System

Abstract

An object of a system according to an embodiment is to individually grasp a state of mental health of a user and provide appropriate advice and support.SOLUTION: A system includes an interaction part, an advice provision part, a trend tracking part, and an expert introduction part. The dialogue unit shares the cause of stress or anxiety through a dialogue with the user. The advice providing unit provides appropriate advice or technique on the basis of the factor of stress or anxiety shared by the dialogue unit. The trend tracker tracks trends in daily activities and emotions. The expert introducer tracks changes and progress in the user's mental health and provides introductions and support to experts as needed.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 grasp the mental health status of individual users and provide appropriate advice and support.

[0005] The system according to the embodiment aims to grasp the mental health state of each user individually and provide appropriate advice and support. [Means for solving the problem]

[0006] The system according to the embodiment includes a dialogue unit, an advice providing unit, a trend tracking unit, and an expert referral unit. The dialogue unit shares stress and anxiety factors through dialogue with the user. The advice providing unit provides appropriate advice and techniques based on the stress and anxiety factors shared by the dialogue unit. The trend tracking unit tracks trends in daily activities and emotions. The expert referral unit tracks changes and progress in the user's mental health and provides referrals to experts and support as needed. [Effects of the Invention]

[0007] The system according to the embodiment can grasp the mental health state of each user individually and provide appropriate advice and support. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The mental health support system according to an embodiment of the present invention allows users to share their stress and anxiety factors through dialogue with an AI, and the AI ​​then provides appropriate advice and techniques based on that information, enabling the mental health support system to effectively manage and improve the user's mental health.

[0029] A mental health support system according to an embodiment includes a dialogue unit, an advice providing unit, a trend tracking unit, and an expert referral unit. The dialogue unit shares stress and anxiety factors with the user through dialogue. For example, the user may tell the AI, "I've been busy at work lately and I'm feeling stressed." The advice providing unit provides appropriate advice and techniques based on the stress and anxiety factors shared by the dialogue unit. For example, the generation AI may provide advice such as, "Take a deep breath and relax. Inhale slowly and exhale slowly." The trend tracking unit tracks trends in daily activities and emotions. For example, the user can record their daily emotions and events like in a diary, and the generation AI can analyze the data to identify changes in the user's emotions. The expert referral unit tracks changes and progress in the user's mental health and provides expert referrals and support as needed. For example, if the user has been feeling stressed for a long period of time, the generation AI may suggest, "You may need expert support. Please consult a counselor." This enables the mental health support system according to an embodiment to effectively manage and improve the user's mental health.

[0030] The dialogue unit can refer to the user's past dialogue history and generate personalized questions to draw out deeper insights. The dialogue unit can refer to the user's past dialogue history and generate personalized questions. For example, if the user talked about work stress in a previous dialogue, the AI ​​can ask, "How is your work situation these days?" This can draw out deeper insights into the user.

[0031] The dialogue unit analyzes non-verbal information provided by the user during the dialogue, thereby improving the quality of the dialogue. For example, the dialogue unit analyzes the user's facial expressions during the dialogue to detect changes in emotions. For example, if the user smiles, the AI ​​will engage in positive dialogue in response to that smile. In this way, the quality of the dialogue can be improved by analyzing non-verbal information.

[0032] The dialogue unit can automatically translate the content of the dialogue to accommodate different cultures and languages ​​and generate responses that take cultural backgrounds into consideration. For example, the dialogue unit can automatically translate the content of the dialogue to accommodate different cultures and languages. For example, it can respond in English to a user speaking Japanese. This makes it possible to have dialogue that accommodates different cultures and languages.

[0033] The advice providing unit can monitor the user's physiological data in real time and adjust advice based on that data. For example, the advice providing unit can monitor the user's heart rate in real time and provide advice based on the user's stress level. For example, if the heart rate is high, the advice providing unit can encourage deep breathing. This allows for more effective support by adjusting advice based on the user's physiological data.

[0034] The advice providing unit can provide advice at optimal timing based on the user's lifestyle and daily schedule. The advice providing unit, for example, analyzes the user's daily schedule and provides advice at optimal timing. For example, advice on how to relax between work sessions can be provided. This makes it possible to provide advice at optimal timing based on the user's lifestyle and schedule.

[0035] The advice providing unit customizes the content of advice according to the user's preferences, and can provide more effective support. The advice providing unit customizes the content of advice according to the user's preferences, for example. For example, if the user likes music, the advice providing unit suggests relaxing music. In this way, more effective support can be provided by providing advice according to the user's preferences.

[0036] The advice providing unit can provide advice in different media formats to deepen the user's understanding. The advice providing unit can provide advice on relaxation techniques or meditation techniques using, for example, audio guidance. For example, the advice providing unit can provide audio guidance to help the user close their eyes and relax. In this way, by providing advice in different media formats, the user's understanding can be deepened.

[0037] The trend tracking unit analyzes data collected from the user's smart device to understand the user's daily activity patterns in detail. For example, the trend tracking unit analyzes location information data collected from the user's smartphone to understand the user's daily movement patterns. For example, it identifies the places the user frequently visits. This allows the user's daily activity patterns to be understood in detail.

[0038] The trend tracking unit can track the user's emotional trends over the long term and analyze the influence of seasons and events. The trend tracking unit, for example, analyzes the user's diary data and tracks the emotional trends over the long term. For example, it identifies patterns of emotional fluctuations at the change of seasons. This makes it possible to track the user's emotional trends over the long term and analyze the influence of seasons and events.

[0039] The trend tracking unit can integrate the user's activity data with other health data to evaluate the overall health status. For example, the trend tracking unit can integrate the user's dietary data and activity data to evaluate the overall health status. For example, the trend tracking unit can evaluate the balance between dietary content and exercise amount. This allows the user's activity data and other health data to be integrated to evaluate the overall health status.

[0040] The trend tracking unit can compare the emotional trends of different users and discover common patterns and tendencies. For example, the trend tracking unit compares the emotional trends of different users and discovers common patterns. For example, it identifies the tendency for emotions to fluctuate in specific seasons. This makes it possible to compare the emotional trends of different users and discover common patterns and tendencies.

[0041] The expert introduction unit can provide a dashboard that records changes in the user's mental health in detail and visualizes the degree of progress. The expert introduction unit, for example, provides a dashboard that records changes in the user's mental health in detail and visualizes the degree of progress. For example, the expert introduction unit displays changes in stress levels in a graph. This makes it possible to record changes in the user's mental health in detail and visualize the degree of progress.

[0042] The expert referral unit can predict changes in the user's mental health and issue an alert if early intervention is required. The expert referral unit, for example, predicts changes in the user's mental health and issue an alert if early intervention is required. For example, an alert is issued if the stress level rises sharply. This makes it possible to predict changes in the user's mental health and issue an alert if early intervention is required.

[0043] The expert referral unit can anonymize the user's mental health data and facilitate data sharing for research purposes. The expert referral unit, for example, anonymizes the user's mental health data and facilitates data sharing for research purposes. For example, the expert referral unit provides data with personal information removed to a research institution. This allows the user's mental health data to be anonymized and facilitates data sharing for research purposes.

[0044] The expert referral unit can cooperate with different mental healthcare platforms and integrate user data to provide comprehensive support. The expert referral unit, for example, cooperates with different mental healthcare platforms and integrates user data to provide comprehensive support. For example, it cooperates with a counseling service to share user data. This allows it to cooperate with different mental healthcare platforms and integrate user data to provide comprehensive support.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The mental health support system can also include a hobby suggestion unit that suggests relaxation methods based on the user's hobbies and interests. For example, if the user likes music, a playlist of relaxing music can be suggested. If the user likes reading, a list of relaxing books can be provided. If the user likes exercise, relaxing yoga or stretching videos can be suggested. This allows for more effective mental health support by suggesting relaxation methods based on the user's hobbies and interests.

[0047] The mental health support system can also include a sleep analysis unit that analyzes the user's sleep data and provides advice to improve the quality of sleep. For example, the system can analyze the user's sleep duration and depth and suggest an appropriate sleep duration. If the user wakes up multiple times during the night, the system can suggest ways to relax before bed. If the user has trouble falling asleep, the system can advise on behaviors and foods to avoid before bed. In this way, the system can provide advice to improve the quality of sleep based on the user's sleep data.

[0048] The mental health support system can also include a nutrition analysis unit that analyzes the user's dietary data and provides advice to improve nutritional balance. For example, the system analyzes the user's diet and suggests a balanced meal menu if the user's nutrition is unbalanced. If the user is feeling stressed, the system suggests recipes containing ingredients that are effective in reducing stress. If the user is feeling tired, the system suggests meals to replenish energy. In this way, advice to improve nutritional balance can be provided based on the user's dietary data.

[0049] The mental health support system can also include an exercise analysis unit that analyzes the user's exercise data and provides advice to maximize the effects of exercise. For example, the system analyzes the user's exercise amount and type and suggests an appropriate exercise plan. If the user is not getting enough exercise, the system suggests exercises that are easy to incorporate into daily life. If the user is exercising too much, the system advises the user to take a rest. If the user likes a particular exercise, the system suggests a method to maximize the effects of that exercise. In this way, advice to maximize the effects of exercise can be provided based on the user's exercise data.

[0050] The mental health support system may further include an activity suggestion unit that suggests new activities based on the user's hobbies and interests. For example, if the user likes outdoor activities, new hiking trails and campsites may be suggested. If the user is interested in art, new art classes and workshops may be suggested. If the user enjoys cooking, new recipes and cooking classes may be suggested. In this way, the quality of life can be improved by suggesting new activities based on the user's hobbies and interests.

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

[0052] Step 1: The dialogue unit shares the causes of stress and anxiety through dialogue with the user. For example, the user tells the AI, "I've been busy at work lately and I'm feeling stressed." Step 2: The advice provider provides appropriate advice and techniques based on the stress and anxiety factors shared by the dialogue component. For example, the generative AI might provide advice such as, "Take a deep breath and relax. Inhale slowly and exhale slowly." Step 3: The trend tracking unit tracks trends in daily activities and emotions. For example, if a user records their daily emotions and events like in a diary, the generation AI analyzes the data and understands changes in the user's emotions. Step 4: The specialist referral unit tracks changes and progress in the user's mental health and provides specialist referrals and support as needed. For example, if a user has been feeling stressed for a long period of time, the generative AI may suggest, "You may need specialist support. Please consider talking to a counselor."

[0053] (Example 2) The mental health support system according to an embodiment of the present invention allows users to share their stress and anxiety factors through dialogue with an AI, and the AI ​​then provides appropriate advice and techniques based on that information, enabling the mental health support system to effectively manage and improve the user's mental health.

[0054] A mental health support system according to an embodiment includes a dialogue unit, an advice providing unit, a trend tracking unit, and an expert referral unit. The dialogue unit shares stress and anxiety factors with the user through dialogue. For example, the user may tell the AI, "I've been busy at work lately and I'm feeling stressed." The advice providing unit provides appropriate advice and techniques based on the stress and anxiety factors shared by the dialogue unit. For example, the generation AI may provide advice such as, "Take a deep breath and relax. Inhale slowly and exhale slowly." The trend tracking unit tracks trends in daily activities and emotions. For example, the user can record their daily emotions and events like in a diary, and the generation AI can analyze the data to identify changes in the user's emotions. The expert referral unit tracks changes and progress in the user's mental health and provides expert referrals and support as needed. For example, if the user has been feeling stressed for a long period of time, the generation AI may suggest, "You may need expert support. Please consult a counselor." This enables the mental health support system according to an embodiment to effectively manage and improve the user's mental health.

[0055] The dialogue unit analyzes the user's tone of voice and speaking style, detects changes in emotion in real time, and adjusts the content of the dialogue. For example, the dialogue unit analyzes the tone of voice and speaking style when the user talks to the AI, and detects changes in emotion in real time. For example, if the user is nervous, the AI ​​will switch to a topic that will help them relax. This makes it possible to have a dialogue that is appropriate to the user's emotions.

[0056] The dialogue unit can refer to the user's past dialogue history and generate personalized questions to draw out deeper insights. The dialogue unit can refer to the user's past dialogue history and generate personalized questions. For example, if the user talked about work stress in a previous dialogue, the AI ​​can ask, "How is your work situation these days?" This can draw out deeper insights into the user.

[0057] The dialogue unit can use the emotion estimation function to estimate the user's emotions and conduct dialogue according to the emotions. For example, the dialogue unit uses the emotion estimation function to estimate the user's emotions in real time and adjust the content of the dialogue. For example, if the user is sad, the AI ​​will offer words of comfort. This makes it possible to conduct dialogue according to the user's emotions.

[0058] The dialogue unit analyzes non-verbal information provided by the user during the dialogue, thereby improving the quality of the dialogue. For example, the dialogue unit analyzes the user's facial expressions during the dialogue to detect changes in emotions. For example, if the user smiles, the AI ​​will engage in positive dialogue in response to that smile. In this way, the quality of the dialogue can be improved by analyzing non-verbal information.

[0059] The dialogue unit can automatically translate the content of the dialogue to accommodate different cultures and languages ​​and generate responses that take cultural backgrounds into consideration. For example, the dialogue unit can automatically translate the content of the dialogue to accommodate different cultures and languages. For example, it can respond in English to a user speaking Japanese. This makes it possible to have dialogue that accommodates different cultures and languages.

[0060] The dialogue unit uses the emotion estimation function to suggest music and videos based on the user's emotions, thereby enhancing the relaxation effect. The dialogue unit, for example, uses the emotion estimation function to suggest music based on the user's emotions. For example, if the user is feeling stressed, the dialogue unit suggests relaxing music. In this way, the relaxation effect can be enhanced by suggesting music and videos that match the user's emotions.

[0061] The advice providing unit can monitor the user's physiological data in real time and adjust advice based on that data. For example, the advice providing unit can monitor the user's heart rate in real time and provide advice based on the user's stress level. For example, if the heart rate is high, the advice providing unit can encourage deep breathing. This allows for more effective support by adjusting advice based on the user's physiological data.

[0062] The advice providing unit can provide advice at optimal timing based on the user's lifestyle and daily schedule. The advice providing unit, for example, analyzes the user's daily schedule and provides advice at optimal timing. For example, advice on how to relax between work sessions can be provided. This makes it possible to provide advice at optimal timing based on the user's lifestyle and schedule.

[0063] The advice providing unit uses the emotion estimation function to provide advice according to the user's emotion, thereby stabilizing the user's emotion. The advice providing unit, for example, uses the emotion estimation function to provide advice according to the user's emotion. For example, if the user is feeling anxious, advice to relax is provided. In this way, by providing advice according to the user's emotion, it is possible to stabilize the user's emotion.

[0064] The advice providing unit customizes the content of advice according to the user's preferences, and can provide more effective support. The advice providing unit customizes the content of advice according to the user's preferences, for example. For example, if the user likes music, the advice providing unit suggests relaxing music. In this way, more effective support can be provided by providing advice according to the user's preferences.

[0065] The advice providing unit can provide advice in different media formats to deepen the user's understanding. The advice providing unit can provide advice on relaxation techniques or meditation techniques using, for example, audio guidance. For example, the advice providing unit can provide audio guidance to help the user close their eyes and relax. In this way, by providing advice in different media formats, the user's understanding can be deepened.

[0066] The advice providing unit can use the emotion estimation function to suggest relaxation techniques based on the user's emotions, thereby reducing stress. The advice providing unit, for example, uses the emotion estimation function to suggest relaxation techniques based on the user's emotions. For example, if the user is feeling stressed, the advice providing unit can suggest deep breathing techniques. In this way, stress can be reduced by suggesting relaxation techniques based on the user's emotions.

[0067] The trend tracking unit analyzes data collected from the user's smart device to understand the user's daily activity patterns in detail. For example, the trend tracking unit analyzes location information data collected from the user's smartphone to understand the user's daily movement patterns. For example, it identifies the places the user frequently visits. This allows the user's daily activity patterns to be understood in detail.

[0068] The trend tracking unit can track the user's emotional trends over the long term and analyze the influence of seasons and events. The trend tracking unit, for example, analyzes the user's diary data and tracks the emotional trends over the long term. For example, it identifies patterns of emotional fluctuations at the change of seasons. This makes it possible to track the user's emotional trends over the long term and analyze the influence of seasons and events.

[0069] The trend tracking unit can use the emotion estimation function to monitor the user's daily emotional fluctuations in real time and provide appropriate advice. The trend tracking unit, for example, uses the emotion estimation function to monitor the user's daily emotional fluctuations in real time. For example, if the user is feeling stressed, advice on how to relax is provided. This makes it possible to monitor the user's daily emotional fluctuations in real time and provide appropriate advice.

[0070] The trend tracking unit can integrate the user's activity data with other health data to evaluate the overall health status. For example, the trend tracking unit can integrate the user's dietary data and activity data to evaluate the overall health status. For example, the trend tracking unit can evaluate the balance between dietary content and exercise amount. This allows the user's activity data and other health data to be integrated to evaluate the overall health status.

[0071] The trend tracking unit can compare the emotional trends of different users and discover common patterns and tendencies. For example, the trend tracking unit compares the emotional trends of different users and discovers common patterns. For example, it identifies the tendency for emotions to fluctuate in specific seasons. This makes it possible to compare the emotional trends of different users and discover common patterns and tendencies.

[0072] The trend tracking unit can use the emotion estimation function to suggest community support based on the user's emotion trends, thereby strengthening social connections. The trend tracking unit, for example, uses the emotion estimation function to suggest community support based on the user's emotion trends. For example, if the user is feeling lonely, the trend tracking unit can suggest joining an online community. In this way, by suggesting community support based on the user's emotion trends, social connections can be strengthened.

[0073] The expert introduction unit can provide a dashboard that records changes in the user's mental health in detail and visualizes the degree of progress. The expert introduction unit, for example, provides a dashboard that records changes in the user's mental health in detail and visualizes the degree of progress. For example, the expert introduction unit displays changes in stress levels in a graph. This makes it possible to record changes in the user's mental health in detail and visualize the degree of progress.

[0074] The expert referral unit can predict changes in the user's mental health and issue an alert if early intervention is required. The expert referral unit, for example, predicts changes in the user's mental health and issue an alert if early intervention is required. For example, an alert is issued if the stress level rises sharply. This makes it possible to predict changes in the user's mental health and issue an alert if early intervention is required.

[0075] The expert introduction unit can use the emotion estimation function to track changes in the user's emotions and evaluate the emotional stability. The expert introduction unit, for example, uses the emotion estimation function to track changes in the user's emotions and evaluate the emotional stability. For example, if there is little emotional fluctuation, the expert introduction unit evaluates the emotions as stable. This makes it possible to track changes in the user's emotions and evaluate the emotional stability.

[0076] The expert referral unit can anonymize the user's mental health data and facilitate data sharing for research purposes. The expert referral unit, for example, anonymizes the user's mental health data and facilitates data sharing for research purposes. For example, the expert referral unit provides data with personal information removed to a research institution. This allows the user's mental health data to be anonymized and facilitates data sharing for research purposes.

[0077] The expert referral unit can cooperate with different mental healthcare platforms and integrate user data to provide comprehensive support. The expert referral unit, for example, cooperates with different mental healthcare platforms and integrates user data to provide comprehensive support. For example, it cooperates with a counseling service to share user data. This allows it to cooperate with different mental healthcare platforms and integrate user data to provide comprehensive support.

[0078] The expert referral unit can use the emotion estimation function to propose a self-care plan based on changes in the user's emotions and promote self-management. The expert referral unit, for example, uses the emotion estimation function to propose a self-care plan based on changes in the user's emotions. For example, if the user is feeling stressed, the expert referral unit proposes a self-care plan for relaxation. In this way, by proposing a self-care plan based on changes in the user's emotions, self-management can be promoted.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The mental health support system can also include a hobby suggestion unit that suggests relaxation methods based on the user's hobbies and interests. For example, if the user likes music, a playlist of relaxing music can be suggested. If the user likes reading, a list of relaxing books can be provided. If the user likes exercise, relaxing yoga or stretching videos can be suggested. This allows for more effective mental health support by suggesting relaxation methods based on the user's hobbies and interests.

[0081] The mental health support system can further include a feedback unit that estimates the user's emotions and provides appropriate feedback based on the estimated emotions. For example, if the user feels anxious, the feedback unit provides feedback such as "Take a deep breath and relax." If the user feels happy, the feedback unit provides positive feedback such as "Keep it up." If the user feels angry, the feedback unit provides feedback to calm down such as "Take a short break." In this way, providing feedback according to the user's emotions can help stabilize emotions.

[0082] The mental health support system can also include a sleep analysis unit that analyzes the user's sleep data and provides advice to improve the quality of sleep. For example, the system can analyze the user's sleep duration and depth and suggest an appropriate sleep duration. If the user wakes up multiple times during the night, the system can suggest ways to relax before bed. If the user has trouble falling asleep, the system can advise on behaviors and foods to avoid before bed. In this way, the system can provide advice to improve the quality of sleep based on the user's sleep data.

[0083] The mental health support system may further include an exercise suggestion unit that estimates the user's emotions and suggests an appropriate exercise plan based on the estimated emotions. For example, if the user is feeling stressed, a relaxing yoga or stretching plan may be suggested. If the user is feeling energetic, a running or dance plan may be suggested. If the user is tired, light walking or other relaxing exercise may be suggested. In this way, by suggesting an exercise plan that matches the user's emotions, mental health can be improved.

[0084] The mental health support system can also include a nutrition analysis unit that analyzes the user's dietary data and provides advice to improve nutritional balance. For example, the system analyzes the user's diet and suggests a balanced meal menu if the user's nutrition is unbalanced. If the user is feeling stressed, the system suggests recipes containing ingredients that are effective in reducing stress. If the user is feeling tired, the system suggests meals to replenish energy. In this way, advice to improve nutritional balance can be provided based on the user's dietary data.

[0085] The mental health support system may further include a self-care suggestion unit that estimates the user's emotions and suggests appropriate self-care activities based on the estimated emotions. For example, if the user is feeling anxious, meditation or mindfulness activities may be suggested. If the user is feeling sad, art therapy or journaling may be suggested. If the user is feeling angry, relaxation exercises or deep breathing techniques may be suggested. In this way, by suggesting self-care activities according to the user's emotions, emotional stability can be achieved.

[0086] The mental health support system can also include an exercise analysis unit that analyzes the user's exercise data and provides advice to maximize the effects of exercise. For example, the system analyzes the user's exercise amount and type and suggests an appropriate exercise plan. If the user is not getting enough exercise, the system suggests exercises that are easy to incorporate into daily life. If the user is exercising too much, the system advises the user to take a rest. If the user likes a particular exercise, the system suggests a method to maximize the effects of that exercise. In this way, advice to maximize the effects of exercise can be provided based on the user's exercise data.

[0087] The mental health support system may further include a communication suggestion unit that estimates the user's emotions and suggests appropriate communication methods based on the estimated emotions. For example, if the user feels lonely, the system may suggest communication methods to encourage communication with friends and family. If the user feels stressed, the system may suggest communication methods to reduce stress. If the user feels happy, the system may suggest communication methods to share that happiness. In this way, social connections can be strengthened by suggesting communication methods that correspond to the user's emotions.

[0088] The mental health support system may further include an activity suggestion unit that suggests new activities based on the user's hobbies and interests. For example, if the user likes outdoor activities, new hiking trails and campsites may be suggested. If the user is interested in art, new art classes and workshops may be suggested. If the user enjoys cooking, new recipes and cooking classes may be suggested. In this way, the quality of life can be improved by suggesting new activities based on the user's hobbies and interests.

[0089] The mental health support system may further include a resource provider that estimates the user's emotions and provides appropriate mental health resources based on the estimated emotions. For example, if the user is feeling anxious, online resources or books to alleviate anxiety may be provided. If the user is feeling stressed, resources for stress management may be provided. If the user is feeling lonely, resources for online communities or support groups may be provided. In this way, providing mental health resources according to the user's emotions can help stabilize emotions.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The dialogue unit shares the causes of stress and anxiety through dialogue with the user. For example, the user tells the AI, "I've been busy at work lately and I'm feeling stressed." Step 2: The advice provider provides appropriate advice and techniques based on the stress and anxiety factors shared by the dialogue component. For example, the generative AI might provide advice such as, "Take a deep breath and relax. Inhale slowly and exhale slowly." Step 3: The trend tracking unit tracks trends in daily activities and emotions. For example, if a user records their daily emotions and events like in a diary, the generation AI analyzes the data and understands changes in the user's emotions. Step 4: The specialist referral unit tracks changes and progress in the user's mental health and provides specialist referrals and support as needed. For example, if a user has been feeling stressed for a long period of time, the generative AI may suggest, "You may need specialist support. Please consider talking to a counselor."

[0092] 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.

[0093] 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.

[0094] 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.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 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.

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

[0098] The 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.

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

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

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

[0102] Fig. 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.

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

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

[0105] In the 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.

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

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

[0108] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0109] The data processing system 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.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

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

[0113] The 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.

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

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

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

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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."

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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]

[0159] 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 dialogue section that shares the causes of stress and anxiety through dialogue with the user; an advice providing unit that provides appropriate advice and techniques based on the stress and anxiety factors shared by the dialogue unit; a trend tracking unit that tracks trends in daily activities and emotions; and a specialist referral unit that tracks changes and progress in the user's mental health and provides specialist referrals and support as needed. A system characterized by:

2. The dialogue unit Analyzing the tone of voice and speaking style of the user, detecting changes in the user's emotions in real time, and adjusting the content of the dialogue.

2. The system of claim 1.

3. The advice providing unit Monitoring the user's physiological data in real time and adjusting advice based thereon 2. The system of claim 1.

4. The trend tracking unit Analyzing data collected from the user's smart device to understand the user's daily activity patterns in detail 2. The system of claim 1.

5. The expert introduction department: providing a dashboard that details the changes in the user's mental health and visualizes the progress; 2. The system of claim 1.

6. The dialogue unit Estimating the user's emotion and conducting a dialogue according to the emotion 2. The system of claim 1.

7. The advice providing unit Providing the advice according to the emotion of the user to stabilize the emotion 2. The system of claim 1.

8. The trend tracking unit Propose community support based on the user's emotional trends and strengthen social connections 2. The system of claim 1.

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