System

Abstract

An object of a system according to an embodiment is to support mental health through natural conversation with a user.SOLUTION: A system according to an embodiment includes a speech generation unit, a mental health analysis unit, and a support provision unit. The conversation generation unit generates a natural conversation with the user. The mental health analysis unit analyzes the conversation content generated by the conversation generation unit. The support providing unit provides appropriate mental health support on the basis of a result analyzed by the mental health analysis 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 do not adequately generate natural conversations to support users' mental health, and there is room for improvement.

[0005] The system according to the embodiment aims to support mental health through natural conversations with the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a conversation generation unit, a mental health analysis unit, and a support provision unit. The conversation generation unit generates natural conversation with a user. The mental health analysis unit analyzes the content of the conversation generated by the conversation generation unit. The support provision unit provides appropriate mental health support based on the results of the analysis by the mental health analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can support mental health through natural conversation with the user. [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) A mental health support system according to an embodiment of the present invention is a system in which a generative AI provides mental health support through natural conversations with a user, thereby alleviating the user's mental disorders and providing peace of mind.

[0029] A mental health support system according to an embodiment includes a conversation generation unit, a mental health analysis unit, and a support provision unit. The conversation generation unit generates natural conversations with a user. For example, the generation AI understands what the user says and generates an appropriate response. For example, if the user says, "I've been feeling stressed lately," the generation AI responds with, "That's tough. What specifically is causing you stress?" The mental health analysis unit analyzes the conversation content generated by the conversation generation unit. For example, the generation AI analyzes the user's mental health status from the user's comments and identifies the cause of stress. For example, if the user says, "I haven't been able to sleep lately," the generation AI provides advice such as, "Try taking deep breaths to relax." The support provision unit provides appropriate mental health support based on the results of the analysis by the mental health analysis unit. For example, the generation AI identifies the cause of stress from the user's comments and suggests ways to deal with it. For example, if the user says, "I'm feeling stressed because of work pressure," the generation AI provides specific advice such as, "Why don't you reconsider your work priorities?" As a result, the mental health support system according to the embodiment can provide mental health support through natural conversation with the user.

[0030] The conversation generation unit can refer to the user's past conversation history and realize personalized dialogue tailored to each individual user. In the conversation generation unit, for example, the generation AI refers to the user's past conversation history and generates a response based on the user's preferences and interests. For example, it may revisit a hobby that the user previously mentioned. In addition, the conversation generation unit can analyze the user's past conversation history to understand the user's mental health state. For example, it may reconfirm the causes of stress that the user previously mentioned. In addition, the conversation generation unit can realize personalized dialogue tailored to the user's preferences and interests based on the user's past conversation history. For example, it may revisit a favorite movie that the user previously mentioned. This makes it possible to realize personalized dialogue tailored to the user.

[0031] The conversation generation unit analyzes the user's non-verbal communication to achieve more natural dialogue. In the conversation generation unit, for example, the generation AI uses a camera to analyze the user's facial expressions and estimate their emotional state. For example, if the user is smiling while speaking, the generation AI generates a positive response. In addition, the conversation generation unit analyzes the user's gestures and estimates their emotional state. For example, if the user is waving, the generation AI estimates that the user is excited and adjusts the response. In addition, the conversation generation unit analyzes the user's gaze and estimates their emotional state. For example, if the user is looking away, the generation AI estimates that the user is feeling anxious and adjusts the response. In this way, the user's non-verbal communication can be analyzed to achieve more natural dialogue.

[0032] The conversation generation unit develops a generation AI that is compatible with different languages ​​and cultures, making it possible to provide natural conversations to global users. For example, the generation AI uses multilingual natural language processing technology to realize dialogue in different languages. For example, it supports multiple languages ​​such as English, French, and Chinese. The conversation generation unit also generates responses that take different cultural backgrounds into account. For example, it uses culturally appropriate expressions and gestures. The conversation generation unit also learns datasets that enable the generation AI to support different languages ​​and cultures, making it possible to provide natural conversations to global users. For example, it generates conversation scenarios based on different languages ​​and cultures. This makes it possible to provide natural conversations that are compatible with different languages ​​and cultures.

[0033] The mental health analysis unit takes into account the user's lifestyle and environmental data to analyze the user's mental health status and provide more accurate support. For example, the generation AI analyzes the user's lifestyle data (e.g., sleep patterns and food records) to assess the user's mental health status. For example, if the user continues to experience lack of sleep, the generation AI recommends rest. The mental health analysis unit also analyzes the user's environmental data (e.g., living environment and work environment) to assess the user's mental health status. For example, if workplace stress is high, the generation AI suggests stress management methods. The mental health analysis unit also analyzes the user's lifestyle data and environmental data to comprehensively assess the user's mental health status. For example, if lack of sleep and workplace stress overlap, the generation AI suggests comprehensive measures. This allows for more accurate mental health support to be provided, taking into account the user's lifestyle and environmental data.

[0034] The mental health analysis unit can monitor a user's mental health status over the long term, analyze trends, and provide preventative support. For example, the generation AI in the mental health analysis unit monitors a user's mental health status over the long term and analyzes trends. For example, regular check-ins are performed to track changes in mental health. The generation AI in the mental health analysis unit also analyzes trends in a user's mental health status and provides preventative support. For example, if stress is on the rise, the generation AI will suggest countermeasures early on. The mental health analysis unit also builds a system in which the generation AI monitors a user's mental health status over the long term, analyzes trends, and provides preventative support. For example, changes in mental health are tracked in real time and alerts are issued as necessary. This makes it possible to monitor a user's mental health status over the long term and provide preventative support.

[0035] The mental health analysis unit analyzes the user's physical health data and can provide support linked to mental health. In the mental health analysis unit, for example, the generation AI analyzes the user's heart rate data and evaluates their mental health state. For example, if the heart rate is high, the generation AI suggests relaxing. In the mental health analysis unit, the generation AI analyzes the user's sleep patterns and evaluates their mental health state. For example, if the user has been experiencing a lack of sleep, the generation AI recommends resting. In the mental health analysis unit, the generation AI comprehensively analyzes the user's physical health data and provides support linked to mental health. For example, the generation AI combines and analyzes heart rate and sleep patterns to evaluate the user's overall health state. This allows the user's physical health data to be analyzed and support linked to mental health to be provided.

[0036] The mental health analysis unit can develop generative AI specialized for different mental health problems and provide individualized support. For example, the mental health analysis unit can develop a generative AI specialized for anxiety to analyze a user's anxiety state and provide appropriate support. For example, it can suggest relaxation methods to a user who is feeling anxious. The mental health analysis unit can also develop a generative AI specialized for depression to analyze a user's depression state and provide appropriate support. For example, it can suggest counseling to a user who is feeling depressed. The mental health analysis unit can also develop a generative AI specialized for stress to analyze a user's stress state and provide appropriate support. For example, it can suggest stress management methods to a user who is feeling stressed. This makes it possible to provide individualized support specialized for different mental health problems.

[0037] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0038] The mental health support system can also suggest relaxation methods based on the user's hobbies and interests. For example, if the user likes music, it can suggest music for relaxation. If the user likes exercise, it can suggest exercise methods for relieving stress. Furthermore, if the user likes reading, it can suggest books that are relaxing. In this way, it is possible to provide relaxation methods that match the user's individual hobbies and interests.

[0039] The mental health support system can also provide advice to improve the user's lifestyle. For example, if the user has an irregular sleep pattern, it can suggest regular sleep habits. If the user has an unhealthy diet, it can suggest a balanced diet. Furthermore, if the user does not exercise enough, it can suggest ways to incorporate daily exercise. This can improve the user's lifestyle and improve their mental health.

[0040] The mental health support system can also provide support to strengthen the user's social connections. For example, if the user feels lonely, it can introduce online communities and support groups. If the user is struggling with relationships with friends or family, it can provide advice on improving communication skills. It can also suggest ways for the user to build social connections through new hobbies or activities. This can strengthen the user's social connections and improve their mental health.

[0041] The mental health support system can also analyze the user's physical health data and provide fitness plans linked to mental health. For example, it can analyze the user's heart rate and exercise data to suggest an appropriate exercise plan. It can also analyze the user's sleep data and provide advice on improving sleep quality. It can also analyze the user's dietary data and suggest a balanced meal plan. This allows for comprehensive support of the user's physical and mental health.

[0042] The mental health support system can also provide mental health support that takes into account the user's work environment. For example, if the user is feeling stressed at work, advice on stress management is provided. If the user is having trouble with interpersonal relationships at work, advice on improving communication skills is provided. Furthermore, if the user is having trouble with the workload at work, suggestions can be made to improve work efficiency or to reassess priorities. In this way, mental health support can be provided that is tailored to the user's work environment.

[0043] The processing flow of the first embodiment will be briefly explained below.

[0044] Step 1: The conversation generation unit generates a natural conversation with the user. For example, the generation AI understands what the user says and generates an appropriate response. For example, if the user says, "I've been feeling very stressed lately," the generation AI will respond by saying, "That's tough. What specifically is causing you stress?" Step 2: The mental health analysis unit analyzes the conversation content generated by the conversation generation unit. For example, the generation AI analyzes the user's mental health state from their comments and identifies the cause of stress. For example, if the user says, "I haven't been able to sleep lately," the generation AI will provide advice such as, "Try taking deep breaths to relax." Step 3: The support provider provides appropriate mental health support based on the results of the analysis by the mental health analysis unit. For example, the generation AI identifies the cause of stress from the user's comments and suggests ways to deal with it. For example, if the user says, "I'm feeling stressed because of work pressure," the generation AI will provide specific advice such as, "Why don't you reconsider your work priorities?"

[0045] (Example 2) A mental health support system according to an embodiment of the present invention is a system in which a generative AI provides mental health support through natural conversations with a user, thereby alleviating the user's mental disorders and providing peace of mind.

[0046] A mental health support system according to an embodiment includes a conversation generation unit, a mental health analysis unit, and a support provision unit. The conversation generation unit generates natural conversations with a user. For example, the generation AI understands what the user says and generates an appropriate response. For example, if the user says, "I've been feeling stressed lately," the generation AI responds with, "That's tough. What specifically is causing you stress?" The mental health analysis unit analyzes the conversation content generated by the conversation generation unit. For example, the generation AI analyzes the user's mental health status from the user's comments and identifies the cause of stress. For example, if the user says, "I haven't been able to sleep lately," the generation AI provides advice such as, "Try taking deep breaths to relax." The support provision unit provides appropriate mental health support based on the results of the analysis by the mental health analysis unit. For example, the generation AI identifies the cause of stress from the user's comments and suggests ways to deal with it. For example, if the user says, "I'm feeling stressed because of work pressure," the generation AI provides specific advice such as, "Why don't you reconsider your work priorities?" As a result, the mental health support system according to the embodiment can provide mental health support through natural conversation with the user.

[0047] The conversation generation unit can analyze the user's tone of voice and speaking rate, estimate their emotional state, and adjust responses accordingly. In the conversation generation unit, for example, the generation AI analyzes the user's tone of voice to estimate their emotional state. For example, if the user speaks in a calm tone, the generation AI generates a relaxed response. In the conversation generation unit, the generation AI also analyzes the user's speaking rate to estimate their emotional state. For example, if the user speaks quickly, the generation AI estimates that the user is stressed and generates a relaxing response. In the conversation generation unit, the generation AI analyzes the user's tone of voice and speaking rate in combination to comprehensively estimate their emotional state. For example, if the user speaks slowly in a calm tone, the generation AI estimates that the user is relaxed and adjusts the response. This makes it possible to generate responses that correspond to the user's emotional state.

[0048] The conversation generation unit can refer to the user's past conversation history and realize personalized dialogue tailored to each individual user. In the conversation generation unit, for example, the generation AI refers to the user's past conversation history and generates a response based on the user's preferences and interests. For example, it may revisit a hobby that the user previously mentioned. In addition, the conversation generation unit can analyze the user's past conversation history to understand the user's mental health state. For example, it may reconfirm the causes of stress that the user previously mentioned. In addition, the conversation generation unit can realize personalized dialogue tailored to the user's preferences and interests based on the user's past conversation history. For example, it may revisit a favorite movie that the user previously mentioned. This makes it possible to realize personalized dialogue tailored to the user.

[0049] The conversation generation unit can use the emotion estimation function to analyze the user's emotions in real time and generate a response that corresponds to the emotion. For example, in the conversation generation unit, the generation AI analyzes the content and tone of the user's speech and estimates the emotion in real time. For example, if the user speaks in a sad voice, the generation AI generates a comforting response. In addition, the conversation generation unit analyzes the user's facial expressions and estimates the emotion in real time. For example, if the user speaks with a smile, the generation AI generates a positive response. In addition, in the conversation generation unit, the generation AI analyzes the user's biometric data (heart rate and electrodermal activity) and estimates the emotion in real time. For example, if the user's heart rate is rising, the generation AI estimates that the user is nervous and generates a response that encourages relaxation. This allows responses that correspond to the user's emotions to be generated in real time.

[0050] The conversation generation unit analyzes the user's non-verbal communication to achieve more natural dialogue. In the conversation generation unit, for example, the generation AI uses a camera to analyze the user's facial expressions and estimate their emotional state. For example, if the user is smiling while speaking, the generation AI generates a positive response. In addition, the conversation generation unit analyzes the user's gestures and estimates their emotional state. For example, if the user is waving, the generation AI estimates that the user is excited and adjusts the response. In addition, the conversation generation unit analyzes the user's gaze and estimates their emotional state. For example, if the user is looking away, the generation AI estimates that the user is feeling anxious and adjusts the response. In this way, the user's non-verbal communication can be analyzed to achieve more natural dialogue.

[0051] The conversation generation unit develops a generation AI that is compatible with different languages ​​and cultures, making it possible to provide natural conversations to global users. For example, the generation AI uses multilingual natural language processing technology to realize dialogue in different languages. For example, it supports multiple languages ​​such as English, French, and Chinese. The conversation generation unit also generates responses that take different cultural backgrounds into account. For example, it uses culturally appropriate expressions and gestures. The conversation generation unit also learns datasets that enable the generation AI to support different languages ​​and cultures, making it possible to provide natural conversations to global users. For example, it generates conversation scenarios based on different languages ​​and cultures. This makes it possible to provide natural conversations that are compatible with different languages ​​and cultures.

[0052] The conversation generation unit can use the emotion estimation function to monitor the stress level felt by the user during a conversation and generate a response to reduce stress. For example, the conversation generation unit uses the generation AI to analyze the user's tone of voice and speaking speed to estimate the stress level. For example, if the user is speaking quickly, the generation AI infers that the user is feeling stressed and generates a response to help them relax. The conversation generation unit also uses the generation AI to analyze the user's facial expressions to estimate the stress level. For example, if the user is frowning, the generation AI infers that the user is feeling stressed and adjusts the response. The conversation generation unit also uses the generation AI to analyze the user's biometric data (heart rate and electrodermal activity) to estimate the stress level. For example, if the user's heart rate is rising, the generation AI infers that the user is feeling stressed and generates a response to help them relax. This makes it possible to monitor the user's stress level and generate a response to reduce stress.

[0053] The mental health analysis unit takes into account the user's lifestyle and environmental data to analyze the user's mental health status and provide more accurate support. For example, the generation AI analyzes the user's lifestyle data (e.g., sleep patterns and food records) to assess the user's mental health status. For example, if the user continues to experience lack of sleep, the generation AI recommends rest. The mental health analysis unit also analyzes the user's environmental data (e.g., living environment and work environment) to assess the user's mental health status. For example, if workplace stress is high, the generation AI suggests stress management methods. The mental health analysis unit also analyzes the user's lifestyle data and environmental data to comprehensively assess the user's mental health status. For example, if lack of sleep and workplace stress overlap, the generation AI suggests comprehensive measures. This allows for more accurate mental health support to be provided, taking into account the user's lifestyle and environmental data.

[0054] The mental health analysis unit can monitor a user's mental health status over the long term, analyze trends, and provide preventative support. For example, the generation AI in the mental health analysis unit monitors a user's mental health status over the long term and analyzes trends. For example, regular check-ins are performed to track changes in mental health. The generation AI in the mental health analysis unit also analyzes trends in a user's mental health status and provides preventative support. For example, if stress is on the rise, the generation AI will suggest countermeasures early on. The mental health analysis unit also builds a system in which the generation AI monitors a user's mental health status over the long term, analyzes trends, and provides preventative support. For example, changes in mental health are tracked in real time and alerts are issued as necessary. This makes it possible to monitor a user's mental health status over the long term and provide preventative support.

[0055] The mental health analysis unit analyzes the user's physical health data and can provide support linked to mental health. In the mental health analysis unit, for example, the generation AI analyzes the user's heart rate data and evaluates their mental health state. For example, if the heart rate is high, the generation AI suggests relaxing. In the mental health analysis unit, the generation AI analyzes the user's sleep patterns and evaluates their mental health state. For example, if the user has been experiencing a lack of sleep, the generation AI recommends resting. In the mental health analysis unit, the generation AI comprehensively analyzes the user's physical health data and provides support linked to mental health. For example, the generation AI combines and analyzes heart rate and sleep patterns to evaluate the user's overall health state. This allows the user's physical health data to be analyzed and support linked to mental health to be provided.

[0056] The mental health analysis unit can develop generative AI specialized for different mental health problems and provide individualized support. For example, the mental health analysis unit can develop a generative AI specialized for anxiety to analyze a user's anxiety state and provide appropriate support. For example, it can suggest relaxation methods to a user who is feeling anxious. The mental health analysis unit can also develop a generative AI specialized for depression to analyze a user's depression state and provide appropriate support. For example, it can suggest counseling to a user who is feeling depressed. The mental health analysis unit can also develop a generative AI specialized for stress to analyze a user's stress state and provide appropriate support. For example, it can suggest stress management methods to a user who is feeling stressed. This makes it possible to provide individualized support specialized for different mental health problems.

[0057] The mental health analysis unit uses the emotion estimation function to provide support for strengthening the positive emotions felt by the user, thereby improving mental health. In the mental health analysis unit, for example, the generation AI analyzes the user's positive emotions in real time and generates a response to strengthen those emotions. For example, if the user is feeling joy, the generation AI generates a response that further brings out that joy. The mental health analysis unit also provides support for the generation AI to strengthen the user's positive emotions. For example, if the user expresses gratitude, the generation AI generates a response that further deepens that gratitude. The mental health analysis unit also builds a system that provides support for the generation AI to strengthen the user's positive emotions. For example, if the user is feeling satisfied, the generation AI generates a response that further enhances that satisfaction. This strengthens the user's positive emotions and improves mental health.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The mental health support system can also suggest relaxation methods based on the user's hobbies and interests. For example, if the user likes music, it can suggest music for relaxation. If the user likes exercise, it can suggest exercise methods for relieving stress. Furthermore, if the user likes reading, it can suggest books that are relaxing. In this way, it is possible to provide relaxation methods that match the user's individual hobbies and interests.

[0060] The mental health support system can also estimate the user's emotions and suggest appropriate relaxation techniques based on the estimated emotions. For example, if the user is feeling anxious, the system can suggest deep breathing or meditation. If the user is feeling angry, the system can suggest exercise or stretching. If the user is feeling sad, the system can suggest relaxing music or art therapy. This makes it possible to provide relaxation techniques tailored to the user's emotions.

[0061] The mental health support system can also provide advice to improve the user's lifestyle. For example, if the user has an irregular sleep pattern, it can suggest regular sleep habits. If the user has an unhealthy diet, it can suggest a balanced diet. Furthermore, if the user does not exercise enough, it can suggest ways to incorporate daily exercise. This can improve the user's lifestyle and improve their mental health.

[0062] The mental health support system can also estimate the user's emotions and provide appropriate mental health resources based on the estimated emotions. For example, if the user is feeling stressed, online resources for stress management can be provided. If the user is feeling anxious, a counseling service can be introduced to alleviate the anxiety. Furthermore, if the user is depressed, professional mental health support can be provided. In this way, appropriate mental health resources can be provided according to the user's emotions.

[0063] The mental health support system can also provide support to strengthen the user's social connections. For example, if the user feels lonely, it can introduce online communities and support groups. If the user is struggling with relationships with friends or family, it can provide advice on improving communication skills. It can also suggest ways for the user to build social connections through new hobbies or activities. This can strengthen the user's social connections and improve their mental health.

[0064] The mental health support system can also estimate the user's emotions and suggest an appropriate relaxation environment based on the estimated emotions. For example, if the user feels like relaxing, it can suggest a quiet place or relaxation in nature. If the user feels like concentrating, it can suggest environmental settings to improve concentration. Furthermore, if the user feels like refreshing, it can suggest places and activities that will help them refresh. In this way, it is possible to provide a relaxation environment that suits the user's emotions.

[0065] The mental health support system can also analyze the user's physical health data and provide fitness plans linked to mental health. For example, it can analyze the user's heart rate and exercise data to suggest an appropriate exercise plan. It can also analyze the user's sleep data and provide advice on improving sleep quality. It can also analyze the user's dietary data and suggest a balanced meal plan. This allows for comprehensive support of the user's physical and mental health.

[0066] The mental health support system can also estimate the user's emotions and suggest appropriate mental health exercises based on the estimated emotions. For example, if the user is feeling stressed, it can suggest breathing exercises or meditation to relieve stress. If the user is feeling anxious, it can suggest mindfulness exercises to reduce anxiety. Furthermore, if the user is feeling depressed, it can suggest positive affirmations to lift the user's mood. This makes it possible to provide mental health exercises that correspond to the user's emotions.

[0067] The mental health support system can also provide mental health support that takes into account the user's work environment. For example, if the user is feeling stressed at work, advice on stress management is provided. If the user is having trouble with interpersonal relationships at work, advice on improving communication skills is provided. Furthermore, if the user is having trouble with the workload at work, suggestions can be made to improve work efficiency or to reassess priorities. In this way, mental health support can be provided that is tailored to the user's work environment.

[0068] The mental health support system can also estimate the user's emotions and provide appropriate mental health resources based on the estimated emotions. For example, if the user is feeling stressed, online resources for stress management can be provided. If the user is feeling anxious, a counseling service can be introduced to alleviate the anxiety. Furthermore, if the user is depressed, professional mental health support can be provided. In this way, appropriate mental health resources can be provided according to the user's emotions.

[0069] The processing flow of the second embodiment will be briefly explained below.

[0070] Step 1: The conversation generation unit generates a natural conversation with the user. For example, the generation AI understands what the user says and generates an appropriate response. For example, if the user says, "I've been feeling very stressed lately," the generation AI will respond by saying, "That's tough. What specifically is causing you stress?" Step 2: The mental health analysis unit analyzes the conversation content generated by the conversation generation unit. For example, the generation AI analyzes the user's mental health state from their comments and identifies the cause of stress. For example, if the user says, "I haven't been able to sleep lately," the generation AI will provide advice such as, "Try taking deep breaths to relax." Step 3: The support provider provides appropriate mental health support based on the results of the analysis by the mental health analysis unit. For example, the generation AI identifies the cause of stress from the user's comments and suggests ways to deal with it. For example, if the user says, "I'm feeling stressed because of work pressure," the generation AI will provide specific advice such as, "Why don't you reconsider your work priorities?"

[0071] 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.

[0072] 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.

[0073] 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.

[0074] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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).

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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).

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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."

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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]

[0138] 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 conversation generation unit that generates natural conversation with a user; a mental health analysis unit that analyzes the conversation content generated by the conversation generation unit; a support providing unit that provides appropriate mental health support based on the results of the analysis by the mental health analysis unit. A system characterized by:

2. The conversation generation unit Analyzing the user's tone of voice and speaking rate to estimate their emotional state and tailor responses 2. The system of claim 1.

3. The conversation generation unit Analyzing the user's non-verbal communication to realize more natural dialogue 2. The system of claim 1.

4. The mental health analysis unit Analyze the mental health status of the user by taking into account their lifestyle and environmental data, and provide more accurate support.

2. The system of claim 1.

5. The mental health analysis unit Using an emotion estimation function, the emotional state of the user is analyzed in real time, and the mental health support according to the emotion is provided.

2. The system of claim 1.

6. The conversation generation unit By referring to the user's past conversation history, a personalized dialogue tailored to each individual user is realized.

2. The system of claim 1.

7. The mental health analysis unit Monitor the user's mental health over time, analyze trends, and provide preventative support 2. The system of claim 1.

8. The mental health analysis unit Analyzing the user's physical health data and providing support linked to mental health 2. The system of claim 1.

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