tired, the voice output unit provides a topic that will make the user laugh. In this way, the voice output unit can provide jokes or light topics that correspond to the user's emotional state, thereby encouraging relaxation.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The mental care system can also collect the user's sleep data and evaluate the quality of their sleep. For example, the data collection unit records the user's heart rate and body movements while they sleep, and analyzes the depth of their sleep and the frequency of interruptions. The diagnosis unit can also evaluate the user's sleep quality based on the collected sleep data and suggest areas for improvement. For example, if there are frequent interruptions to their sleep, the diagnosis unit can suggest improvements to the bedroom environment or relaxation techniques. This allows the user's overall health to be understood in more detail and appropriate measures to be taken.

[0068] The mental care system can further collect data on the user's social activities and evaluate the user's social health. For example, the data collection unit records the user's frequency of social networking site use and message exchanges, and analyzes the user's social activity patterns. The diagnosis unit can evaluate the user's social health based on the collected social activity data and suggest areas for improvement. For example, if the user is not socially active, the diagnosis unit can suggest taking up a new hobby or joining a new community. This can improve the user's social health.

[0069] The mental care system can also suggest relaxation methods and activities based on the user's hobbies and interests. For example, the suggestion unit can suggest relaxing content based on the user's favorite music or movies. The system can also suggest activities that interest the user, helping to reduce stress. For example, if the user likes outdoor activities, the system can suggest hiking or camping. This makes it possible to provide relaxation methods and activities based on the user's hobbies and interests.

[0070] The mental care system can also make specific work improvement suggestions to reduce stress, taking into account the user's work environment and work content. For example, the suggestion unit can suggest ways to improve the user's work efficiency and how to take breaks. It can also suggest improvements to the work environment, such as desk arrangement and lighting adjustments. This makes it possible to make work improvement suggestions to reduce stress based on the user's work environment and work content.

[0071] The mental care system can also collect the user's dietary and exercise data to diagnose their overall health condition. For example, the data collection unit records the user's dietary habits and exercise frequency to evaluate their overall health condition. The diagnosis unit can also evaluate the user's health condition based on the collected data and suggest areas for improvement. For example, it can evaluate the nutritional balance of meals and the effectiveness of exercise, and provide specific advice for improving their health condition. This allows the user's overall health condition to be diagnosed and appropriate measures to be taken.

[0072] The mental care system can further use the user's emotion estimation function to analyze emotional fluctuations when the user is in a specific location and identify stress factors for each location. For example, the data collection unit analyzes emotional fluctuations when the user is at work or at home and identifies stress factors. The suggestion unit can also suggest methods for reducing stress in specific locations. For example, if the stress level at work is high, it can suggest ways to improve the work environment. This allows stress factors for each location to be identified and appropriate measures to be taken.

[0073] The mental care system further uses the user's emotion estimation function to suggest music and video content that matches the user's emotional state, thereby stabilizing the user's emotions. For example, if the user is feeling stressed, the suggestion unit can suggest relaxing music and videos. Also, if the user is feeling anxious, the system can suggest content that will help change the user's mood. This allows the system to provide music and video content that matches the user's emotional state, thereby stabilizing the user's emotions.

[0074] The mental care system can further use the user's emotion estimation function to speak in a tone and tempo that corresponds to the user's emotional state. For example, the voice output unit can speak in a calm tone if the user is feeling stressed. On the other hand, it can speak in a bright tone if the user is relaxed. This allows the system to speak in a tone and tempo that corresponds to the user's emotional state, resulting in a more friendly dialogue.

[0075] The mental care system can further use the user's emotion estimation function to provide jokes or light topics that correspond to the user's emotional state, encouraging relaxation. For example, if the user is feeling stressed, the audio output unit can tell a relaxing joke. Also, if the user is feeling anxious, it can provide a topic that will help change their mood. In this way, jokes or light topics that correspond to the user's emotional state can be provided, encouraging relaxation.

[0076] The mental care system can further use the user's emotion estimation function to analyze the stress the user feels during a specific time period and suggest relaxation methods appropriate for that time period. For example, the suggestion unit can analyze the stress the user feels after work and suggest relaxation methods. It can also suggest methods for reducing stress in the morning and methods for relaxing during lunch breaks. This makes it possible to suggest relaxation methods appropriate for the stress the user feels during a specific time period.

[0077] The processing flow of the second embodiment will be briefly explained below.

[0078] Step 1: The data collection unit collects physiological data from the wearable device, such as heart rate, body temperature, and electrodermal activity, and can also transmit the data to the application in real time via the emotion sensor. Step 2: The diagnosis unit analyzes the physiological data collected by the data collection unit to diagnose the user's mental state. For example, it detects sudden increases in heart rate and changes in electrodermal activity to recognize signs of stress or anxiety. Generative AI can also be used to diagnose the user's mental state based on the collected data. Step 3: The suggestion unit generates positive suggestions based on the results of the diagnosis by the diagnosis unit. For example, it provides specific advice to the user in the form of, "After the ●● meeting, your central nervous system is disrupted. Taking a short rest will double your productivity," or "Was there anything unpleasant that happened? I'd be happy to listen if you'd like." Generative AI can also be used to make positive suggestions to users based on the diagnosis results. Step 4: The voice output unit communicates the positive suggestions generated by the suggestion unit to the user by voice. For example, the suggestions generated by the generation AI are communicated to the user by voice. In addition, a voice robot can be used to reduce stress and anxiety through dialogue with the user.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0083] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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).

[0088] 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.

[0089] 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.

[0090] 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.

[0091] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0092] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0105] 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.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0107] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0113] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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."

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0145] 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]

[0146] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a data collection unit that collects physiological data from the wearable device; a diagnosis unit that analyzes the physiological data collected by the data collection unit and diagnoses a mental state; a suggestion unit that generates positive suggestions based on the results of the diagnosis by the diagnosis unit; a voice output unit that outputs the positive suggestion generated by the suggestion unit to the user by voice. A system characterized by:

2. The diagnostic unit Detects sudden increases in heart rate and changes in electrodermal activity to recognize signs of stress and anxiety 2. The system of claim 1.

3. The proposal unit Learn from the user's past response history to provide more personalized positive suggestions 2. The system of claim 1.

4. The audio output unit Analyzing the user's tone of voice and speaking style and providing real-time feedback on their emotional state.

2. The system of claim 1.

5. The data collection unit The user's diet and exercise data will also be collected to diagnose their overall health.

2. The system of claim 1.

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