System

Abstract

An object of a system according to an embodiment is to visualize a stress state of an individual and provide appropriate advice.SOLUTION: A system according to an embodiment includes a wearable device, generated AI, a data-collecting unit, a data-analyzing unit, a visualizing unit, and an advice-providing unit. The data collector is configured to collect biological data collected by a wearable device. The data analyzer analyzes the vital signs data collected by the data collector. The visualizer visualizes the stress status analyzed by the data analyzer. The advice providing unit provides advice based on the stress status visualized by the visualization unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to accurately grasp an individual's stress level and provide appropriate advice.

[0005] The system according to the embodiment aims to visualize an individual's stress state and provide appropriate advice. [Means for solving the problem]

[0006] The system according to the embodiment includes a wearable device, a generation AI, a data collection unit, a data analysis unit, a visualization unit, and an advice provision unit. The data collection unit collects biometric data collected by the wearable device. The data analysis unit analyzes the biometric data collected by the data collection unit. The visualization unit visualizes the stress situation analyzed by the data analysis unit. The advice provision unit provides advice based on the stress situation visualized by the visualization unit. [Effects of the Invention]

[0007] The system according to the embodiment can visualize the stress state of an individual and provide appropriate advice. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) A stress-free advisor according to an embodiment of the present invention is a system for reducing an individual's stress by combining a wearable device and a generative AI. This system uses a wearable device to collect an individual's biometric data, which is then analyzed by a generative AI to visualize stressful situations and provide appropriate advice. This allows the stress-free advisor to grasp an individual's stress state in real time and take effective measures.

[0029] A stress-free advisor according to an embodiment includes a wearable device, a generation AI, a data collection unit, a data analysis unit, a visualization unit, and an advice provision unit. The wearable device collects an individual's biometric data. For example, a wristwatch-type device measures heart rate and estimates stress levels. Furthermore, measuring electrodermal activity (EDA) can identify the degree of tension or excitement. These data are used to monitor the individual's stress state in detail. The data collection unit collects the biometric data collected by the wearable device. For example, it collects data such as heart rate, EDA, and body temperature in real time. The data analysis unit analyzes the biometric data collected by the data collection unit. For example, the generation AI analyzes fluctuations in heart rate and EDA to identify specific situations in which the individual feels stressed. The generation AI analyzes the data using a text generation AI (e.g., LLM) or a multimodal generation AI. The visualization unit visualizes the stress levels analyzed by the data analysis unit. For example, the generation AI visualizes information such as "my heart rate spiked during a meeting" or "my EDA increased during my commute." The advice providing unit provides advice based on the stress situation visualized by the visualization unit. For example, specific advice such as "take a deep breath and relax" or "take a short break" is generated. In this way, the stress-free advisor according to the embodiment visualizes the stress of an individual and provides appropriate advice to help reduce stress.

[0030] The data collection unit may be equipped with a voice recognition function that estimates the stress level from the tone and speed of the user's voice. For example, the data collection unit may be equipped with a microphone in the wearable device and analyze the tone and speed of the user's voice in real time. For example, the data collection unit may measure the pitch and speed of the voice during a conversation to estimate the stress level. This allows for more detailed stress analysis by estimating the stress level from the tone and speed of the user's voice.

[0031] The data collection unit can be equipped with an environmental sensor that measures the ambient noise level and light intensity. For example, the data collection unit can be equipped with a noise sensor in a wearable device to measure the ambient noise level in real time. For example, the data collection unit can analyze stress levels in noisy environments. By collecting ambient environmental data, the accuracy of stress level analysis can be improved.

[0032] The data collection unit can be equipped with a function to collect biological data from pets. For example, the data collection unit can develop a wearable device for pets to measure heart rate and activity level. For example, a sensor can be attached to a dog's collar to monitor stress levels. This allows for health management of pets by monitoring their stress levels.

[0033] The data collection unit can be provided with a function to link with a home smart home system and adjust the home environment. For example, the data collection unit links a wearable device with the smart home system to adjust the temperature and humidity in the home in real time. For example, the data collection unit can automatically adjust the air conditioner when the stress level is high. This makes it possible to reduce stress by adjusting the home environment.

[0034] The data analysis unit can be equipped with a function to visualize the user's stress level in a 3D graph. For example, the data analysis unit develops a system that visualizes the user's stress level in a 3D graph based on the data analyzed by the generation AI. For example, it displays fluctuations in stress level along a time axis. This makes it possible to track changes over time by visualizing the user's stress level in a 3D graph.

[0035] The data analysis unit can be equipped with a function to display the user's stress level in association with other health data. For example, the data analysis unit will develop a system that displays the user's stress level in association with sleep patterns and dietary content based on data analyzed by the generation AI. For example, it will analyze the impact of lack of sleep on stress. This allows the user's stress level to be displayed in association with other health data, allowing the user to understand their overall health condition.

[0036] The data analysis unit can have a function to compare the user's stress level with other users and display the relative stress level. For example, the data analysis unit develops a system that compares the user's stress level with other users based on the data analyzed by the generation AI. For example, it compares the user's stress level with users of the same age or occupation. This allows the user to understand their relative stress state by comparing their stress level with other users.

[0037] The data analysis unit can be equipped with a function to display the user's stress level on a map and visualize the stress level at a specific location. For example, the data analysis unit may develop a system that displays the user's stress level on a map based on the data analyzed by the generation AI. For example, the stress level at a specific location may be indicated by color. This allows the user's stress level to be displayed on a map, making it possible to understand the stress situation at a specific location.

[0038] The advice providing unit can have a function to provide personalized advice based on the user's past successful cases of stress reduction. For example, the advice providing unit develops a system in which a generation AI analyzes the user's past successful cases of stress reduction and provides personalized advice based on the results. For example, it suggests relaxation methods that have been effective in the past. This enables effective stress reduction by providing personalized advice based on the user's past successful cases of stress reduction.

[0039] The advice providing unit can be equipped with a function to suggest specific activities that take into account the user's hobbies and interests. For example, the advice providing unit will develop a system in which a generation AI analyzes the user's hobbies and interests and suggests specific activities based on them. For example, it may suggest relaxation methods related to hobbies. This allows for effective stress reduction by suggesting specific activities that take into account the user's hobbies and interests.

[0040] The advice providing unit can also have a function to share advice with the user's family and friends and encourage support. For example, the advice providing unit will develop a system that shares advice provided by the generation AI with the user's family and friends. For example, it will notify family members of advice for stress reduction. This will allow the advice to be shared with the user's family and friends, encouraging support and enabling effective stress reduction.

[0041] The advice providing unit can have a function to adapt to the user's work environment and support stress reduction in the workplace. The advice providing unit, for example, develops a system that adapts the advice provided by the generation AI to the user's work environment. For example, it proposes methods for reducing stress in the workplace. This makes it possible to reduce stress in the workplace by adapting to the user's work environment.

[0042] The data analysis unit can be equipped with a function to analyze past data, predict a user's stress patterns, and propose preventative measures. For example, the data analysis unit develops a system in which the generative AI analyzes past data and predicts a user's stress patterns. For example, it identifies trends in increased stress during specific periods or situations. This allows the system to analyze past data, predict a user's stress patterns, and propose preventative measures.

[0043] The data analysis unit can be equipped with a function that analyzes a user's stress level by season based on past data and suggests seasonal countermeasures. For example, the data analysis unit develops a system in which the generation AI analyzes a user's stress level by season based on past data. For example, it identifies a tendency for stress to increase in winter. This makes it possible to suggest seasonal countermeasures by analyzing a user's stress level by season based on past data.

[0044] The data analysis unit can be equipped with a function that associates a user's stress level with other health data based on past data and suggests long-term measures. For example, the data analysis unit develops a system in which the generative AI associates a user's stress level with the amount of exercise and diet based on past data and suggests long-term measures. For example, it analyzes the impact of lack of exercise on stress. This enables comprehensive health management by associating a user's stress level with other health data based on past data and suggesting long-term measures.

[0045] The data analysis unit can have a function to compare the user's stress level with other users based on past data and display the relative stress level. For example, the data analysis unit develops a system in which the generation AI compares the user's stress level with other users based on past data. For example, it compares the user's stress level with users of the same age or occupation. This makes it possible to compare the user's stress level with other users based on past data and display the relative stress level, thereby enabling objective stress evaluation.

[0046] The advice providing unit can have a function of analyzing the user's schedule and sending reminders during times when stress is likely to increase. For example, the advice providing unit develops a system in which a generation AI analyzes the user's schedule and sends reminders during times when stress is likely to increase. For example, reminders are sent before meetings or during commute times. This enables preventative stress management by analyzing the user's schedule and sending reminders during times when stress is likely to increase.

[0047] The advice providing unit can have a function to customize reminders for specific situations based on past stress data. For example, the advice providing unit develops a system in which a generation AI customizes reminders for specific situations based on a user's past stress data. For example, a reminder is sent in situations where stress has increased in the past. This enables effective stress management by customizing reminders for specific situations based on past stress data.

[0048] The advice providing unit can also have a function to send reminders to the user's family and friends to encourage support. For example, the advice providing unit develops a system in which the generation AI sends reminders to the user's family and friends. For example, it notifies family members of a reminder to reduce stress. This allows the user's family and friends to receive reminders and encourage support, enabling effective stress management.

[0049] The advice providing unit may have a function of adapting to the user's work environment and providing a reminder to reduce stress at work. For example, the advice providing unit develops a reminder system that the generation AI adapts to the user's work environment. For example, the advice providing unit sends a reminder suggesting ways to reduce stress at work. This enables effective stress management by adapting to the user's work environment and providing a reminder to reduce stress at work.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The Stress Free Advisor can also be equipped with a nutritional analysis unit that collects the user's dietary data and analyzes nutritional balance. For example, when the user inputs their dietary information into the app, the AI ​​analyzes the nutritional balance and provides dietary advice that is effective in reducing stress. This allows for comprehensive health management by analyzing the user's dietary information and providing stress reduction advice that takes nutritional balance into consideration.

[0052] The Stress Free Advisor can also be equipped with an exercise analysis unit that collects the user's exercise data and analyzes their exercise patterns. For example, a wearable device records the number of steps taken and the duration of exercise, and the AI ​​generator analyzes the exercise patterns. This allows the system to analyze the user's exercise patterns and provide appropriate exercise advice to help reduce stress and maintain health.

[0053] The Stress Free Advisor can also be equipped with a sleep analysis unit that collects the user's sleep data and analyzes the quality of their sleep. For example, a wearable device could record their heart rate and movements while they sleep, and the AI ​​generator could analyze the quality of their sleep. This would allow the system to analyze the user's sleep quality and provide advice for improvement, helping to reduce stress and maintain good health.

[0054] The Stress Free Advisor can also be equipped with a hobby analysis unit that suggests relaxation methods based on the user's hobbies and interests. For example, if the user inputs their hobbies and interests into the app, the generation AI will suggest relaxation methods based on them. This allows for effective stress reduction by suggesting relaxation methods based on the user's hobbies and interests.

[0055] The Stress-Free Advisor can also be equipped with a workplace analysis unit that collects data on the user's workplace environment and analyzes workplace stressors. For example, when a user inputs workplace stressors into the app, the generative AI analyzes them and suggests ways to reduce workplace stress. This enables effective stress management by analyzing the user's workplace environment data and suggesting ways to reduce workplace stress.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: Wearable devices collect personal biometric data. For example, a wristwatch-style device can measure heart rate and estimate stress levels. It can also measure electrodermal activity to gauge tension and arousal levels. Step 2: The data collection unit collects the biological data collected by the wearable device, such as heart rate, electrodermal activity, and body temperature, in real time. Step 3: The data analysis unit analyzes the biometric data collected by the data collection unit. For example, the generation AI analyzes fluctuations in heart rate and electrodermal activity to identify specific situations that cause stress to the individual. The generation AI analyzes the data using text generation AI (e.g., LLM) and multimodal generation AI. Step 4: The visualization unit visualizes the stress situation analyzed by the data analysis unit. For example, the generation AI visualizes information such as "heart rate spiked during a meeting" or "electrodermal activity increased during commuting." Step 5: The advice providing unit provides advice based on the stress situation visualized by the visualization unit. For example, it generates specific advice such as "take a deep breath and relax" or "take a short break."

[0058] (Example 2) A stress-free advisor according to an embodiment of the present invention is a system for reducing an individual's stress by combining a wearable device and a generative AI. This system uses a wearable device to collect an individual's biometric data, which is then analyzed by a generative AI to visualize stressful situations and provide appropriate advice. This allows the stress-free advisor to grasp an individual's stress state in real time and take effective measures.

[0059] A stress-free advisor according to an embodiment includes a wearable device, a generation AI, a data collection unit, a data analysis unit, a visualization unit, and an advice provision unit. The wearable device collects an individual's biometric data. For example, a wristwatch-type device measures heart rate and estimates stress levels. Furthermore, measuring electrodermal activity (EDA) can identify the degree of tension or excitement. These data are used to monitor the individual's stress state in detail. The data collection unit collects the biometric data collected by the wearable device. For example, it collects data such as heart rate, EDA, and body temperature in real time. The data analysis unit analyzes the biometric data collected by the data collection unit. For example, the generation AI analyzes fluctuations in heart rate and EDA to identify specific situations in which the individual feels stressed. The generation AI analyzes the data using a text generation AI (e.g., LLM) or a multimodal generation AI. The visualization unit visualizes the stress levels analyzed by the data analysis unit. For example, the generation AI visualizes information such as "my heart rate spiked during a meeting" or "my EDA increased during my commute." The advice providing unit provides advice based on the stress situation visualized by the visualization unit. For example, specific advice such as "take a deep breath and relax" or "take a short break" is generated. In this way, the stress-free advisor according to the embodiment visualizes the stress of an individual and provides appropriate advice to help reduce stress.

[0060] The data collection unit may be equipped with a voice recognition function that estimates the stress level from the tone and speed of the user's voice. For example, the data collection unit may be equipped with a microphone in the wearable device and analyze the tone and speed of the user's voice in real time. For example, the data collection unit may measure the pitch and speed of the voice during a conversation to estimate the stress level. This allows for more detailed stress analysis by estimating the stress level from the tone and speed of the user's voice.

[0061] The data collection unit can be equipped with an environmental sensor that measures the ambient noise level and light intensity. For example, the data collection unit can be equipped with a noise sensor in a wearable device to measure the ambient noise level in real time. For example, the data collection unit can analyze stress levels in noisy environments. By collecting ambient environmental data, the accuracy of stress level analysis can be improved.

[0062] The data collection unit can be equipped with an emotion estimation function that analyzes the user's facial expressions. For example, the data collection unit is equipped with a camera in the wearable device and analyzes the user's facial expressions in real time. For example, it measures the wrinkles between the eyebrows and the degree to which the corners of the mouth turn down to estimate the stress level. This allows for more detailed stress analysis by estimating the stress level from the user's facial expressions.

[0063] The data collection unit can be equipped with a function to collect biological data from pets. For example, the data collection unit can develop a wearable device for pets to measure heart rate and activity level. For example, a sensor can be attached to a dog's collar to monitor stress levels. This allows for health management of pets by monitoring their stress levels.

[0064] The data collection unit can be provided with a function to link with a home smart home system and adjust the home environment. For example, the data collection unit links a wearable device with the smart home system to adjust the temperature and humidity in the home in real time. For example, the data collection unit can automatically adjust the air conditioner when the stress level is high. This makes it possible to reduce stress by adjusting the home environment.

[0065] The data collection unit can be equipped with a function to automatically play relaxing music when the user feels stressed. For example, the data collection unit will be equipped with an emotion estimation function in a wearable device, and a system will be developed that automatically plays relaxing music when the user feels stressed. For example, music can be selected based on heart rate and changes in facial expression. This will enable stress reduction by playing relaxing music when the user feels stressed.

[0066] The data analysis unit can be equipped with a function to visualize the user's stress level in a 3D graph. For example, the data analysis unit develops a system that visualizes the user's stress level in a 3D graph based on the data analyzed by the generation AI. For example, it displays fluctuations in stress level along a time axis. This makes it possible to track changes over time by visualizing the user's stress level in a 3D graph.

[0067] The data analysis unit can be equipped with a function to display the user's stress level in association with other health data. For example, the data analysis unit will develop a system that displays the user's stress level in association with sleep patterns and dietary content based on data analyzed by the generation AI. For example, it will analyze the impact of lack of sleep on stress. This allows the user's stress level to be displayed in association with other health data, allowing the user to understand their overall health condition.

[0068] The data analysis unit can have a function for visualizing changes in a user's emotions in real time. The data analysis unit can develop a system for visualizing changes in a user's emotions in real time, for example, using an emotion estimation function. For example, the emotion score can be displayed in a graph to show the correlation with stress levels. This makes it possible to visualize changes in a user's emotions in real time and show the correlation between stress levels and emotions.

[0069] The data analysis unit can have a function to compare the user's stress level with other users and display the relative stress level. For example, the data analysis unit develops a system that compares the user's stress level with other users based on the data analyzed by the generation AI. For example, it compares the user's stress level with users of the same age or occupation. This allows the user to understand their relative stress state by comparing their stress level with other users.

[0070] The data analysis unit can be equipped with a function to display the user's stress level on a map and visualize the stress level at a specific location. For example, the data analysis unit may develop a system that displays the user's stress level on a map based on the data analyzed by the generation AI. For example, the stress level at a specific location may be indicated by color. This allows the user's stress level to be displayed on a map, making it possible to understand the stress situation at a specific location.

[0071] The data analysis unit can have a function to automatically display visual content to help the user relax when they feel stressed. For example, the data analysis unit uses the emotion estimation function to develop a system that automatically displays visual content to help the user relax when they feel stressed. For example, natural scenery or artworks can be displayed. This makes it possible to reduce stress by displaying visual content to help the user relax when they feel stressed.

[0072] The advice providing unit can have a function to provide personalized advice based on the user's past successful cases of stress reduction. For example, the advice providing unit develops a system in which a generation AI analyzes the user's past successful cases of stress reduction and provides personalized advice based on the results. For example, it suggests relaxation methods that have been effective in the past. This enables effective stress reduction by providing personalized advice based on the user's past successful cases of stress reduction.

[0073] The advice providing unit can be equipped with a function to suggest specific activities that take into account the user's hobbies and interests. For example, the advice providing unit will develop a system in which a generation AI analyzes the user's hobbies and interests and suggests specific activities based on them. For example, it may suggest relaxation methods related to hobbies. This allows for effective stress reduction by suggesting specific activities that take into account the user's hobbies and interests.

[0074] The advice providing unit can have a function to provide advice in real time according to the user's emotional state. For example, the advice providing unit uses an emotion estimation function to analyze the user's emotional state in real time and develop a system to provide advice based on that analysis. For example, the system may suggest a relaxation method according to the emotion score. This allows for effective stress reduction by providing advice in real time according to the user's emotional state.

[0075] The advice providing unit can also have a function to share advice with the user's family and friends and encourage support. For example, the advice providing unit will develop a system that shares advice provided by the generation AI with the user's family and friends. For example, it will notify family members of advice for stress reduction. This will allow the advice to be shared with the user's family and friends, encouraging support and enabling effective stress reduction.

[0076] The advice providing unit can have a function to adapt to the user's work environment and support stress reduction in the workplace. The advice providing unit, for example, develops a system that adapts the advice provided by the generation AI to the user's work environment. For example, it proposes methods for reducing stress in the workplace. This makes it possible to reduce stress in the workplace by adapting to the user's work environment.

[0077] The advice providing unit may have a function to automatically activate an aroma diffuser to relax the user when the user feels stressed. The advice providing unit may use, for example, an emotion estimation function to develop a system that automatically activates an aroma diffuser when the user feels stressed. For example, an aroma with a relaxing effect may be emitted when the stress level increases. In this way, the user can achieve a relaxing effect by activating the aroma diffuser when they feel stressed.

[0078] The data analysis unit can be equipped with a function to analyze past data, predict a user's stress patterns, and propose preventative measures. For example, the data analysis unit develops a system in which the generative AI analyzes past data and predicts a user's stress patterns. For example, it identifies trends in increased stress during specific periods or situations. This allows the system to analyze past data, predict a user's stress patterns, and propose preventative measures.

[0079] The data analysis unit can be equipped with a function that analyzes a user's stress level by season based on past data and suggests seasonal countermeasures. For example, the data analysis unit develops a system in which the generation AI analyzes a user's stress level by season based on past data. For example, it identifies a tendency for stress to increase in winter. This makes it possible to suggest seasonal countermeasures by analyzing a user's stress level by season based on past data.

[0080] The data analysis unit can be equipped with a function that uses the emotion estimation function to manage the user's emotion history and propose long-term stress countermeasures based on changes in emotion. The data analysis unit, for example, uses the emotion estimation function to develop a system that manages the user's emotion history. For example, it records past changes in emotion and analyzes the association with stressful situations. This enables effective stress management by managing the emotion history using the emotion estimation function and proposing long-term stress countermeasures based on changes in emotion.

[0081] The data analysis unit can be equipped with a function that associates a user's stress level with other health data based on past data and suggests long-term measures. For example, the data analysis unit develops a system in which the generative AI associates a user's stress level with the amount of exercise and diet based on past data and suggests long-term measures. For example, it analyzes the impact of lack of exercise on stress. This enables comprehensive health management by associating a user's stress level with other health data based on past data and suggesting long-term measures.

[0082] The data analysis unit can have a function to compare the user's stress level with other users based on past data and display the relative stress level. For example, the data analysis unit develops a system in which the generation AI compares the user's stress level with other users based on past data. For example, it compares the user's stress level with users of the same age or occupation. This makes it possible to compare the user's stress level with other users based on past data and display the relative stress level, thereby enabling objective stress evaluation.

[0083] The data analysis unit can be provided with a function that uses the emotion estimation function to propose long-term stress countermeasures in response to changes in emotions based on the user's emotion history. The data analysis unit, for example, uses the emotion estimation function to develop a system that proposes long-term stress countermeasures based on the user's emotion history. For example, past changes in emotions are recorded and the correlation with stressful situations is analyzed. This enables effective stress management by using the emotion estimation function to propose long-term stress countermeasures in response to changes in emotions based on the emotion history.

[0084] The advice providing unit can have a function of analyzing the user's schedule and sending reminders during times when stress is likely to increase. For example, the advice providing unit develops a system in which a generation AI analyzes the user's schedule and sends reminders during times when stress is likely to increase. For example, reminders are sent before meetings or during commute times. This enables preventative stress management by analyzing the user's schedule and sending reminders during times when stress is likely to increase.

[0085] The advice providing unit can have a function to customize reminders for specific situations based on past stress data. For example, the advice providing unit develops a system in which a generation AI customizes reminders for specific situations based on a user's past stress data. For example, a reminder is sent in situations where stress has increased in the past. This enables effective stress management by customizing reminders for specific situations based on past stress data.

[0086] The advice providing unit can have a function of using an emotion estimation function to send reminders in real time according to the user's emotional state. For example, the advice providing unit develops a system that uses the emotion estimation function to analyze the user's emotional state in real time and sends reminders based on the analysis. For example, the system sends reminders according to an emotion score. This enables effective stress management by using the emotion estimation function to send reminders in real time according to the user's emotional state.

[0087] The advice providing unit can also have a function to send reminders to the user's family and friends to encourage support. For example, the advice providing unit develops a system in which the generation AI sends reminders to the user's family and friends. For example, it notifies family members of a reminder to reduce stress. This allows the user's family and friends to receive reminders and encourage support, enabling effective stress management.

[0088] The advice providing unit may have a function of adapting to the user's work environment and providing a reminder to reduce stress at work. For example, the advice providing unit develops a reminder system that the generation AI adapts to the user's work environment. For example, the advice providing unit sends a reminder suggesting ways to reduce stress at work. This enables effective stress management by adapting to the user's work environment and providing a reminder to reduce stress at work.

[0089] The advice providing unit can be equipped with a function of using the emotion estimation function to automatically send a reminder to the user to relax when the user feels stressed. For example, the advice providing unit develops a system that uses the emotion estimation function to automatically send a reminder to the user to relax when the user feels stressed. For example, the reminder is sent when the stress level increases. This enables effective stress management by using the emotion estimation function to automatically send a reminder to the user to relax when the user feels stressed.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The Stress Free Advisor can also be equipped with a nutritional analysis unit that collects the user's dietary data and analyzes nutritional balance. For example, when the user inputs their dietary information into the app, the AI ​​analyzes the nutritional balance and provides dietary advice that is effective in reducing stress. This allows for comprehensive health management by analyzing the user's dietary information and providing stress reduction advice that takes nutritional balance into consideration.

[0092] The Stress Free Advisor can also be equipped with an exercise analysis unit that collects the user's exercise data and analyzes their exercise patterns. For example, a wearable device records the number of steps taken and the duration of exercise, and the AI ​​generator analyzes the exercise patterns. This allows the system to analyze the user's exercise patterns and provide appropriate exercise advice to help reduce stress and maintain health.

[0093] The Stress Free Advisor can also be equipped with a sleep analysis unit that collects the user's sleep data and analyzes the quality of their sleep. For example, a wearable device could record their heart rate and movements while they sleep, and the AI ​​generator could analyze the quality of their sleep. This would allow the system to analyze the user's sleep quality and provide advice for improvement, helping to reduce stress and maintain good health.

[0094] The Stress Free Advisor can also be equipped with a hobby analysis unit that suggests relaxation methods based on the user's hobbies and interests. For example, if the user inputs their hobbies and interests into the app, the generation AI will suggest relaxation methods based on them. This allows for effective stress reduction by suggesting relaxation methods based on the user's hobbies and interests.

[0095] The Stress-Free Advisor can also be equipped with a workplace analysis unit that collects data on the user's workplace environment and analyzes workplace stressors. For example, when a user inputs workplace stressors into the app, the generative AI analyzes them and suggests ways to reduce workplace stress. This enables effective stress management by analyzing the user's workplace environment data and suggesting ways to reduce workplace stress.

[0096] The Stress-Free Advisor can further include an emotion analysis unit that analyzes the user's emotional state and suggests relaxation methods based on changes in emotions. For example, the emotion estimation function can be used to analyze the user's emotional state in real time and suggest relaxation methods based on that analysis. This allows for effective stress reduction by suggesting relaxation methods based on the user's emotional state.

[0097] The Stress Free Advisor can also be equipped with a function to analyze the user's emotional state and automatically play music based on changes in emotion. For example, the emotion estimation function can be used to analyze the user's emotional state in real time and automatically play relaxing music based on that analysis. This allows for effective stress reduction by automatically playing music based on the user's emotional state.

[0098] The Stress-Free Advisor can also be equipped with a function to analyze the user's emotional state and display visual content based on changes in the user's emotions. For example, the Stress-Free Advisor can use an emotion estimation function to analyze the user's emotional state in real time and display visual content to help them relax based on that analysis. This allows for effective stress reduction by displaying visual content based on the user's emotional state.

[0099] The Stress Free Advisor can also analyze the user's emotional state and automatically activate the aroma diffuser based on changes in emotion. For example, it can use the emotion estimation function to analyze the user's emotional state in real time and release a relaxing aroma based on that. This allows for effective stress reduction by automatically activating the aroma diffuser based on the user's emotional state.

[0100] The Stress Free Advisor can also be equipped with a function to analyze the user's emotional state and send reminders based on changes in the user's emotions. For example, the Stress Free Advisor can use an emotion estimation function to analyze the user's emotional state in real time and send reminders based on that analysis. This allows for effective stress management by sending reminders based on the user's emotional state.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: Wearable devices collect personal biometric data. For example, a wristwatch-style device can measure heart rate and estimate stress levels. It can also measure electrodermal activity to gauge tension and arousal levels. Step 2: The data collection unit collects the biological data collected by the wearable device, such as heart rate, electrodermal activity, and body temperature, in real time. Step 3: The data analysis unit analyzes the biometric data collected by the data collection unit. For example, the generation AI analyzes fluctuations in heart rate and electrodermal activity to identify specific situations that cause stress to the individual. The generation AI analyzes the data using text generation AI (e.g., LLM) and multimodal generation AI. Step 4: The visualization unit visualizes the stress situation analyzed by the data analysis unit. For example, the generation AI visualizes information such as "heart rate spiked during a meeting" or "electrodermal activity increased during commuting." Step 5: The advice providing unit provides advice based on the stress situation visualized by the visualization unit. For example, it generates specific advice such as "take a deep breath and relax" or "take a short break."

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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]

[0170] 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. Wearable devices and Generative AI and a data collection unit that collects biological data collected by the wearable device; a data analysis unit that analyzes the biological data collected by the data collection unit; a visualization unit that visualizes the stress situation analyzed by the data analysis unit; an advice providing unit that provides advice based on the stress situation visualized by the visualization unit; A system characterized by:

2. The data collection unit Equipped with environmental sensors that measure ambient noise levels and light intensity 2. The system of claim 1.

3. The data analysis unit Equipped with a function to visualize the user's stress level in a 3D graph 2. The system of claim 1.

4. The advice providing unit It has the ability to provide personalized advice based on the user's past successes in reducing stress.

2. The system of claim 1.

5. The data collection unit Equipped with emotion estimation function that analyzes the user's facial expressions 2. The system of claim 1.

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