system

Abstract

The system according to the embodiment aims to diagnose mental conditions early and provide appropriate positive suggestions. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a suggestion unit, and an audio output unit. The collection unit collects biometric data. The analysis unit analyzes the biometric data collected by the collection unit and diagnoses the mental state. The suggestion unit makes positive suggestions based on the diagnosis results obtained by the analysis unit. The audio output unit outputs the content suggested by the suggestion unit by audio.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that it is difficult to detect changes in mental state early and take appropriate measures.

[0005] The system according to the embodiment aims to diagnose mental conditions early and provide appropriate positive suggestions. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a suggestion unit, and an audio output unit. The collection unit collects biometric data. The analysis unit analyzes the biometric data collected by the collection unit and diagnoses the mental state. The suggestion unit makes positive suggestions based on the diagnosis results obtained by the analysis unit. The audio output unit outputs the content suggested by the suggestion unit by audio. [Effects of the Invention]

[0007] The system according to the embodiment can diagnose mental conditions early and provide appropriate positive suggestions. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An integrated application according to an embodiment of the present invention is a system for detecting mental illnesses and providing mental care. In this system, a user wears a wearable device, collects biometric data collected during daily life, and a multimodal AI analyzes the data to diagnose the user's mental state. Based on the diagnosis, a voice robot provides positive suggestions to the user. For example, the system may suggest, "Your central nervous system is disrupted after the meeting. Taking a short rest will double your productivity." or "Did something bother you? I'd be happy to listen." This enables advance detection of mental illnesses and mental care, thereby preventing the onset of mental illnesses. For example, when a user feels stressed, the voice robot can provide appropriate advice, reducing stress and preventing the onset of mental illnesses. Furthermore, by understanding their own mental state, users can take appropriate measures in their daily lives. This is expected to reduce the number of people suffering from mental illnesses and improve people's well-being.

[0029] A mental care system according to an embodiment includes a collection unit, an analysis unit, a suggestion unit, and an audio output unit. The collection unit collects biological data. The biological data includes, for example, heart rate, body temperature, and activity level, but is not limited to these examples. For example, the collection unit collects heart rate data using a sensor that measures heart rate. The collection unit can also collect body temperature data using a sensor that measures body temperature. The collection unit can also collect activity level data using a sensor that measures activity level. For example, the collection unit measures a user's heart rate in real time using a heart rate sensor and collects data. The collection unit can also measure a user's body temperature in real time using a body temperature sensor and collect data. The collection unit can also measure a user's activity level in real time using an activity level sensor and collect data. The analysis unit analyzes the biological data collected by the collection unit and diagnoses the user's mental state. The analysis is performed using, for example, statistical analysis of data or a machine learning algorithm, but is not limited to these examples. For example, the analysis unit may statistically analyze collected heart rate data and determine that a sudden increase in heart rate is a sign of stress. The analysis unit may also statistically analyze collected body temperature data and determine that a change in body temperature is a sign of stress. The analysis unit may also statistically analyze collected activity data and determine that a change in activity is a sign of stress. For example, the analysis unit may use a machine learning algorithm to analyze collected heart rate data and estimate a stress level. The analysis unit may also use a machine learning algorithm to analyze collected body temperature data and estimate a stress level. The analysis unit may also use a machine learning algorithm to analyze collected activity data and estimate a stress level. The suggestion unit makes positive suggestions based on the diagnosis results obtained by the analysis unit. Examples of suggestions include, but are not limited to, relaxation techniques and encouraging words. For example, the suggestion unit may make a suggestion based on the diagnosis results, such as, "Your central nervous system is disrupted after the meeting. Taking a short rest will double your productivity." Based on the diagnosis results, the proposal department can also make suggestions such as, "Was there anything that bothered you? I'd be happy to listen if you'd like."The suggestion unit can also suggest relaxation methods based on the diagnosis results. For example, the suggestion unit can suggest deep breathing or meditation based on the diagnosis results. The suggestion unit can also suggest light exercise based on the diagnosis results. The suggestion unit can also suggest listening to relaxing music based on the diagnosis results. The audio output unit outputs the content suggested by the suggestion unit as audio. The audio output can be performed using, for example, voice synthesis technology, but is not limited to such an example. For example, the audio output unit can output the content suggested by the suggestion unit as audio using voice synthesis technology. The audio output unit can also adjust the tone and speed of the audio before outputting. For example, the audio output unit can output the content suggested by the suggestion unit in a calm tone. The audio output unit can also output the content suggested in a bright tone. The audio output unit can also output the content suggested in a quick and concise tone. As a result, the mental care system according to the embodiment can realize early detection of mental illness and mental care, and prevent the onset of mental illness. For example, when a user feels stressed, the voice robot can provide appropriate advice, thereby reducing stress and preventing the onset of mental illness. Furthermore, by allowing users to understand their own mental state, they can take appropriate measures in their daily lives, which is expected to reduce the number of patients with mental illness and improve people's well-being.

[0030] The collection unit can collect biological data such as heart rate, body temperature, and activity level in real time. Real-time includes, but is not limited to, the frequency and delay time of data collection. For example, the collection unit collects heart rate data in real time using a sensor that measures heart rate. The collection unit can also collect body temperature data in real time using a sensor that measures body temperature. The collection unit can also collect activity level data in real time using a sensor that measures activity level. For example, the collection unit measures the user's heart rate in real time using a heart rate sensor and collects data. The collection unit can also measure the user's body temperature in real time and collect data using a body temperature sensor. The collection unit can also measure the user's activity level in real time and collect data using an activity level sensor. By collecting biological data in real time, the user's mental state can be accurately understood. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data acquired by a heart rate sensor into the generation AI and have the generation AI perform real-time data analysis.

[0031] The analysis unit analyzes the collected biological data and can determine that a sudden increase in heart rate or a change in body temperature is a sign of stress. A sudden increase includes, for example, but is not limited to, a range or time range of heart rate fluctuations. A change in body temperature includes, for example, but is not limited to, a range or time range of body temperature fluctuations. For example, the analysis unit statistically analyzes the collected heart rate data and determines that a sudden increase in heart rate is a sign of stress. The analysis unit can also statistically analyze the collected body temperature data and determine that a change in body temperature is a sign of stress. The analysis unit can also statistically analyze the collected activity data and determine that a change in activity level is a sign of stress. For example, the analysis unit uses a machine learning algorithm to analyze the collected heart rate data and estimate a stress level. The analysis unit can also use a machine learning algorithm to analyze the collected body temperature data and estimate a stress level. The analysis unit can also use a machine learning algorithm to analyze the collected activity data and estimate a stress level. This allows for early detection of signs of stress and the implementation of appropriate measures. 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 may input collected biometric data into the generation AI and have the generation AI determine whether there are signs of stress.

[0032] The suggestion unit can make positive suggestions based on the diagnosis result, such as, "After the meeting, your central nervous system is disrupted. Taking a short rest will double your productivity." or "Did something bother you? I'd be happy to listen to you." Positive suggestions include, but are not limited to, relaxation techniques and words of encouragement. For example, the suggestion unit can make a suggestion based on the diagnosis result, such as, "After the meeting, your central nervous system is disrupted. Taking a short rest will double your productivity." The suggestion unit can also make a suggestion based on the diagnosis result, such as, "Did something bother you? I'd be happy to listen to you." The suggestion unit can also suggest relaxation techniques based on the diagnosis result. For example, the suggestion unit can suggest deep breathing or meditation based on the diagnosis result. The suggestion unit can also suggest light exercise based on the diagnosis result. The suggestion unit can also suggest listening to relaxing music based on the diagnosis result. This allows appropriate advice to be provided to the user, improving their mental state. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or without AI. For example, the suggestion unit can input the diagnosis results into the generation AI and cause the generation AI to generate positive suggestions.

[0033] The audio output unit can output the content proposed by the suggestion unit by audio. Examples of audio output include, but are not limited to, the type of speech synthesis technology and the tone and speed of the speech. The audio output unit can output the content proposed by the suggestion unit by audio, for example, using speech synthesis technology. The audio output unit can also adjust the tone and speed of the speech before outputting. For example, the audio output unit can output the proposed content in a calm tone. The audio output unit can also output the proposed content in a bright tone. The audio output unit can also output the proposed content in a quick and concise tone. This can provide audio advice to the user and improve their mental state. Some or all of the above-described processing in the audio output unit can be performed using, for example, AI, or can be performed without using AI. For example, the audio output unit can input the content proposed by the suggestion unit to a generation AI and have the generation AI perform speech synthesis.

[0034] The collection unit can analyze the user's past biological data and select the optimal collection timing. The optimal collection timing includes, but is not limited to, timing setting based on the results of past data analysis. For example, the collection unit can identify time periods when stress levels are high from the user's past data and intensively collect data during those time periods. The collection unit can also identify time periods when the user is relaxed from the user's past data and refrain from collecting data during those time periods. The collection unit can also collect data before and after specific events (e.g., meetings, exercise) from the user's past data. For example, the collection unit can analyze the user's past heart rate data to identify time periods when stress levels are high. The collection unit can also analyze the user's past body temperature data to identify time periods when the user is relaxed. The collection unit can also analyze the user's past activity data to collect data before and after specific events. This enables efficient data collection by collecting data at the optimal timing based on past data. Some or all of the above-described processing performed by the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input the user's past biometric data into the generation AI and have the generation AI select the optimal collection timing.

[0035] When collecting biometric data, the collection unit can filter the biometric data based on the user's current activity status. Filtering includes, but is not limited to, a method of selecting data according to the activity status. For example, when the user is exercising, the collection unit prioritizes collecting heart rate and activity level data and filters out other data. Furthermore, when the user is resting, the collection unit can prioritize collecting body temperature and heart rate data and filtering out activity level data. Furthermore, when the user is working, the collection unit can prioritize collecting data indicating a stress level and filtering out other data. For example, the collection unit can detect the user's activity status with a sensor and prioritize collecting heart rate and activity level data when the user is exercising. Furthermore, the collection unit can detect the user's activity status with a sensor and prioritize collecting body temperature and heart rate data when the user is resting. Furthermore, the collection unit can detect the user's activity status with a sensor and prioritize collecting stress level data when the user is working. This allows only necessary data to be collected by filtering data according to the user's activity status. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's activity status data into the generation AI and have the generation AI perform filtering.

[0036] When collecting biometric data, the collection unit can select the optimal collection means depending on the user's input method. The optimal collection means includes, but is not limited to, the type and placement of sensors. For example, when a user uses voice input, the collection unit analyzes the voice data to detect changes in stress and emotions. Furthermore, when a user uses text input, the collection unit can analyze the text data to detect changes in emotions. Furthermore, when a user uses image input, the collection unit can analyze the image data to detect changes in emotions from facial expressions and posture. For example, the collection unit analyzes the voice data of a user using voice input to detect changes in stress and emotions. Furthermore, the collection unit can analyze the text data of a user using text input to detect changes in emotions. Furthermore, the collection unit can analyze the image data of a user using image input to detect changes in emotions from facial expressions and posture. This enables efficient data collection by collecting data using the optimal means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input user input data into the generation AI and have the generation AI select the optimal collection means.

[0037] When collecting biometric data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. For example, the collection unit prioritizes collecting data indicating a stress level when the user is in the office. Furthermore, the collection unit can also prioritize collecting data indicating a relaxed state when the user is at home. Furthermore, the collection unit can prioritize collecting activity level and heart rate data when the user is out. For example, the collection unit can acquire the user's geographical location information using GPS data, and prioritize collecting data indicating a stress level when the user is in the office. Furthermore, the collection unit can acquire the user's geographical location information using a location information service, and prioritize collecting data indicating a relaxed state when the user is at home. Furthermore, the collection unit can acquire the user's geographical location information using GPS data, and prioritize collecting activity level and heart rate data when the user is out. This allows for more accurate data to be obtained by collecting highly relevant data based on the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's geographical location information to the generation AI and cause the generation AI to perform preferential collection of highly relevant data.

[0038] When collecting biometric data, the collection unit may analyze the user's social media activity and collect related data. Social media activity includes, but is not limited to, the content of posts and the number of likes. For example, if a user posts on social media that they are feeling stressed, the collection unit may prioritize collecting heart rate and body temperature data. Furthermore, if a user posts on social media that they are relaxing, the collection unit may prioritize collecting activity level data. Furthermore, if a user posts on social media that they are rushing, the collection unit may prioritize collecting heart rate and activity level data. For example, the collection unit may analyze the user's social media posts and prioritize collecting heart rate and body temperature data if they are feeling stressed. Furthermore, the collection unit may analyze the user's social media posts and prioritize collecting activity level data if they are relaxing. Furthermore, the collection unit may analyze the user's social media posts and prioritize collecting heart rate and activity level data if they are rushing. In this way, by collecting related data based on social media activity, the user's mental state can be more accurately understood. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's social media data into the generation AI and have the generation AI collect related data.

[0039] When collecting biometric data, the collection unit can customize the collection method by reflecting the user's past feedback. Feedback includes, for example, the user's ratings and comments, but is not limited to these examples. For example, if the user previously requested collection of heart rate data, the collection unit may prioritize collecting heart rate data. Furthermore, if the user previously requested collection of body temperature data, the collection unit may prioritize collecting body temperature data. Furthermore, if the user previously requested collection of activity amount data, the collection unit may prioritize collecting activity amount data. For example, the collection unit may analyze the user's past feedback and prioritize collecting heart rate data if the user previously requested collection of heart rate data. Furthermore, the collection unit may analyze the user's past feedback and prioritize collecting body temperature data if the user previously requested collection of body temperature data. Furthermore, the collection unit may analyze the user's past feedback and prioritize collecting activity amount data if the user previously requested collection of activity amount data. This allows data to be collected in a manner optimal for the user by reflecting past feedback. Some or all of the above-described processing by the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input the user's past feedback data into the generation AI and have the generation AI customize the collection method.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the biological data. The importance includes, for example, but is not limited to, the impact and urgency of the data. Adjusting the level of detail includes, for example, but is not limited to, adjusting the depth and scope of the analysis according to the importance. For example, the analysis unit performs a detailed analysis when a sudden change in heart rate is detected. The analysis unit can also perform a detailed analysis when an abnormal change in body temperature is detected. The analysis unit can also perform a detailed analysis when a sudden change in activity level is detected. For example, the analysis unit detects a sudden change in heart rate data and performs a detailed analysis. The analysis unit can also detect an abnormal change in body temperature data and perform a detailed analysis. The analysis unit can also detect a sudden change in activity level data and perform a detailed analysis. By adjusting the level of detail of the analysis according to the importance of the biological data, efficient analysis is possible. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input collected biometric data into the generation AI and have the generation AI adjust the level of detail of the analysis based on importance.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the category of biological data. The categories include, but are not limited to, for example, the type of data and its purpose. Applying an analysis algorithm includes, but is not limited to, for example, selecting an algorithm depending on the category. For example, the analysis unit applies a heart rate variability analysis algorithm to heart rate data. The analysis unit can also apply a body temperature variability analysis algorithm to body temperature data. The analysis unit can also apply an activity variability analysis algorithm to activity data. For example, the analysis unit applies a heart rate variability analysis algorithm to heart rate data to perform analysis. The analysis unit can also apply a body temperature variability analysis algorithm to body temperature data to perform analysis. The analysis unit can also apply an activity variability analysis algorithm to activity data to perform analysis. This enables accurate analysis by applying an appropriate analysis algorithm depending on the category of biological data. 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 collected biometric data into the generation AI and have the generation AI apply an analysis algorithm according to the category.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Past analysis results include, but are not limited to, past data analysis results and trends. For example, the analysis unit analyzes current heart rate data by referring to the user's past heart rate analysis results. The analysis unit can also analyze current body temperature data by referring to the user's past body temperature analysis results. The analysis unit can also analyze current activity data by referring to the user's past activity analysis results. For example, the analysis unit analyzes current heart rate data by referring to the user's past heart rate analysis results. The analysis unit can also analyze current body temperature data by referring to the user's past body temperature analysis results. The analysis unit can also analyze current activity data by referring to the user's past activity analysis results. In this way, by referring to the past analysis results, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or without AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0043] During analysis, the analysis unit can determine the priority of analysis based on the time of collection of biological data. The collection time includes, for example, but is not limited to, the timing and frequency of data collection. Determining the priority includes, for example, but is not limited to, setting the importance of data according to the time of collection. For example, the analysis unit prioritizes analysis of recently collected data. The analysis unit can also prioritize analysis of data collected before and after a specific event (e.g., a meeting, exercise, etc.). The analysis unit can also prioritize analysis of data in which an abnormality has been detected compared with the user's past data. For example, the analysis unit prioritizes analysis of recently collected heart rate data. The analysis unit can also prioritize analysis of body temperature data collected before and after a specific event. The analysis unit can also prioritize analysis of activity amount data in which an abnormality has been detected compared with the user's past data. This enables efficient analysis by determining the priority of analysis based on the time of collection. 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 collected biometric data into the generation AI and have the generation AI determine analysis priorities based on the time of collection.

[0044] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. Examples of the level of expertise include, but are not limited to, the user's occupation and educational background. Adjusting the use of technical terminology includes, but is not limited to, selecting terms according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide the analysis results using detailed technical terminology. For example, if the user does not have technical expertise, the analysis unit can provide the analysis results in simple language. The analysis unit can also adjust the level of detail of the analysis results according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide the analysis results using detailed technical terminology. For example, if the user does not have technical expertise, the analysis unit can provide the analysis results in simple language. The analysis unit can also adjust the level of detail of the analysis results according to the user's level of expertise. This allows the analysis results to be easily understood by providing them according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's expertise level data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0045] The suggestion unit may adjust the level of detail of the proposal based on the importance of the diagnostic result when making a proposal. The importance may include, but is not limited to, the impact and urgency of the diagnostic result, for example. The adjustment of the level of detail may include, but is not limited to, the depth and scope of the proposal based on the importance. For example, the suggestion unit may provide a detailed proposal for an important diagnostic result. The suggestion unit may also provide a concise proposal for a less important diagnostic result. The suggestion unit may also adjust the level of detail of the proposal based on the importance of the diagnostic result. For example, the suggestion unit may provide a detailed proposal for an important diagnostic result. The suggestion unit may also provide a concise proposal for a less important diagnostic result. The suggestion unit may also adjust the level of detail of the proposal based on the importance of the diagnostic result. This enables efficient proposals by adjusting the level of detail of the proposal based on the importance of the diagnostic result. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit may input diagnostic result data to a generation AI and cause the generation AI to adjust the level of detail of the proposal based on the importance.

[0046] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of the diagnostic result. The categories include, for example, but are not limited to, the type and purpose of the diagnostic result. Applying the suggestion algorithm includes, for example, but are not limited to, selecting an algorithm according to the category. For example, the suggestion unit can suggest stress reduction for a stress-related diagnostic result. The suggestion unit can also suggest relaxation methods for a relaxation-related diagnostic result. The suggestion unit can also suggest exercise for an activity-related diagnostic result. For example, the suggestion unit can suggest stress reduction for a stress-related diagnostic result. The suggestion unit can also suggest relaxation methods for a relaxation-related diagnostic result. The suggestion unit can also suggest exercise for an activity-related diagnostic result. This enables accurate suggestions by applying an appropriate suggestion algorithm depending on the category of the diagnostic result. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input diagnostic result data to a generation AI and cause the generation AI to apply a suggestion algorithm according to the category.

[0047] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. Past proposal results include, for example, past proposal content and effects, but are not limited to such examples. For example, the suggestion unit makes a current proposal by referring to the user's past proposal results. The suggestion unit can also prioritize effective proposals from the user's past proposal results. The suggestion unit can also analyze the user's past proposal results and make an optimal proposal. For example, the suggestion unit makes a current proposal by referring to the user's past proposal results. The suggestion unit can also prioritize effective proposals from the user's past proposal results. The suggestion unit can also analyze the user's past proposal results and make an optimal proposal. As a result, the accuracy of the proposal is improved by referring to the past proposal results. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past proposal result data to the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0048] When making a proposal, the suggestion unit can determine the priority of the proposal based on the time of submission of the diagnostic results. The submission time includes, for example, but is not limited to, the timing and frequency of submission of the diagnostic results. Determining the priority includes, for example, but is not limited to, setting the importance of the proposal according to the time of submission. The suggestion unit, for example, prioritizes the proposal based on the most recent diagnostic result. The suggestion unit can also make the proposal based on diagnostic results submitted before or after a specific event (e.g., a meeting, exercise, etc.). The suggestion unit can also make the proposal based on diagnostic results in which an abnormality is detected when compared with the user's past data. For example, the suggestion unit prioritizes the proposal based on the most recent diagnostic result. The suggestion unit can also make the proposal based on diagnostic results submitted before or after a specific event. The suggestion unit can also make the proposal based on diagnostic results in which an abnormality is detected when compared with the user's past data. This enables efficient proposals by determining the priority of the proposals based on the time of submission. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input diagnostic result data into the generation AI and have the generation AI determine the priority of suggestions based on the time of submission.

[0049] When making a proposal, the suggestion unit can adjust the order of suggestions based on the relevance of the diagnostic results. The relevance includes, for example, but is not limited to, the relevance or correlation of the diagnostic results. Adjusting the order includes, for example, but is not limited to, setting the order of suggestions based on the relevance. For example, when there are multiple stress-related diagnostic results, the suggestion unit can make suggestions in descending order of relevance. Furthermore, when there are multiple relaxation-related diagnostic results, the suggestion unit can make suggestions in descending order of relevance. Furthermore, when there are multiple activity-amount-related diagnostic results, the suggestion unit can make suggestions in descending order of relevance. For example, when there are multiple stress-related diagnostic results, the suggestion unit can make suggestions in descending order of relevance. Furthermore, when there are multiple relaxation-related diagnostic results, the suggestion unit can make suggestions in descending order of relevance. Thus, adjusting the order of suggestions based on the relevance of the diagnostic results enables efficient suggestions. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input diagnostic result data to the generation AI and cause the generation AI to adjust the order of suggestions based on relevance.

[0050] When making a proposal, the suggestion unit may adjust the use of technical terminology in the proposal according to the user's level of expertise. Examples of the level of expertise include, but are not limited to, the user's occupation and educational background. Adjusting the use of technical terminology may include, but is not limited to, selecting terms according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit may make the proposal using detailed technical terminology. For example, if the user does not have technical expertise, the suggestion unit may make the proposal using simple language. The suggestion unit may also adjust the level of detail in the proposal according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit may make the proposal using detailed technical terminology. For example, if the user does not have technical expertise, the suggestion unit may make the proposal using simple language. The suggestion unit may also adjust the level of detail in the proposal according to the user's level of expertise. This enables the proposal to be easy to understand by providing it according to the user's level of expertise. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit may input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0051] The audio output unit can adjust the level of detail of the audio based on the importance of the proposed content when outputting the audio. The level of importance includes, but is not limited to, for example, the impact and urgency of the proposed content. Adjusting the level of detail includes, but is not limited to, setting the level of detail of the audio according to the importance. For example, the audio output unit can output detailed audio when the proposed content is important. The audio output unit can also output concise audio when the proposed content is not so important. The audio output unit can also adjust the level of detail of the audio according to the importance of the proposed content. For example, the audio output unit can output detailed audio when the proposed content is important. The audio output unit can also output concise audio when the proposed content is not so important. The audio output unit can also adjust the level of detail of the audio according to the importance of the proposed content. As a result, adjusting the level of detail of the audio according to the importance of the proposed content enables efficient audio output. Some or all of the above-mentioned processing in the audio output unit may be performed using, for example, AI, or may be performed without using AI. For example, the audio output unit can input the proposal content data to the generation AI and cause the generation AI to adjust the level of detail of the audio based on the importance.

[0052] When outputting audio, the audio output unit can apply different audio output algorithms depending on the category of the suggested content. The category may include, but is not limited to, the type and purpose of the suggested content. Applying an audio output algorithm may include, but is not limited to, selecting an algorithm depending on the category. For example, the audio output unit may output audio in a calm tone for a suggestion to reduce stress. The audio output unit may also output audio in a bright tone for a suggestion to relax. The audio output unit may also output audio in a cheerful tone for a suggestion to exercise. For example, the audio output unit may output audio in a calm tone for a suggestion to reduce stress. The audio output unit may also output audio in a cheerful tone for a suggestion to relax. The audio output unit may also output audio in a cheerful tone for a suggestion to exercise. This allows accurate audio output by applying an appropriate audio output algorithm depending on the category of the suggested content. Some or all of the above-described processing in the audio output unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice output unit can input proposal content data to the generation AI and cause the generation AI to apply a voice output algorithm according to the category.

[0053] When outputting audio, the audio output unit can improve the accuracy of the audio output by referring to the user's past audio output results. Past audio output results include, for example, past audio output content and effects, but are not limited to these examples. For example, the audio output unit performs current audio output by referring to the user's past audio output results. The audio output unit can also prioritize effective audio output from the user's past audio output results. The audio output unit can also analyze the user's past audio output results and perform optimal audio output. For example, the audio output unit performs current audio output by referring to the user's past audio output results. The audio output unit can also prioritize effective audio output from the user's past audio output results. The audio output unit can also analyze the user's past audio output results and perform optimal audio output. As a result, the accuracy of the audio output is improved by referring to the past audio output results. Some or all of the above-mentioned processing in the audio output unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice output unit can input the user's past voice output result data into the generation AI and cause the generation AI to improve the accuracy of the voice output.

[0054] When outputting audio, the audio output unit can determine the priority of audio output based on the submission time of the proposal content. The submission time includes, for example, but is not limited to, the timing and frequency of submission of the proposal content. Determining the priority includes, for example, but is not limited to, setting the importance of audio output according to the submission time. The audio output unit, for example, prioritizes audio output of the most recent proposal content. The audio output unit can also prioritize audio output of proposal content submitted before or after a specific event (e.g., a meeting, exercise, etc.). The audio output unit can also prioritize audio output of proposal content for which an abnormality has been detected compared with the user's past data. For example, the audio output unit prioritizes audio output of the most recent proposal content. The audio output unit can also prioritize audio output of proposal content submitted before or after a specific event. The audio output unit can also prioritize audio output of proposal content for which an abnormality has been detected compared with the user's past data. In this way, determining the priority of audio output based on the submission time enables efficient audio output. Some or all of the above-described processing in the audio output unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice output unit can input proposal content data into the generation AI and have the generation AI determine the priority of voice output based on the submission time.

[0055] When outputting audio, the audio output unit can adjust the order of audio output based on the relevance of the suggested content. The relevance includes, for example, but is not limited to, the relevance or correlation of the suggested content. Adjusting the order includes, for example, but is not limited to, setting the order of audio output according to the relevance. For example, when there are multiple suggestions for stress reduction, the audio output unit can output the audio in order of relevance. Furthermore, when there are multiple suggestions for relaxation methods, the audio output unit can output the audio in order of relevance. Furthermore, when there are multiple suggestions for exercise, the audio output unit can output the audio in order of relevance. For example, when there are multiple suggestions for stress reduction, the audio output unit can output the audio in order of relevance. Furthermore, when there are multiple suggestions for relaxation methods, the audio output unit can output the audio in order of relevance. Furthermore, when there are multiple suggestions for exercise, the audio output unit can output the audio in order of relevance. Adjusting the order of audio output based on the relevance of the suggested content enables efficient audio output. Some or all of the above-described processing in the audio output unit may be performed using, for example, AI, or may be performed without using AI. For example, the audio output unit may input proposal content data to a generation AI and cause the generation AI to adjust the order of audio output based on relevance.

[0056] The audio output unit can adjust the use of technical terms in the audio output according to the user's level of expertise when outputting audio. Examples of the level of expertise include, but are not limited to, the user's occupation and educational background. Adjusting the use of technical terms includes, but is not limited to, selecting terms according to the user's level of expertise. For example, if the user has specialized knowledge, the audio output unit can use detailed technical terms to output the audio. Furthermore, if the user does not have specialized knowledge, the audio output unit can also use simple language to output the audio. Furthermore, the audio output unit can adjust the level of detail of the audio output according to the user's level of expertise. For example, if the user has specialized knowledge, the audio output unit can use detailed technical terms to output the audio. Furthermore, if the user does not have specialized knowledge, the audio output unit can also use simple language to output the audio. Furthermore, the audio output unit can adjust the level of detail of the audio output according to the user's level of expertise. This allows for audio output that is easy to understand by providing audio output according to the user's level of expertise. Some or all of the above-described processing in the audio output unit can be performed using, for example, AI, or without AI. For example, the voice output unit can input the user's expertise level data to the generation AI and cause the generation AI to adjust the use of technical terms.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] When collecting a user's biometric data, the collection unit can learn the user's activity patterns and automatically adjust the optimal data collection timing. For example, if the user jogs every morning, heart rate and activity data can be collected intensively during that time period. Furthermore, the frequency of collecting body temperature and heart rate data can be reduced during the user's relaxing time at night. Furthermore, the collection unit can identify times when the user is likely to feel stressed and strengthen data collection during those times. This enables efficient data collection that matches the user's lifestyle rhythm.

[0059] When analyzing collected biometric data, the analysis unit can improve the accuracy of the analysis by taking into account the user's past health history. For example, for a user who has been diagnosed with a high risk of heart disease in the past, the analysis of heart rate data can be focused. Also, for a user who has had stress-related problems in the past, the analysis of stress levels can be strengthened. Furthermore, past data can be compared with current data, and detailed analysis can be performed if abnormal fluctuations are found. This enables highly accurate analysis tailored to the user's individual health condition.

[0060] When making suggestions based on the diagnosis results, the suggestion unit can adjust the suggestions by taking into account the user's current environmental information. For example, if the user is in the office, the suggestion unit can suggest relaxation methods that can be performed in a short time. If the user is at home, the suggestion unit can suggest more detailed relaxation methods or longer rest periods. Furthermore, if the user is out, the suggestion unit can suggest easy stretches or deep breathing exercises. This makes it possible to make appropriate suggestions according to the user's environment.

[0061] When outputting the content suggested by the suggestion unit as a voice, the voice output unit can adjust the tone and speed of the voice by referring to the user's past voice output history. For example, if voice output in a calm tone has been effective in the past, the voice output unit can output in a similar tone. Also, if quick voice output has been preferred in the past, the voice output unit can output at a similar speed. Furthermore, the voice output history in the past can be analyzed to select the optimal voice output method. This makes it possible to provide the optimal voice output for the user.

[0062] The collection unit can prioritize collection of highly relevant data taking into account the user's geographical location information. For example, when the user is in the office, data indicating a stress level can be collected preferentially. When the user is at home, data indicating a relaxed state can be collected preferentially. Furthermore, when the user is out, data on activity level and heart rate can be collected preferentially. In this way, by collecting highly relevant data based on the user's geographical location information, more accurate data can be obtained.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The collection unit collects biological data. The biological data includes heart rate, body temperature, activity level, etc. For example, the collection unit collects heart rate data using a heart rate sensor, body temperature data using a body temperature sensor, and activity level data using an activity level sensor. Step 2: The analysis unit analyzes the biometric data collected by the collection unit and diagnoses the mental state. The analysis is performed using statistical analysis of the data and machine learning algorithms. For example, it determines that a sudden increase in heart rate data, fluctuations in body temperature, or fluctuations in activity level are signs of stress. Step 3: The suggestion section makes positive suggestions based on the diagnostic results obtained by the analysis section. These suggestions include relaxation techniques and encouraging words. For example, a suggestion might be, "After the meeting, your central nervous system is feeling disrupted. Taking a short rest will double your productivity." Step 4: The voice output unit outputs the content proposed by the suggestion unit by voice. The voice output is performed using voice synthesis technology. For example, the voice output unit outputs the content of the suggestion in a calm tone or a bright tone.

[0065] (Example 2) An integrated application according to an embodiment of the present invention is a system for detecting mental illnesses and providing mental care. In this system, a user wears a wearable device, collects biometric data collected during daily life, and a multimodal AI analyzes the data to diagnose the user's mental state. Based on the diagnosis, a voice robot provides positive suggestions to the user. For example, the system may suggest, "Your central nervous system is disrupted after the meeting. Taking a short rest will double your productivity." or "Did something bother you? I'd be happy to listen." This enables advance detection of mental illnesses and mental care, thereby preventing the onset of mental illnesses. For example, when a user feels stressed, the voice robot can provide appropriate advice, reducing stress and preventing the onset of mental illnesses. Furthermore, by understanding their own mental state, users can take appropriate measures in their daily lives. This is expected to reduce the number of people suffering from mental illnesses and improve people's well-being.

[0066] A mental care system according to an embodiment includes a collection unit, an analysis unit, a suggestion unit, and an audio output unit. The collection unit collects biological data. The biological data includes, for example, heart rate, body temperature, and activity level, but is not limited to these examples. For example, the collection unit collects heart rate data using a sensor that measures heart rate. The collection unit can also collect body temperature data using a sensor that measures body temperature. The collection unit can also collect activity level data using a sensor that measures activity level. For example, the collection unit measures a user's heart rate in real time using a heart rate sensor and collects data. The collection unit can also measure a user's body temperature in real time using a body temperature sensor and collect data. The collection unit can also measure a user's activity level in real time using an activity level sensor and collect data. The analysis unit analyzes the biological data collected by the collection unit and diagnoses the user's mental state. The analysis is performed using, for example, statistical analysis of data or a machine learning algorithm, but is not limited to these examples. For example, the analysis unit may statistically analyze collected heart rate data and determine that a sudden increase in heart rate is a sign of stress. The analysis unit may also statistically analyze collected body temperature data and determine that a change in body temperature is a sign of stress. The analysis unit may also statistically analyze collected activity data and determine that a change in activity is a sign of stress. For example, the analysis unit may use a machine learning algorithm to analyze collected heart rate data and estimate a stress level. The analysis unit may also use a machine learning algorithm to analyze collected body temperature data and estimate a stress level. The analysis unit may also use a machine learning algorithm to analyze collected activity data and estimate a stress level. The suggestion unit makes positive suggestions based on the diagnosis results obtained by the analysis unit. Examples of suggestions include, but are not limited to, relaxation techniques and encouraging words. For example, the suggestion unit may make a suggestion based on the diagnosis results, such as, "Your central nervous system is disrupted after the meeting. Taking a short rest will double your productivity." Based on the diagnosis results, the proposal department can also make suggestions such as, "Was there anything that bothered you? I'd be happy to listen if you'd like."The suggestion unit can also suggest relaxation methods based on the diagnosis results. For example, the suggestion unit can suggest deep breathing or meditation based on the diagnosis results. The suggestion unit can also suggest light exercise based on the diagnosis results. The suggestion unit can also suggest listening to relaxing music based on the diagnosis results. The audio output unit outputs the content suggested by the suggestion unit as audio. The audio output can be performed using, for example, voice synthesis technology, but is not limited to such an example. For example, the audio output unit can output the content suggested by the suggestion unit as audio using voice synthesis technology. The audio output unit can also adjust the tone and speed of the audio before outputting. For example, the audio output unit can output the content suggested by the suggestion unit in a calm tone. The audio output unit can also output the content suggested in a bright tone. The audio output unit can also output the content suggested in a quick and concise tone. As a result, the mental care system according to the embodiment can realize early detection of mental illness and mental care, and prevent the onset of mental illness. For example, when a user feels stressed, the voice robot can provide appropriate advice, thereby reducing stress and preventing the onset of mental illness. Furthermore, by allowing users to understand their own mental state, they can take appropriate measures in their daily lives, which is expected to reduce the number of patients with mental illness and improve people's well-being.

[0067] The collection unit can collect biological data such as heart rate, body temperature, and activity level in real time. Real-time includes, but is not limited to, the frequency and delay time of data collection. For example, the collection unit collects heart rate data in real time using a sensor that measures heart rate. The collection unit can also collect body temperature data in real time using a sensor that measures body temperature. The collection unit can also collect activity level data in real time using a sensor that measures activity level. For example, the collection unit measures the user's heart rate in real time using a heart rate sensor and collects data. The collection unit can also measure the user's body temperature in real time and collect data using a body temperature sensor. The collection unit can also measure the user's activity level in real time and collect data using an activity level sensor. By collecting biological data in real time, the user's mental state can be accurately understood. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data acquired by a heart rate sensor into the generation AI and have the generation AI perform real-time data analysis.

[0068] The analysis unit analyzes the collected biological data and can determine that a sudden increase in heart rate or a change in body temperature is a sign of stress. A sudden increase includes, for example, but is not limited to, a range or time range of heart rate fluctuations. A change in body temperature includes, for example, but is not limited to, a range or time range of body temperature fluctuations. For example, the analysis unit statistically analyzes the collected heart rate data and determines that a sudden increase in heart rate is a sign of stress. The analysis unit can also statistically analyze the collected body temperature data and determine that a change in body temperature is a sign of stress. The analysis unit can also statistically analyze the collected activity data and determine that a change in activity level is a sign of stress. For example, the analysis unit uses a machine learning algorithm to analyze the collected heart rate data and estimate a stress level. The analysis unit can also use a machine learning algorithm to analyze the collected body temperature data and estimate a stress level. The analysis unit can also use a machine learning algorithm to analyze the collected activity data and estimate a stress level. This allows for early detection of signs of stress and the implementation of appropriate measures. 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 may input collected biometric data into the generation AI and have the generation AI determine whether there are signs of stress.

[0069] The suggestion unit can make positive suggestions based on the diagnosis result, such as, "After the meeting, your central nervous system is disrupted. Taking a short rest will double your productivity." or "Did something bother you? I'd be happy to listen to you." Positive suggestions include, but are not limited to, relaxation techniques and words of encouragement. For example, the suggestion unit can make a suggestion based on the diagnosis result, such as, "After the meeting, your central nervous system is disrupted. Taking a short rest will double your productivity." The suggestion unit can also make a suggestion based on the diagnosis result, such as, "Did something bother you? I'd be happy to listen to you." The suggestion unit can also suggest relaxation techniques based on the diagnosis result. For example, the suggestion unit can suggest deep breathing or meditation based on the diagnosis result. The suggestion unit can also suggest light exercise based on the diagnosis result. The suggestion unit can also suggest listening to relaxing music based on the diagnosis result. This allows appropriate advice to be provided to the user, improving their mental state. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or without AI. For example, the suggestion unit can input the diagnosis results into the generation AI and cause the generation AI to generate positive suggestions.

[0070] The audio output unit can output the content proposed by the suggestion unit by audio. Examples of audio output include, but are not limited to, the type of speech synthesis technology and the tone and speed of the speech. The audio output unit can output the content proposed by the suggestion unit by audio, for example, using speech synthesis technology. The audio output unit can also adjust the tone and speed of the speech before outputting. For example, the audio output unit can output the proposed content in a calm tone. The audio output unit can also output the proposed content in a bright tone. The audio output unit can also output the proposed content in a quick and concise tone. This can provide audio advice to the user and improve their mental state. Some or all of the above-described processing in the audio output unit can be performed using, for example, AI, or can be performed without using AI. For example, the audio output unit can input the content proposed by the suggestion unit to a generation AI and have the generation AI perform speech synthesis.

[0071] The collection unit can estimate the user's emotion and adjust the frequency of biometric data collection based on the estimated user emotion. Examples of emotion estimation include, but are not limited to, facial expression recognition and voice analysis. Examples of adjusting the collection frequency include, but are not limited to, setting a collection interval according to the user's emotional state. For example, if the user is feeling stressed, the collection unit can collect heart rate and body temperature data more frequently. Also, if the user is relaxed, the collection unit can reduce the frequency of data collection and collect the minimum amount of data necessary. Also, if the user is in a hurry, the collection unit can prioritize collecting activity data and adjust the collection frequency of other data. For example, the collection unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Also, the collection unit can record the user's voice and estimate the emotion using voice analysis technology. Also, the collection unit can collect the user's biometric data (such as heart rate and body temperature) with a sensor and estimate the emotion using an emotion estimation algorithm. In this way, by adjusting the frequency of data collection according to the user's emotion, more accurate data can be collected. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input user emotion data to the generation AI and cause the generation AI to adjust the collection frequency.

[0072] The collection unit can analyze the user's past biological data and select the optimal collection timing. The optimal collection timing includes, but is not limited to, timing setting based on the results of past data analysis. For example, the collection unit can identify time periods when stress levels are high from the user's past data and intensively collect data during those time periods. The collection unit can also identify time periods when the user is relaxed from the user's past data and refrain from collecting data during those time periods. The collection unit can also collect data before and after specific events (e.g., meetings, exercise) from the user's past data. For example, the collection unit can analyze the user's past heart rate data to identify time periods when stress levels are high. The collection unit can also analyze the user's past body temperature data to identify time periods when the user is relaxed. The collection unit can also analyze the user's past activity data to collect data before and after specific events. This enables efficient data collection by collecting data at the optimal timing based on past data. Some or all of the above-described processing performed by the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input the user's past biometric data into the generation AI and have the generation AI select the optimal collection timing.

[0073] When collecting biometric data, the collection unit can filter the biometric data based on the user's current activity status. Filtering includes, but is not limited to, a method of selecting data according to the activity status. For example, when the user is exercising, the collection unit prioritizes collecting heart rate and activity level data and filters out other data. Furthermore, when the user is resting, the collection unit can prioritize collecting body temperature and heart rate data and filtering out activity level data. Furthermore, when the user is working, the collection unit can prioritize collecting data indicating a stress level and filtering out other data. For example, the collection unit can detect the user's activity status with a sensor and prioritize collecting heart rate and activity level data when the user is exercising. Furthermore, the collection unit can detect the user's activity status with a sensor and prioritize collecting body temperature and heart rate data when the user is resting. Furthermore, the collection unit can detect the user's activity status with a sensor and prioritize collecting stress level data when the user is working. This allows only necessary data to be collected by filtering data according to the user's activity status. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's activity status data into the generation AI and have the generation AI perform filtering.

[0074] When collecting biometric data, the collection unit can select the optimal collection means depending on the user's input method. The optimal collection means includes, but is not limited to, the type and placement of sensors. For example, when a user uses voice input, the collection unit analyzes the voice data to detect changes in stress and emotions. Furthermore, when a user uses text input, the collection unit can analyze the text data to detect changes in emotions. Furthermore, when a user uses image input, the collection unit can analyze the image data to detect changes in emotions from facial expressions and posture. For example, the collection unit analyzes the voice data of a user using voice input to detect changes in stress and emotions. Furthermore, the collection unit can analyze the text data of a user using text input to detect changes in emotions. Furthermore, the collection unit can analyze the image data of a user using image input to detect changes in emotions from facial expressions and posture. This enables efficient data collection by collecting data using the optimal means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input user input data into the generation AI and have the generation AI select the optimal collection means.

[0075] The collection unit can estimate the user's emotion and determine the priority of biometric data to be collected based on the estimated user's emotion. Examples of emotion estimation include, but are not limited to, facial expression recognition and voice analysis. Examples of prioritization include, but are not limited to, setting the importance of data according to the user's emotional state. For example, the collection unit prioritizes collecting heart rate and body temperature data when the user is stressed. The collection unit can also prioritize collecting activity level data when the user is relaxed. The collection unit can also prioritize collecting heart rate and activity level data when the user is in a hurry. For example, the collection unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The collection unit can also record the user's voice and estimate the emotion using voice analysis technology. The collection unit can also collect the user's biometric data (such as heart rate and body temperature) with a sensor and estimate the emotion using an emotion estimation algorithm. In this way, by determining the priority of data according to the user's emotion, important data can be collected preferentially. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input user emotion data to the generation AI and have the generation AI determine the priority of the biometric data to be collected.

[0076] When collecting biometric data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. For example, the collection unit prioritizes collecting data indicating a stress level when the user is in the office. Furthermore, the collection unit can also prioritize collecting data indicating a relaxed state when the user is at home. Furthermore, the collection unit can prioritize collecting activity level and heart rate data when the user is out. For example, the collection unit can acquire the user's geographical location information using GPS data, and prioritize collecting data indicating a stress level when the user is in the office. Furthermore, the collection unit can acquire the user's geographical location information using a location information service, and prioritize collecting data indicating a relaxed state when the user is at home. Furthermore, the collection unit can acquire the user's geographical location information using GPS data, and prioritize collecting activity level and heart rate data when the user is out. This allows for more accurate data to be obtained by collecting highly relevant data based on the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's geographical location information to the generation AI and cause the generation AI to perform preferential collection of highly relevant data.

[0077] When collecting biometric data, the collection unit may analyze the user's social media activity and collect related data. Social media activity includes, but is not limited to, the content of posts and the number of likes. For example, if a user posts on social media that they are feeling stressed, the collection unit may prioritize collecting heart rate and body temperature data. Furthermore, if a user posts on social media that they are relaxing, the collection unit may prioritize collecting activity level data. Furthermore, if a user posts on social media that they are rushing, the collection unit may prioritize collecting heart rate and activity level data. For example, the collection unit may analyze the user's social media posts and prioritize collecting heart rate and body temperature data if they are feeling stressed. Furthermore, the collection unit may analyze the user's social media posts and prioritize collecting activity level data if they are relaxing. Furthermore, the collection unit may analyze the user's social media posts and prioritize collecting heart rate and activity level data if they are rushing. In this way, by collecting related data based on social media activity, the user's mental state can be more accurately understood. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's social media data into the generation AI and have the generation AI collect related data.

[0078] When collecting biometric data, the collection unit can customize the collection method by reflecting the user's past feedback. Feedback includes, for example, the user's ratings and comments, but is not limited to these examples. For example, if the user previously requested collection of heart rate data, the collection unit may prioritize collecting heart rate data. Furthermore, if the user previously requested collection of body temperature data, the collection unit may prioritize collecting body temperature data. Furthermore, if the user previously requested collection of activity amount data, the collection unit may prioritize collecting activity amount data. For example, the collection unit may analyze the user's past feedback and prioritize collecting heart rate data if the user previously requested collection of heart rate data. Furthermore, the collection unit may analyze the user's past feedback and prioritize collecting body temperature data if the user previously requested collection of body temperature data. Furthermore, the collection unit may analyze the user's past feedback and prioritize collecting activity amount data if the user previously requested collection of activity amount data. This allows data to be collected in a manner optimal for the user by reflecting past feedback. Some or all of the above-described processing by the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input the user's past feedback data into the generation AI and have the generation AI customize the collection method.

[0079] The analysis unit can estimate the user's emotion and adjust the analysis algorithm based on the estimated user emotion. Estimating the emotion includes, but is not limited to, facial expression recognition and voice analysis. Adjusting the analysis algorithm includes, but is not limited to, setting algorithm parameters according to the user's emotional state. For example, if the user is feeling stressed, the analysis unit can prioritize stress-related data in the analysis. Furthermore, if the user is relaxed, the analysis unit can prioritize relaxation-related data in the analysis. Furthermore, if the user is in a hurry, the analysis unit can prioritize activity-related data in the analysis. For example, the analysis unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the analysis unit can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the analysis unit can collect the user's biometric data (such as heart rate and body temperature) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows for more accurate analysis by adjusting the analysis algorithm according to the user's emotion. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input user emotion data into the generation AI and have the generation AI adjust the analysis algorithm.

[0080] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the biological data. The importance includes, for example, but is not limited to, the impact and urgency of the data. Adjusting the level of detail includes, for example, but is not limited to, adjusting the depth and scope of the analysis according to the importance. For example, the analysis unit performs a detailed analysis when a sudden change in heart rate is detected. The analysis unit can also perform a detailed analysis when an abnormal change in body temperature is detected. The analysis unit can also perform a detailed analysis when a sudden change in activity level is detected. For example, the analysis unit detects a sudden change in heart rate data and performs a detailed analysis. The analysis unit can also detect an abnormal change in body temperature data and perform a detailed analysis. The analysis unit can also detect a sudden change in activity level data and perform a detailed analysis. By adjusting the level of detail of the analysis according to the importance of the biological data, efficient analysis is possible. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input collected biometric data into the generation AI and have the generation AI adjust the level of detail of the analysis based on importance.

[0081] During analysis, the analysis unit can apply different analysis algorithms depending on the category of biological data. The categories include, but are not limited to, for example, the type of data and its purpose. Applying an analysis algorithm includes, but is not limited to, for example, selecting an algorithm depending on the category. For example, the analysis unit applies a heart rate variability analysis algorithm to heart rate data. The analysis unit can also apply a body temperature variability analysis algorithm to body temperature data. The analysis unit can also apply an activity variability analysis algorithm to activity data. For example, the analysis unit applies a heart rate variability analysis algorithm to heart rate data to perform analysis. The analysis unit can also apply a body temperature variability analysis algorithm to body temperature data to perform analysis. The analysis unit can also apply an activity variability analysis algorithm to activity data to perform analysis. This enables accurate analysis by applying an appropriate analysis algorithm depending on the category of biological data. 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 collected biometric data into the generation AI and have the generation AI apply an analysis algorithm according to the category.

[0082] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Past analysis results include, but are not limited to, past data analysis results and trends. For example, the analysis unit analyzes current heart rate data by referring to the user's past heart rate analysis results. The analysis unit can also analyze current body temperature data by referring to the user's past body temperature analysis results. The analysis unit can also analyze current activity data by referring to the user's past activity analysis results. For example, the analysis unit analyzes current heart rate data by referring to the user's past heart rate analysis results. The analysis unit can also analyze current body temperature data by referring to the user's past body temperature analysis results. The analysis unit can also analyze current activity data by referring to the user's past activity analysis results. In this way, by referring to the past analysis results, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or without AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0083] The analysis unit can estimate the user's emotions and determine an analysis priority based on the estimated user emotions. Examples of emotion estimation include, but are not limited to, facial expression recognition and voice analysis. Examples of priority determination include, but are not limited to, setting the importance of data according to the user's emotional state. For example, if the user is feeling stressed, the analysis unit can prioritize analyzing stress-related data. Also, if the user is relaxed, the analysis unit can prioritize analyzing relaxation-related data. Also, if the user is in a hurry, the analysis unit can prioritize analyzing activity-related data. For example, the analysis unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Also, the analysis unit can record the user's voice and estimate the emotion using voice analysis technology. Also, the analysis unit can collect the user's biometric data (such as heart rate and body temperature) with a sensor and estimate the emotion using an emotion estimation algorithm. In this way, by determining the analysis priority according to the user's emotions, important data can be analyzed preferentially. 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 may input user emotion data to the generation AI and have the generation AI determine the priority of analysis.

[0084] During analysis, the analysis unit can determine the priority of analysis based on the time of collection of biological data. The collection time includes, for example, but is not limited to, the timing and frequency of data collection. Determining the priority includes, for example, but is not limited to, setting the importance of data according to the time of collection. For example, the analysis unit prioritizes analysis of recently collected data. The analysis unit can also prioritize analysis of data collected before and after a specific event (e.g., a meeting, exercise, etc.). The analysis unit can also prioritize analysis of data in which an abnormality has been detected compared with the user's past data. For example, the analysis unit prioritizes analysis of recently collected heart rate data. The analysis unit can also prioritize analysis of body temperature data collected before and after a specific event. The analysis unit can also prioritize analysis of activity amount data in which an abnormality has been detected compared with the user's past data. This enables efficient analysis by determining the priority of analysis based on the time of collection. 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 collected biometric data into the generation AI and have the generation AI determine analysis priorities based on the time of collection.

[0085] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. Examples of the level of expertise include, but are not limited to, the user's occupation and educational background. Adjusting the use of technical terminology includes, but is not limited to, selecting terms according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide the analysis results using detailed technical terminology. For example, if the user does not have technical expertise, the analysis unit can provide the analysis results in simple language. The analysis unit can also adjust the level of detail of the analysis results according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide the analysis results using detailed technical terminology. For example, if the user does not have technical expertise, the analysis unit can provide the analysis results in simple language. The analysis unit can also adjust the level of detail of the analysis results according to the user's level of expertise. This allows the analysis results to be easily understood by providing them according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's expertise level data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0086] The suggestion unit can estimate the user's emotion and adjust the way the suggestion is expressed based on the estimated user's emotion. Estimating the emotion includes, but is not limited to, facial expression recognition and voice analysis. Adjusting the way the suggestion is expressed includes, but is not limited to, selecting an expression based on the user's emotional state. For example, if the user is stressed, the suggestion unit can make a suggestion using gentle words. If the user is relaxed, the suggestion unit can also make a detailed suggestion. If the user is in a hurry, the suggestion unit can also make a concise suggestion. For example, the suggestion unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The suggestion unit can also record the user's voice and estimate the emotion using voice analysis technology. The suggestion unit can also collect the user's biometric data (such as heart rate and body temperature) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows for more effective suggestions by adjusting the way the suggestion is expressed based on the user's emotion. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can input the user's emotional data into the generation AI and have the generation AI adjust the way the suggestion is expressed.

[0087] The suggestion unit may adjust the level of detail of the proposal based on the importance of the diagnostic result when making a proposal. The importance may include, but is not limited to, the impact and urgency of the diagnostic result, for example. The adjustment of the level of detail may include, but is not limited to, the depth and scope of the proposal based on the importance. For example, the suggestion unit may provide a detailed proposal for an important diagnostic result. The suggestion unit may also provide a concise proposal for a less important diagnostic result. The suggestion unit may also adjust the level of detail of the proposal based on the importance of the diagnostic result. For example, the suggestion unit may provide a detailed proposal for an important diagnostic result. The suggestion unit may also provide a concise proposal for a less important diagnostic result. The suggestion unit may also adjust the level of detail of the proposal based on the importance of the diagnostic result. This enables efficient proposals by adjusting the level of detail of the proposal based on the importance of the diagnostic result. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit may input diagnostic result data to a generation AI and cause the generation AI to adjust the level of detail of the proposal based on the importance.

[0088] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of the diagnostic result. The categories include, for example, but are not limited to, the type and purpose of the diagnostic result. Applying the suggestion algorithm includes, for example, but are not limited to, selecting an algorithm according to the category. For example, the suggestion unit can suggest stress reduction for a stress-related diagnostic result. The suggestion unit can also suggest relaxation methods for a relaxation-related diagnostic result. The suggestion unit can also suggest exercise for an activity-related diagnostic result. For example, the suggestion unit can suggest stress reduction for a stress-related diagnostic result. The suggestion unit can also suggest relaxation methods for a relaxation-related diagnostic result. The suggestion unit can also suggest exercise for an activity-related diagnostic result. This enables accurate suggestions by applying an appropriate suggestion algorithm depending on the category of the diagnostic result. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input diagnostic result data to a generation AI and cause the generation AI to apply a suggestion algorithm according to the category.

[0089] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. Past proposal results include, for example, past proposal content and effects, but are not limited to such examples. For example, the suggestion unit makes a current proposal by referring to the user's past proposal results. The suggestion unit can also prioritize effective proposals from the user's past proposal results. The suggestion unit can also analyze the user's past proposal results and make an optimal proposal. For example, the suggestion unit makes a current proposal by referring to the user's past proposal results. The suggestion unit can also prioritize effective proposals from the user's past proposal results. The suggestion unit can also analyze the user's past proposal results and make an optimal proposal. As a result, the accuracy of the proposal is improved by referring to the past proposal results. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past proposal result data to the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0090] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. Estimating the emotion includes, but is not limited to, facial expression recognition and voice analysis. Adjusting the length of the suggestion includes, but is not limited to, setting the length of the suggestion according to the user's emotional state. For example, if the user is stressed, the suggestion unit can provide short and to-the-point suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions. For example, the suggestion unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The suggestion unit can also record the user's voice and estimate the emotion using voice analysis technology. The suggestion unit can also collect the user's biometric data (such as heart rate and body temperature) with a sensor and estimate the emotion using an emotion estimation algorithm. This enables more effective suggestions by adjusting the length of the suggestion based on the user's emotion. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can input the user's emotional data into the generation AI and cause the generation AI to adjust the length of the suggestion.

[0091] When making a proposal, the suggestion unit can determine the priority of the proposal based on the time of submission of the diagnostic results. The submission time includes, for example, but is not limited to, the timing and frequency of submission of the diagnostic results. Determining the priority includes, for example, but is not limited to, setting the importance of the proposal according to the time of submission. The suggestion unit, for example, prioritizes the proposal based on the most recent diagnostic result. The suggestion unit can also make the proposal based on diagnostic results submitted before or after a specific event (e.g., a meeting, exercise, etc.). The suggestion unit can also make the proposal based on diagnostic results in which an abnormality is detected when compared with the user's past data. For example, the suggestion unit prioritizes the proposal based on the most recent diagnostic result. The suggestion unit can also make the proposal based on diagnostic results submitted before or after a specific event. The suggestion unit can also make the proposal based on diagnostic results in which an abnormality is detected when compared with the user's past data. This enables efficient proposals by determining the priority of the proposals based on the time of submission. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input diagnostic result data into the generation AI and have the generation AI determine the priority of suggestions based on the time of submission.

[0092] When making a proposal, the suggestion unit can adjust the order of suggestions based on the relevance of the diagnostic results. The relevance includes, for example, but is not limited to, the relevance or correlation of the diagnostic results. Adjusting the order includes, for example, but is not limited to, setting the order of suggestions based on the relevance. For example, when there are multiple stress-related diagnostic results, the suggestion unit can make suggestions in descending order of relevance. Furthermore, when there are multiple relaxation-related diagnostic results, the suggestion unit can make suggestions in descending order of relevance. Furthermore, when there are multiple activity-amount-related diagnostic results, the suggestion unit can make suggestions in descending order of relevance. For example, when there are multiple stress-related diagnostic results, the suggestion unit can make suggestions in descending order of relevance. Furthermore, when there are multiple relaxation-related diagnostic results, the suggestion unit can make suggestions in descending order of relevance. Thus, adjusting the order of suggestions based on the relevance of the diagnostic results enables efficient suggestions. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input diagnostic result data to the generation AI and cause the generation AI to adjust the order of suggestions based on relevance.

[0093] When making a proposal, the suggestion unit may adjust the use of technical terminology in the proposal according to the user's level of expertise. Examples of the level of expertise include, but are not limited to, the user's occupation and educational background. Adjusting the use of technical terminology may include, but is not limited to, selecting terms according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit may make the proposal using detailed technical terminology. For example, if the user does not have technical expertise, the suggestion unit may make the proposal using simple language. The suggestion unit may also adjust the level of detail in the proposal according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit may make the proposal using detailed technical terminology. For example, if the user does not have technical expertise, the suggestion unit may make the proposal using simple language. The suggestion unit may also adjust the level of detail in the proposal according to the user's level of expertise. This enables the proposal to be easy to understand by providing it according to the user's level of expertise. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit may input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0094] The audio output unit can estimate the user's emotion and adjust the tone of the audio output based on the estimated user's emotion. Estimating the emotion includes, but is not limited to, facial expression recognition and voice analysis. Adjusting the tone includes, but is not limited to, setting the tone of the audio according to the user's emotional state. For example, the audio output unit can output audio in a calm tone when the user is stressed. The audio output unit can also output audio in a bright tone when the user is relaxed. The audio output unit can also output audio in a quick and concise tone when the user is in a hurry. For example, the audio output unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The audio output unit can also record the user's voice and estimate the emotion using voice analysis technology. The audio output unit can also collect the user's biometric data (such as heart rate and body temperature) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows for more effective audio output by adjusting the tone of the audio output according to the user's emotion. Some or all of the above-described processing in the audio output unit may be performed using, for example, AI, or may be performed without using AI. For example, the audio output unit may input user emotion data to the generation AI and have the generation AI adjust the tone of the audio output.

[0095] The audio output unit can adjust the level of detail of the audio based on the importance of the proposed content when outputting the audio. The level of importance includes, but is not limited to, for example, the impact and urgency of the proposed content. Adjusting the level of detail includes, but is not limited to, setting the level of detail of the audio according to the importance. For example, the audio output unit can output detailed audio when the proposed content is important. The audio output unit can also output concise audio when the proposed content is not so important. The audio output unit can also adjust the level of detail of the audio according to the importance of the proposed content. For example, the audio output unit can output detailed audio when the proposed content is important. The audio output unit can also output concise audio when the proposed content is not so important. The audio output unit can also adjust the level of detail of the audio according to the importance of the proposed content. As a result, adjusting the level of detail of the audio according to the importance of the proposed content enables efficient audio output. Some or all of the above-mentioned processing in the audio output unit may be performed using, for example, AI, or may be performed without using AI. For example, the audio output unit can input the proposal content data to the generation AI and cause the generation AI to adjust the level of detail of the audio based on the importance.

[0096] When outputting audio, the audio output unit can apply different audio output algorithms depending on the category of the suggested content. The category may include, but is not limited to, the type and purpose of the suggested content. Applying an audio output algorithm may include, but is not limited to, selecting an algorithm depending on the category. For example, the audio output unit may output audio in a calm tone for a suggestion to reduce stress. The audio output unit may also output audio in a bright tone for a suggestion to relax. The audio output unit may also output audio in a cheerful tone for a suggestion to exercise. For example, the audio output unit may output audio in a calm tone for a suggestion to reduce stress. The audio output unit may also output audio in a cheerful tone for a suggestion to relax. The audio output unit may also output audio in a cheerful tone for a suggestion to exercise. This allows accurate audio output by applying an appropriate audio output algorithm depending on the category of the suggested content. Some or all of the above-described processing in the audio output unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice output unit can input proposal content data to the generation AI and cause the generation AI to apply a voice output algorithm according to the category.

[0097] When outputting audio, the audio output unit can improve the accuracy of the audio output by referring to the user's past audio output results. Past audio output results include, for example, past audio output content and effects, but are not limited to these examples. For example, the audio output unit performs current audio output by referring to the user's past audio output results. The audio output unit can also prioritize effective audio output from the user's past audio output results. The audio output unit can also analyze the user's past audio output results and perform optimal audio output. For example, the audio output unit performs current audio output by referring to the user's past audio output results. The audio output unit can also prioritize effective audio output from the user's past audio output results. The audio output unit can also analyze the user's past audio output results and perform optimal audio output. As a result, the accuracy of the audio output is improved by referring to the past audio output results. Some or all of the above-mentioned processing in the audio output unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice output unit can input the user's past voice output result data into the generation AI and cause the generation AI to improve the accuracy of the voice output.

[0098] The audio output unit can estimate the user's emotion and adjust the speed of the audio output based on the estimated user's emotion. Estimating the emotion includes, but is not limited to, facial expression recognition and voice analysis. Adjusting the speed includes, but is not limited to, setting the speed of the audio according to the user's emotional state. For example, the audio output unit can output audio at a slower speed when the user is stressed. Furthermore, the audio output unit can output audio at a normal speed when the user is relaxed. Furthermore, the audio output unit can output audio at a faster speed when the user is in a hurry. For example, the audio output unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the audio output unit can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the audio output unit can collect the user's biometric data (such as heart rate and body temperature) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows for more effective audio output by adjusting the speed of the audio output according to the user's emotion. Some or all of the above-described processing in the audio output unit may be performed using, for example, AI, or may be performed without using AI. For example, the audio output unit may input user emotion data to the generation AI and have the generation AI adjust the speed of the audio output.

[0099] When outputting audio, the audio output unit can determine the priority of audio output based on the submission time of the proposal content. The submission time includes, for example, but is not limited to, the timing and frequency of submission of the proposal content. Determining the priority includes, for example, but is not limited to, setting the importance of audio output according to the submission time. The audio output unit, for example, prioritizes audio output of the most recent proposal content. The audio output unit can also prioritize audio output of proposal content submitted before or after a specific event (e.g., a meeting, exercise, etc.). The audio output unit can also prioritize audio output of proposal content for which an abnormality has been detected compared with the user's past data. For example, the audio output unit prioritizes audio output of the most recent proposal content. The audio output unit can also prioritize audio output of proposal content submitted before or after a specific event. The audio output unit can also prioritize audio output of proposal content for which an abnormality has been detected compared with the user's past data. In this way, determining the priority of audio output based on the submission time enables efficient audio output. Some or all of the above-described processing in the audio output unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice output unit can input proposal content data into the generation AI and have the generation AI determine the priority of voice output based on the submission time.

[0100] When outputting audio, the audio output unit can adjust the order of audio output based on the relevance of the suggested content. The relevance includes, for example, but is not limited to, the relevance or correlation of the suggested content. Adjusting the order includes, for example, but is not limited to, setting the order of audio output according to the relevance. For example, when there are multiple suggestions for stress reduction, the audio output unit can output the audio in order of relevance. Furthermore, when there are multiple suggestions for relaxation methods, the audio output unit can output the audio in order of relevance. Furthermore, when there are multiple suggestions for exercise, the audio output unit can output the audio in order of relevance. For example, when there are multiple suggestions for stress reduction, the audio output unit can output the audio in order of relevance. Furthermore, when there are multiple suggestions for relaxation methods, the audio output unit can output the audio in order of relevance. Furthermore, when there are multiple suggestions for exercise, the audio output unit can output the audio in order of relevance. Adjusting the order of audio output based on the relevance of the suggested content enables efficient audio output. Some or all of the above-described processing in the audio output unit may be performed using, for example, AI, or may be performed without using AI. For example, the audio output unit may input proposal content data to a generation AI and cause the generation AI to adjust the order of audio output based on relevance.

[0101] The audio output unit can adjust the use of technical terms in the audio output according to the user's level of expertise when outputting audio. Examples of the level of expertise include, but are not limited to, the user's occupation and educational background. Adjusting the use of technical terms includes, but is not limited to, selecting terms according to the user's level of expertise. For example, if the user has specialized knowledge, the audio output unit can use detailed technical terms to output the audio. Furthermore, if the user does not have specialized knowledge, the audio output unit can also use simple language to output the audio. Furthermore, the audio output unit can adjust the level of detail of the audio output according to the user's level of expertise. For example, if the user has specialized knowledge, the audio output unit can use detailed technical terms to output the audio. Furthermore, if the user does not have specialized knowledge, the audio output unit can also use simple language to output the audio. Furthermore, the audio output unit can adjust the level of detail of the audio output according to the user's level of expertise. This allows for audio output that is easy to understand by providing audio output according to the user's level of expertise. Some or all of the above-described processing in the audio output unit can be performed using, for example, AI, or without AI. For example, the voice output unit can input the user's expertise level data to the generation AI and cause the generation AI to adjust the use of technical terms. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, suggestion unit, and audio output unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects biometric data using a heart rate sensor or a body temperature sensor of the smart device 14. The analysis unit analyzes the collected biometric data by the specific processing unit 290 of the data processing device 12 and diagnoses the mental state. The suggestion unit generates positive suggestions based on the diagnosis results by the specific processing unit 290 of the data processing device 12. The audio output unit outputs the suggestions by audio via the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, suggestion unit, and audio output 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 collection unit collects biometric data using a heart rate sensor or a body temperature sensor of the smart glasses 214. The analysis unit analyzes the collected biometric data by the specific processing unit 290 of the data processing device 12 and diagnoses the mental state. The suggestion unit generates positive suggestions based on the diagnosis results by the specific processing unit 290 of the data processing device 12. The audio output unit outputs the suggestions by audio via the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, suggestion unit, and audio output 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 collection unit collects biometric data using a heart rate sensor or a body temperature sensor of the headset type terminal 314. The analysis unit analyzes the collected biometric data by the specific processing unit 290 of the data processing device 12 and diagnoses the mental state. The suggestion unit generates a positive suggestion based on the diagnosis result by the specific processing unit 290 of the data processing device 12. The audio output unit outputs the suggestion content by voice via the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, suggestion unit, and audio output unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects biometric data using a heart rate sensor or a body temperature sensor of the robot 414. The analysis unit analyzes the collected biometric data by the specific processing unit 290 of the data processing device 12 and diagnoses the mental state. The suggestion unit generates a positive suggestion based on the diagnosis result by the specific processing unit 290 of the data processing device 12. The audio output unit outputs the suggestion content by voice via the control unit 46A of the robot 414.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] When collecting a user's biometric data, the collection unit can learn the user's activity patterns and automatically adjust the optimal data collection timing. For example, if the user jogs every morning, heart rate and activity data can be collected intensively during that time period. Furthermore, the frequency of collecting body temperature and heart rate data can be reduced during the user's relaxing time at night. Furthermore, the collection unit can identify times when the user is likely to feel stressed and strengthen data collection during those times. This enables efficient data collection that matches the user's lifestyle rhythm.

[0104] When analyzing collected biometric data, the analysis unit can improve the accuracy of the analysis by taking into account the user's past health history. For example, for a user who has been diagnosed with a high risk of heart disease in the past, the analysis of heart rate data can be focused. Also, for a user who has had stress-related problems in the past, the analysis of stress levels can be strengthened. Furthermore, past data can be compared with current data, and detailed analysis can be performed if abnormal fluctuations are found. This enables highly accurate analysis tailored to the user's individual health condition.

[0105] When making suggestions based on the diagnosis results, the suggestion unit can adjust the suggestions by taking into account the user's current environmental information. For example, if the user is in the office, the suggestion unit can suggest relaxation methods that can be performed in a short time. If the user is at home, the suggestion unit can suggest more detailed relaxation methods or longer rest periods. Furthermore, if the user is out, the suggestion unit can suggest easy stretches or deep breathing exercises. This makes it possible to make appropriate suggestions according to the user's environment.

[0106] When outputting the content suggested by the suggestion unit as a voice, the voice output unit can adjust the tone and speed of the voice by referring to the user's past voice output history. For example, if voice output in a calm tone has been effective in the past, the voice output unit can output in a similar tone. Also, if quick voice output has been preferred in the past, the voice output unit can output at a similar speed. Furthermore, the voice output history in the past can be analyzed to select the optimal voice output method. This makes it possible to provide the optimal voice output for the user.

[0107] The collection unit can estimate the user's emotions and adjust the type of biometric data to be collected based on the estimated emotions. For example, if the user is feeling stressed, data on heart rate and body temperature can be collected with priority. Also, if the user is relaxed, data on activity level can be collected with priority. Furthermore, if the user is in a hurry, data on heart rate and activity level can be collected with priority. This makes it possible to collect appropriate data according to the user's emotions.

[0108] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is feeling stressed, the analysis can prioritize stress-related data. If the user is relaxed, the analysis can prioritize relaxation-related data. Furthermore, if the user is in a hurry, the analysis can prioritize activity-related data. This enables appropriate analysis according to the user's emotions.

[0109] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated emotions. For example, if the user is feeling stressed, the suggestion unit can make gentle suggestions. If the user is relaxed, the suggestion unit can make detailed suggestions. If the user is in a hurry, the suggestion unit can make concise suggestions. This makes it possible to make appropriate suggestions according to the user's emotions.

[0110] The audio output unit can estimate the user's emotions and adjust the tone of the audio output based on the estimated emotions. For example, if the user is feeling stressed, the audio output can be in a calm tone. If the user is relaxed, the audio output can be in a bright tone. Furthermore, if the user is in a hurry, the audio output can be in a quick and concise tone. This makes it possible to output appropriate audio according to the user's emotions.

[0111] The audio output unit can estimate the user's emotion and adjust the audio output speed based on the estimated emotion. For example, if the user is feeling stressed, the audio output can be performed at a slow speed. If the user is relaxed, the audio output can be performed at a normal speed. Furthermore, if the user is in a hurry, the audio output can be performed at a fast speed. This makes it possible to output appropriate audio according to the user's emotion.

[0112] The collection unit can prioritize collection of highly relevant data taking into account the user's geographical location information. For example, when the user is in the office, data indicating a stress level can be collected preferentially. When the user is at home, data indicating a relaxed state can be collected preferentially. Furthermore, when the user is out, data on activity level and heart rate can be collected preferentially. In this way, by collecting highly relevant data based on the user's geographical location information, more accurate data can be obtained.

[0113] The processing flow of the second embodiment will be briefly explained below.

[0114] Step 1: The collection unit collects biological data. The biological data includes heart rate, body temperature, activity level, etc. For example, the collection unit collects heart rate data using a heart rate sensor, body temperature data using a body temperature sensor, and activity level data using an activity level sensor. Step 2: The analysis unit analyzes the biometric data collected by the collection unit and diagnoses the mental state. The analysis is performed using statistical analysis of the data and machine learning algorithms. For example, it determines that a sudden increase in heart rate data, fluctuations in body temperature, or fluctuations in activity level are signs of stress. Step 3: The suggestion section makes positive suggestions based on the diagnostic results obtained by the analysis section. These suggestions include relaxation techniques and encouraging words. For example, a suggestion might be, "After the meeting, your central nervous system is feeling disrupted. Taking a short rest will double your productivity." Step 4: The voice output unit outputs the content proposed by the suggestion unit by voice. The voice output is performed using voice synthesis technology. For example, the voice output unit outputs the content of the suggestion in a calm tone or a bright tone.

[0115] 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.

[0116] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] 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.

[0118] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0119] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] 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.

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

[0136] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[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 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.

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

[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 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.

[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 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.

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

[0149] 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.

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

[0152] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[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 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.

[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 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).

[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] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0166] 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.

[0167] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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).

[0172] 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.

[0173] 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."

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] [Explanation of symbols]

[0187] 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 collection unit that collects biometric data; an analysis unit that analyzes the biological data collected by the collection unit and diagnoses the mental state; a suggestion unit that makes positive suggestions based on the diagnosis results obtained by the analysis unit; an audio output unit that outputs the content proposed by the proposal unit by voice; A system characterized by:

2. The collecting unit Collects real-time biometric data such as heart rate, body temperature, and activity levels 2. The system of claim 1.

3. The analysis unit The collected biometric data is analyzed to determine if a sudden increase in heart rate or fluctuations in body temperature are signs of stress.

2. The system of claim 1.

4. The audio output unit The content proposed by the proposal unit is output by voice.

2. The system of claim 1.

5. The collecting unit The method estimates the user's emotions and adjusts the frequency of biometric data collection based on the estimated user emotions.

2. The system of claim 1.

6. The collecting unit Analyze the user's past biometric data and select the optimal collection timing 2. The system of claim 1.

7. The collecting unit When collecting biometric data, filtering is performed based on the user's current activity status.

2. The system of claim 1.

8. The collecting unit When collecting biometric data, select the optimal collection method depending on the user's input method.

2. The system of claim 1.

9. The collecting unit Estimate the user's emotions and prioritize the biometric data to be collected based on the estimated user emotions.

2. The system of claim 1.

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