System

Abstract

To provide a system which analyzes a user's biological sensor data, and proposes an appropriate relaxation method by evaluating a psychological health state.SOLUTION: A system includes a collection part, an analysis part, a provision part, a dialog part, an evaluation part, and a proposal part. The collection part collects biological sensor data of a user. The analysis part analyzes the biological sensor data collected by the collection part. The provision part provides the user with an analysis result according to the analysis part. The dialog part conducts a specific dialog content with the user in accordance with the user's age. The evaluation part evaluates a psychological health state of the user, based on the specific dialog with the user by the dialog part. The proposal part proposes a relaxation method, based on a stress factor specified by the evaluation part.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have the problem that they do not adequately analyze a user's biosensor data, but rather evaluate their psychological health state and suggest appropriate relaxation methods.

[0005] The system according to the embodiment aims to analyze biosensor data of a user, evaluate the psychological health state, and suggest an appropriate relaxation method. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a provision unit, a conversation unit, an evaluation unit, and a suggestion unit. The collection unit collects biosensor data of a user. The analysis unit analyzes the biosensor data collected by the collection unit. The provision unit provides the user with the analysis results by the analysis unit. The conversation unit has specific conversation content with the user according to the user's age. The evaluation unit evaluates the user's psychological health state based on the specific conversation between the conversation unit and the user. The suggestion unit suggests a relaxation method based on the stress factors identified by the evaluation unit. Effect of the Invention

[0007] The system according to the embodiment can analyze the biosensor data of the user, evaluate the psychological health state, and suggest appropriate relaxation methods. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The AI ​​healthcare system according to the embodiment of the present invention is a system that can be used by a wide range of age groups, from elementary school students to the elderly. This system has a function of collecting biosensor data (such as heart rate) of the user and analyzing the data with AI. It also includes a function of generating a conversation according to the user's age and evaluating the psychological health state through text and voice. This allows the user to talk to the AI ​​about things that they cannot talk about with other people, making it possible to quickly and accurately identify stress factors. Furthermore, based on the identified stress factors, the AI ​​proposes relaxation methods and assists instructors and caregivers. For example, the user's biosensor data is collected. For example, data such as heart rate and body temperature is collected. Next, the collected data is analyzed by the AI. The analysis results are provided to the user. Furthermore, the AI ​​generates a conversation according to the user's age and has a conversation with the user. Based on this conversation, the AI ​​evaluates the user's psychological health state. Based on the evaluation results, the AI ​​identifies stress factors and proposes relaxation methods. For example, for elementary school students, the AI ​​generates a conversation about school life and friendships to evaluate the psychological health state. For elderly people, the AI ​​generates a conversation about health and daily life to evaluate the psychological health state. This makes it possible to suggest relaxation methods suitable for each age group. The system can also be used as an assistant to instructors and caregivers. For example, using the system in nursing facilities makes it easier for caregivers to understand the psychological health of users and provide appropriate care. Using the system in schools also makes it easier for teachers to understand the psychological health of students and provide appropriate guidance. This allows the AI ​​healthcare system to quickly and accurately evaluate the user's psychological health and suggest appropriate relaxation methods.

[0029] The AI ​​healthcare system according to the embodiment includes a collection unit, an analysis unit, a provision unit, a conversation unit, an evaluation unit, and a proposal unit. The collection unit collects biosensor data of a user. The biosensor data of a user includes, for example, a heart rate, blood pressure, body temperature, and the like, but is not limited to such examples. The collection unit collects heart rate data using, for example, a heart rate sensor. The collection unit can also collect body temperature data using a body temperature sensor. Furthermore, the collection unit can collect blood pressure data using a blood pressure monitor. For example, the collection unit wears a heart rate sensor on the user's wrist and collects heart rate data in real time. The body temperature sensor is worn on the user's forehead and collects body temperature data. The blood pressure monitor is worn on the user's upper arm and collects blood pressure data. The analysis unit analyzes the biosensor data collected by the collection unit. The analysis is performed, for example, using statistical analysis of data or a machine learning algorithm, but is not limited to such examples. For example, the analysis unit performs statistical analysis of heart rate data to detect abnormal heart rate patterns. The analysis unit can also detect an abnormality in the body temperature data using a machine learning algorithm. Furthermore, the analysis unit can also analyze the blood pressure data and detect an abnormal blood pressure pattern. The provision unit provides the analysis result by the analysis unit to the user. The provision is performed in a format such as text, audio, visual, etc., but is not limited to such examples. For example, the provision unit provides the analysis result to the user in a text format. Furthermore, the provision unit can also provide the analysis result in an audio format. Furthermore, the provision unit can also provide the analysis result in a visual format. The conversation unit uses the generation AI to generate a conversation according to the age of the user and to have a conversation with the user. The conversation includes, for example, questions about health and topics about stress, but is not limited to such examples. For example, the conversation unit uses the generation AI to generate questions about health according to the age of the user. Furthermore, the conversation unit can also generate topics about the user's stress using the generation AI. Furthermore, the conversation unit can also generate topics according to the user's interests using the generation AI. The evaluation unit evaluates the user's psychological health state based on the conversation between the user by the conversation unit and the analysis result by the analysis unit.The evaluation is performed based on criteria such as, for example, stress level, anxiety level, and happiness level, but is not limited to such examples. For example, the evaluation unit performs emotion analysis of the conversation content and integrated analysis of the biometric data to evaluate the stress level of the user. The evaluation unit can also perform emotion analysis of the conversation content and integrated analysis of the biometric data to evaluate the anxiety level of the user. Furthermore, the evaluation unit can also perform emotion analysis of the conversation content and integrated analysis of the biometric data to evaluate the happiness level of the user. The suggestion unit suggests a relaxation method based on the stress factor identified by the evaluation unit. The relaxation method includes, for example, deep breathing, meditation, yoga, etc., but is not limited to such examples. For example, the suggestion unit suggests deep breathing when the user's stress level is high. Furthermore, the suggestion unit can also suggest meditation when the user's anxiety level is high. Furthermore, the suggestion unit can also suggest yoga when the user's happiness level is low. As a result, the AI ​​healthcare system according to the embodiment can quickly and accurately evaluate the psychological health state of the user and suggest an appropriate relaxation method.

[0030] The collection unit collects biosensor data of the user. The biosensor data of the user includes, for example, heart rate, blood pressure, body temperature, etc., but is not limited to such examples. For example, the collection unit collects heart rate data using a heart rate sensor. The collection unit can also collect body temperature data using a body temperature sensor. Furthermore, the collection unit can collect blood pressure data using a blood pressure monitor. For example, the collection unit wears a heart rate sensor on the user's wrist and collects heart rate data in real time. The body temperature sensor is worn on the user's forehead and collects body temperature data. The blood pressure monitor is worn on the user's upper arm and collects blood pressure data. These sensors transmit the collected data to a central database using wireless communication technology such as Bluetooth (registered trademark) and Wi-Fi. The collection unit can centrally manage these data and cooperate with other systems and departments as necessary. For example, the collected data is stored in a cloud server and can be accessed by the analysis unit and the provision unit. In addition, by adjusting the frequency and accuracy of data collection, flexible responses according to specific situations and conditions are possible. This allows the collection unit to collect data efficiently and effectively, improving the performance of the entire system. Furthermore, the collection unit can encrypt data and control access to the data in order to protect the privacy of users. This allows the collection unit to achieve safe and reliable data collection, enhancing the sense of security of users.

[0031] The analysis unit analyzes the biosensor data collected by the collection unit. The analysis is performed, for example, using statistical analysis of data or machine learning algorithms, but is not limited to such examples. For example, the analysis unit performs statistical analysis of heart rate data to detect abnormal heart rate patterns. The analysis unit can also detect abnormalities in body temperature data using machine learning algorithms. Furthermore, the analysis unit can analyze blood pressure data to detect abnormal blood pressure patterns. Specifically, the analysis unit applies an algorithm for time-series analysis of heart rate data and detecting abnormal fluctuations and patterns. For example, if the heart rate increases suddenly or decreases abnormally, this is detected as an abnormality. Furthermore, for the body temperature data, a model for detecting abnormally high or low temperatures is constructed taking into account fluctuations due to seasons and time periods. For the blood pressure data, both the systolic blood pressure and the diastolic blood pressure are analyzed to identify abnormal high or low blood pressure patterns. The analysis unit comprehensively analyzes these data and generates an index for evaluating the health state of the user. Furthermore, the analysis unit can utilize past data and statistical information to evaluate long-term health risks and perform trend analysis. For example, based on past heart rate data, the analysis unit can predict risk fluctuations at specific times of the day or activity levels and evaluate future health risks. The analysis unit can also use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings. This allows the analysis unit to not only grasp the situation in real time, but also handle long-term health management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The providing unit provides the analysis result by the analysis unit to the user. The provision is performed in a format such as text, audio, visual, etc., but is not limited to such examples. For example, the providing unit provides the analysis result to the user in a text format. The providing unit can also provide the analysis result in an audio format. Furthermore, the providing unit can also provide the analysis result in a visual format. Specifically, the providing unit provides an application for notifying the user of the analysis result to a smartphone or tablet of the user. This application displays the user's health condition in an easy-to-understand manner and prompts the user to take necessary actions. In addition, the analysis result can be notified by voice using a voice assistant and specific advice can be provided to the user. In the visual format, the fluctuation of the user's health condition is visually displayed using graphs and charts, and a warning is displayed when an abnormality occurs. The providing unit provides customizable notification settings in consideration of the user's convenience. For example, the user can select the frequency and format of notifications and receive the analysis result in a way that is optimal for him or her. Furthermore, the providing unit can collect user feedback and continuously improve the accuracy and effectiveness of the provided content. For example, the user's reaction and behavior to the analysis result are monitored to help improve the provided content. In addition, the providing unit can reliably transmit information using multiple communication methods. For example, important information can be reliably delivered not only by smartphone notifications, but also by voice calls, SMS, emails, etc. This allows the providing unit to quickly and reliably provide analysis results to the user and support health management.

[0033] The conversation unit uses the generation AI to generate a conversation according to the user's age and to have a conversation with the user. The conversation includes, for example, questions about health and topics about stress, but is not limited to such examples. For example, the conversation unit uses the generation AI to generate questions about health according to the user's age. The conversation unit can also use the generation AI to generate topics about the user's stress. Furthermore, the conversation unit can also use the generation AI to generate topics according to the user's interests. Specifically, the generation AI generates optimal conversation content taking into account the user's age, gender, past conversation history, health data, etc. For example, advice on exercise and diet is provided to younger users, and information on the prevention and management of chronic diseases is provided to middle-aged and older users. The generation AI also analyzes the user's stress level and emotional state in real time and selects appropriate topics. For example, if the user is feeling stressed, advice on relaxation methods and stress management is provided. Furthermore, the generation AI can also suggest health-related topics based on the user's interests and concerns. For example, if the user is interested in a particular sport or hobby, information on the impact of that activity on health is provided. The conversation unit can grasp the user's health and psychological state through natural dialogue with the user and provide appropriate advice. Furthermore, the conversation unit can collect user feedback and continuously improve the conversation content of the generation AI. This allows the conversation unit to provide personalized health management support to the user and increase the user's health awareness.

[0034] The evaluation unit evaluates the user's psychological health state based on the conversation between the user and the conversation unit and the analysis result by the analysis unit. The evaluation is performed based on criteria such as, for example, stress level, anxiety level, and happiness level, but is not limited to such examples. For example, the evaluation unit performs emotion analysis of the conversation content and integrated analysis of the biometric data to evaluate the user's stress level. The evaluation unit can also perform emotion analysis of the conversation content and integrated analysis of the biometric data to evaluate the user's anxiety level. Furthermore, the evaluation unit can also perform emotion analysis of the conversation content and integrated analysis of the biometric data to evaluate the user's happiness level. Specifically, the evaluation unit uses natural language processing technology to analyze the user's emotions and psychological state from the conversation content. For example, signs of stress and anxiety are detected from the tone and content of the user's remarks. In addition, the evaluation unit integrates the collected biometric data and the conversation content to comprehensively evaluate the user's psychological health state. For example, the evaluation unit evaluates the user's stress level by combining fluctuations in heart rate and blood pressure with the emotion analysis result of the conversation content. The evaluation unit generates information for providing appropriate advice and support to the user based on these evaluation results. In addition, the evaluation unit can also predict long-term psychological health fluctuations by taking into account past data and trends. For example, based on past stress level data, it can predict risk fluctuations at specific times and situations and plan future countermeasures. The evaluation unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data and issue early warnings. This allows the evaluation unit to not only grasp the situation in real time, but also handle long-term psychological health management and anomaly detection, improving the reliability and safety of the entire system.

[0035] The suggestion unit suggests a relaxation method based on the stress factors identified by the evaluation unit. Examples of the relaxation method include, but are not limited to, deep breathing, meditation, yoga, and the like. For example, the suggestion unit suggests deep breathing when the user's stress level is high. The suggestion unit can also suggest meditation when the user's anxiety level is high. Furthermore, the suggestion unit can also suggest yoga when the user's happiness level is low. Specifically, the suggestion unit selects an optimal relaxation method based on the result of evaluating the user's psychological health state, and provides the user with a specific implementation method. For example, when suggesting deep breathing, the suggestion unit explains a specific breathing rhythm and method, and guides the user to easily practice. When suggesting meditation, the suggestion unit explains basic steps and precautions for meditation, and provides advice to create an environment in which the user can relax. When suggesting yoga, the suggestion unit introduces simple poses and stretches for beginners, and supports the user to practice without difficulty. The suggestion unit can collect user feedback and continuously improve the accuracy and effectiveness of the suggestions. For example, the suggestion unit monitors the user's impressions and effects after practicing the relaxation method, and uses them to improve the suggestions. The suggestion unit can also suggest customized relaxation methods according to the user's lifestyle and preferences. For example, if the user wants to relax during a specific time period, the suggestion unit can suggest a relaxation method suitable for that time period. In this way, the suggestion unit can provide the user with a personalized relaxation method, thereby improving the user's psychological health.

[0036] The conversation unit can have a conversation according to the analysis result by the analysis unit. The conversation unit generates a conversation about the user's health condition based on the analysis result by the analysis unit, for example. For example, when the analysis result indicates an increase in stress level, the conversation unit generates a conversation about stress reduction. In addition, when the analysis result indicates an abnormality in body temperature, the conversation unit can also generate a conversation about body temperature management. Furthermore, when the analysis result indicates an abnormality in heart rate, the conversation unit can also generate a conversation about heart rate management. This makes it possible to conduct a conversation based on the analysis result, and to more appropriately evaluate the psychological health condition. A part or all of the above-mentioned processing in the conversation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the conversation unit inputs the analysis result to the generation AI, and the generation AI can generate the conversation content based on the analysis result.

[0037] The conversation unit can have a conversation according to the evaluation result by the evaluation unit. The conversation unit generates a conversation about the user's psychological health state based on, for example, the evaluation result by the evaluation unit. For example, when the evaluation result indicates an increase in stress level, the conversation unit generates a conversation about stress reduction. In addition, when the evaluation result indicates an increase in anxiety level, the conversation unit can also generate a conversation about anxiety reduction. Furthermore, when the evaluation result indicates a decrease in happiness level, the conversation unit can also generate a conversation about happiness improvement. This makes it possible to grasp the user's psychological health state more accurately by having a conversation based on the evaluation result. Part or all of the above-mentioned processing in the conversation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the conversation unit inputs the evaluation result to the generation AI, and the generation AI can generate the conversation content based on the evaluation result.

[0038] The evaluation unit can evaluate the user's psychological health state based on the conversation between the user and the conversation unit and the analysis result by the analysis unit. The evaluation unit, for example, integrates the content of the conversation between the user and the conversation unit and the analysis result by the analysis unit to evaluate the user's psychological health state. For example, the evaluation unit performs an emotional analysis of the conversation content and an integrated analysis of the heart rate data to evaluate the user's stress level. The evaluation unit can also perform an emotional analysis of the conversation content and an integrated analysis of the body temperature data to evaluate the user's anxiety level. Furthermore, the evaluation unit can also perform an emotional analysis of the conversation content and an integrated analysis of the blood pressure data to evaluate the user's happiness level. This makes it possible to evaluate the psychological health state more accurately by performing an evaluation based on both the conversation and the analysis result. Part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can input the conversation content and the analysis result to AI, which can perform an integrated analysis and output the evaluation result.

[0039] The collection unit can analyze the user's past biometric data history and select an appropriate collection method. The collection unit, for example, analyzes the user's past biometric data history and selects the optimal collection method. For example, the collection unit analyzes the user's past heart rate data and focuses on collecting data during time periods when abnormalities were observed. The collection unit can also analyze the user's past body temperature data and collect data during time periods when body temperature fluctuates rapidly. Furthermore, the collection unit can analyze the user's past sleep data and optimize data collection during sleep. This allows the optimal collection method to be selected by analyzing the past data history. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the past biometric data history into AI, which can select the optimal collection method.

[0040] The collection unit can perform filtering based on the user's current activity status and environment when collecting biosensor data. The collection unit performs filtering based on the user's current activity status and environment when collecting biosensor data, for example. For example, when the user is exercising, the collection unit filters out a temporary increase in heart rate due to exercise. In addition, when the user is stationary, the collection unit can also preferentially collect data at rest. Furthermore, when the user is out, the collection unit can also collect data by removing environmental noise. This enables more accurate data collection by filtering data based on the activity status and environment. Part or all of the above-mentioned 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 the user's activity status and environmental data to AI, and the AI ​​can perform filtering.

[0041] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the user when collecting biosensor data. For example, the collection unit preferentially collects highly relevant data in consideration of the geographical location information of the user when collecting biosensor data. For example, when the user is at high altitude, the collection unit preferentially collects oxygen saturation data. In addition, when the user is in an urban area, the collection unit can also preferentially collect heart rate data. Furthermore, when the user is at home, the collection unit can also preferentially collect sleep data. In this way, highly relevant data can be preferentially collected by considering the geographical location information. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to AI, and the AI ​​can preferentially collect highly relevant data.

[0042] The collection unit can analyze the user's social media activity and collect related data when collecting the biosensor data. For example, the collection unit analyzes the user's social media activity and collects related data when collecting the biosensor data. For example, when the user posts that he / she is feeling stressed, the collection unit can preferentially collect heart rate data. In addition, when the user posts that he / she is relaxing, the collection unit can also preferentially collect body temperature data. Furthermore, when the user posts about exercise, the collection unit can also preferentially collect respiratory rate data. In this way, related data can be collected by analyzing social media activity. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data into AI, and the AI ​​can collect related data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected biometric data during the analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the collected biometric data during the analysis. For example, the analysis unit performs a detailed analysis when the heart rate data is abnormal. The analysis unit can also perform a detailed analysis when the body temperature data fluctuates rapidly. Furthermore, the analysis unit can also perform a detailed analysis when the respiratory rate data is abnormal. As a result, important data can be analyzed in detail by adjusting the level of detail of the analysis based on the importance of the data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected biometric data to AI, which evaluates the importance of the data, and adjust the level of detail of the analysis based on the result.

[0044] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. The analysis unit can apply different analysis algorithms depending on the category of data during analysis, for example. 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. Furthermore, the analysis unit can apply a breathing pattern analysis algorithm to breathing rate data. This allows for more appropriate analysis by applying different analysis algorithms depending on the category of data. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the category of data to AI, which can select and apply an appropriate analysis algorithm.

[0045] The analysis unit can determine the priority of the analysis based on the collection time of the data during the analysis. The analysis unit, for example, determines the priority of the analysis based on the collection time of the data during the analysis. For example, the analysis unit prioritizes the analysis of recently collected data. The analysis unit can also prioritize the analysis of data collected in a specific time period. Furthermore, the analysis unit can also prioritize the analysis of data from a time period in which an abnormality was observed. In this way, important data can be analyzed preferentially by determining the priority of the analysis based on the collection time of the data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collection time of the data to AI, which determines the priority, and perform analysis based on the result.

[0046] The analysis unit can adjust the order of analysis based on the relevance of data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of data during analysis. For example, the analysis unit adjusts the order of analysis taking into consideration the relevance of heart rate data and body temperature data. The analysis unit can also adjust the order of analysis taking into consideration the relevance of respiratory rate data and exercise data. Furthermore, the analysis unit can adjust the order of analysis taking into consideration the relevance of sleep data and stress data. This allows for more efficient analysis by adjusting the order of analysis based on the relevance of data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of data to AI, which can adjust the order of analysis.

[0047] The providing unit can adjust the level of detail of the provision based on the importance of the analysis result at the time of provision. For example, the providing unit adjusts the level of detail of the provision based on the importance of the analysis result at the time of provision. For example, the providing unit provides important analysis results in detail. The providing unit can also provide general analysis results concisely. Furthermore, the providing unit can provide analysis results in which an abnormality is found in detail. Thereby, important information can be provided in detail by adjusting the level of detail of the provision based on the importance of the analysis result. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the importance of the analysis result to AI, and the AI ​​can adjust the level of detail of the provision.

[0048] The providing unit can apply different providing methods depending on the user's level of understanding at the time of providing. For example, the providing unit applies different providing methods depending on the user's level of understanding at the time of providing. For example, the providing unit provides information using graphs and charts so that the user can easily understand. In addition, when the user requests detailed information, the providing unit can also provide detailed information in text. Furthermore, when the user can easily understand the information visually, the providing unit can also provide the information using visual aids. This makes it possible to provide more appropriate information by applying a providing method according to the user's level of understanding. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's level of understanding to AI, and the AI ​​can select an appropriate providing method.

[0049] The providing unit can select an appropriate providing method in consideration of the device information of the user at the time of providing. For example, the providing unit selects an appropriate providing method in consideration of the device information of the user at the time of providing. For example, when the user is using a smartphone, the providing unit provides information according to the screen size. In addition, when the user is using a tablet, the providing unit can also provide information optimized for a large screen. Furthermore, when the user is using a smartwatch, the providing unit can also provide concise and highly visible information. This makes it possible to select an optimal providing method by considering the device information of the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit inputs the device information of the user into AI, and the AI ​​can select an appropriate providing method.

[0050] The providing unit can customize the provided content by referring to the user's past feedback at the time of providing. The providing unit, for example, customizes the provided content by referring to the user's past feedback at the time of providing. For example, the providing unit preferentially provides an information format that the user has preferred in the past. The providing unit can also exclude and provide an information format that the user has avoided in the past. Furthermore, the providing unit can also provide an optimal information format based on the user's past feedback. This makes it possible to customize the provided content by referring to the past feedback. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's past feedback into AI, and the AI ​​can customize the provided content.

[0051] The conversation unit can generate optimal conversation content by referring to the user's past conversation history during conversation. The conversation unit, for example, generates optimal conversation content by referring to the user's past conversation history during conversation. For example, the conversation unit generates related conversation content based on topics that the user has talked about in the past. The conversation unit can also preferentially include topics that the user has shown interest in in the conversation content. Furthermore, the conversation unit can generate conversation content by excluding topics that the user has avoided in the past. In this way, optimal conversation content can be generated by referring to the past conversation history. Part or all of the above-mentioned processing in the conversation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the conversation unit can input the user's past conversation history into the generation AI, and the generation AI can generate optimal conversation content.

[0052] The conversation unit can apply different conversation algorithms depending on the age and interests of the user during conversation. The conversation unit, for example, applies different conversation algorithms depending on the age and interests of the user during conversation. For example, the conversation unit generates a conversation about school life and friendships for elementary school students. The conversation unit can also generate a conversation about health conditions and daily life for elderly people. Furthermore, the conversation unit can generate a conversation about hobbies and interests depending on the user's interests. This makes it possible to have a more appropriate conversation by applying a conversation algorithm according to the user's age and interests. Some or all of the above-mentioned processing in the conversation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the conversation unit inputs the user's age and interest data into the generation AI, which can select and apply an appropriate conversation algorithm.

[0053] The conversation unit can generate highly relevant conversation content in consideration of the geographical location information of the user during conversation. The conversation unit generates highly relevant conversation content in consideration of the geographical location information of the user during conversation, for example. For example, when the user is at a tourist spot, the conversation unit generates a conversation about tourist information for the area. In addition, when the user is at home, the conversation unit can also generate a conversation about a method of relaxing at home. Furthermore, when the user is at work, the conversation unit can also generate a conversation about work stress management. In this way, highly relevant conversation content can be generated by taking into account the geographical location information. Part or all of the above-mentioned processing in the conversation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the conversation unit inputs the geographical location information of the user into the generation AI, and the generation AI can generate highly relevant conversation content.

[0054] The conversation unit can analyze the user's social media activity during the conversation and generate related conversation content. The conversation unit can, for example, analyze the user's social media activity during the conversation and generate related conversation content. For example, the conversation unit generates a conversation about a topic in which the user showed interest on social media. The conversation unit can also generate conversation content by excluding topics that the user avoided on social media. Furthermore, the conversation unit can identify a current interest from the user's social media activity and generate a conversation based on the current interest. In this way, related conversation content can be generated by analyzing social media activity. Part or all of the above-mentioned processing in the conversation unit can be performed, for example, using a generation AI or can be performed without using a generation AI. For example, the conversation unit can input the user's social media activity data to the generation AI, and the generation AI can generate related conversation content.

[0055] The evaluation unit can improve the accuracy of the evaluation by considering the interrelationship between the conversation content and the biometric data during the evaluation. The evaluation unit can improve the accuracy of the evaluation by considering the interrelationship between the conversation content and the biometric data during the evaluation. For example, the evaluation unit performs the evaluation by considering the correlation between the stress level during the conversation and the heart rate data. The evaluation unit can also perform the evaluation by considering the correlation between the relaxation level during the conversation and the body temperature data. Furthermore, the evaluation unit can also perform the evaluation by considering the correlation between the excitement level during the conversation and the respiratory rate data. As a result, the accuracy of the evaluation can be improved by considering the interrelationship between the conversation content and the biometric data. Part or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the conversation content and the biometric data into AI, which analyzes the interrelationship, and perform the evaluation based on the result.

[0056] The evaluation unit can perform the evaluation while taking into consideration the attribute information of the user. For example, the evaluation unit performs the evaluation while taking into consideration the attribute information of the user. For example, the evaluation unit applies evaluation criteria according to the age of the user. The evaluation unit can also apply evaluation criteria according to the gender of the user. Furthermore, the evaluation unit can also apply evaluation criteria according to the health condition of the user. This enables a more appropriate evaluation by taking into consideration the attribute information of the user. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit inputs the attribute information of the user into AI, which can select and apply appropriate evaluation criteria.

[0057] The evaluation unit can perform the evaluation taking into consideration the geographical distribution of the user at the time of evaluation. The evaluation unit performs the evaluation taking into consideration the geographical distribution of the user, for example, at the time of evaluation. For example, when the user lives in an urban area, the evaluation unit performs the evaluation taking into consideration stress factors specific to the urban area. In addition, when the user lives in a rural area, the evaluation unit can also perform the evaluation taking into consideration stress factors specific to the rural area. Furthermore, when the user lives overseas, the evaluation unit can also perform the evaluation taking into consideration stress factors specific to that area. This allows for a more appropriate evaluation by taking into consideration the geographical distribution. Part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit inputs the geographical distribution data of the user into AI, which can select and apply appropriate evaluation criteria.

[0058] The evaluation unit can improve the accuracy of the evaluation by referring to related literature at the time of evaluation. The evaluation unit can improve the accuracy of the evaluation by referring to related literature, for example, at the time of evaluation. For example, the evaluation unit updates the evaluation criteria by referring to the latest medical research. The evaluation unit can also adjust the evaluation criteria by referring to related psychology literature. Furthermore, the evaluation unit can also optimize the evaluation criteria by referring to literature related to the attributes of the user. This makes it possible to improve the accuracy of the evaluation by referring to related literature. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can input related literature data into AI, which updates the evaluation criteria, and perform evaluation based on the results.

[0059] The suggestion unit can analyze the user's past stress factors and suggest the optimal relaxation method at the time of suggestion. For example, the suggestion unit can analyze the user's past stress factors and suggest the optimal relaxation method at the time of suggestion. For example, the suggestion unit preferentially suggests relaxation methods that have been effective for the user in the past. The suggestion unit can also exclude relaxation methods that the user has avoided in the past and suggest them. Furthermore, the suggestion unit can analyze the user's past stress factors and suggest a relaxation method based on them. In this way, the optimal relaxation method can be suggested by analyzing past stress factors. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the user's past stress factor data into AI, which can select and suggest the optimal relaxation method.

[0060] The suggestion unit can customize the relaxation method based on the user's current living situation at the time of suggestion. The suggestion unit customizes the relaxation method based on the user's current living situation at the time of suggestion, for example. For example, when the user is busy, the suggestion unit suggests a relaxation method that is effective in a short time. Also, when the user has time to relax, the suggestion unit can also suggest a long-term relaxation method. Furthermore, when the user is in a specific environment, the suggestion unit can also suggest a relaxation method suitable for that environment. This allows for more appropriate suggestions by customizing the relaxation method based on the current living situation. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's current living situation data into AI, which can select and suggest the optimal relaxation method.

[0061] The suggestion unit can suggest an optimal relaxation method in consideration of the geographical location information of the user at the time of suggestion. For example, the suggestion unit suggests an optimal relaxation method in consideration of the geographical location information of the user at the time of suggestion. For example, when the user is in an urban area, the suggestion unit suggests a relaxation method that can be performed in the urban area. In addition, when the user is in a natural environment, the suggestion unit can also suggest a relaxation method that utilizes nature. Furthermore, when the user is at home, the suggestion unit can also suggest a relaxation method that can be performed at home. In this way, the optimal relaxation method can be suggested by taking into account the geographical location information. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the geographical location information of the user to AI, which can select and suggest the optimal relaxation method.

[0062] The suggestion unit can analyze the user's social media activity at the time of suggestion to suggest a relaxation method. For example, the suggestion unit analyzes the user's social media activity at the time of suggestion to suggest a relaxation method. For example, the suggestion unit suggests a relaxation method in which the user showed interest on social media. The suggestion unit can also exclude relaxation methods that the user avoided on social media and make suggestions. Furthermore, the suggestion unit can identify current interests from the user's social media activity and suggest a relaxation method based on the identified interests. In this way, the optimal relaxation method can be suggested to the user by analyzing social media activity. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the user's social media activity data into AI, which can select and suggest the optimal relaxation method.

[0063] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0064] The collection unit can analyze the user's past biometric data history and select an appropriate collection method. For example, the collection unit can analyze the user's past heart rate data and collect data focusing on the time period when abnormalities were observed. The collection unit can also analyze the user's past body temperature data and collect data during the time period when the body temperature fluctuates rapidly. Furthermore, the collection unit can analyze the user's past sleep data and optimize data collection during sleep. In this way, the optimal collection method can be selected by analyzing the past data history. Part or all of the above-mentioned 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 the past biometric data history into AI, which can select the optimal collection method.

[0065] The collection unit can perform filtering based on the user's current activity status and environment when collecting biosensor data. For example, when the user is exercising, the collection unit filters out a temporary increase in heart rate due to exercise. In addition, when the user is stationary, the collection unit can also preferentially collect data at rest. Furthermore, when the user is out, the collection unit can also collect data by removing environmental noise. This enables more accurate data collection by filtering data based on the activity status and environment. Some or all of the above-mentioned 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 the user's activity status and environmental data to AI, and the AI ​​can perform filtering.

[0066] When collecting biosensor data, the collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the user. For example, when the user is at high altitude, the collection unit preferentially collects oxygen saturation data. In addition, when the user is in an urban area, the collection unit can also preferentially collect heart rate data. Furthermore, when the user is at home, the collection unit can also preferentially collect sleep data. In this way, highly relevant data can be preferentially collected by considering the geographical location information. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to AI, and the AI ​​can preferentially collect highly relevant data.

[0067] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected biometric data during the analysis. For example, the analysis unit performs a detailed analysis when the heart rate data is abnormal. The analysis unit can also perform a detailed analysis when the body temperature data fluctuates rapidly. Furthermore, the analysis unit can perform a detailed analysis when the respiratory rate data is abnormal. As a result, important data can be analyzed in detail by adjusting the level of detail of the analysis based on the importance of the data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected biometric data to AI, which evaluates the importance of the data, and adjust the level of detail of the analysis based on the result.

[0068] The evaluation unit can perform the evaluation by taking into account the attribute information of the user. For example, the evaluation unit applies evaluation criteria according to the age of the user. The evaluation unit can also apply evaluation criteria according to the gender of the user. Furthermore, the evaluation unit can apply evaluation criteria according to the health condition of the user. This allows for a more appropriate evaluation by taking into account the attribute information of the user. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the attribute information of the user to AI, which can select and apply appropriate evaluation criteria.

[0069] The process flow of the first embodiment will be briefly described below.

[0070] Step 1: The collection unit collects biosensor data of a user. The biosensor data of a user includes, for example, a heart rate, blood pressure, and body temperature. The collection unit collects heart rate data using a heart rate sensor, collects body temperature data using a body temperature sensor, and collects blood pressure data using a blood pressure meter. For example, the heart rate sensor is attached to the user's wrist, the body temperature sensor is attached to the user's forehead, and the blood pressure meter is attached to the user's upper arm. Step 2: The analysis unit analyzes the biosensor data collected by the collection unit. The analysis is performed using statistical analysis of the data and machine learning algorithms. For example, statistical analysis of heart rate data is performed to detect abnormal heart rate patterns, abnormalities in body temperature data are detected using machine learning algorithms, and blood pressure data is analyzed to detect abnormal blood pressure patterns. Step 3: The providing unit provides the analysis result by the analysis unit to the user. The analysis result is provided in a format such as text, audio, or visual. For example, the analysis result is provided to the user in a text format, audio format, or visual format. Step 4: The conversation unit uses the generation AI to generate a conversation appropriate to the user's age and engages in a conversation with the user. The conversation includes questions about health and topics about stress. For example, the generation AI is used to generate questions about health and topics about stress appropriate to the user's age. Step 5: The evaluation unit evaluates the user's psychological health state based on the conversation between the user and the conversation unit and the analysis results by the analysis unit. The evaluation is based on criteria such as stress level, anxiety level, and happiness level. For example, an emotional analysis of the conversation content and an integrated analysis of biometric data are performed to evaluate the user's stress level, anxiety level, and happiness level. Step 6: The suggestion unit suggests relaxation techniques based on the stress factors identified by the evaluation unit. The relaxation techniques include deep breathing, meditation, yoga, etc. For example, if the user's stress level is high, deep breathing is suggested, if the anxiety level is high, meditation is suggested, and if the happiness level is low, yoga is suggested.

[0071] (Example 2) The AI ​​healthcare system according to the embodiment of the present invention is a system that can be used by a wide range of age groups, from elementary school students to the elderly. This system has a function of collecting biosensor data (such as heart rate) of the user and analyzing the data with AI. It also includes a function of generating a conversation according to the user's age and evaluating the psychological health state through text and voice. This allows the user to talk to the AI ​​about things that they cannot talk about with other people, making it possible to quickly and accurately identify stress factors. Furthermore, based on the identified stress factors, the AI ​​proposes relaxation methods and assists instructors and caregivers. For example, the user's biosensor data is collected. For example, data such as heart rate and body temperature is collected. Next, the collected data is analyzed by the AI. The analysis results are provided to the user. Furthermore, the AI ​​generates a conversation according to the user's age and has a conversation with the user. Based on this conversation, the AI ​​evaluates the user's psychological health state. Based on the evaluation results, the AI ​​identifies stress factors and proposes relaxation methods. For example, for elementary school students, the AI ​​generates a conversation about school life and friendships to evaluate the psychological health state. For elderly people, the AI ​​generates a conversation about health and daily life to evaluate the psychological health state. This makes it possible to suggest relaxation methods suitable for each age group. The system can also be used as an assistant to instructors and caregivers. For example, using the system in nursing facilities makes it easier for caregivers to understand the psychological health of users and provide appropriate care. Using the system in schools also makes it easier for teachers to understand the psychological health of students and provide appropriate guidance. This allows the AI ​​healthcare system to quickly and accurately evaluate the user's psychological health and suggest appropriate relaxation methods.

[0072] The AI ​​healthcare system according to the embodiment includes a collection unit, an analysis unit, a provision unit, a conversation unit, an evaluation unit, and a proposal unit. The collection unit collects biosensor data of a user. The biosensor data of a user includes, for example, a heart rate, blood pressure, body temperature, and the like, but is not limited to such examples. The collection unit collects heart rate data using, for example, a heart rate sensor. The collection unit can also collect body temperature data using a body temperature sensor. Furthermore, the collection unit can collect blood pressure data using a blood pressure monitor. For example, the collection unit wears a heart rate sensor on the user's wrist and collects heart rate data in real time. The body temperature sensor is worn on the user's forehead and collects body temperature data. The blood pressure monitor is worn on the user's upper arm and collects blood pressure data. The analysis unit analyzes the biosensor data collected by the collection unit. The analysis is performed, for example, using statistical analysis of data or a machine learning algorithm, but is not limited to such examples. For example, the analysis unit performs statistical analysis of heart rate data to detect abnormal heart rate patterns. The analysis unit can also detect an abnormality in the body temperature data using a machine learning algorithm. Furthermore, the analysis unit can also analyze the blood pressure data and detect an abnormal blood pressure pattern. The provision unit provides the analysis result by the analysis unit to the user. The provision is performed in a format such as text, audio, visual, etc., but is not limited to such examples. For example, the provision unit provides the analysis result to the user in a text format. Furthermore, the provision unit can also provide the analysis result in an audio format. Furthermore, the provision unit can also provide the analysis result in a visual format. The conversation unit uses the generation AI to generate a conversation according to the age of the user and to have a conversation with the user. The conversation includes, for example, questions about health and topics about stress, but is not limited to such examples. For example, the conversation unit uses the generation AI to generate questions about health according to the age of the user. Furthermore, the conversation unit can also generate topics about the user's stress using the generation AI. Furthermore, the conversation unit can also generate topics according to the user's interests using the generation AI. The evaluation unit evaluates the user's psychological health state based on the conversation between the user by the conversation unit and the analysis result by the analysis unit.The evaluation is performed based on criteria such as, for example, stress level, anxiety level, and happiness level, but is not limited to such examples. For example, the evaluation unit performs emotion analysis of the conversation content and integrated analysis of the biometric data to evaluate the stress level of the user. The evaluation unit can also perform emotion analysis of the conversation content and integrated analysis of the biometric data to evaluate the anxiety level of the user. Furthermore, the evaluation unit can also perform emotion analysis of the conversation content and integrated analysis of the biometric data to evaluate the happiness level of the user. The suggestion unit suggests a relaxation method based on the stress factor identified by the evaluation unit. The relaxation method includes, for example, deep breathing, meditation, yoga, etc., but is not limited to such examples. For example, the suggestion unit suggests deep breathing when the user's stress level is high. Furthermore, the suggestion unit can also suggest meditation when the user's anxiety level is high. Furthermore, the suggestion unit can also suggest yoga when the user's happiness level is low. As a result, the AI ​​healthcare system according to the embodiment can quickly and accurately evaluate the psychological health state of the user and suggest an appropriate relaxation method.

[0073] The collection unit collects biosensor data of the user. The biosensor data of the user includes, for example, heart rate, blood pressure, body temperature, etc., but is not limited to such examples. For example, the collection unit collects heart rate data using a heart rate sensor. The collection unit can also collect body temperature data using a body temperature sensor. Furthermore, the collection unit can collect blood pressure data using a blood pressure monitor. For example, the collection unit wears a heart rate sensor on the user's wrist and collects heart rate data in real time. The body temperature sensor is worn on the user's forehead and collects body temperature data. The blood pressure monitor is worn on the user's upper arm and collects blood pressure data. These sensors transmit the collected data to a central database using wireless communication technology such as Bluetooth or Wi-Fi. The collection unit can centrally manage these data and cooperate with other systems and departments as necessary. For example, the collected data is stored in a cloud server and can be accessed by the analysis unit and the provision unit. In addition, by adjusting the frequency and accuracy of data collection, flexible responses according to specific situations and conditions are possible. This allows the collection unit to collect data efficiently and effectively, improving the performance of the entire system. Furthermore, the collection unit can encrypt data and control access to the data in order to protect the privacy of users. This allows the collection unit to achieve safe and reliable data collection, enhancing the sense of security of users.

[0074] The analysis unit analyzes the biosensor data collected by the collection unit. The analysis is performed, for example, using statistical analysis of data or machine learning algorithms, but is not limited to such examples. For example, the analysis unit performs statistical analysis of heart rate data to detect abnormal heart rate patterns. The analysis unit can also detect abnormalities in body temperature data using machine learning algorithms. Furthermore, the analysis unit can analyze blood pressure data to detect abnormal blood pressure patterns. Specifically, the analysis unit applies an algorithm for time-series analysis of heart rate data and detecting abnormal fluctuations and patterns. For example, if the heart rate increases suddenly or decreases abnormally, this is detected as an abnormality. Furthermore, for the body temperature data, a model for detecting abnormally high or low temperatures is constructed taking into account fluctuations due to seasons and time periods. For the blood pressure data, both the systolic blood pressure and the diastolic blood pressure are analyzed to identify abnormal high or low blood pressure patterns. The analysis unit comprehensively analyzes these data and generates an index for evaluating the health state of the user. Furthermore, the analysis unit can utilize past data and statistical information to evaluate long-term health risks and perform trend analysis. For example, based on past heart rate data, the analysis unit can predict risk fluctuations at specific times of the day or activity levels and evaluate future health risks. The analysis unit can also use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings. This allows the analysis unit to not only grasp the situation in real time, but also handle long-term health management and anomaly detection, improving the reliability and safety of the entire system.

[0075] The providing unit provides the analysis result by the analysis unit to the user. The provision is performed in a format such as text, audio, visual, etc., but is not limited to such examples. For example, the providing unit provides the analysis result to the user in a text format. The providing unit can also provide the analysis result in an audio format. Furthermore, the providing unit can also provide the analysis result in a visual format. Specifically, the providing unit provides an application for notifying the user of the analysis result to a smartphone or tablet of the user. This application displays the user's health condition in an easy-to-understand manner and prompts the user to take necessary actions. In addition, the analysis result can be notified by voice using a voice assistant and specific advice can be provided to the user. In the visual format, the fluctuation of the user's health condition is visually displayed using graphs and charts, and a warning is displayed when an abnormality occurs. The providing unit provides customizable notification settings in consideration of the user's convenience. For example, the user can select the frequency and format of notifications and receive the analysis result in a way that is optimal for him or her. Furthermore, the providing unit can collect user feedback and continuously improve the accuracy and effectiveness of the provided content. For example, the user's reaction and behavior to the analysis result are monitored to help improve the provided content. In addition, the providing unit can reliably transmit information using multiple communication methods. For example, important information can be reliably delivered not only by smartphone notifications, but also by voice calls, SMS, emails, etc. This allows the providing unit to quickly and reliably provide analysis results to the user and support health management.

[0076] The conversation unit uses the generation AI to generate a conversation according to the user's age and to have a conversation with the user. The conversation includes, for example, questions about health and topics about stress, but is not limited to such examples. For example, the conversation unit uses the generation AI to generate questions about health according to the user's age. The conversation unit can also use the generation AI to generate topics about the user's stress. Furthermore, the conversation unit can also use the generation AI to generate topics according to the user's interests. Specifically, the generation AI generates optimal conversation content taking into account the user's age, gender, past conversation history, health data, etc. For example, advice on exercise and diet is provided to younger users, and information on the prevention and management of chronic diseases is provided to middle-aged and older users. The generation AI also analyzes the user's stress level and emotional state in real time and selects appropriate topics. For example, if the user is feeling stressed, advice on relaxation methods and stress management is provided. Furthermore, the generation AI can also suggest health-related topics based on the user's interests and concerns. For example, if the user is interested in a particular sport or hobby, information on the impact of that activity on health is provided. The conversation unit can grasp the user's health and psychological state through natural dialogue with the user and provide appropriate advice. Furthermore, the conversation unit can collect user feedback and continuously improve the conversation content of the generation AI. This allows the conversation unit to provide personalized health management support to the user and increase the user's health awareness.

[0077] The evaluation unit evaluates the user's psychological health state based on the conversation between the user and the conversation unit and the analysis result by the analysis unit. The evaluation is performed based on criteria such as, for example, stress level, anxiety level, and happiness level, but is not limited to such examples. For example, the evaluation unit performs emotion analysis of the conversation content and integrated analysis of the biometric data to evaluate the user's stress level. The evaluation unit can also perform emotion analysis of the conversation content and integrated analysis of the biometric data to evaluate the user's anxiety level. Furthermore, the evaluation unit can also perform emotion analysis of the conversation content and integrated analysis of the biometric data to evaluate the user's happiness level. Specifically, the evaluation unit uses natural language processing technology to analyze the user's emotions and psychological state from the conversation content. For example, signs of stress and anxiety are detected from the tone and content of the user's remarks. In addition, the evaluation unit integrates the collected biometric data and the conversation content to comprehensively evaluate the user's psychological health state. For example, the evaluation unit evaluates the user's stress level by combining fluctuations in heart rate and blood pressure with the emotion analysis result of the conversation content. The evaluation unit generates information for providing appropriate advice and support to the user based on these evaluation results. In addition, the evaluation unit can also predict long-term psychological health fluctuations by taking into account past data and trends. For example, based on past stress level data, it can predict risk fluctuations at specific times and situations and plan future countermeasures. The evaluation unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data and issue early warnings. This allows the evaluation unit to not only grasp the situation in real time, but also handle long-term psychological health management and anomaly detection, improving the reliability and safety of the entire system.

[0078] The suggestion unit suggests a relaxation method based on the stress factors identified by the evaluation unit. Examples of the relaxation method include, but are not limited to, deep breathing, meditation, yoga, and the like. For example, the suggestion unit suggests deep breathing when the user's stress level is high. The suggestion unit can also suggest meditation when the user's anxiety level is high. Furthermore, the suggestion unit can also suggest yoga when the user's happiness level is low. Specifically, the suggestion unit selects an optimal relaxation method based on the result of evaluating the user's psychological health state, and provides the user with a specific implementation method. For example, when suggesting deep breathing, the suggestion unit explains a specific breathing rhythm and method, and guides the user to easily practice. When suggesting meditation, the suggestion unit explains basic steps and precautions for meditation, and provides advice to create an environment in which the user can relax. When suggesting yoga, the suggestion unit introduces simple poses and stretches for beginners, and supports the user to practice without difficulty. The suggestion unit can collect user feedback and continuously improve the accuracy and effectiveness of the suggestions. For example, the suggestion unit monitors the user's impressions and effects after practicing the relaxation method, and uses them to improve the suggestions. The suggestion unit can also suggest customized relaxation methods according to the user's lifestyle and preferences. For example, if the user wants to relax during a specific time period, the suggestion unit can suggest a relaxation method suitable for that time period. In this way, the suggestion unit can provide the user with a personalized relaxation method, thereby improving the user's psychological health.

[0079] The conversation unit can have a conversation according to the analysis result by the analysis unit. The conversation unit generates a conversation about the user's health condition based on the analysis result by the analysis unit, for example. For example, when the analysis result indicates an increase in stress level, the conversation unit generates a conversation about stress reduction. In addition, when the analysis result indicates an abnormality in body temperature, the conversation unit can also generate a conversation about body temperature management. Furthermore, when the analysis result indicates an abnormality in heart rate, the conversation unit can also generate a conversation about heart rate management. This makes it possible to conduct a conversation based on the analysis result, and to more appropriately evaluate the psychological health condition. A part or all of the above-mentioned processing in the conversation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the conversation unit inputs the analysis result to the generation AI, and the generation AI can generate the conversation content based on the analysis result.

[0080] The conversation unit can have a conversation according to the evaluation result by the evaluation unit. The conversation unit generates a conversation about the user's psychological health state based on, for example, the evaluation result by the evaluation unit. For example, when the evaluation result indicates an increase in stress level, the conversation unit generates a conversation about stress reduction. In addition, when the evaluation result indicates an increase in anxiety level, the conversation unit can also generate a conversation about anxiety reduction. Furthermore, when the evaluation result indicates a decrease in happiness level, the conversation unit can also generate a conversation about happiness improvement. This makes it possible to grasp the user's psychological health state more accurately by having a conversation based on the evaluation result. Part or all of the above-mentioned processing in the conversation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the conversation unit inputs the evaluation result to the generation AI, and the generation AI can generate the conversation content based on the evaluation result.

[0081] The evaluation unit can evaluate the user's psychological health state based on the conversation between the user and the conversation unit and the analysis result by the analysis unit. The evaluation unit, for example, integrates the content of the conversation between the user and the conversation unit and the analysis result by the analysis unit to evaluate the user's psychological health state. For example, the evaluation unit performs an emotional analysis of the conversation content and an integrated analysis of the heart rate data to evaluate the user's stress level. The evaluation unit can also perform an emotional analysis of the conversation content and an integrated analysis of the body temperature data to evaluate the user's anxiety level. Furthermore, the evaluation unit can also perform an emotional analysis of the conversation content and an integrated analysis of the blood pressure data to evaluate the user's happiness level. This makes it possible to evaluate the psychological health state more accurately by performing an evaluation based on both the conversation and the analysis result. Part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can input the conversation content and the analysis result to AI, which can perform an integrated analysis and output the evaluation result.

[0082] The collection unit can estimate the user's emotion and adjust the collection timing of the biosensor data based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion and adjusts the collection timing of the biosensor data based on the estimated emotion. For example, when the user is stressed, the collection unit increases the collection frequency and acquires data in real time. Also, when the user is relaxed, the collection unit can reduce the collection frequency and acquire data periodically. Furthermore, when the user is exercising, the collection unit can also collect data at the peak of the exercise. This enables more appropriate data collection by adjusting the collection timing according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data to the generation AI, which performs emotion estimation, and adjust the collection timing based on the result.

[0083] The collection unit can analyze the user's past biometric data history and select an appropriate collection method. The collection unit, for example, analyzes the user's past biometric data history and selects the optimal collection method. For example, the collection unit analyzes the user's past heart rate data and focuses on collecting data during time periods when abnormalities were observed. The collection unit can also analyze the user's past body temperature data and collect data during time periods when body temperature fluctuates rapidly. Furthermore, the collection unit can analyze the user's past sleep data and optimize data collection during sleep. This allows the optimal collection method to be selected by analyzing the past data history. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the past biometric data history into AI, which can select the optimal collection method.

[0084] The collection unit can perform filtering based on the user's current activity status and environment when collecting biosensor data. The collection unit performs filtering based on the user's current activity status and environment when collecting biosensor data, for example. For example, when the user is exercising, the collection unit filters out a temporary increase in heart rate due to exercise. In addition, when the user is stationary, the collection unit can also preferentially collect data at rest. Furthermore, when the user is out, the collection unit can also collect data by removing environmental noise. This enables more accurate data collection by filtering data based on the activity status and environment. Part or all of the above-mentioned 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 the user's activity status and environmental data to AI, and the AI ​​can perform filtering.

[0085] The collection unit can estimate the user's emotion and determine the priority of the biometric data to be collected based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion and determines the priority of the biometric data to be collected based on the estimated emotion. For example, when the user is stressed, the collection unit preferentially collects heart rate data. Also, when the user is relaxed, the collection unit can preferentially collect body temperature data. Furthermore, when the user is exercising, the collection unit can preferentially collect respiratory rate data. In this way, important data can be preferentially collected by determining the priority of data according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data to the generation AI, which performs emotion estimation, and determine the priority of the data based on the result.

[0086] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the user when collecting biosensor data. For example, the collection unit preferentially collects highly relevant data in consideration of the geographical location information of the user when collecting biosensor data. For example, when the user is at high altitude, the collection unit preferentially collects oxygen saturation data. In addition, when the user is in an urban area, the collection unit can also preferentially collect heart rate data. Furthermore, when the user is at home, the collection unit can also preferentially collect sleep data. In this way, highly relevant data can be preferentially collected by considering the geographical location information. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to AI, and the AI ​​can preferentially collect highly relevant data.

[0087] The collection unit can analyze the user's social media activity and collect related data when collecting the biosensor data. For example, the collection unit analyzes the user's social media activity and collects related data when collecting the biosensor data. For example, when the user posts that he / she is feeling stressed, the collection unit can preferentially collect heart rate data. In addition, when the user posts that he / she is relaxing, the collection unit can also preferentially collect body temperature data. Furthermore, when the user posts about exercise, the collection unit can also preferentially collect respiratory rate data. In this way, related data can be collected by analyzing social media activity. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data into AI, and the AI ​​can collect related data.

[0088] The analysis unit can estimate the user's emotion and adjust the analysis algorithm based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the analysis algorithm based on the estimated emotion. For example, when the user is feeling stressed, the analysis unit applies an algorithm that emphasizes stress-related data. In addition, when the user is relaxed, the analysis unit can also apply an algorithm that emphasizes relaxation-related data. In addition, when the user is exercising, the analysis unit can also apply an algorithm that emphasizes exercise-related data. This allows for more appropriate analysis by adjusting the analysis algorithm according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI, which performs emotion estimation, and adjust the analysis algorithm based on the result.

[0089] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected biometric data during the analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the collected biometric data during the analysis. For example, the analysis unit performs a detailed analysis when the heart rate data is abnormal. The analysis unit can also perform a detailed analysis when the body temperature data fluctuates rapidly. Furthermore, the analysis unit can also perform a detailed analysis when the respiratory rate data is abnormal. As a result, important data can be analyzed in detail by adjusting the level of detail of the analysis based on the importance of the data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected biometric data to AI, which evaluates the importance of the data, and adjust the level of detail of the analysis based on the result.

[0090] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. The analysis unit can apply different analysis algorithms depending on the category of data during analysis, for example. 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. Furthermore, the analysis unit can apply a breathing pattern analysis algorithm to breathing rate data. This allows for more appropriate analysis by applying different analysis algorithms depending on the category of data. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the category of data to AI, which can select and apply an appropriate analysis algorithm.

[0091] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the display method of the analysis result based on the estimated emotion. For example, the analysis unit provides a simple and highly visible display method when the user is stressed. In addition, the analysis unit can also provide a display method including detailed information when the user is relaxed. Furthermore, the analysis unit can also provide a display method that focuses on the main points when the user is in a hurry. This makes it possible to provide more appropriate information by adjusting the display method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI, which performs emotion estimation, and adjust the display method based on the result.

[0092] The analysis unit can determine the priority of the analysis based on the collection time of the data during the analysis. The analysis unit, for example, determines the priority of the analysis based on the collection time of the data during the analysis. For example, the analysis unit prioritizes the analysis of recently collected data. The analysis unit can also prioritize the analysis of data collected in a specific time period. Furthermore, the analysis unit can also prioritize the analysis of data from a time period in which an abnormality was observed. In this way, important data can be analyzed preferentially by determining the priority of the analysis based on the collection time of the data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collection time of the data to AI, which determines the priority, and perform analysis based on the result.

[0093] The analysis unit can adjust the order of analysis based on the relevance of data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of data during analysis. For example, the analysis unit adjusts the order of analysis taking into consideration the relevance of heart rate data and body temperature data. The analysis unit can also adjust the order of analysis taking into consideration the relevance of respiratory rate data and exercise data. Furthermore, the analysis unit can adjust the order of analysis taking into consideration the relevance of sleep data and stress data. This allows for more efficient analysis by adjusting the order of analysis based on the relevance of data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of data to AI, which can adjust the order of analysis.

[0094] The providing unit can estimate the user's emotion and adjust the expression method of the information to be provided based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the expression method of the information to be provided based on the estimated emotion. For example, when the user is feeling stressed, the providing unit provides simple and highly visible information. In addition, when the user is relaxed, the providing unit can also provide detailed information. Furthermore, when the user is in a hurry, the providing unit can also provide information that covers the main points. This makes it possible to provide more appropriate information by adjusting the expression method of the information according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI, which can perform emotion estimation and adjust the expression method of the information based on the result.

[0095] The providing unit can adjust the level of detail of the provision based on the importance of the analysis result at the time of provision. For example, the providing unit adjusts the level of detail of the provision based on the importance of the analysis result at the time of provision. For example, the providing unit provides important analysis results in detail. The providing unit can also provide general analysis results concisely. Furthermore, the providing unit can provide analysis results in which an abnormality is found in detail. Thereby, important information can be provided in detail by adjusting the level of detail of the provision based on the importance of the analysis result. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the importance of the analysis result to AI, and the AI ​​can adjust the level of detail of the provision.

[0096] The providing unit can apply different providing methods depending on the user's level of understanding at the time of providing. For example, the providing unit applies different providing methods depending on the user's level of understanding at the time of providing. For example, the providing unit provides information using graphs and charts so that the user can easily understand. In addition, when the user requests detailed information, the providing unit can also provide detailed information in text. Furthermore, when the user can easily understand the information visually, the providing unit can also provide the information using visual aids. This makes it possible to provide more appropriate information by applying a providing method according to the user's level of understanding. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's level of understanding to AI, and the AI ​​can select an appropriate providing method.

[0097] The providing unit can estimate the user's emotion and determine the priority of information to be provided based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and determines the priority of information to be provided based on the estimated emotion. For example, when the user is feeling stressed, the providing unit can provide stress-related information preferentially. Also, when the user is relaxed, the providing unit can provide relaxation-related information preferentially. Furthermore, when the user is exercising, the providing unit can provide exercise-related information preferentially. In this way, important information can be provided preferentially by determining the priority of information according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI, which can perform emotion estimation and determine the priority of information based on the result.

[0098] The providing unit can select an appropriate providing method in consideration of the device information of the user at the time of providing. For example, the providing unit selects an appropriate providing method in consideration of the device information of the user at the time of providing. For example, when the user is using a smartphone, the providing unit provides information according to the screen size. In addition, when the user is using a tablet, the providing unit can also provide information optimized for a large screen. Furthermore, when the user is using a smartwatch, the providing unit can also provide concise and highly visible information. This makes it possible to select an optimal providing method by considering the device information of the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit inputs the device information of the user into AI, and the AI ​​can select an appropriate providing method.

[0099] The providing unit can customize the provided content by referring to the user's past feedback at the time of providing. The providing unit, for example, customizes the provided content by referring to the user's past feedback at the time of providing. For example, the providing unit preferentially provides an information format that the user has preferred in the past. The providing unit can also exclude and provide an information format that the user has avoided in the past. Furthermore, the providing unit can also provide an optimal information format based on the user's past feedback. This makes it possible to customize the provided content by referring to the past feedback. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's past feedback into AI, and the AI ​​can customize the provided content.

[0100] The conversation unit can estimate the user's emotion and adjust the content of the conversation based on the estimated user's emotion. The conversation unit, for example, estimates the user's emotion and adjusts the content of the conversation based on the estimated emotion. For example, the conversation unit provides a topic that can relax the user when the user is stressed. Furthermore, the conversation unit can also provide an interesting topic when the user is relaxed. Furthermore, the conversation unit can provide a conversation that focuses on the main points when the user is in a hurry. This allows for a more appropriate conversation by adjusting the content of the conversation according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the conversation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the conversation unit can input the user's emotion data to the generation AI, which performs emotion estimation, and adjust the content of the conversation based on the result.

[0101] The conversation unit can generate optimal conversation content by referring to the user's past conversation history during conversation. The conversation unit, for example, generates optimal conversation content by referring to the user's past conversation history during conversation. For example, the conversation unit generates related conversation content based on topics that the user has talked about in the past. The conversation unit can also preferentially include topics that the user has shown interest in in the conversation content. Furthermore, the conversation unit can generate conversation content by excluding topics that the user has avoided in the past. In this way, optimal conversation content can be generated by referring to the past conversation history. Part or all of the above-mentioned processing in the conversation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the conversation unit can input the user's past conversation history into the generation AI, and the generation AI can generate optimal conversation content.

[0102] The conversation unit can apply different conversation algorithms depending on the age and interests of the user during conversation. The conversation unit, for example, applies different conversation algorithms depending on the age and interests of the user during conversation. For example, the conversation unit generates a conversation about school life and friendships for elementary school students. The conversation unit can also generate a conversation about health conditions and daily life for elderly people. Furthermore, the conversation unit can generate a conversation about hobbies and interests depending on the user's interests. This makes it possible to have a more appropriate conversation by applying a conversation algorithm according to the user's age and interests. Some or all of the above-mentioned processing in the conversation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the conversation unit inputs the user's age and interest data into the generation AI, which can select and apply an appropriate conversation algorithm.

[0103] The conversation unit can estimate the user's emotion and adjust the tone of the conversation based on the estimated user's emotion. The conversation unit, for example, estimates the user's emotion and adjusts the tone of the conversation based on the estimated emotion. For example, the conversation unit can conduct the conversation in a calm tone when the user is nervous. In addition, the conversation unit can also conduct the conversation in a bright tone when the user is relaxed. Furthermore, the conversation unit can also conduct the conversation in a quick and concise tone when the user is in a hurry. This allows for a more appropriate conversation by adjusting the tone of the conversation according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the conversation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the conversation unit can input the user's emotion data to the generation AI, which can perform emotion estimation and adjust the tone of the conversation based on the result.

[0104] The conversation unit can generate highly relevant conversation content in consideration of the geographical location information of the user during conversation. The conversation unit generates highly relevant conversation content in consideration of the geographical location information of the user during conversation, for example. For example, when the user is at a tourist spot, the conversation unit generates a conversation about tourist information for the area. In addition, when the user is at home, the conversation unit can also generate a conversation about a method of relaxing at home. Furthermore, when the user is at work, the conversation unit can also generate a conversation about work stress management. In this way, highly relevant conversation content can be generated by taking into account the geographical location information. Part or all of the above-mentioned processing in the conversation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the conversation unit inputs the geographical location information of the user into the generation AI, and the generation AI can generate highly relevant conversation content.

[0105] The conversation unit can analyze the user's social media activity during the conversation and generate related conversation content. The conversation unit can, for example, analyze the user's social media activity during the conversation and generate related conversation content. For example, the conversation unit generates a conversation about a topic in which the user showed interest on social media. The conversation unit can also generate conversation content by excluding topics that the user avoided on social media. Furthermore, the conversation unit can identify a current interest from the user's social media activity and generate a conversation based on the current interest. In this way, related conversation content can be generated by analyzing social media activity. Part or all of the above-mentioned processing in the conversation unit can be performed, for example, using a generation AI or can be performed without using a generation AI. For example, the conversation unit can input the user's social media activity data to the generation AI, and the generation AI can generate related conversation content.

[0106] The evaluation unit can estimate the user's emotion and adjust the evaluation criteria based on the estimated user's emotion. The evaluation unit, for example, estimates the user's emotion and adjusts the evaluation criteria based on the estimated emotion. For example, when the user is feeling stressed, the evaluation unit emphasizes stress-related evaluation criteria. In addition, when the user is relaxed, the evaluation unit can also emphasize relaxation-related evaluation criteria. Furthermore, when the user is exercising, the evaluation unit can also emphasize exercise-related evaluation criteria. This allows for more appropriate evaluation by adjusting the evaluation criteria according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the evaluation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the evaluation unit can input the user's emotion data to the generation AI, which performs emotion estimation, and adjust the evaluation criteria based on the result.

[0107] The evaluation unit can improve the accuracy of the evaluation by considering the interrelationship between the conversation content and the biometric data during the evaluation. The evaluation unit can improve the accuracy of the evaluation by considering the interrelationship between the conversation content and the biometric data during the evaluation. For example, the evaluation unit performs the evaluation by considering the correlation between the stress level during the conversation and the heart rate data. The evaluation unit can also perform the evaluation by considering the correlation between the relaxation level during the conversation and the body temperature data. Furthermore, the evaluation unit can also perform the evaluation by considering the correlation between the excitement level during the conversation and the respiratory rate data. As a result, the accuracy of the evaluation can be improved by considering the interrelationship between the conversation content and the biometric data. Part or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the conversation content and the biometric data into AI, which analyzes the interrelationship, and perform the evaluation based on the result.

[0108] The evaluation unit can perform the evaluation while taking into consideration the attribute information of the user. For example, the evaluation unit performs the evaluation while taking into consideration the attribute information of the user. For example, the evaluation unit applies evaluation criteria according to the age of the user. The evaluation unit can also apply evaluation criteria according to the gender of the user. Furthermore, the evaluation unit can also apply evaluation criteria according to the health condition of the user. This enables a more appropriate evaluation by taking into consideration the attribute information of the user. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit inputs the attribute information of the user into AI, which can select and apply appropriate evaluation criteria.

[0109] The evaluation unit can estimate the user's emotion and adjust the display method of the evaluation result based on the estimated user's emotion. The evaluation unit, for example, estimates the user's emotion and adjusts the display method of the evaluation result based on the estimated emotion. For example, when the user is stressed, the evaluation unit provides a simple and highly visible display method. In addition, when the user is relaxed, the evaluation unit can also provide a display method including detailed information. Furthermore, when the user is in a hurry, the evaluation unit can also provide a display method that focuses on the main points. This makes it possible to provide more appropriate information by adjusting the display method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the evaluation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the evaluation unit can input the user's emotion data to the generation AI, which performs emotion estimation, and adjust the display method based on the result.

[0110] The evaluation unit can perform the evaluation taking into consideration the geographical distribution of the user at the time of evaluation. The evaluation unit performs the evaluation taking into consideration the geographical distribution of the user, for example, at the time of evaluation. For example, when the user lives in an urban area, the evaluation unit performs the evaluation taking into consideration stress factors specific to the urban area. In addition, when the user lives in a rural area, the evaluation unit can also perform the evaluation taking into consideration stress factors specific to the rural area. Furthermore, when the user lives overseas, the evaluation unit can also perform the evaluation taking into consideration stress factors specific to that area. This allows for a more appropriate evaluation by taking into consideration the geographical distribution. Part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit inputs the geographical distribution data of the user into AI, which can select and apply appropriate evaluation criteria.

[0111] The evaluation unit can improve the accuracy of the evaluation by referring to related literature at the time of evaluation. The evaluation unit can improve the accuracy of the evaluation by referring to related literature, for example, at the time of evaluation. For example, the evaluation unit updates the evaluation criteria by referring to the latest medical research. The evaluation unit can also adjust the evaluation criteria by referring to related psychology literature. Furthermore, the evaluation unit can also optimize the evaluation criteria by referring to literature related to the attributes of the user. This makes it possible to improve the accuracy of the evaluation by referring to related literature. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can input related literature data into AI, which updates the evaluation criteria, and perform evaluation based on the results.

[0112] The suggestion unit can estimate the user's emotion and adjust the proposed content based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion and adjusts the proposed content based on the estimated emotion. For example, when the user is feeling stressed, the suggestion unit suggests a relaxation method. Also, when the user is relaxed, the suggestion unit can make a suggestion for maintaining health. Furthermore, when the user is exercising, the suggestion unit can suggest a recovery method after exercise. This allows for more appropriate suggestions by adjusting the proposed content according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data to the generation AI, which performs emotion estimation, and adjust the proposed content based on the result.

[0113] The suggestion unit can analyze the user's past stress factors and suggest the optimal relaxation method at the time of suggestion. For example, the suggestion unit can analyze the user's past stress factors and suggest the optimal relaxation method at the time of suggestion. For example, the suggestion unit preferentially suggests relaxation methods that have been effective for the user in the past. The suggestion unit can also exclude relaxation methods that the user has avoided in the past and suggest them. Furthermore, the suggestion unit can analyze the user's past stress factors and suggest a relaxation method based on them. In this way, the optimal relaxation method can be suggested by analyzing past stress factors. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the user's past stress factor data into AI, which can select and suggest the optimal relaxation method.

[0114] The suggestion unit can customize the relaxation method based on the user's current living situation at the time of suggestion. The suggestion unit customizes the relaxation method based on the user's current living situation at the time of suggestion, for example. For example, when the user is busy, the suggestion unit suggests a relaxation method that is effective in a short time. Also, when the user has time to relax, the suggestion unit can also suggest a long-term relaxation method. Furthermore, when the user is in a specific environment, the suggestion unit can also suggest a relaxation method suitable for that environment. This allows for more appropriate suggestions by customizing the relaxation method based on the current living situation. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's current living situation data into AI, which can select and suggest the optimal relaxation method.

[0115] The suggestion unit can estimate the user's emotion and determine the priority of the suggestions based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion and determines the priority of the suggestions based on the estimated emotion. For example, when the user is feeling stressed, the suggestion unit prioritizes a suggestion for stress reduction. Also, when the user is relaxed, the suggestion unit can prioritize a suggestion for maintaining health. Furthermore, when the user is exercising, the suggestion unit can prioritize a suggestion for a post-exercise recovery method. In this way, by determining the priority of the suggestions according to the user's emotion, important suggestions can be made with priority. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data to the generation AI, which performs emotion estimation, and determine the priority of the suggestions based on the result.

[0116] The suggestion unit can suggest an optimal relaxation method in consideration of the geographical location information of the user at the time of suggestion. For example, the suggestion unit suggests an optimal relaxation method in consideration of the geographical location information of the user at the time of suggestion. For example, when the user is in an urban area, the suggestion unit suggests a relaxation method that can be performed in the urban area. In addition, when the user is in a natural environment, the suggestion unit can also suggest a relaxation method that utilizes nature. Furthermore, when the user is at home, the suggestion unit can also suggest a relaxation method that can be performed at home. In this way, the optimal relaxation method can be suggested by taking into account the geographical location information. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the geographical location information of the user to AI, which can select and suggest the optimal relaxation method.

[0117] The suggestion unit can analyze the user's social media activity at the time of suggestion to suggest a relaxation method. For example, the suggestion unit analyzes the user's social media activity at the time of suggestion to suggest a relaxation method. For example, the suggestion unit suggests a relaxation method in which the user showed interest on social media. The suggestion unit can also exclude relaxation methods that the user avoided on social media and make suggestions. Furthermore, the suggestion unit can identify current interests from the user's social media activity and suggest a relaxation method based on the identified interests. In this way, the optimal relaxation method can be suggested to the user by analyzing social media activity. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the user's social media activity data into AI, which can select and suggest the optimal relaxation method.

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

[0119] The analysis unit can estimate the user's emotion and adjust the analysis algorithm based on the estimated emotion. For example, when the user is feeling stressed, the analysis unit applies an algorithm that emphasizes stress-related data. In addition, when the user is relaxed, the analysis unit can also apply an algorithm that emphasizes relaxation-related data. In addition, when the user is exercising, the analysis unit can also apply an algorithm that emphasizes exercise-related data. This allows for more appropriate analysis by adjusting the analysis algorithm according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI, which performs emotion estimation, and adjust the analysis algorithm based on the result.

[0120] The providing unit can estimate the user's emotion and adjust the way of expressing the information to be provided based on the estimated emotion. For example, when the user is feeling stressed, the providing unit provides simple and highly visible information. In addition, when the user is relaxed, the providing unit can also provide detailed information. Furthermore, when the user is in a hurry, the providing unit can also provide information with the main points. This makes it possible to provide more appropriate information by adjusting the way of expressing information according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI, which performs emotion estimation, and adjust the way of expressing information based on the result.

[0121] The conversation unit can estimate the user's emotions and adjust the content of the conversation based on the estimated emotions. For example, the conversation unit can provide a topic that can relax the user when the user is stressed. Furthermore, the conversation unit can also provide an interesting topic when the user is relaxed. Furthermore, the conversation unit can provide a conversation that focuses on the main points when the user is in a hurry. This allows for a more appropriate conversation by adjusting the content of the conversation according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the conversation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the conversation unit can input the user's emotion data to the generation AI, which can perform emotion estimation and adjust the content of the conversation based on the result.

[0122] The evaluation unit can estimate the user's emotion and adjust the evaluation criteria based on the estimated emotion. For example, when the user is feeling stressed, the evaluation unit can emphasize stress-related evaluation criteria. Also, when the user is relaxed, the evaluation unit can emphasize relaxation-related evaluation criteria. Furthermore, when the user is exercising, the evaluation unit can emphasize exercise-related evaluation criteria. This allows for more appropriate evaluation by adjusting the evaluation criteria according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the evaluation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the evaluation unit can input the user's emotion data to the generation AI, which performs emotion estimation, and adjust the evaluation criteria based on the result.

[0123] The suggestion unit can estimate the user's emotion and adjust the proposed content based on the estimated emotion. For example, when the user is feeling stressed, the suggestion unit can suggest a relaxation method. Also, when the user is relaxed, the suggestion unit can make a suggestion for maintaining health. Furthermore, when the user is exercising, the suggestion unit can also suggest a recovery method after exercise. This allows for more appropriate suggestions by adjusting the proposed content according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data to the generation AI, which performs emotion estimation, and adjust the proposed content based on the result.

[0124] The collection unit can analyze the user's past biometric data history and select an appropriate collection method. For example, the collection unit can analyze the user's past heart rate data and collect data focusing on the time period when abnormalities were observed. The collection unit can also analyze the user's past body temperature data and collect data during the time period when the body temperature fluctuates rapidly. Furthermore, the collection unit can analyze the user's past sleep data and optimize data collection during sleep. In this way, the optimal collection method can be selected by analyzing the past data history. Part or all of the above-mentioned 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 the past biometric data history into AI, which can select the optimal collection method.

[0125] The collection unit can perform filtering based on the user's current activity status and environment when collecting biosensor data. For example, when the user is exercising, the collection unit filters out a temporary increase in heart rate due to exercise. In addition, when the user is stationary, the collection unit can also preferentially collect data at rest. Furthermore, when the user is out, the collection unit can also collect data by removing environmental noise. This enables more accurate data collection by filtering data based on the activity status and environment. Some or all of the above-mentioned 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 the user's activity status and environmental data to AI, and the AI ​​can perform filtering.

[0126] When collecting biosensor data, the collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the user. For example, when the user is at high altitude, the collection unit preferentially collects oxygen saturation data. In addition, when the user is in an urban area, the collection unit can also preferentially collect heart rate data. Furthermore, when the user is at home, the collection unit can also preferentially collect sleep data. In this way, highly relevant data can be preferentially collected by considering the geographical location information. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to AI, and the AI ​​can preferentially collect highly relevant data.

[0127] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected biometric data during the analysis. For example, the analysis unit performs a detailed analysis when the heart rate data is abnormal. The analysis unit can also perform a detailed analysis when the body temperature data fluctuates rapidly. Furthermore, the analysis unit can perform a detailed analysis when the respiratory rate data is abnormal. As a result, important data can be analyzed in detail by adjusting the level of detail of the analysis based on the importance of the data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected biometric data to AI, which evaluates the importance of the data, and adjust the level of detail of the analysis based on the result.

[0128] The evaluation unit can perform the evaluation by taking into account the attribute information of the user. For example, the evaluation unit applies evaluation criteria according to the age of the user. The evaluation unit can also apply evaluation criteria according to the gender of the user. Furthermore, the evaluation unit can apply evaluation criteria according to the health condition of the user. This allows for a more appropriate evaluation by taking into account the attribute information of the user. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the attribute information of the user to AI, which can select and apply appropriate evaluation criteria.

[0129] The process flow of the second embodiment will be briefly described below.

[0130] Step 1: The collection unit collects biosensor data of a user. The biosensor data of a user includes, for example, a heart rate, blood pressure, and body temperature. The collection unit collects heart rate data using a heart rate sensor, collects body temperature data using a body temperature sensor, and collects blood pressure data using a blood pressure meter. For example, the heart rate sensor is attached to the user's wrist, the body temperature sensor is attached to the user's forehead, and the blood pressure meter is attached to the user's upper arm. Step 2: The analysis unit analyzes the biosensor data collected by the collection unit. The analysis is performed using statistical analysis of the data and machine learning algorithms. For example, statistical analysis of heart rate data is performed to detect abnormal heart rate patterns, abnormalities in body temperature data are detected using machine learning algorithms, and blood pressure data is analyzed to detect abnormal blood pressure patterns. Step 3: The providing unit provides the analysis result by the analysis unit to the user. The analysis result is provided in a format such as text, audio, or visual. For example, the analysis result is provided to the user in a text format, audio format, or visual format. Step 4: The conversation unit uses the generation AI to generate a conversation appropriate to the user's age and engages in a conversation with the user. The conversation includes questions about health and topics about stress. For example, the generation AI is used to generate questions about health and topics about stress appropriate to the user's age. Step 5: The evaluation unit evaluates the user's psychological health state based on the conversation between the user and the conversation unit and the analysis results by the analysis unit. The evaluation is based on criteria such as stress level, anxiety level, and happiness level. For example, an emotional analysis of the conversation content and an integrated analysis of biometric data are performed to evaluate the user's stress level, anxiety level, and happiness level. Step 6: The suggestion unit suggests relaxation techniques based on the stress factors identified by the evaluation unit. The relaxation techniques include deep breathing, meditation, yoga, etc. For example, if the user's stress level is high, deep breathing is suggested, if the anxiety level is high, meditation is suggested, and if the happiness level is low, yoga is suggested.

[0131] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0132] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0133] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0134] For example, the collection unit can collect biosensor data of the user using a heart rate sensor or a body temperature sensor of the smart device 14. For example, the analysis unit can analyze data collected by the specific processing unit 290 of the data processing device 12. For example, the provision unit can provide the analysis result to the user using the display 40A or the speaker 40B of the smart device 14. For example, the conversation unit can generate a conversation according to the user's age using a generation AI by the control unit 46A of the smart device 14 and have a conversation with the user. For example, the evaluation unit can evaluate the user's psychological health state based on the conversation content and the analysis result by the specific processing unit 290 of the data processing device 12. For example, the suggestion unit can suggest a relaxation method based on the stress factor identified by the specific processing unit 290 of the data processing device 12. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

[0136] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0137] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0138] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0139] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average 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 performed in a secure state.

[0142] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0143] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0145] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0146] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0147] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0149] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0150] For example, the collection unit can collect biosensor data of the user using a heart rate sensor or a body temperature sensor of the smart glasses 214. For example, the analysis unit can analyze data collected by the specific processing unit 290 of the data processing device 12. For example, the provision unit can provide the analysis result to the user using the display or speaker of the smart glasses 214. For example, the conversation unit can generate a conversation according to the user's age using a generation AI by the control unit 46A of the smart glasses 214 and have a conversation with the user. For example, the evaluation unit can evaluate the user's psychological health state based on the conversation content and the analysis result by the specific processing unit 290 of the data processing device 12. For example, the suggestion unit can suggest a relaxation method based on the stress factor identified by the specific processing unit 290 of the data processing device 12. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

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

[0153] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0154] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0155] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average 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 performed in a secure state.

[0158] 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.

[0159] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0161] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0162] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0163] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for 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.

[0164] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0165] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0166] For example, the collection unit can collect biosensor data of the user using a heart rate sensor or a body temperature sensor of the headset type terminal 314. For example, the analysis unit can analyze data collected by the specific processing unit 290 of the data processing device 12. For example, the provision unit can provide the analysis result to the user using the display or speaker of the headset type terminal 314. For example, the conversation unit can generate a conversation according to the user's age using a generation AI by the control unit 46A of the headset type terminal 314 and have a conversation with the user. For example, the evaluation unit can evaluate the user's psychological health state based on the conversation content and the analysis result by the specific processing unit 290 of the data processing device 12. For example, the suggestion unit can suggest a relaxation method based on the stress factor identified by the specific processing unit 290 of the data processing device 12. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

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

[0169] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0170] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0171] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0172] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0173] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0174] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0175] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0176] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0178] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0179] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0180] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0181] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0182] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0183] For example, the collection unit can collect biosensor data of the user using a heart rate sensor or a body temperature sensor of the robot 414. For example, the analysis unit can analyze data collected by the specific processing unit 290 of the data processing device 12. For example, the provision unit can provide the analysis result to the user using the display or speaker of the robot 414. For example, the conversation unit can generate a conversation according to the age of the user using a generation AI by the control unit 46A of the robot 414 and have a conversation with the user. For example, the evaluation unit can evaluate the psychological health state of the user based on the conversation content and the analysis result by the specific processing unit 290 of the data processing device 12. For example, the suggestion unit can suggest a relaxation method based on the stress factor identified by the specific processing unit 290 of the data processing device 12. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0184] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0185] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0186] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0187] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0188] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0189] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0190] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0191] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0192] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0193] In addition, 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 upon request from the data processing device 12.

[0194] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0195] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0196] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0197] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0198] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0199] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0200] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0201] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0202] (Appendix 1) A collection unit that collects biosensor data of a user; an analysis unit that analyzes the biosensor data collected by the collection unit; a providing unit that provides the user with an analysis result by the analysis unit; A conversation unit that carries out a specific conversation content with the user according to the age of the user; an evaluation unit that evaluates a psychological health state of the user based on a specific conversation between the user and the conversation unit; A suggestion unit that suggests a relaxation method based on the stress factor identified by the evaluation unit. A system characterized by: (Appendix 2) The conversation unit includes: A conversation is carried out according to the analysis result by the analysis unit. 2. The system of claim 1, (Appendix 3) The conversation unit includes: A conversation is conducted according to the evaluation result by the evaluation unit. 2. The system of claim 1, (Appendix 4) The evaluation unit is Evaluating the psychological health state of the user based on a specific conversation between the user and the conversation unit and an analysis result by the analysis unit. 2. The system of claim 1, (Appendix 5) The collecting unit includes: To estimate a user's emotion and adjust a collection timing of biosensor data based on the estimated user's emotion. 2. The system of claim 1, (Appendix 6) The collecting unit includes: Analyze the user's past biometric data history and select the appropriate collection method 2. The system of claim 1, (Appendix 7) The collecting unit includes: When biosensor data is collected, it is filtered based on the user's current activity and environment. 2. The system of claim 1, (Appendix 8) The collecting unit includes: To estimate a user's emotion and determine a priority order of biometric data to be collected based on the estimated user's emotion. 2. The system of claim 1, (Appendix 9) The collecting unit includes: When collecting biosensor data, the system prioritizes collection of relevant data by taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 10) The collecting unit includes: Upon collection of biosensor data, the user's social media activity is analyzed and relevant data is collected. 2. The system of claim 1, (Appendix 11) The analysis unit is Inferring user emotions and adjusting analysis algorithms based on the inferred user emotions 2. The system of claim 1, (Appendix 12) The analysis unit is During analysis, adjust the level of detail based on the importance of the collected biometric data 2. The system of claim 1, (Appendix 13) The analysis unit is During analysis, apply different analysis algorithms depending on the category of data 2. The system of claim 1, (Appendix 14) The analysis unit is Infer user emotions and adjust the way analysis results are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, prioritize analysis based on when the data was collected 2. The system of claim 1, (Appendix 16) The analysis unit is During analysis, adjust the order of analysis based on data relevance 2. The system of claim 1, (Appendix 17) The providing unit is Estimate the user's emotions and adjust the way information is presented based on the estimated user's emotions 2. The system of claim 1, (Appendix 18) The providing unit is At the time of provision, the level of detail provided will be adjusted based on the importance of the analysis results. 2. The system of claim 1, (Appendix 19) The providing unit is When providing information, apply different methods of providing information depending on the user's level of understanding. 2. The system of claim 1, (Appendix 20) The providing unit is Estimate the user's emotions and prioritize the information to be provided based on the estimated user's emotions. 2. The system of claim 1, (Appendix 21) The providing unit is When providing information, the appropriate method of providing the information is selected taking into account the user's device information. 2. The system of claim 1, (Appendix 22) The providing unit is When delivering, refer to past user feedback to customize the delivery 2. The system of claim 1, (Appendix 23) The conversation unit includes: Estimate user emotions and adjust the content of the conversation based on the estimated user emotions 2. The system of claim 1, (Appendix 24) The conversation unit includes: During a conversation, the system generates optimal conversation content by referring to the user's past conversation history 2. The system of claim 1, (Appendix 25) The conversation unit includes: During a conversation, different conversation algorithms are applied depending on the user's age and interests. 2. The system of claim 1, (Appendix 26) The conversation unit includes: Infer user emotions and adjust the tone of the conversation based on the inferred user emotions 2. The system of claim 1, (Appendix 27) The conversation unit includes: During conversations, the app takes into account the user's geographic location to generate relevant conversation content 2. The system of claim 1, (Appendix 28) The conversation unit includes: During conversations, it analyzes the user's social media activity and generates relevant conversation content 2. The system of claim 1, (Appendix 29) The evaluation unit is Inferring a user's emotion and adjusting the evaluation criteria based on the estimated user's emotion 2. The system of claim 1, (Appendix 30) The evaluation unit is Improve the accuracy of the evaluation by considering the correlation between the conversation content and biometric data during the evaluation. 2. The system of claim 1, (Appendix 31) The evaluation unit is When evaluating, user attribute information is taken into consideration 2. The system of claim 1, (Appendix 32) The evaluation unit is Inferring user emotion and adjusting the way rating results are displayed based on the estimated user emotion 2. The system of claim 1, (Appendix 33) The evaluation unit is During evaluation, the geographical distribution of users is taken into account. 2. The system of claim 1, (Appendix 34) The evaluation unit is During evaluation, refer to related literature to improve the accuracy of the evaluation. 2. The system of claim 1, (Appendix 35) The suggestion unit, Infer user emotions and adjust suggestions based on the inferred user emotions 2. The system of claim 1, (Appendix 36) The suggestion unit, When making suggestions, the system analyzes the user's past stress factors and suggests optimal relaxation methods. 2. The system of claim 1, (Appendix 37) The suggestion unit, Customize relaxation techniques based on the user's current life situation when making suggestions 2. The system of claim 1, (Appendix 38) The suggestion unit, Estimate user sentiment and prioritize suggestions based on the estimated user sentiment 2. The system of claim 1, (Appendix 39) The suggestion unit, When proposing, the system takes into account the user's geographic location information to suggest optimal relaxation methods. 2. The system of claim 1, (Appendix 40) The suggestion unit, When making suggestions, the app analyzes the user's social media activity to suggest relaxation techniques. 2. The system of claim 1, [Explanation of symbols]

[0203] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection unit that collects biosensor data of a user; an analysis unit that analyzes the biosensor data collected by the collection unit; a providing unit that provides the user with an analysis result by the analysis unit; A conversation unit that carries out a specific conversation content with the user according to the age of the user; an evaluation unit that evaluates a psychological health state of the user based on a specific conversation between the user and the conversation unit; A suggestion unit that suggests a relaxation method based on the stress factor identified by the evaluation unit. A system characterized by:

2. The conversation unit includes: A conversation is carried out according to the analysis result by the analysis unit.

2. The system of claim 1 .

3. The conversation unit includes: A conversation is conducted according to the evaluation result by the evaluation unit.

2. The system of claim 1 .

4. The evaluation unit is Evaluating the psychological health state of the user based on a specific conversation between the user and the conversation unit and an analysis result by the analysis unit.

2. The system of claim 1 .

5. The collecting unit includes: The emotion of the user is estimated, and a collection timing of biosensor data is adjusted based on the estimated emotion of the user.

2. The system of claim 1 .

6. The collecting unit includes: Analyzing the user's past biometric data history and selecting an appropriate collection method 2. The system of claim 1 .

7. The collecting unit includes: When biosensor data is collected, it is filtered based on the user's current activity and environment.

2. The system of claim 1 .

8. The collecting unit includes: Estimating the emotion of the user and prioritizing the biometric data to be collected based on the estimated emotion of the user.

2. The system of claim 1 .

9. The collecting unit includes: When collecting biosensor data, the biosensor takes into consideration the geographical location information of the user and preferentially collects highly relevant data.

2. The system of claim 1 .

10. The collecting unit includes: Upon collection of biosensor data, analyze the user's social media activity and collect related data.

2. The system of claim 1 .

Images