system

Abstract

The system according to the embodiment aims to provide real-time support to people who have difficulty controlling their emotions. [Solution] A system according to an embodiment includes a detection unit, an advice unit, and an intervention unit. The detection unit detects the emotion of a subject using voice recognition or facial expression recognition. The advice unit provides advice based on the emotion detected by the detection unit. The intervention unit notifies the subject of the advice provided by the advice unit in real time.

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] Existing technologies are not sufficient to provide real-time support to people who have difficulty managing their emotions, and there is room for improvement.

[0005] The system according to the embodiment aims to provide real-time support to people who have difficulty controlling their emotions. [Means for solving the problem]

[0006] The system according to the embodiment includes a detection unit, an advice unit, and an intervention unit. The detection unit detects the emotion of a subject using voice recognition or facial expression recognition. The advice unit provides advice based on the emotion detected by the detection unit. The intervention unit notifies the subject of the advice provided by the advice unit in real time. [Effects of the Invention]

[0007] The system according to the embodiment can provide real-time support to people who have difficulty controlling their emotions. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An emotion regulation system according to an embodiment of the present invention supports people who suffer from poor emotional regulation, such as those who are unable to control their emotions and end up hurting others or blaming themselves. When a subject attempts to speak or act impulsively, the emotion regulation AI provides calming encouragement and advice tailored to the situation. First, the subject's daily actions and comments are recorded to identify situations in which they are likely to become emotional. For example, the subject's emotions are recorded in a diary-like format to understand the emotions they are likely to feel in specific situations. These records are then input into the emotion regulation AI for analysis. Next, the emotion regulation AI identifies situations in which the subject is likely to become emotional and provides advice tailored to the situation. For example, if the subject feels anger, the emotion regulation AI will encourage them to "take a deep breath and calm down." Similarly, if the subject feels sad, the AI ​​will provide advice such as "Try to talk to someone." Furthermore, the emotion regulation AI detects the moment when the subject is about to speak or act impulsively and intervenes in real time. For example, if a subject becomes angry and is about to make an aggressive remark toward someone, the emotion regulation AI will warn them, saying, "What you say may hurt the other person. Please think about it for a moment before speaking." This service helps people who struggle with emotion regulation to better control their emotions and improve their relationships with those around them. It also helps subjects learn how to control their emotions in a positive way without blaming themselves. In this way, the emotion regulation system helps people who struggle with emotional control to effectively manage their emotions and improve their relationships with those around them.

[0029] An emotion regulation system according to an embodiment includes a detection unit, an advice unit, and an intervention unit. The detection unit detects the emotion of a subject using voice recognition or facial expression recognition. For example, the detection unit uses voice recognition technology to analyze the tone of voice and speaking rate of the subject to detect the emotion. The detection unit can also use facial expression recognition technology to analyze changes in the subject's facial expression to detect the emotion. For example, the voice recognition technology analyzes voice data to detect changes in emotion in real time. The facial expression recognition technology analyzes facial expression data captured by a camera to detect changes in emotion. The advice unit provides appropriate advice based on the emotion detected by the detection unit. For example, if the subject is feeling angry, the advice unit may provide advice encouraging the subject to take a deep breath to calm down. If the subject is feeling sad, the advice unit may also suggest taking a walk to change their mood. For example, the advice unit analyzes the emotion data and generates appropriate advice. The intervention unit notifies the subject of the advice provided by the advice unit in real time. For example, the intervention unit may notify the subject of the advice provided by the advice unit via a smartphone or a wearable device. The intervention unit can also provide advice using a voice message or a text message. For example, the intervention unit can send a push notification to the subject's device to provide advice in real time. This allows the emotion regulation system according to the embodiment to detect the subject's emotions in real time and provide appropriate advice to support emotion regulation.

[0030] The emotion regulation system includes a recording unit that records the subject's daily actions or statements. The recording unit records the subject's daily actions and statements. For example, the recording unit records what actions the subject takes in what situations. The recording unit can also record what statements the subject makes. For example, the recording unit supports the subject in recording their emotions in a diary-like format. The recording unit saves the subject's actions and statements as digital data and uses it for later analysis. By recording the subject's daily actions and statements, it is possible to identify situations in which the subject is likely to become emotional. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the subject's actions and statements into AI, which then analyzes and records the data.

[0031] The emotion regulation system includes an analysis unit that analyzes recorded behaviors or statements and identifies situations in which the subject is likely to become emotional. The analysis unit analyzes the recorded behaviors and statements and identifies situations in which the subject is likely to become emotional. For example, the analysis unit uses a data analysis algorithm to identify situations in which the subject is likely to become emotional. The analysis unit can also analyze patterns of the subject's behavior and statements and identify triggers that make the subject likely to become emotional. For example, the analysis unit analyzes what emotions the subject is likely to feel in certain situations. By identifying situations in which the subject is likely to become emotional, appropriate advice can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input recorded data into AI, which can analyze the data and identify situations in which the subject is likely to become emotional.

[0032] The emotion regulation system includes a notification unit that notifies the subject via a smartphone or a wearable device. The notification unit notifies the subject via the smartphone or the wearable device. For example, the notification unit provides advice using a push notification on the smartphone. The notification unit can also notify using the vibration function of the wearable device. For example, the notification unit notifies the advice using a smartwatch. This makes it possible to intervene in real time by notifying via the smartphone or the wearable device. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can notify the smartphone or the wearable device of advice generated by AI.

[0033] The detection unit can detect the emotions of the subject using voice recognition or facial expression recognition. For example, the detection unit uses voice recognition technology to analyze the tone of the subject's voice and speaking rate to detect emotions. For example, the detection unit analyzes voice data and detects changes in emotions in real time. The detection unit can also use facial expression recognition technology to analyze changes in the subject's facial expressions to detect emotions. For example, the detection unit analyzes facial expression data captured by a camera to detect changes in emotions. This improves the accuracy of emotion detection by using voice recognition and facial expression recognition. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input voice data and facial expression data into AI, which then detects emotions.

[0034] The advice unit can provide appropriate advice based on the detected emotion. For example, if the subject is feeling angry, the advice unit can provide advice encouraging the subject to take a deep breath to calm down. For example, the advice unit can analyze emotion data and generate appropriate advice. Furthermore, if the subject is feeling sad, the advice unit can also suggest taking a walk to change their mood. For example, the advice unit can generate advice based on emotion data and provide it to the subject. In this way, by providing advice based on the detected emotion, it is possible to support the subject in regulating their emotions. Some or all of the above-described processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input emotion data into AI, which can then generate appropriate advice.

[0035] The detection unit can estimate the subject's emotions and improve detection accuracy based on the estimated emotions. The detection unit can, for example, analyze the subject's voice tone and speaking rate to detect changes in emotions in real time. For example, the detection unit can analyze voice data to detect changes in emotions. The detection unit can also capture subtle changes in the subject's facial expressions with a high-precision camera to estimate emotions. For example, the detection unit can analyze facial expression data to estimate changes in emotions. Furthermore, the detection unit can refer to the subject's past emotional data to more accurately estimate the subject's current emotions. For example, the detection unit can learn patterns of emotional changes based on past emotional data to improve detection accuracy. This improves detection accuracy based on the estimated emotions, enabling more accurate emotion detection. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without AI. For example, the detection unit can input voice data and facial expression data into AI, which can then estimate emotions.

[0036] During detection, the detection unit can improve the detection algorithm by referring to the subject's past emotional data. For example, the detection unit stores in a database situations in which the subject has become emotional in the past and predicts the subject's emotions in similar situations. For example, the detection unit learns patterns of emotional changes based on the past emotional data and improves detection accuracy. The detection unit can also store the subject's past emotional data in the cloud and share it with other devices to optimize the detection algorithm. For example, the detection unit accesses a database on the cloud and improves the detection algorithm by referring to the past emotional data. By referring to the past emotional data, the accuracy of the detection algorithm is improved. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without AI. For example, the detection unit can input past emotional data into AI, which then optimizes the detection algorithm.

[0037] The detection unit can detect emotions by taking into account the environmental sounds and background sounds of the subject. For example, the detection unit measures the noise level in the environment where the subject is present and adjusts the accuracy of emotion detection. For example, the detection unit monitors the noise level in real time and incorporates it into the emotion detection algorithm. The detection unit can also detect specific sounds (e.g., yelling or laughter) from background sounds and reflect them in emotion estimation. For example, the detection unit analyzes background sounds, detects specific sounds, and uses them to estimate emotions. Furthermore, the detection unit can monitor changes in environmental sounds in real time and incorporate them into the emotion detection algorithm. For example, the detection unit detects changes in environmental sounds and estimates emotions based on that information. This improves the accuracy of emotion detection by taking environmental sounds and background sounds into consideration. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without AI. For example, the detection unit can input environmental sound data into AI, which then detects emotions.

[0038] The detection unit can estimate the subject's emotions and adjust the timing of detection based on the estimated emotions. For example, the detection unit can predict time periods when the subject is likely to become emotional and focus on detecting emotions during those time periods. For example, the detection unit can identify time periods when the subject is likely to become emotional based on past data and increase the detection frequency during those time periods. The detection unit can also immediately detect sudden changes in the subject's emotions. For example, the detection unit can detect sudden changes in emotions and respond in real time. Furthermore, the detection unit can reduce the detection frequency when the subject's emotions are stable, thereby saving resources. For example, the detection unit can set a low detection frequency when the emotions are stable. This enables efficient emotion detection by adjusting the detection timing based on the estimated emotions. Some or all of the above-described processing in the detection unit may be performed using, or without, AI. For example, the detection unit can input emotion data into AI, which can then adjust the detection timing.

[0039] The detection unit can detect emotions by analyzing the subject's physical movements and posture during detection. The detection unit, for example, monitors changes in the subject's walking speed and posture to estimate changes in emotions. For example, the detection unit analyzes walking speed and posture data to detect changes in emotions. The detection unit can also analyze the subject's hand movements and gestures and reflect this in emotion estimation. For example, the detection unit analyzes hand movement and gesture data to estimate changes in emotions. Furthermore, the detection unit can detect changes in the subject's sitting or standing style to predict changes in emotions. For example, the detection unit analyzes sitting and standing style data to detect changes in emotions. This improves the accuracy of emotion detection by analyzing physical movements and posture. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without AI. For example, the detection unit can input physical movement and posture data into AI, which can then detect emotions.

[0040] The detection unit can detect emotions using the subject's biometric information during detection. The detection unit, for example, monitors fluctuations in the subject's heart rate in real time to detect changes in emotions. For example, the detection unit analyzes heart rate data to detect changes in emotions. The detection unit can also measure the subject's electrodermal response and estimate changes in emotions. For example, the detection unit analyzes electrodermal response data to estimate changes in emotions. Furthermore, the detection unit can analyze the subject's breathing pattern to predict changes in emotions. For example, the detection unit analyzes breathing pattern data to detect changes in emotions. This improves the accuracy of emotion detection by using biometric information. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input biometric information data into AI, which can then detect emotions.

[0041] The advice unit can estimate the emotion of the subject and adjust the content of the advice based on the estimated emotion. For example, if the subject is feeling angry, the advice unit provides advice encouraging the subject to take a deep breath to calm down. For example, the advice unit analyzes emotion data and generates appropriate advice. Furthermore, if the subject is feeling sad, the advice unit can suggest taking a walk to change their mood. For example, the advice unit generates advice based on emotion data and provides it to the subject. Furthermore, if the subject is feeling anxious, the advice unit can provide advice recommending meditation to relax. For example, the advice unit generates advice based on emotion data and provides it to the subject. In this way, by adjusting the content of the advice based on the estimated emotion, more effective advice can be provided. Some or all of the above-described processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input emotion data to AI, which generates appropriate advice.

[0042] When providing advice, the advice unit can improve the effectiveness of the advice by referring to the subject's past reaction data. For example, the advice unit can re-provide advice that was effective for the subject in the past. For example, the advice unit selects the most effective advice based on the past reaction data. The advice unit can also analyze the subject's past reaction data and customize the content of the advice. For example, the advice unit adjusts the content of the advice based on the past reaction data. In this way, the effectiveness of the advice is optimized by referring to the past reaction data. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input past reaction data into AI, which can optimize the effectiveness of the advice.

[0043] When providing advice, the advice unit can change the format of the advice depending on the current situation and environment of the subject. For example, when the subject is in a public place, the advice unit provides the advice by text message. For example, the advice unit selects a format suitable for providing advice in a public place. Furthermore, when the subject is at home, the advice unit can also provide advice by voice. For example, the advice unit selects a format suitable for providing advice at home. Furthermore, when the subject is driving, the advice unit can also provide concise and safe advice. For example, the advice unit selects a format suitable for providing advice while driving. In this way, by changing the format of advice depending on the situation and environment, more appropriate advice is provided. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input situation and environmental data into AI, which can adjust the format of the advice.

[0044] The advice unit can estimate the emotions of the subject and adjust the timing of advice based on the estimated emotions. The advice unit, for example, provides advice during times when the subject is likely to become emotional. For example, the advice unit identifies times when the subject is likely to become emotional based on past data and provides advice during those times. The advice unit can also provide advice immediately when the subject's emotions change suddenly. For example, the advice unit detects sudden changes in emotions and provides advice in real time. Furthermore, the advice unit can reduce the frequency of advice when the subject's emotions are stable, thereby saving resources. For example, the advice unit sets the frequency of advice low when the emotions are stable. In this way, more effective advice can be provided by adjusting the timing of advice based on the estimated emotions. Some or all of the above-described processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input emotion data into AI, which can adjust the timing of advice.

[0045] When providing advice, the advice unit can customize the way the advice is expressed based on the subject's preferences and tastes. For example, the advice unit provides the advice with the subject's favorite music playing in the background. For example, the advice unit provides the advice while playing the subject's favorite music. The advice unit can also provide the advice using language preferred by the subject. For example, the advice unit provides the advice using language that matches the subject's preferences. Furthermore, the advice unit can also provide the advice in the voice of a character preferred by the subject. For example, the advice unit provides the advice using the voice of a character preferred by the subject. In this way, by customizing the way the advice is expressed based on the subject's preferences and tastes, more acceptable advice is provided. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input preference and taste data into AI, which can customize the way the advice is expressed.

[0046] When providing advice, the advice unit can provide the advice while taking into consideration the social relationships of the subject. For example, if the subject is with a friend, the advice unit provides advice to ask the friend for cooperation. For example, the advice unit provides advice to ask the friend for cooperation. Furthermore, if the subject is with family, the advice unit can also provide advice to ask the family to listen to the subject. For example, the advice unit provides advice to ask the family to listen to the subject. Furthermore, if the subject is alone, the advice unit can also suggest a way for the subject to control their own emotions. For example, the advice unit suggests a way for the subject to control their own emotions. In this way, more appropriate advice is provided by taking social relationships into consideration. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input social relationship data into AI, which can then provide advice.

[0047] The intervention unit can estimate the subject's emotions and adjust the notification method based on the estimated emotions. For example, if the subject is feeling angry, the intervention unit sends a notification encouraging the subject to take a deep breath to calm down. For example, the intervention unit can notify the subject of advice to calm down based on the emotion data. Furthermore, if the subject is feeling sad, the intervention unit can also send a notification suggesting that the subject take a walk to change their mood. For example, the intervention unit can notify the subject of advice to change their mood based on the emotion data. Furthermore, if the subject is feeling anxious, the intervention unit can send a notification recommending meditation to relax. For example, the intervention unit can notify the subject of advice to relax based on the emotion data. In this way, by adjusting the notification method based on the estimated emotions, more effective notifications can be provided. Some or all of the above-described processing in the intervention unit may be performed using, for example, AI, or may be performed without using AI. For example, the intervention unit can input emotion data into AI, which can then adjust the notification method.

[0048] When sending a notification, the intervention unit can optimize the effectiveness of the notification by referring to the subject's past response data. For example, the intervention unit resends a notification that was effective for the subject in the past. For example, the intervention unit selects the most effective notification based on the past response data. The intervention unit can also analyze the subject's past response data and customize the content of the notification. For example, the intervention unit adjusts the content of the notification based on the past response data. In this way, the effectiveness of the notification is optimized by referring to the past response data. Some or all of the above-mentioned processing in the intervention unit may be performed using, for example, AI, or may be performed without using AI. For example, the intervention unit can input past response data into AI, which can optimize the effectiveness of the notification.

[0049] When notifying the subject, the intervention unit can change the notification format depending on the subject's current activity or situation. For example, if the subject is in a public place, the intervention unit notifies the subject by text message. For example, the intervention unit selects a format suitable for notification in a public place. Furthermore, if the subject is at home, the intervention unit can also notify the subject by voice. For example, the intervention unit selects a format suitable for notification at home. Furthermore, if the subject is driving, the intervention unit can also provide a concise and safe notification. For example, the intervention unit selects a format suitable for notification while driving. In this way, by changing the notification format depending on the activity or situation, more appropriate notification can be provided. Some or all of the above-mentioned processing in the intervention unit may be performed using, for example, AI, or may be performed without using AI. For example, the intervention unit can input activity and situation data into AI, which can adjust the notification format.

[0050] The intervention unit can estimate the subject's emotions and adjust the timing of notifications based on the estimated emotions. The intervention unit, for example, provides notifications during time periods when the subject is likely to become emotional. For example, the intervention unit identifies time periods when the subject is likely to become emotional based on past data and provides notifications during those time periods. The intervention unit can also provide instant notifications when the subject's emotions suddenly change. For example, the intervention unit detects sudden changes in emotions and provides notifications in real time. Furthermore, the intervention unit can reduce the frequency of notifications when the subject's emotions are stable, thereby saving resources. For example, the intervention unit sets the notification frequency low when the emotions are stable. This allows for more effective notifications by adjusting the timing of notifications based on the estimated emotions. Some or all of the above-described processing in the intervention unit may be performed using, for example, AI, or may be performed without AI. For example, the intervention unit can input emotion data into AI, which then adjusts the timing of notifications.

[0051] When notifying, the intervention unit can customize the notification method depending on the type of device of the subject. For example, if the subject is using a smartphone, the intervention unit can notify by push notification. For example, the intervention unit can provide advice using a push notification on the smartphone. Furthermore, if the subject is using a wearable device, the intervention unit can also notify by vibration. For example, the intervention unit can notify using the vibration function of the wearable device. Furthermore, if the subject is using a personal computer, the intervention unit can also notify by pop-up notification. For example, the intervention unit can provide advice using a pop-up notification on the personal computer. In this way, by customizing the notification method depending on the type of device, more appropriate notification can be provided. Some or all of the above-mentioned processing in the intervention unit may be performed using, for example, AI, or may be performed without using AI. For example, the intervention unit can input device type data into AI, which can customize the notification method.

[0052] When notifying, the intervention unit can select an appropriate notification method taking into account the location information of the subject. For example, if the subject is at home, the intervention unit notifies by voice. For example, the intervention unit selects a format suitable for notification at home. Furthermore, if the subject is out, the intervention unit can also notify by text message. For example, the intervention unit selects a format suitable for notification while out. Furthermore, if the subject is driving, the intervention unit can also provide a concise and safe notification. For example, the intervention unit selects a format suitable for notification while driving. In this way, more appropriate notification is provided by taking the location information into consideration. Some or all of the above-mentioned processing in the intervention unit may be performed using, for example, AI, or may be performed without using AI. For example, the intervention unit can input location information data into AI, which can select the notification method.

[0053] The recording unit can estimate the subject's emotion and adjust the content of the record based on the estimated emotion. For example, if the subject is feeling anger, the recording unit records the situation and cause in detail. For example, the recording unit records the situation in which the subject felt anger and the cause as text data. Furthermore, if the subject is feeling sad, the recording unit can also record the intensity and duration of the emotion. For example, the recording unit records the intensity and duration of the emotion when the subject felt sad as data. Furthermore, if the subject is feeling anxious, the recording unit can also record the cause and how to deal with it. For example, the recording unit records in detail the cause and how to deal with it when the subject felt anxious. This allows for more detailed recording by adjusting the content of the record based on the estimated emotion. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without AI. For example, the recording unit can input emotion data into AI, which can then adjust the content of the record.

[0054] During recording, the recording unit can improve the accuracy of the recording by referring to the subject's past behavioral data. For example, the recording unit stores situations in which the subject became emotional in the past in a database and records emotions in similar situations. For example, the recording unit learns patterns of emotional changes based on the past behavioral data and improves the recording accuracy. The recording unit can also improve the recording accuracy by storing the subject's past behavioral data in the cloud and sharing it with other devices. For example, the recording unit accesses a database on the cloud and improves the recording accuracy by referring to the past behavioral data. In this way, the recording accuracy is improved by referring to the past behavioral data. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the past behavioral data into AI, which can improve the recording accuracy.

[0055] The recording unit can take into consideration environmental information about the subject when recording. For example, the recording unit records weather information about the environment in which the subject is located and associates it with changes in emotion. For example, the recording unit records changes in emotion based on weather data. The recording unit can also record information about the location in which the subject is located and associate it with changes in emotion. For example, the recording unit records changes in emotion based on location data. Furthermore, the recording unit can record the sound environment around the subject and associate it with changes in emotion. For example, the recording unit records changes in emotion based on sound environment data. This improves the accuracy of recording by taking environmental information into consideration. Some or all of the above-mentioned processing in the recording unit can be performed using, for example, AI, or can be performed without using AI. For example, the recording unit can input environmental information data into AI, and the AI ​​can perform recording.

[0056] The recording unit can estimate the subject's emotions and adjust the recording frequency based on the estimated emotions. For example, the recording unit can increase the recording frequency during times when the subject is likely to become emotional. For example, the recording unit can identify times when the subject is likely to become emotional based on past data and increase the recording frequency during those times. The recording unit can also reduce the recording frequency when the subject's emotions are stable, thereby saving resources. For example, the recording unit can set the recording frequency low when the emotions are stable. Furthermore, the recording unit can immediately record when the subject's emotions suddenly change. For example, the recording unit can detect sudden changes in emotions and record in real time. This enables efficient recording by adjusting the recording frequency based on the estimated emotions. Some or all of the above-described processing in the recording unit can be performed using, for example, AI, or without AI. For example, the recording unit can input emotion data into AI, which can then adjust the recording frequency.

[0057] The recording unit can customize the recording method depending on the type of device used by the subject when recording. For example, if the subject is using a smartphone, the recording unit records using a voice memo. For example, the recording unit records using the smartphone's voice memo function. Furthermore, if the subject is using a wearable device, the recording unit can automatically record the heart rate and number of steps. For example, the recording unit records the heart rate and number of steps using a sensor in the wearable device. Furthermore, if the subject is using a computer, the recording unit can also record by text input. For example, the recording unit records using the computer's text input function. This allows for more appropriate recording by customizing the recording method depending on the type of device. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without AI. For example, the recording unit can input device type data into AI, which can then customize the recording method.

[0058] The analysis unit can estimate the subject's emotions and improve the accuracy of the analysis based on the estimated emotions. The analysis unit can, for example, analyze the subject's voice tone and speaking rate to analyze changes in emotions in real time. For example, the analysis unit can analyze voice data to detect changes in emotions. The analysis unit can also capture subtle changes in the subject's facial expressions with a high-precision camera to estimate emotions. For example, the analysis unit can analyze facial expression data to estimate changes in emotions. Furthermore, the analysis unit can refer to the subject's past emotional data to more accurately analyze the subject's current emotions. For example, the analysis unit can learn patterns of emotional changes based on past emotional data to improve the accuracy of the analysis. This improves the accuracy of the analysis based on the estimated emotions, enabling more accurate analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input voice data and facial expression data into AI, which can then estimate emotions.

[0059] During analysis, the analysis unit can optimize the analysis algorithm by referring to the subject's past emotional data. For example, the analysis unit stores in a database situations in which the subject became emotional in the past and analyzes emotions in similar situations. For example, the analysis unit learns patterns of emotional changes based on the past emotional data and improves the accuracy of the analysis. The analysis unit can also store the subject's past emotional data in the cloud and share it with other devices to optimize the analysis algorithm. For example, the analysis unit accesses a database on the cloud and optimizes the analysis algorithm by referring to the past emotional data. In this way, the accuracy of the analysis algorithm is improved by referring to the past emotional data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past emotional data into AI, which then optimizes the analysis algorithm.

[0060] The analysis unit can take into account environmental information about the subject during analysis. The analysis unit, for example, analyzes weather information about the environment in which the subject is located and associates it with changes in emotion. For example, the analysis unit analyzes changes in emotion based on weather data. The analysis unit can also analyze information about the location in which the subject is located and associate it with changes in emotion. For example, the analysis unit analyzes changes in emotion based on location data. Furthermore, the analysis unit can analyze the sound environment around the subject and associate it with changes in emotion. For example, the analysis unit analyzes changes in emotion based on sound environment data. This improves the accuracy of the analysis by taking environmental information into consideration. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input environmental information data into AI, which then performs the analysis.

[0061] The analysis unit can estimate the subject's emotions and adjust the timing of analysis based on the estimated emotions. The analysis unit, for example, increases the frequency of analysis during times when the subject is likely to become emotional. For example, the analysis unit identifies times when the subject is likely to become emotional based on past data and increases the frequency of analysis during those times. The analysis unit can also reduce the frequency of analysis when the subject's emotions are stable, thereby saving resources. For example, the analysis unit sets the analysis frequency low when the subject's emotions are stable. Furthermore, the analysis unit can immediately perform analysis when the subject's emotions suddenly change. For example, the analysis unit detects sudden changes in emotions and performs analysis in real time. This enables efficient analysis by adjusting the timing of analysis based on the estimated emotions. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input emotion data into AI, which then adjusts the timing of analysis.

[0062] The analysis unit can take into account the social relationships of the subject when performing the analysis. For example, when the subject is with friends, the analysis unit analyzes the content of conversations with friends. For example, the analysis unit analyzes changes in emotions based on conversation data with friends. Furthermore, when the subject is with family, the analysis unit can also analyze the content of conversations with family. For example, the analysis unit analyzes changes in emotions based on conversation data with family. Furthermore, when the subject is alone, the analysis unit can analyze the subject's own monologue and behavior. For example, the analysis unit analyzes changes in emotions based on monologue and behavior data. This allows for more appropriate analysis by taking social relationships into consideration. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input social relationship data into AI, which then performs the analysis.

[0063] The notification unit can estimate the emotion of the target person and adjust the content of the notification based on the estimated emotion. For example, if the target person is feeling angry, the notification unit sends a notification encouraging the target person to take a deep breath to calm down. For example, the notification unit can send advice to calm down based on the emotion data. Furthermore, if the target person is feeling sad, the notification unit can send a notification suggesting taking a walk to change their mood. For example, the notification unit can send advice to change their mood based on the emotion data. Furthermore, if the target person is feeling anxious, the notification unit can send a notification recommending meditation to relax. For example, the notification unit can send advice to relax based on the emotion data. In this way, by adjusting the content of the notification based on the estimated emotion, more effective notification is provided. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input emotion data into AI, which can adjust the content of the notification.

[0064] When notifying, the notification unit can optimize the effectiveness of the notification by referring to the target person's past reaction data. For example, the notification unit resends a notification that was effective for the target person in the past. For example, the notification unit selects the most effective notification based on the past reaction data. The notification unit can also analyze the target person's past reaction data and customize the content of the notification. For example, the notification unit adjusts the content of the notification based on the past reaction data. In this way, the effectiveness of the notification is optimized by referring to the past reaction data. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input past reaction data into AI, which can optimize the effectiveness of the notification.

[0065] The notification unit can change the notification format depending on the subject's current activity or situation when notifying the subject. For example, if the subject is in a public place, the notification unit notifies the subject via text message. For example, the notification unit selects a format suitable for notification in a public place. The notification unit can also notify the subject by voice when the subject is at home. For example, the notification unit selects a format suitable for notification at home. Furthermore, the notification unit can also provide a concise and safe notification when the subject is driving. For example, the notification unit selects a format suitable for notification while driving. In this way, by changing the notification format depending on the activity or situation, more appropriate notification is provided. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input activity and situation data into AI, which can adjust the notification format.

[0066] The notification unit can estimate the emotion of the target person and adjust the timing of the notification based on the estimated emotion. The notification unit, for example, provides a notification during a time period when the target person is likely to become emotional. For example, the notification unit identifies a time period when the target person is likely to become emotional based on past data and provides a notification during that time period. The notification unit can also provide an immediate notification when the target person's emotion changes suddenly. For example, the notification unit detects a sudden change in emotion and provides a notification in real time. Furthermore, the notification unit can reduce the frequency of notifications when the target person's emotion is stable, thereby saving resources. For example, the notification unit sets the notification frequency low when the emotion is stable. This allows for more effective notification by adjusting the timing of notifications based on the estimated emotion. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without AI. For example, the notification unit can input emotion data into AI, which can adjust the timing of notifications.

[0067] When notifying, the notification unit can customize the notification method depending on the type of device the subject uses. For example, if the subject uses a smartphone, the notification unit can provide the notification via push notification. For example, the notification unit can provide advice using a push notification on the smartphone. Furthermore, if the subject uses a wearable device, the notification unit can also provide the notification via vibration. For example, the notification unit can provide the notification using the vibration function of the wearable device. Furthermore, if the subject uses a personal computer, the notification unit can also provide the notification via pop-up notification. For example, the notification unit can provide advice using a pop-up notification on the personal computer. In this way, by customizing the notification method depending on the type of device, more appropriate notification can be provided. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input device type data into AI, which can then customize the notification method.

[0068] When notifying, the notification unit can select an appropriate notification method taking into account the target person's location information. For example, if the target person is at home, the notification unit notifies by voice. For example, the notification unit selects a format suitable for notification at home. Furthermore, if the target person is out, the notification unit can also notify by text message. For example, the notification unit selects a format suitable for notification while out. Furthermore, if the target person is driving, the notification unit can also provide a concise and safe notification. For example, the notification unit selects a format suitable for notification while driving. In this way, more appropriate notification is provided by taking the location information into consideration. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input location information data into AI, which then selects the notification method.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The emotion regulation system can further include a health management unit that monitors the subject's health condition. The health management unit monitors the subject's biometric information, such as heart rate, blood pressure, and body temperature, in real time and correlates it with changes in emotions. For example, if the subject's heart rate suddenly rises, the health management unit can provide advice to relax, as this may indicate stress. If blood pressure is high, the system can also provide advice on deep breathing or meditation. Furthermore, the system can monitor changes in body temperature and, if poor physical condition is affecting emotions, can advise the subject to take a rest. This enables emotion regulation that takes health status into account, providing more effective support.

[0071] The emotion regulation system can further include a hobby management unit that takes into account the subject's hobbies and interests. The hobby management unit collects information about the subject's hobbies and interests and uses it to regulate emotions. For example, if the subject likes listening to music, it can suggest relaxing music when the subject is feeling emotional. If the subject likes reading, it can also recommend books to help them change their mood. Furthermore, if the subject enjoys sports, it can suggest ways to relieve stress through exercise. This makes it possible to regulate emotions based on hobbies and interests, and provides advice that is more easily accepted by the subject.

[0072] The emotion regulation system can further include a network management unit that utilizes the subject's social network. The network management unit monitors the subject's relationships with friends and family to help regulate their emotions. For example, if the subject is feeling stressed, it can advise the subject to contact a trusted friend. Also, if there is a lack of communication with family, it can suggest that the subject spend more time with their family. Furthermore, if the subject is feeling lonely, it can recommend that the subject join a community where they share hobbies and interests. This makes it possible to regulate emotions by utilizing social networks, thereby reducing the subject's sense of isolation.

[0073] The emotion regulation system may further include a sleep management unit that monitors the subject's sleep patterns. The sleep management unit monitors the subject's sleep duration and quality and correlates them with changes in emotions. For example, if the subject continues to lack sleep, the sleep management unit may advise the subject to get enough sleep, as this may increase emotional instability. If the sleep quality is poor, the system may also suggest a relaxation routine. Furthermore, if the subject wakes up frequently during the night, the system may suggest ways to improve the bedroom environment. This enables emotion regulation that takes sleep patterns into account, thereby improving the subject's overall health.

[0074] The emotion regulation system can further include a dietary management unit that monitors the subject's eating patterns. The dietary management unit monitors the subject's dietary content and intake times and correlates them with changes in emotions. For example, if the subject's diet is unbalanced, it may increase emotional instability, so the system can advise the subject to eat a balanced diet. Also, if the subject's meal times are irregular, it can suggest that the subject try to eat regular meals. Furthermore, it can take into account the emotional impact of certain foods and advise the subject to choose appropriate meals. This enables emotion regulation that takes dietary patterns into account, thereby improving the subject's overall health.

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

[0076] Step 1: The detection unit detects the subject's emotions using voice recognition or facial expression recognition. For example, voice recognition technology can be used to analyze the subject's tone of voice and speaking rate to detect emotions. Facial expression recognition technology can also be used to analyze changes in the subject's facial expressions to detect emotions. Voice recognition technology analyzes voice data and detects changes in emotions in real time. Facial expression recognition technology analyzes facial expression data captured by a camera to detect changes in emotions. Step 2: The advice unit provides appropriate advice based on the emotions detected by the detection unit. For example, if the subject is feeling angry, the advice unit may suggest taking a deep breath to calm down. If the subject is feeling sad, the advice unit may suggest taking a walk to change their mood. The advice unit analyzes the emotion data and generates appropriate advice. Step 3: The intervention unit notifies the subject of the advice provided by the advice unit in real time. For example, the advice may be provided via a smartphone or wearable device. The advice may also be provided using a voice message or text message. The intervention unit sends a push notification to the subject's device and provides the advice in real time.

[0077] (Example 2) An emotion regulation system according to an embodiment of the present invention supports people who suffer from poor emotional regulation, such as those who are unable to control their emotions and end up hurting others or blaming themselves. When a subject attempts to speak or act impulsively, the emotion regulation AI provides calming encouragement and advice tailored to the situation. First, the subject's daily actions and comments are recorded to identify situations in which they are likely to become emotional. For example, the subject's emotions are recorded in a diary-like format to understand the emotions they are likely to feel in specific situations. These records are then input into the emotion regulation AI for analysis. Next, the emotion regulation AI identifies situations in which the subject is likely to become emotional and provides advice tailored to the situation. For example, if the subject feels anger, the emotion regulation AI will encourage them to "take a deep breath and calm down." Similarly, if the subject feels sad, the AI ​​will provide advice such as "Try to talk to someone." Furthermore, the emotion regulation AI detects the moment when the subject is about to speak or act impulsively and intervenes in real time. For example, if a subject becomes angry and is about to make an aggressive remark toward someone, the emotion regulation AI will warn them, saying, "What you say may hurt the other person. Please think about it for a moment before speaking." This service helps people who struggle with emotion regulation to better control their emotions and improve their relationships with those around them. It also helps subjects learn how to control their emotions in a positive way without blaming themselves. In this way, the emotion regulation system helps people who struggle with emotional control to effectively manage their emotions and improve their relationships with those around them.

[0078] An emotion regulation system according to an embodiment includes a detection unit, an advice unit, and an intervention unit. The detection unit detects the emotion of a subject using voice recognition or facial expression recognition. For example, the detection unit uses voice recognition technology to analyze the tone of voice and speaking rate of the subject to detect the emotion. The detection unit can also use facial expression recognition technology to analyze changes in the subject's facial expression to detect the emotion. For example, the voice recognition technology analyzes voice data to detect changes in emotion in real time. The facial expression recognition technology analyzes facial expression data captured by a camera to detect changes in emotion. The advice unit provides appropriate advice based on the emotion detected by the detection unit. For example, if the subject is feeling angry, the advice unit may provide advice encouraging the subject to take a deep breath to calm down. If the subject is feeling sad, the advice unit may also suggest taking a walk to change their mood. For example, the advice unit analyzes the emotion data and generates appropriate advice. The intervention unit notifies the subject of the advice provided by the advice unit in real time. For example, the intervention unit may notify the subject of the advice provided by the advice unit via a smartphone or a wearable device. The intervention unit can also provide advice using a voice message or a text message. For example, the intervention unit can send a push notification to the subject's device to provide advice in real time. This allows the emotion regulation system according to the embodiment to detect the subject's emotions in real time and provide appropriate advice to support emotion regulation.

[0079] The emotion regulation system includes a recording unit that records the subject's daily actions or statements. The recording unit records the subject's daily actions and statements. For example, the recording unit records what actions the subject takes in what situations. The recording unit can also record what statements the subject makes. For example, the recording unit supports the subject in recording their emotions in a diary-like format. The recording unit saves the subject's actions and statements as digital data and uses it for later analysis. By recording the subject's daily actions and statements, it is possible to identify situations in which the subject is likely to become emotional. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the subject's actions and statements into AI, which then analyzes and records the data.

[0080] The emotion regulation system includes an analysis unit that analyzes recorded behaviors or statements and identifies situations in which the subject is likely to become emotional. The analysis unit analyzes the recorded behaviors and statements and identifies situations in which the subject is likely to become emotional. For example, the analysis unit uses a data analysis algorithm to identify situations in which the subject is likely to become emotional. The analysis unit can also analyze patterns of the subject's behavior and statements and identify triggers that make the subject likely to become emotional. For example, the analysis unit analyzes what emotions the subject is likely to feel in certain situations. By identifying situations in which the subject is likely to become emotional, appropriate advice can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input recorded data into AI, which can analyze the data and identify situations in which the subject is likely to become emotional.

[0081] The emotion regulation system includes a notification unit that notifies the subject via a smartphone or a wearable device. The notification unit notifies the subject via the smartphone or the wearable device. For example, the notification unit provides advice using a push notification on the smartphone. The notification unit can also notify using the vibration function of the wearable device. For example, the notification unit notifies the advice using a smartwatch. This makes it possible to intervene in real time by notifying via the smartphone or the wearable device. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can notify the smartphone or the wearable device of advice generated by AI.

[0082] The detection unit can detect the emotions of the subject using voice recognition or facial expression recognition. For example, the detection unit uses voice recognition technology to analyze the tone of the subject's voice and speaking rate to detect emotions. For example, the detection unit analyzes voice data and detects changes in emotions in real time. The detection unit can also use facial expression recognition technology to analyze changes in the subject's facial expressions to detect emotions. For example, the detection unit analyzes facial expression data captured by a camera to detect changes in emotions. This improves the accuracy of emotion detection by using voice recognition and facial expression recognition. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input voice data and facial expression data into AI, which then detects emotions.

[0083] The advice unit can provide appropriate advice based on the detected emotion. For example, if the subject is feeling angry, the advice unit can provide advice encouraging the subject to take a deep breath to calm down. For example, the advice unit can analyze emotion data and generate appropriate advice. Furthermore, if the subject is feeling sad, the advice unit can also suggest taking a walk to change their mood. For example, the advice unit can generate advice based on emotion data and provide it to the subject. In this way, by providing advice based on the detected emotion, it is possible to support the subject in regulating their emotions. Some or all of the above-described processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input emotion data into AI, which can then generate appropriate advice.

[0084] The detection unit can estimate the subject's emotions and improve detection accuracy based on the estimated emotions. The detection unit can, for example, analyze the subject's voice tone and speaking rate to detect changes in emotions in real time. For example, the detection unit can analyze voice data to detect changes in emotions. The detection unit can also capture subtle changes in the subject's facial expressions with a high-precision camera to estimate emotions. For example, the detection unit can analyze facial expression data to estimate changes in emotions. Furthermore, the detection unit can refer to the subject's past emotional data to more accurately estimate the subject's current emotions. For example, the detection unit can learn patterns of emotional changes based on past emotional data to improve detection accuracy. This improves detection accuracy based on the estimated emotions, enabling more accurate emotion detection. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without AI. For example, the detection unit can input voice data and facial expression data into AI, which can then estimate emotions.

[0085] During detection, the detection unit can improve the detection algorithm by referring to the subject's past emotional data. For example, the detection unit stores in a database situations in which the subject has become emotional in the past and predicts the subject's emotions in similar situations. For example, the detection unit learns patterns of emotional changes based on the past emotional data and improves detection accuracy. The detection unit can also store the subject's past emotional data in the cloud and share it with other devices to optimize the detection algorithm. For example, the detection unit accesses a database on the cloud and improves the detection algorithm by referring to the past emotional data. By referring to the past emotional data, the accuracy of the detection algorithm is improved. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without AI. For example, the detection unit can input past emotional data into AI, which then optimizes the detection algorithm.

[0086] The detection unit can detect emotions by taking into account the environmental sounds and background sounds of the subject. For example, the detection unit measures the noise level in the environment where the subject is present and adjusts the accuracy of emotion detection. For example, the detection unit monitors the noise level in real time and incorporates it into the emotion detection algorithm. The detection unit can also detect specific sounds (e.g., yelling or laughter) from background sounds and reflect them in emotion estimation. For example, the detection unit analyzes background sounds, detects specific sounds, and uses them to estimate emotions. Furthermore, the detection unit can monitor changes in environmental sounds in real time and incorporate them into the emotion detection algorithm. For example, the detection unit detects changes in environmental sounds and estimates emotions based on that information. This improves the accuracy of emotion detection by taking environmental sounds and background sounds into consideration. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without AI. For example, the detection unit can input environmental sound data into AI, which then detects emotions.

[0087] The detection unit can estimate the subject's emotions and adjust the timing of detection based on the estimated emotions. For example, the detection unit can predict time periods when the subject is likely to become emotional and focus on detecting emotions during those time periods. For example, the detection unit can identify time periods when the subject is likely to become emotional based on past data and increase the detection frequency during those time periods. The detection unit can also immediately detect sudden changes in the subject's emotions. For example, the detection unit can detect sudden changes in emotions and respond in real time. Furthermore, the detection unit can reduce the detection frequency when the subject's emotions are stable, thereby saving resources. For example, the detection unit can set a low detection frequency when the emotions are stable. This enables efficient emotion detection by adjusting the detection timing based on the estimated emotions. Some or all of the above-described processing in the detection unit may be performed using, or without, AI. For example, the detection unit can input emotion data into AI, which can then adjust the detection timing.

[0088] The detection unit can detect emotions by analyzing the subject's physical movements and posture during detection. The detection unit, for example, monitors changes in the subject's walking speed and posture to estimate changes in emotions. For example, the detection unit analyzes walking speed and posture data to detect changes in emotions. The detection unit can also analyze the subject's hand movements and gestures and reflect this in emotion estimation. For example, the detection unit analyzes hand movement and gesture data to estimate changes in emotions. Furthermore, the detection unit can detect changes in the subject's sitting or standing style to predict changes in emotions. For example, the detection unit analyzes sitting and standing style data to detect changes in emotions. This improves the accuracy of emotion detection by analyzing physical movements and posture. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without AI. For example, the detection unit can input physical movement and posture data into AI, which can then detect emotions.

[0089] The detection unit can detect emotions using the subject's biometric information during detection. For example, the detection unit monitors fluctuations in the subject's heart rate in real time to detect changes in emotions. For example, the detection unit analyzes heart rate data to detect changes in emotions. The detection unit can also measure the subject's electrodermal response and estimate changes in emotions. For example, the detection unit analyzes electrodermal response data to estimate changes in emotions. Furthermore, the detection unit can analyze the subject's breathing pattern to predict changes in emotions. For example, the detection unit analyzes breathing pattern data to detect changes in emotions. This improves the accuracy of emotion detection by using biometric information. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input biometric information data to AI, which can then detect emotions.

[0090] The advice unit can estimate the emotion of the subject and adjust the content of the advice based on the estimated emotion. For example, if the subject is feeling angry, the advice unit provides advice encouraging the subject to take a deep breath to calm down. For example, the advice unit analyzes emotion data and generates appropriate advice. Furthermore, if the subject is feeling sad, the advice unit can suggest taking a walk to change their mood. For example, the advice unit generates advice based on emotion data and provides it to the subject. Furthermore, if the subject is feeling anxious, the advice unit can provide advice recommending meditation to relax. For example, the advice unit generates advice based on emotion data and provides it to the subject. In this way, by adjusting the content of the advice based on the estimated emotion, more effective advice can be provided. Some or all of the above-described processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input emotion data to AI, which generates appropriate advice.

[0091] When providing advice, the advice unit can improve the effectiveness of the advice by referring to the subject's past reaction data. For example, the advice unit can re-provide advice that was effective for the subject in the past. For example, the advice unit selects the most effective advice based on the past reaction data. The advice unit can also analyze the subject's past reaction data and customize the content of the advice. For example, the advice unit adjusts the content of the advice based on the past reaction data. In this way, the effectiveness of the advice is optimized by referring to the past reaction data. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input past reaction data into AI, which can optimize the effectiveness of the advice.

[0092] When providing advice, the advice unit can change the format of the advice depending on the current situation and environment of the subject. For example, when the subject is in a public place, the advice unit provides the advice by text message. For example, the advice unit selects a format suitable for providing advice in a public place. Furthermore, when the subject is at home, the advice unit can also provide advice by voice. For example, the advice unit selects a format suitable for providing advice at home. Furthermore, when the subject is driving, the advice unit can also provide concise and safe advice. For example, the advice unit selects a format suitable for providing advice while driving. In this way, by changing the format of advice depending on the situation and environment, more appropriate advice is provided. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input situation and environmental data into AI, which can adjust the format of the advice.

[0093] The advice unit can estimate the emotions of the subject and adjust the timing of advice based on the estimated emotions. The advice unit, for example, provides advice during times when the subject is likely to become emotional. For example, the advice unit identifies times when the subject is likely to become emotional based on past data and provides advice during those times. The advice unit can also provide advice immediately when the subject's emotions change suddenly. For example, the advice unit detects sudden changes in emotions and provides advice in real time. Furthermore, the advice unit can reduce the frequency of advice when the subject's emotions are stable, thereby saving resources. For example, the advice unit sets the frequency of advice low when the emotions are stable. In this way, more effective advice can be provided by adjusting the timing of advice based on the estimated emotions. Some or all of the above-described processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input emotion data into AI, which can adjust the timing of advice.

[0094] When providing advice, the advice unit can customize the way the advice is expressed based on the subject's preferences and tastes. For example, the advice unit provides the advice with the subject's favorite music playing in the background. For example, the advice unit provides the advice while playing the subject's favorite music. The advice unit can also provide the advice using language preferred by the subject. For example, the advice unit provides the advice using language that matches the subject's preferences. Furthermore, the advice unit can also provide the advice in the voice of a character preferred by the subject. For example, the advice unit provides the advice using the voice of a character preferred by the subject. In this way, by customizing the way the advice is expressed based on the subject's preferences and tastes, more acceptable advice is provided. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input preference and taste data into AI, which can customize the way the advice is expressed.

[0095] When providing advice, the advice unit can provide the advice while taking into consideration the social relationships of the subject. For example, if the subject is with a friend, the advice unit provides advice to ask the friend for cooperation. For example, the advice unit provides advice to ask the friend for cooperation. Furthermore, if the subject is with family, the advice unit can also provide advice to ask the family to listen to the subject. For example, the advice unit provides advice to ask the family to listen to the subject. Furthermore, if the subject is alone, the advice unit can also suggest a way for the subject to control their own emotions. For example, the advice unit suggests a way for the subject to control their own emotions. In this way, more appropriate advice is provided by taking social relationships into consideration. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input social relationship data into AI, which can then provide advice.

[0096] The intervention unit can estimate the subject's emotions and adjust the notification method based on the estimated emotions. For example, if the subject is feeling angry, the intervention unit sends a notification encouraging the subject to take a deep breath to calm down. For example, the intervention unit can notify the subject of advice to calm down based on the emotion data. Furthermore, if the subject is feeling sad, the intervention unit can also send a notification suggesting that the subject take a walk to change their mood. For example, the intervention unit can notify the subject of advice to change their mood based on the emotion data. Furthermore, if the subject is feeling anxious, the intervention unit can send a notification recommending meditation to relax. For example, the intervention unit can notify the subject of advice to relax based on the emotion data. In this way, by adjusting the notification method based on the estimated emotions, more effective notifications can be provided. Some or all of the above-described processing in the intervention unit may be performed using, for example, AI, or may be performed without using AI. For example, the intervention unit can input emotion data into AI, which can then adjust the notification method.

[0097] When sending a notification, the intervention unit can optimize the effectiveness of the notification by referring to the subject's past response data. For example, the intervention unit resends a notification that was effective for the subject in the past. For example, the intervention unit selects the most effective notification based on the past response data. The intervention unit can also analyze the subject's past response data and customize the content of the notification. For example, the intervention unit adjusts the content of the notification based on the past response data. In this way, the effectiveness of the notification is optimized by referring to the past response data. Some or all of the above-mentioned processing in the intervention unit may be performed using, for example, AI, or may be performed without using AI. For example, the intervention unit can input past response data into AI, which can optimize the effectiveness of the notification.

[0098] When notifying the subject, the intervention unit can change the notification format depending on the subject's current activity or situation. For example, if the subject is in a public place, the intervention unit notifies the subject by text message. For example, the intervention unit selects a format suitable for notification in a public place. Furthermore, if the subject is at home, the intervention unit can also notify the subject by voice. For example, the intervention unit selects a format suitable for notification at home. Furthermore, if the subject is driving, the intervention unit can also provide a concise and safe notification. For example, the intervention unit selects a format suitable for notification while driving. In this way, by changing the notification format depending on the activity or situation, more appropriate notification can be provided. Some or all of the above-mentioned processing in the intervention unit may be performed using, for example, AI, or may be performed without using AI. For example, the intervention unit can input activity and situation data into AI, which can adjust the notification format.

[0099] The intervention unit can estimate the subject's emotions and adjust the timing of notifications based on the estimated emotions. The intervention unit, for example, provides notifications during time periods when the subject is likely to become emotional. For example, the intervention unit identifies time periods when the subject is likely to become emotional based on past data and provides notifications during those time periods. The intervention unit can also provide instant notifications when the subject's emotions suddenly change. For example, the intervention unit detects sudden changes in emotions and provides notifications in real time. Furthermore, the intervention unit can reduce the frequency of notifications when the subject's emotions are stable, thereby saving resources. For example, the intervention unit sets the notification frequency low when the emotions are stable. This allows for more effective notifications by adjusting the timing of notifications based on the estimated emotions. Some or all of the above-described processing in the intervention unit may be performed using, for example, AI, or may be performed without AI. For example, the intervention unit can input emotion data into AI, which then adjusts the timing of notifications.

[0100] When notifying, the intervention unit can customize the notification method depending on the type of device of the subject. For example, if the subject is using a smartphone, the intervention unit can notify by push notification. For example, the intervention unit can provide advice using a push notification on the smartphone. Furthermore, if the subject is using a wearable device, the intervention unit can also notify by vibration. For example, the intervention unit can notify using the vibration function of the wearable device. Furthermore, if the subject is using a personal computer, the intervention unit can also notify by pop-up notification. For example, the intervention unit can provide advice using a pop-up notification on the personal computer. In this way, by customizing the notification method depending on the type of device, more appropriate notification can be provided. Some or all of the above-mentioned processing in the intervention unit may be performed using, for example, AI, or may be performed without using AI. For example, the intervention unit can input device type data into AI, which can customize the notification method.

[0101] When notifying, the intervention unit can select an appropriate notification method taking into account the location information of the subject. For example, if the subject is at home, the intervention unit notifies by voice. For example, the intervention unit selects a format suitable for notification at home. Furthermore, if the subject is out, the intervention unit can also notify by text message. For example, the intervention unit selects a format suitable for notification while out. Furthermore, if the subject is driving, the intervention unit can also provide a concise and safe notification. For example, the intervention unit selects a format suitable for notification while driving. In this way, more appropriate notification is provided by taking the location information into consideration. Some or all of the above-mentioned processing in the intervention unit may be performed using, for example, AI, or may be performed without using AI. For example, the intervention unit can input location information data into AI, which can select the notification method.

[0102] The recording unit can estimate the subject's emotion and adjust the content of the record based on the estimated emotion. For example, if the subject is feeling anger, the recording unit records the situation and cause in detail. For example, the recording unit records the situation in which the subject felt anger and the cause as text data. Furthermore, if the subject is feeling sad, the recording unit can also record the intensity and duration of the emotion. For example, the recording unit records the intensity and duration of the emotion when the subject felt sad as data. Furthermore, if the subject is feeling anxious, the recording unit can also record the cause and how to deal with it. For example, the recording unit records in detail the cause and how to deal with it when the subject felt anxious. This allows for more detailed recording by adjusting the content of the record based on the estimated emotion. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without AI. For example, the recording unit can input emotion data into AI, which can then adjust the content of the record.

[0103] During recording, the recording unit can improve the accuracy of the recording by referring to the subject's past behavioral data. For example, the recording unit stores situations in which the subject became emotional in the past in a database and records emotions in similar situations. For example, the recording unit learns patterns of emotional changes based on the past behavioral data and improves the recording accuracy. The recording unit can also improve the recording accuracy by storing the subject's past behavioral data in the cloud and sharing it with other devices. For example, the recording unit accesses a database on the cloud and improves the recording accuracy by referring to the past behavioral data. In this way, the recording accuracy is improved by referring to the past behavioral data. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the past behavioral data into AI, which can improve the recording accuracy.

[0104] The recording unit can take into consideration environmental information about the subject when recording. For example, the recording unit records weather information about the environment in which the subject is located and associates it with changes in emotion. For example, the recording unit records changes in emotion based on weather data. The recording unit can also record information about the location in which the subject is located and associate it with changes in emotion. For example, the recording unit records changes in emotion based on location data. Furthermore, the recording unit can record the sound environment around the subject and associate it with changes in emotion. For example, the recording unit records changes in emotion based on sound environment data. This improves the accuracy of recording by taking environmental information into consideration. Some or all of the above-mentioned processing in the recording unit can be performed using, for example, AI, or can be performed without using AI. For example, the recording unit can input environmental information data into AI, and the AI ​​can perform recording.

[0105] The recording unit can estimate the subject's emotions and adjust the recording frequency based on the estimated emotions. For example, the recording unit can increase the recording frequency during times when the subject is likely to become emotional. For example, the recording unit can identify times when the subject is likely to become emotional based on past data and increase the recording frequency during those times. The recording unit can also reduce the recording frequency when the subject's emotions are stable, thereby saving resources. For example, the recording unit can set the recording frequency low when the emotions are stable. Furthermore, the recording unit can immediately record when the subject's emotions suddenly change. For example, the recording unit can detect sudden changes in emotions and record in real time. This enables efficient recording by adjusting the recording frequency based on the estimated emotions. Some or all of the above-described processing in the recording unit can be performed using, for example, AI, or without AI. For example, the recording unit can input emotion data into AI, which can then adjust the recording frequency.

[0106] The recording unit can customize the recording method depending on the type of device used by the subject when recording. For example, if the subject is using a smartphone, the recording unit records using a voice memo. For example, the recording unit records using the smartphone's voice memo function. Furthermore, if the subject is using a wearable device, the recording unit can automatically record the heart rate and number of steps. For example, the recording unit records the heart rate and number of steps using a sensor in the wearable device. Furthermore, if the subject is using a computer, the recording unit can also record by text input. For example, the recording unit records using the computer's text input function. This allows for more appropriate recording by customizing the recording method depending on the type of device. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without AI. For example, the recording unit can input device type data into AI, which can then customize the recording method.

[0107] The analysis unit can estimate the subject's emotions and improve the accuracy of the analysis based on the estimated emotions. The analysis unit can, for example, analyze the subject's voice tone and speaking rate to analyze changes in emotions in real time. For example, the analysis unit can analyze voice data to detect changes in emotions. The analysis unit can also capture subtle changes in the subject's facial expressions with a high-precision camera to estimate emotions. For example, the analysis unit can analyze facial expression data to estimate changes in emotions. Furthermore, the analysis unit can refer to the subject's past emotional data to more accurately analyze the subject's current emotions. For example, the analysis unit can learn patterns of emotional changes based on past emotional data to improve the accuracy of the analysis. This improves the accuracy of the analysis based on the estimated emotions, enabling more accurate analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input voice data and facial expression data into AI, which can then estimate emotions.

[0108] During analysis, the analysis unit can optimize the analysis algorithm by referring to the subject's past emotional data. For example, the analysis unit stores in a database situations in which the subject became emotional in the past and analyzes emotions in similar situations. For example, the analysis unit learns patterns of emotional changes based on the past emotional data and improves the accuracy of the analysis. The analysis unit can also store the subject's past emotional data in the cloud and share it with other devices to optimize the analysis algorithm. For example, the analysis unit accesses a database on the cloud and optimizes the analysis algorithm by referring to the past emotional data. In this way, the accuracy of the analysis algorithm is improved by referring to the past emotional data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past emotional data into AI, which then optimizes the analysis algorithm.

[0109] The analysis unit can take into account environmental information about the subject during analysis. The analysis unit, for example, analyzes weather information about the environment in which the subject is located and associates it with changes in emotion. For example, the analysis unit analyzes changes in emotion based on weather data. The analysis unit can also analyze information about the location in which the subject is located and associate it with changes in emotion. For example, the analysis unit analyzes changes in emotion based on location data. Furthermore, the analysis unit can analyze the sound environment around the subject and associate it with changes in emotion. For example, the analysis unit analyzes changes in emotion based on sound environment data. This improves the accuracy of the analysis by taking environmental information into consideration. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input environmental information data into AI, which then performs the analysis.

[0110] The analysis unit can estimate the subject's emotions and adjust the timing of analysis based on the estimated emotions. The analysis unit, for example, increases the frequency of analysis during times when the subject is likely to become emotional. For example, the analysis unit identifies times when the subject is likely to become emotional based on past data and increases the frequency of analysis during those times. The analysis unit can also reduce the frequency of analysis when the subject's emotions are stable, thereby saving resources. For example, the analysis unit sets the analysis frequency low when the subject's emotions are stable. Furthermore, the analysis unit can immediately perform analysis when the subject's emotions suddenly change. For example, the analysis unit detects sudden changes in emotions and performs analysis in real time. This enables efficient analysis by adjusting the timing of analysis based on the estimated emotions. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input emotion data into AI, which then adjusts the timing of analysis.

[0111] The analysis unit can take into account the social relationships of the subject when performing the analysis. For example, when the subject is with friends, the analysis unit analyzes the content of conversations with friends. For example, the analysis unit analyzes changes in emotions based on conversation data with friends. Furthermore, when the subject is with family, the analysis unit can also analyze the content of conversations with family. For example, the analysis unit analyzes changes in emotions based on conversation data with family. Furthermore, when the subject is alone, the analysis unit can analyze the subject's own monologue and behavior. For example, the analysis unit analyzes changes in emotions based on monologue and behavior data. This allows for more appropriate analysis by taking social relationships into consideration. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input social relationship data into AI, which then performs the analysis.

[0112] The notification unit can estimate the emotion of the target person and adjust the content of the notification based on the estimated emotion. For example, if the target person is feeling angry, the notification unit sends a notification encouraging the target person to take a deep breath to calm down. For example, the notification unit can send advice to calm down based on the emotion data. Furthermore, if the target person is feeling sad, the notification unit can send a notification suggesting taking a walk to change their mood. For example, the notification unit can send advice to change their mood based on the emotion data. Furthermore, if the target person is feeling anxious, the notification unit can send a notification recommending meditation to relax. For example, the notification unit can send advice to relax based on the emotion data. In this way, by adjusting the content of the notification based on the estimated emotion, more effective notification is provided. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input emotion data into AI, which can adjust the content of the notification.

[0113] When notifying, the notification unit can optimize the effectiveness of the notification by referring to the target person's past reaction data. For example, the notification unit resends a notification that was effective for the target person in the past. For example, the notification unit selects the most effective notification based on the past reaction data. The notification unit can also analyze the target person's past reaction data and customize the content of the notification. For example, the notification unit adjusts the content of the notification based on the past reaction data. In this way, the effectiveness of the notification is optimized by referring to the past reaction data. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input past reaction data into AI, which can optimize the effectiveness of the notification.

[0114] The notification unit can change the notification format depending on the subject's current activity or situation when notifying the subject. For example, if the subject is in a public place, the notification unit notifies the subject via text message. For example, the notification unit selects a format suitable for notification in a public place. The notification unit can also notify the subject by voice when the subject is at home. For example, the notification unit selects a format suitable for notification at home. Furthermore, the notification unit can also provide a concise and safe notification when the subject is driving. For example, the notification unit selects a format suitable for notification while driving. In this way, by changing the notification format depending on the activity or situation, more appropriate notification is provided. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input activity and situation data into AI, which can adjust the notification format.

[0115] The notification unit can estimate the emotion of the target person and adjust the timing of the notification based on the estimated emotion. The notification unit, for example, provides a notification during a time period when the target person is likely to become emotional. For example, the notification unit identifies a time period when the target person is likely to become emotional based on past data and provides a notification during that time period. The notification unit can also provide an immediate notification when the target person's emotion changes suddenly. For example, the notification unit detects a sudden change in emotion and provides a notification in real time. Furthermore, the notification unit can reduce the frequency of notifications when the target person's emotion is stable, thereby saving resources. For example, the notification unit sets the notification frequency low when the emotion is stable. This allows for more effective notification by adjusting the timing of notifications based on the estimated emotion. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without AI. For example, the notification unit can input emotion data into AI, which can adjust the timing of notifications.

[0116] When notifying, the notification unit can customize the notification method depending on the type of device the subject uses. For example, if the subject uses a smartphone, the notification unit can provide the notification via push notification. For example, the notification unit can provide advice using a push notification on the smartphone. Furthermore, if the subject uses a wearable device, the notification unit can also provide the notification via vibration. For example, the notification unit can provide the notification using the vibration function of the wearable device. Furthermore, if the subject uses a personal computer, the notification unit can also provide the notification via pop-up notification. For example, the notification unit can provide advice using a pop-up notification on the personal computer. In this way, by customizing the notification method depending on the type of device, more appropriate notification can be provided. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input device type data into AI, which can then customize the notification method.

[0117] When notifying, the notification unit can select an appropriate notification method taking into account the target person's location information. For example, if the target person is at home, the notification unit notifies by voice. For example, the notification unit selects a format suitable for notification at home. Furthermore, if the target person is out, the notification unit can also notify by text message. For example, the notification unit selects a format suitable for notification while out. Furthermore, if the target person is driving, the notification unit can also provide a concise and safe notification. For example, the notification unit selects a format suitable for notification while driving. In this way, more appropriate notification is provided by taking the location information into consideration. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input location information data into AI, which then selects the notification method. === Hard Collateral 1-1 === Each of the multiple elements, including the detection unit, advice unit, intervention unit, recording unit, analysis unit, and notification unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the detection unit detects the subject's emotions using the camera 42 and microphone 38B of the smart device 14, and the detection unit analyzes the emotions by the control unit 46A. The advice unit is realized by the identification processing unit 290 of the data processing device 12 and generates appropriate advice using an emotion identification model 59. The intervention unit is realized by the control unit 46A of the smart device 14 and notifies the subject of the advice in real time. The recording unit records the subject's daily actions and statements in the storage 50 of the smart device 14, which are later analyzed by the identification processing unit 290 of the data processing device 12. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the recorded data to identify situations that are likely to cause emotional outbursts. The notification unit sends notifications to a smartphone or a wearable device via the communication I / F 44 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the detection unit, advice unit, intervention unit, recording unit, analysis unit, and notification unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the detection unit detects the subject's emotions using the camera 42 and microphone 238 of the smart glasses 214, and the detection unit is analyzed by the control unit 46A. The advice unit is realized by the identification processing unit 290 of the data processing device 12 and generates appropriate advice using an emotion identification model 59. The intervention unit is realized by the control unit 46A of the smart glasses 214 and notifies the subject of advice in real time. The recording unit records the subject's daily actions and statements in the storage 50 of the smart glasses 214, which are later analyzed by the identification processing unit 290 of the data processing device 12. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the recorded data to identify situations that are likely to cause emotional outbursts. The notification unit sends notifications to a smartphone or wearable device via the communication I / F 44 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements, including the detection unit, advice unit, intervention unit, recording unit, analysis unit, and notification unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the detection unit detects the subject's emotions using the camera 42 and microphone 238 of the headset-type terminal 314, and the detection unit analyzes the emotions by the control unit 46A. The advice unit is realized by the identification processing unit 290 of the data processing device 12 and generates appropriate advice using an emotion identification model 59. The intervention unit is realized by the control unit 46A of the headset-type terminal 314 and notifies the subject of the advice in real time. The recording unit records the subject's daily actions and statements in the storage 50 of the headset-type terminal 314, which are later analyzed by the identification processing unit 290 of the data processing device 12. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the recorded data to identify situations that are likely to cause emotional outbursts. The notification unit sends notifications to a smartphone or a wearable device via the communication I / F 44 of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements, including the detection unit, advice unit, intervention unit, recording unit, analysis unit, and notification unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the detection unit detects the subject's emotions using the camera 42 and microphone 238 of the robot 414, and the detection unit is analyzed by the control unit 46A. The advice unit is realized by the identification processing unit 290 of the data processing device 12 and generates appropriate advice using an emotion identification model 59. The intervention unit is realized by the control unit 46A of the robot 414 and notifies the subject of the advice in real time. The recording unit records the subject's daily actions and statements in the storage 50 of the robot 414, which are later analyzed by the identification processing unit 290 of the data processing device 12. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the recorded data to identify situations that are likely to cause emotional outbursts. The notification unit sends notifications to a smartphone or a wearable device via the communication I / F 44 of the robot 414.

[0118] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0119] The emotion regulation system can further include a health management unit that monitors the subject's health condition. The health management unit monitors the subject's biometric information, such as heart rate, blood pressure, and body temperature, in real time and correlates it with changes in emotions. For example, if the subject's heart rate suddenly rises, the health management unit can provide advice to relax, as this may indicate stress. If blood pressure is high, the system can also provide advice on deep breathing or meditation. Furthermore, the system can monitor changes in body temperature and, if poor physical condition is affecting emotions, can advise the subject to take a rest. This enables emotion regulation that takes health status into account, providing more effective support.

[0120] The emotion regulation system can further include a hobby management unit that takes into account the subject's hobbies and interests. The hobby management unit collects information about the subject's hobbies and interests and uses it to regulate emotions. For example, if the subject likes listening to music, it can suggest relaxing music when the subject is feeling emotional. If the subject likes reading, it can also recommend books to help them change their mood. Furthermore, if the subject enjoys sports, it can suggest ways to relieve stress through exercise. This makes it possible to regulate emotions based on hobbies and interests, and provides advice that is more easily accepted by the subject.

[0121] The emotion regulation system can further include a network management unit that utilizes the subject's social network. The network management unit monitors the subject's relationships with friends and family to help regulate their emotions. For example, if the subject is feeling stressed, it can advise the subject to contact a trusted friend. Also, if there is a lack of communication with family, it can suggest that the subject spend more time with their family. Furthermore, if the subject is feeling lonely, it can recommend that the subject join a community where they share hobbies and interests. This makes it possible to regulate emotions by utilizing social networks, thereby reducing the subject's sense of isolation.

[0122] The emotion regulation system may further include a sleep management unit that monitors the subject's sleep patterns. The sleep management unit monitors the subject's sleep duration and quality and correlates them with changes in emotions. For example, if the subject continues to lack sleep, the sleep management unit may advise the subject to get enough sleep, as this may increase emotional instability. If the sleep quality is poor, the system may also suggest a relaxation routine. Furthermore, if the subject wakes up frequently during the night, the system may suggest ways to improve the bedroom environment. This enables emotion regulation that takes sleep patterns into account, thereby improving the subject's overall health.

[0123] The emotion regulation system can further include a dietary management unit that monitors the subject's eating patterns. The dietary management unit monitors the subject's dietary content and intake times and correlates them with changes in emotions. For example, if the subject's diet is unbalanced, it may increase emotional instability, so the system can advise the subject to eat a balanced diet. Also, if the subject's meal times are irregular, it can suggest that the subject try to eat regular meals. Furthermore, it can take into account the emotional impact of certain foods and advise the subject to choose appropriate meals. This enables emotion regulation that takes dietary patterns into account, thereby improving the subject's overall health.

[0124] The emotion regulation system can further estimate the emotion of the subject and adjust the content of advice based on the estimated emotion. For example, if the subject is feeling angry, the system can provide advice encouraging the subject to take a deep breath to calm down. For example, the advice unit analyzes emotion data and generates appropriate advice. Furthermore, if the subject is feeling sad, the advice unit can also suggest taking a walk to change their mood. For example, the advice unit generates advice based on emotion data and provides it to the subject. In this way, by providing advice based on the detected emotion, it is possible to support the subject in regulating their emotions. Some or all of the above-described processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input emotion data into AI, which then generates appropriate advice.

[0125] The emotion adjustment system can further estimate the emotion of the subject and adjust the timing of advice based on the estimated emotion. For example, advice is provided during times when the subject is likely to become emotional. For example, the advice unit identifies times when the subject is likely to become emotional based on past data and provides advice during those times. The advice unit can also provide advice immediately when the subject's emotion changes suddenly. For example, the advice unit detects sudden changes in emotion and provides advice in real time. In this way, more effective advice can be provided by adjusting the timing of advice based on the estimated emotion. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input emotion data into AI, which can adjust the timing of advice.

[0126] The emotion adjustment system can further estimate the emotion of the subject and adjust the format of the advice based on the estimated emotion. For example, if the subject is in a public place, the advice is provided by text message. For example, the advice unit selects a format suitable for providing advice in a public place. Furthermore, if the subject is at home, the advice unit can also provide advice by voice. For example, the advice unit selects a format suitable for providing advice at home. In this way, more appropriate advice can be provided by changing the format of advice depending on the situation and environment. Some or all of the above-described processing in the advice unit may be performed using, or without, AI. For example, the advice unit can input situation and environmental data into AI, which can adjust the format of advice.

[0127] The emotion adjustment system can further estimate the emotion of the subject and customize the content of the advice based on the estimated emotion. For example, the advice is provided with the subject's favorite music in the background. For example, the advice unit provides the advice while playing the subject's favorite music. The advice unit can also provide the advice using language preferred by the subject. For example, the advice unit provides the advice using language that matches the subject's preferences. In this way, by customizing the way the advice is expressed based on the subject's preferences and tastes, more acceptable advice is provided. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input preference and taste data into AI, which can customize the way the advice is expressed.

[0128] The emotion regulation system can further estimate the emotion of the subject and evaluate the effectiveness of advice based on the estimated emotion. For example, it monitors how the emotion of the subject changes after providing advice. For example, the advice unit analyzes emotion data and evaluates the effectiveness of the advice. The advice unit can also collect feedback from the subject and improve the content of the advice. For example, the advice unit adjusts the content of the advice based on the subject's feedback. This enables more effective emotion regulation by evaluating the effectiveness of the advice and continuously improving it. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input emotion data and feedback data into AI, which can evaluate the effectiveness of the advice.

[0129] The processing flow of the second embodiment will be briefly explained below.

[0130] Step 1: The detection unit detects the subject's emotions using voice recognition or facial expression recognition. For example, voice recognition technology can be used to analyze the subject's tone of voice and speaking rate to detect emotions. Facial expression recognition technology can also be used to analyze changes in the subject's facial expressions to detect emotions. Voice recognition technology analyzes voice data and detects changes in emotions in real time. Facial expression recognition technology analyzes facial expression data captured by a camera to detect changes in emotions. Step 2: The advice unit provides appropriate advice based on the emotions detected by the detection unit. For example, if the subject is feeling angry, the advice unit may suggest taking a deep breath to calm down. If the subject is feeling sad, the advice unit may suggest taking a walk to change their mood. The advice unit analyzes the emotion data and generates appropriate advice. Step 3: The intervention unit notifies the subject of the advice provided by the advice unit in real time. For example, the advice may be provided via a smartphone or wearable device. The advice may also be provided using a voice message or text message. The intervention unit sends a push notification to the subject's device and provides the advice in real time.

[0131] 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.

[0132] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0133] 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.

[0134] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0135] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0145] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] 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.

[0147] 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.

[0148] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0149] 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.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0152] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0159] 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.

[0160] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0161] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0162] 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.

[0163] 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.

[0164] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0165] 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.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0168] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0178] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0179] 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.

[0180] 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.

[0181] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0182] 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.

[0183] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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).

[0188] 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.

[0189] 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."

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] [Explanation of symbols]

[0203] 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 detection unit that detects the emotion of a subject using voice recognition or facial expression recognition; an advice unit that provides advice based on the emotion detected by the detection unit; an intervention unit that notifies the subject of the advice provided by the advice unit in real time. A system characterized by:

2. Equipped with a recording section to record the subject's daily actions or statements 2. The system of claim 1.

3. Equipped with an analysis unit that analyzes recorded actions or statements to identify situations that are likely to cause emotional reactions 2. The system of claim 1.

4. Equipped with a notification unit that notifies the target person via a smartphone or wearable device 2. The system of claim 1.

5. The detection unit Detecting a subject's emotions using voice or facial recognition 2. The system of claim 1.

6. The advice unit Providing appropriate advice based on detected emotions 2. The system of claim 1.

7. The detection unit Estimate the target's emotions and improve detection accuracy based on the estimated emotions 2. The system of claim 1.

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