system

Abstract

The system according to this embodiment aims to provide a relaxation method tailored to the individual. [Solution] The system according to the embodiment comprises an acquisition unit, an analysis unit, and a provision unit. The acquisition unit acquires heart rate and environmental data in real time. The analysis unit analyzes the data acquired by the acquisition unit. The provision unit provides relaxation instructions based on the data analyzed by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to find a relaxation method suitable for an individual.

[0005] The system according to the embodiment aims to provide a relaxation method suitable for an individual.

Means for Solving the Problems

[0006] The system according to the embodiment includes an acquisition unit, an analysis unit, and a provision unit. The acquisition unit acquires heart rate and environmental data in real time. The analysis unit analyzes the data acquired by the acquisition unit. The provision unit provides a relaxation instruction based on the data analyzed by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment can provide a relaxation method tailored to the individual. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 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". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

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

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An AI agent system according to an embodiment of the present invention is a system that analyzes heart rate and environmental data to provide personalized relaxation. This AI agent system acquires heart rate and environmental data in real time, analyzes the acquired data, and automatically generates an individually tailored wellness plan. Furthermore, it provides specific relaxation instructions based on the analysis results. For example, the AI ​​agent system acquires heart rate and environmental data in real time using a smartwatch. Next, the AI ​​agent analyzes the acquired data and automatically generates an individually tailored wellness plan. For example, the AI ​​agent system integrates and analyzes heart rate, exercise data, stress indicators, and environmental data. Furthermore, it provides specific relaxation instructions based on the analysis results. For example, the AI ​​agent system provides specific instructions on sauna usage time, cold bath usage timing, and outdoor air bathing time. As a result, the AI ​​agent system aims to promote health and reduce stress, making it extremely convenient for users. The AI ​​agent system can provide additional value to fitness facilities and improve user satisfaction. In today's world, where personalized health management is becoming increasingly important due to growing health awareness, this system is extremely useful. As a result, the AI ​​agent system can achieve healthy physical condition management and stress reduction.

[0029] The AI ​​agent system according to this embodiment comprises an acquisition unit, an analysis unit, and a provision unit. The acquisition unit acquires heart rate and environmental data in real time. The acquisition unit acquires heart rate and environmental data in real time, for example, using a smartwatch. The acquisition unit can use, for example, an optical heart rate sensor to measure heart rate. The acquisition unit can use, for example, a temperature sensor or a humidity sensor to acquire environmental data. The acquisition unit can acquire data in real time, for example, every second. The analysis unit analyzes the data acquired by the acquisition unit. The analysis unit integrates and analyzes, for example, heart rate, exercise data, stress indicators, and environmental data. The analysis unit can use, for example, a heart rate variability analysis algorithm to analyze heart rate data. The analysis unit can analyze, for example, fluctuations in temperature and humidity to analyze environmental data. The analysis unit can use, for example, an algorithm to evaluate stress levels to analyze stress indicators. The provision unit provides relaxation instructions based on the data analyzed by the analysis unit. The service provider can, for example, give specific instructions regarding the duration of sauna use, the timing of cold bath use, and the duration of outdoor relaxation. The service provider can use, for example, voice guidance to provide relaxation instructions. The service provider can use, for example, a smartphone app to provide relaxation instructions. The service provider can use, for example, a display in a fitness facility to provide relaxation instructions. As a result, the AI ​​agent system according to the embodiment can analyze heart rate and environmental data to provide relaxation optimized for the individual.

[0030] The data acquisition unit acquires heart rate and environmental data in real time. For example, the data acquisition unit uses a smartwatch to acquire heart rate and environmental data in real time. Smartwatches are equipped with optical heart rate sensors, which allow for accurate measurement of heart rate. The optical heart rate sensor detects changes in blood flow by shining light onto the skin and measuring the reflected light, thereby calculating the heart rate. Furthermore, smartwatches are also equipped with temperature and humidity sensors, which can be used to acquire ambient environmental data. The temperature sensor measures the ambient temperature, and the humidity sensor measures the humidity in the air. The data acquisition unit acquires this data every second, collecting data in real time. This allows the data acquisition unit to always have up-to-date information on the user's heart rate and ambient environmental data. In addition, the data acquisition unit transmits this data to a central database, making it accessible to the analysis and provisioning units. This allows the data acquisition unit to collect data efficiently and accurately, improving the overall system performance.

[0031] The analysis unit analyzes the data acquired by the acquisition unit. For example, the analysis unit integrates and analyzes heart rate, exercise data, stress indicators, and environmental data. For heart rate data analysis, a heart rate variability analysis algorithm can be used. The heart rate variability analysis algorithm analyzes the variability pattern of heart rate and evaluates the user's stress level and relaxation state. For example, if the heart rate rises sharply, it can be determined that the user is likely experiencing stress. For exercise data analysis, data from acceleration sensors and gyroscope sensors is used to evaluate the user's exercise volume and activity level. For environmental data analysis, temperature and humidity fluctuations are analyzed to evaluate an environment in which the user can spend time comfortably. For stress indicator analysis, an algorithm that evaluates stress levels is used to quantitatively evaluate the user's stress state. As a result, the analysis unit can integrate and analyze the acquired data to accurately grasp the user's health status and stress level. Furthermore, the analysis unit can also utilize past data and statistical information to analyze long-term changes and trends in health status. As a result, the analysis unit can not only grasp the situation in real time but also respond to long-term health management and prevention.

[0032] The service provider provides relaxation instructions based on data analyzed by the analysis unit. For example, the service provider can provide specific instructions regarding sauna usage time, cold bath usage timing, and outdoor air bathing time. The service provider can also use voice guidance to provide relaxation instructions. Voice guidance provides users with specific relaxation methods and timings, enhancing the relaxation effect. For example, when sauna usage time ends, the voice guidance can prompt the user to use the cold bath. The service provider can also provide relaxation instructions using a smartphone app. The smartphone app displays user data in real time and notifies users of relaxation instructions. For example, the app can count down sauna usage time and notify the user to use the cold bath when it ends. The service provider can also provide relaxation instructions using displays in fitness facilities. The displays show user data and relaxation instructions, allowing users to act accordingly. This enables the service provider to provide users with appropriate relaxation instructions, reducing stress and promoting health. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the instructions. This allows the service provider to provide users with optimal relaxation and improve the overall reliability and effectiveness of the system.

[0033] The service provider can give specific instructions regarding sauna usage time, cold bath usage timing, and outdoor relaxation time. For example, the service provider can instruct the user to use the sauna for 10 minutes. For example, the service provider can instruct the user to use the cold bath immediately after using the sauna. For example, the service provider can instruct the user to relax outdoors for 5 minutes. By providing specific relaxation instructions, user satisfaction can be improved. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the sauna usage time, cold bath usage timing, and outdoor relaxation time into a generating AI, which can then generate optimal instructions.

[0034] The acquisition unit can acquire heart rate and environmental data in real time using a smartwatch. For example, the acquisition unit measures heart rate using the smartwatch's optical heart rate sensor. For example, the acquisition unit measures ambient temperature using the smartwatch's temperature sensor. For example, the acquisition unit measures ambient humidity using the smartwatch's humidity sensor. This allows data to be acquired in real time using a smartwatch. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input data acquired from the smartwatch into a generating AI, which can then analyze the data.

[0035] The analysis unit can integrate and analyze heart rate, exercise data, stress indicators, and environmental data. For example, the analysis unit can integrate and analyze heart rate data and exercise data. For example, the analysis unit can integrate and analyze heart rate data and stress indicators. For example, the analysis unit can integrate and analyze heart rate data and environmental data. By integrating and analyzing multiple data, more accurate analysis results can be obtained. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input heart rate data, exercise data, stress indicators, and environmental data into a generating AI, which can then integrate and analyze the data.

[0036] The service provider can offer additional value to fitness facilities. For example, the service provider can provide personalized relaxation plans to users of fitness facilities. For example, the service provider can provide health management advice to users of fitness facilities. For example, the service provider can provide instructions for stress reduction to users of fitness facilities. By providing additional value to fitness facilities, user satisfaction can be improved. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data of fitness facility users into a generating AI, and the generating AI can generate personalized relaxation plans.

[0037] The data acquisition unit can analyze the user's past health data and select the optimal data acquisition method. For example, the data acquisition unit can analyze the user's past heart rate data and, if there is a tendency for heart rate to increase during certain time periods, focus on acquiring data during those times. For example, the data acquisition unit can analyze the user's past environmental data and, if stress increases under certain environmental conditions, increase the frequency of data acquisition under those conditions. For example, the data acquisition unit can analyze the user's past exercise data and adjust the data acquisition timing considering changes in heart rate after exercise. In this way, the optimal data acquisition method can be selected by analyzing past health data. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the user's past health data into a generating AI, which can then select the optimal data acquisition method.

[0038] The data acquisition unit can filter data based on the user's current activity status and environmental conditions during data acquisition. For example, if the user is exercising, the data acquisition unit filters heart rate data according to exercise intensity and excludes outliers. For example, if the user is stationary, the data acquisition unit filters out fluctuations in environmental data to acquire stable data. For example, if the user is outdoors, the data acquisition unit filters data considering fluctuations in temperature and humidity. By performing filtering during data acquisition, more accurate data can be obtained. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the user's activity status and environmental conditions into a generating AI, which can then filter the data.

[0039] The data acquisition unit can prioritize the acquisition of highly relevant data by considering the user's geographical location information during data acquisition. For example, if the user is outdoors, the data acquisition unit prioritizes the acquisition of environmental data such as temperature and humidity. For example, if the user is indoors, the data acquisition unit prioritizes the acquisition of environmental data such as room temperature and illuminance. For example, if the user is in a specific location, the data acquisition unit prioritizes the acquisition of data appropriate to the characteristics of that location. This allows for the priority acquisition of highly relevant data by considering geographical location information. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the user's geographical location information into a generating AI, which can then prioritize the acquisition of highly relevant data.

[0040] The data acquisition unit can analyze the user's social media activity and acquire relevant data when acquiring data. For example, if the user is experiencing stress on social media, the data acquisition unit will prioritize acquiring heart rate data. For example, if the user is relaxing on social media, the data acquisition unit will prioritize acquiring environmental data. For example, if the user is posting about exercise on social media, the data acquisition unit will prioritize acquiring exercise data. In this way, relevant data can be acquired by analyzing social media activity. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the user's social media activity data into a generating AI, and the generating AI can acquire relevant data.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of heart rate and environmental data during the analysis. For example, if the heart rate is high, the analysis unit performs a detailed analysis to identify the cause of stress. For example, if the environmental data is fluctuating, the analysis unit performs a detailed analysis to evaluate the influence of the environment. For example, if the heart rate and environmental data are stable, the analysis unit performs a simplified analysis to evaluate the relaxation effect. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of heart rate and environmental data into a generating AI, which can then adjust the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a stress analysis algorithm to heart rate data. For example, the analysis unit applies an environmental impact analysis algorithm to environmental data. For example, the analysis unit applies an exercise effect analysis algorithm to exercise data. By applying an analysis algorithm according to the data category, more accurate analysis results can be obtained. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI, and the generating AI can apply an analysis algorithm according to the category.

[0043] The analysis unit can determine the priority of analysis based on the data acquisition timing during the analysis. For example, the analysis unit may prioritize the analysis of the latest data to reflect real-time conditions. For example, the analysis unit may refer to past data to evaluate long-term trends. For example, the analysis unit may prioritize the analysis of data from a specific event to evaluate its impact. By determining the priority of analysis based on the data acquisition timing, it becomes possible to perform analysis that reflects real-time conditions. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data acquisition timing into a generating AI, which can then determine the priority of analysis based on the acquisition timing.

[0044] The analysis unit can adjust the order of analysis based on the relationships between the data during the analysis. For example, the analysis unit evaluates the relationship between heart rate data and environmental data and determines the order of analysis. For example, the analysis unit evaluates the relationship between exercise data and heart rate data and determines the order of analysis. For example, the analysis unit evaluates the relationship between environmental data and stress indicators and determines the order of analysis. This allows for efficient analysis by adjusting the order of analysis based on the relationships between the data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relationships between the data into a generating AI, which can then adjust the order of analysis based on the relationships.

[0045] The service provider can adjust the level of detail in the instructions based on the importance of relaxation at the time of delivery. For example, if relaxation is important, the service provider will provide detailed instructions. For example, if relaxation is not so important, the service provider will provide simplified instructions. For example, the service provider will adjust the level of detail in the instructions in stages according to the importance of relaxation. This allows for the provision of appropriate instructions by adjusting the level of detail based on the importance of relaxation. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the importance of relaxation into a generating AI, and the generating AI can adjust the level of detail in the instructions.

[0046] The service provider can apply different instruction algorithms depending on the relaxation category at the time of service provision. For example, the service provider applies a meditation instruction algorithm for meditation. For example, the service provider applies a stretching instruction algorithm for stretching. For example, the service provider applies a deep breathing instruction algorithm for deep breathing. By applying an analysis algorithm according to the relaxation category, more accurate analysis results can be obtained. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the relaxation category into a generating AI, and the generating AI can apply an instruction algorithm according to the category.

[0047] The service provider can determine the priority of instructions based on the timing of relaxation at the time of provision. For example, the service provider will provide instructions preferentially when relaxation is needed. For example, the service provider will adjust the priority of instructions according to the timing of relaxation. For example, the service provider will adjust the content of the instructions based on the timing of relaxation. By determining the priority of instructions based on the timing of relaxation, relaxation can be provided at the appropriate time. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the timing of relaxation into a generating AI, and the generating AI can determine the priority of instructions based on the timing.

[0048] The service provider can adjust the order of instructions based on their relevance to relaxation during delivery. For example, the service provider prioritizes providing instructions with a high relevance to relaxation. For example, the service provider adjusts the order of instructions according to their relevance to relaxation. For example, the service provider adjusts the content of the instructions based on their relevance to relaxation. By adjusting the order of instructions based on their relevance to relaxation, more effective relaxation can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the relevance to relaxation into a generating AI, which can then adjust the order of instructions based on the relevance.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The acquisition unit acquires the user's sleep data, and the analysis unit analyzes the acquired sleep data to evaluate the user's sleep quality. For example, the acquisition unit monitors the user's heart rate and movement during sleep and records the depth of sleep and the frequency of interruptions. The analysis unit analyzes this data to evaluate the user's sleep quality and identify areas for improvement. Based on the analysis results, the provision unit can provide the user with specific advice to improve their sleep quality. For example, it can instruct them on relaxation methods before bedtime and how to create an appropriate sleep environment. This allows the user to get better quality sleep and contributes to an improvement in their overall health.

[0051] The analysis unit can acquire and analyze the user's dietary data to evaluate its impact on their health. For example, the acquisition unit records the content and calories of the meals the user consumes. The analysis unit analyzes this data to evaluate the user's nutritional balance and the quality of their diet. Based on the analysis results, the provision unit can provide the user with advice on healthy eating. For example, it can provide advice on ingredients to increase the intake of specific nutrients or on the timing of meals. This allows the user to lead a healthier lifestyle.

[0052] The acquisition unit acquires user activity data, and the analysis unit analyzes the acquired activity data to evaluate the user's exercise habits. For example, the acquisition unit records the user's steps and exercise time. The analysis unit analyzes this data to evaluate the user's exercise volume and quality. Based on the analysis results, the provision unit can point out areas for improvement in the user's exercise habits and provide a specific exercise plan. For example, it can provide advice on adjusting the frequency and intensity of exercise, or recommend specific exercises. This allows the user to develop more effective exercise habits.

[0053] The acquisition unit acquires the user's water intake data, and the analysis unit analyzes the acquired water intake data to evaluate the user's hydration status. For example, the acquisition unit records the amount of water the user has consumed. The analysis unit analyzes this data to evaluate the user's hydration status and calculate the required amount of water intake. Based on the analysis results, the provision unit can provide the user with appropriate hydration advice. For example, it can provide specific instructions on the timing and amount of water to hydrate after exercise or on hot days. This enables the user to hydrate appropriately and contributes to maintaining good health.

[0054] The analysis unit can monitor users' health data over the long term and analyze trends in their health status. For example, the acquisition unit records the user's heart rate, exercise data, and sleep data over a long period. The analysis unit analyzes this data to evaluate trends in the user's health status and predict health risks. Based on the analysis results, the provision unit can provide users with specific advice to mitigate health risks. For example, it may recommend improving exercise habits, reviewing diet, and undergoing regular health checkups. This allows users to manage their health over the long term and reduce health risks.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The acquisition unit acquires heart rate and environmental data in real time. For example, a smartwatch can be used to acquire heart rate and environmental data in real time. An optical heart rate sensor can be used to measure heart rate, and a temperature sensor or humidity sensor can be used to acquire environmental data. Data is acquired every second. Step 2: The analysis unit analyzes the data acquired by the acquisition unit. For example, it integrates and analyzes heart rate, exercise data, stress indicators, and environmental data. A heart rate variability analysis algorithm can be used to analyze heart rate data, and temperature and humidity fluctuations can be analyzed to analyze environmental data. An algorithm for evaluating stress levels can be used to analyze stress indicators. Step 3: The service unit provides relaxation instructions based on the data analyzed by the analysis unit. For example, it provides specific instructions on how long to use the sauna, when to use the cold bath, and how long to spend in the fresh air. Audio guides, smartphone apps, and fitness facility displays can be used to provide relaxation instructions.

[0057] (Example of form 2) An AI agent system according to an embodiment of the present invention is a system that analyzes heart rate and environmental data to provide personalized relaxation. This AI agent system acquires heart rate and environmental data in real time, analyzes the acquired data, and automatically generates an individually tailored wellness plan. Furthermore, it provides specific relaxation instructions based on the analysis results. For example, the AI ​​agent system acquires heart rate and environmental data in real time using a smartwatch. Next, the AI ​​agent analyzes the acquired data and automatically generates an individually tailored wellness plan. For example, the AI ​​agent system integrates and analyzes heart rate, exercise data, stress indicators, and environmental data. Furthermore, it provides specific relaxation instructions based on the analysis results. For example, the AI ​​agent system provides specific instructions on sauna usage time, cold bath usage timing, and outdoor air bathing time. As a result, the AI ​​agent system aims to promote health and reduce stress, making it extremely convenient for users. The AI ​​agent system can provide additional value to fitness facilities and improve user satisfaction. In today's world, where personalized health management is becoming increasingly important due to growing health awareness, this system is extremely useful. As a result, the AI ​​agent system can achieve healthy physical condition management and stress reduction.

[0058] The AI ​​agent system according to this embodiment comprises an acquisition unit, an analysis unit, and a provision unit. The acquisition unit acquires heart rate and environmental data in real time. The acquisition unit acquires heart rate and environmental data in real time, for example, using a smartwatch. The acquisition unit can use, for example, an optical heart rate sensor to measure heart rate. The acquisition unit can use, for example, a temperature sensor or a humidity sensor to acquire environmental data. The acquisition unit can acquire data in real time, for example, every second. The analysis unit analyzes the data acquired by the acquisition unit. The analysis unit integrates and analyzes, for example, heart rate, exercise data, stress indicators, and environmental data. The analysis unit can use, for example, a heart rate variability analysis algorithm to analyze heart rate data. The analysis unit can analyze, for example, fluctuations in temperature and humidity to analyze environmental data. The analysis unit can use, for example, an algorithm to evaluate stress levels to analyze stress indicators. The provision unit provides relaxation instructions based on the data analyzed by the analysis unit. The service provider can, for example, give specific instructions regarding the duration of sauna use, the timing of cold bath use, and the duration of outdoor relaxation. The service provider can use, for example, voice guidance to provide relaxation instructions. The service provider can use, for example, a smartphone app to provide relaxation instructions. The service provider can use, for example, a display in a fitness facility to provide relaxation instructions. As a result, the AI ​​agent system according to the embodiment can analyze heart rate and environmental data to provide relaxation optimized for the individual.

[0059] The data acquisition unit acquires heart rate and environmental data in real time. For example, the data acquisition unit uses a smartwatch to acquire heart rate and environmental data in real time. Smartwatches are equipped with optical heart rate sensors, which allow for accurate measurement of heart rate. The optical heart rate sensor detects changes in blood flow by shining light onto the skin and measuring the reflected light, thereby calculating the heart rate. Furthermore, smartwatches are also equipped with temperature and humidity sensors, which can be used to acquire ambient environmental data. The temperature sensor measures the ambient temperature, and the humidity sensor measures the humidity in the air. The data acquisition unit acquires this data every second, collecting data in real time. This allows the data acquisition unit to always have up-to-date information on the user's heart rate and ambient environmental data. In addition, the data acquisition unit transmits this data to a central database, making it accessible to the analysis and provisioning units. This allows the data acquisition unit to collect data efficiently and accurately, improving the overall system performance.

[0060] The analysis unit analyzes the data acquired by the acquisition unit. For example, the analysis unit integrates and analyzes heart rate, exercise data, stress indicators, and environmental data. For heart rate data analysis, a heart rate variability analysis algorithm can be used. The heart rate variability analysis algorithm analyzes the variability pattern of heart rate and evaluates the user's stress level and relaxation state. For example, if the heart rate rises sharply, it can be determined that the user is likely experiencing stress. For exercise data analysis, data from acceleration sensors and gyroscope sensors is used to evaluate the user's exercise volume and activity level. For environmental data analysis, temperature and humidity fluctuations are analyzed to evaluate an environment in which the user can spend time comfortably. For stress indicator analysis, an algorithm that evaluates stress levels is used to quantitatively evaluate the user's stress state. As a result, the analysis unit can integrate and analyze the acquired data to accurately grasp the user's health status and stress level. Furthermore, the analysis unit can also utilize past data and statistical information to analyze long-term changes and trends in health status. As a result, the analysis unit can not only grasp the situation in real time but also respond to long-term health management and prevention.

[0061] The service provider provides relaxation instructions based on data analyzed by the analysis unit. For example, the service provider can provide specific instructions regarding sauna usage time, cold bath usage timing, and outdoor air bathing time. The service provider can also use voice guidance to provide relaxation instructions. Voice guidance provides users with specific relaxation methods and timings, enhancing the relaxation effect. For example, when sauna usage time ends, the voice guidance can prompt the user to use the cold bath. The service provider can also provide relaxation instructions using a smartphone app. The smartphone app displays user data in real time and notifies users of relaxation instructions. For example, the app can count down sauna usage time and notify the user to use the cold bath when it ends. The service provider can also provide relaxation instructions using displays in fitness facilities. The displays show user data and relaxation instructions, allowing users to act accordingly. This enables the service provider to provide users with appropriate relaxation instructions, reducing stress and promoting health. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the instructions. This allows the service provider to provide users with optimal relaxation and improve the overall reliability and effectiveness of the system.

[0062] The service provider can give specific instructions regarding sauna usage time, cold bath usage timing, and outdoor relaxation time. For example, the service provider can instruct the user to use the sauna for 10 minutes. For example, the service provider can instruct the user to use the cold bath immediately after using the sauna. For example, the service provider can instruct the user to relax outdoors for 5 minutes. By providing specific relaxation instructions, user satisfaction can be improved. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the sauna usage time, cold bath usage timing, and outdoor relaxation time into a generating AI, which can then generate optimal instructions.

[0063] The acquisition unit can acquire heart rate and environmental data in real time using a smartwatch. For example, the acquisition unit measures heart rate using the smartwatch's optical heart rate sensor. For example, the acquisition unit measures ambient temperature using the smartwatch's temperature sensor. For example, the acquisition unit measures ambient humidity using the smartwatch's humidity sensor. This allows data to be acquired in real time using a smartwatch. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input data acquired from the smartwatch into a generating AI, which can then analyze the data.

[0064] The analysis unit can integrate and analyze heart rate, exercise data, stress indicators, and environmental data. For example, the analysis unit can integrate and analyze heart rate data and exercise data. For example, the analysis unit can integrate and analyze heart rate data and stress indicators. For example, the analysis unit can integrate and analyze heart rate data and environmental data. By integrating and analyzing multiple data, more accurate analysis results can be obtained. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input heart rate data, exercise data, stress indicators, and environmental data into a generating AI, which can then integrate and analyze the data.

[0065] The service provider can offer additional value to fitness facilities. For example, the service provider can provide personalized relaxation plans to users of fitness facilities. For example, the service provider can provide health management advice to users of fitness facilities. For example, the service provider can provide instructions for stress reduction to users of fitness facilities. By providing additional value to fitness facilities, user satisfaction can be improved. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data of fitness facility users into a generating AI, and the generating AI can generate personalized relaxation plans.

[0066] The data acquisition unit can estimate the user's emotions and adjust the timing of heart rate and environmental data acquisition based on the estimated emotions. For example, if the user is stressed, the data acquisition unit increases the frequency of heart rate acquisition and shortens the interval for environmental data acquisition. For example, if the user is relaxed, the data acquisition unit decreases the frequency of heart rate acquisition and lengthens the interval for environmental data acquisition. For example, if the user is exercising, the data acquisition unit maintains a constant frequency of heart rate acquisition and acquires environmental data in real time. This allows for the acquisition of more appropriate data by adjusting the timing of data acquisition according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the user's emotion data into the generative AI, which can then adjust the timing of data acquisition based on the emotions.

[0067] The data acquisition unit can analyze the user's past health data and select the optimal data acquisition method. For example, the data acquisition unit can analyze the user's past heart rate data and, if there is a tendency for heart rate to increase during certain time periods, focus on acquiring data during those times. For example, the data acquisition unit can analyze the user's past environmental data and, if stress increases under certain environmental conditions, increase the frequency of data acquisition under those conditions. For example, the data acquisition unit can analyze the user's past exercise data and adjust the data acquisition timing considering changes in heart rate after exercise. In this way, the optimal data acquisition method can be selected by analyzing past health data. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the user's past health data into a generating AI, which can then select the optimal data acquisition method.

[0068] The data acquisition unit can filter data based on the user's current activity status and environmental conditions during data acquisition. For example, if the user is exercising, the data acquisition unit filters heart rate data according to exercise intensity and excludes outliers. For example, if the user is stationary, the data acquisition unit filters out fluctuations in environmental data to acquire stable data. For example, if the user is outdoors, the data acquisition unit filters data considering fluctuations in temperature and humidity. By performing filtering during data acquisition, more accurate data can be obtained. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the user's activity status and environmental conditions into a generating AI, which can then filter the data.

[0069] The data acquisition unit can estimate the user's emotions and determine the priority of data to acquire based on the estimated user emotions. For example, if the user is stressed, the data acquisition unit will prioritize acquiring heart rate data. For example, if the user is relaxed, the data acquisition unit will prioritize acquiring environmental data. For example, if the user is exercising, the data acquisition unit will prioritize acquiring exercise data. In this way, important data can be acquired preferentially by determining the priority of data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or not using AI. For example, the data acquisition unit can input the user's emotion data into the generative AI, and the generative AI can determine the priority of data based on the emotions.

[0070] The data acquisition unit can prioritize the acquisition of highly relevant data by considering the user's geographical location information during data acquisition. For example, if the user is outdoors, the data acquisition unit prioritizes the acquisition of environmental data such as temperature and humidity. For example, if the user is indoors, the data acquisition unit prioritizes the acquisition of environmental data such as room temperature and illuminance. For example, if the user is in a specific location, the data acquisition unit prioritizes the acquisition of data appropriate to the characteristics of that location. This allows for the priority acquisition of highly relevant data by considering geographical location information. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the user's geographical location information into a generating AI, which can then prioritize the acquisition of highly relevant data.

[0071] The data acquisition unit can analyze the user's social media activity and acquire relevant data when acquiring data. For example, if the user is experiencing stress on social media, the data acquisition unit will prioritize acquiring heart rate data. For example, if the user is relaxing on social media, the data acquisition unit will prioritize acquiring environmental data. For example, if the user is posting about exercise on social media, the data acquisition unit will prioritize acquiring exercise data. In this way, relevant data can be acquired by analyzing social media activity. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the user's social media activity data into a generating AI, and the generating AI can acquire relevant data.

[0072] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is stressed, the analysis unit applies an analysis algorithm that focuses on stress reduction. For example, if the user is relaxed, the analysis unit applies an analysis algorithm that maximizes the relaxation effect. For example, if the user is exercising, the analysis unit applies an analysis algorithm that evaluates the effects of exercise. By adjusting the analysis algorithm according to the user's emotions, more appropriate analysis results can be obtained. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into a generative AI, and the generative AI can adjust the analysis algorithm based on the emotions.

[0073] The analysis unit can adjust the level of detail of the analysis based on the importance of heart rate and environmental data during the analysis. For example, if the heart rate is high, the analysis unit performs a detailed analysis to identify the cause of stress. For example, if the environmental data is fluctuating, the analysis unit performs a detailed analysis to evaluate the influence of the environment. For example, if the heart rate and environmental data are stable, the analysis unit performs a simplified analysis to evaluate the relaxation effect. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of heart rate and environmental data into a generating AI, which can then adjust the level of detail of the analysis.

[0074] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a stress analysis algorithm to heart rate data. For example, the analysis unit applies an environmental impact analysis algorithm to environmental data. For example, the analysis unit applies an exercise effect analysis algorithm to exercise data. By applying an analysis algorithm according to the data category, more accurate analysis results can be obtained. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI, and the generating AI can apply an analysis algorithm according to the category.

[0075] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit provides a simple and highly visible display method. For example, if the user is relaxed, the analysis unit provides a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit provides a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into the generative AI, and the generative AI can adjust the display method of the analysis results based on the emotions.

[0076] The analysis unit can determine the priority of analysis based on the data acquisition timing during the analysis. For example, the analysis unit may prioritize the analysis of the latest data to reflect real-time conditions. For example, the analysis unit may refer to past data to evaluate long-term trends. For example, the analysis unit may prioritize the analysis of data from a specific event to evaluate its impact. By determining the priority of analysis based on the data acquisition timing, it becomes possible to perform analysis that reflects real-time conditions. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data acquisition timing into a generating AI, which can then determine the priority of analysis based on the acquisition timing.

[0077] The analysis unit can adjust the order of analysis based on the relationships between the data during the analysis. For example, the analysis unit evaluates the relationship between heart rate data and environmental data and determines the order of analysis. For example, the analysis unit evaluates the relationship between exercise data and heart rate data and determines the order of analysis. For example, the analysis unit evaluates the relationship between environmental data and stress indicators and determines the order of analysis. This allows for efficient analysis by adjusting the order of analysis based on the relationships between the data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relationships between the data into a generating AI, which can then adjust the order of analysis based on the relationships.

[0078] The service provider can estimate the user's emotions and adjust the relaxation instructions based on those emotions. For example, if the user is feeling stressed, the service provider may provide instructions for deep breathing or meditation. If the user is relaxed, the service provider may provide instructions for light stretching or relaxation music. If the user has just exercised, the service provider may provide instructions for cooling down. By adjusting the relaxation instructions according to the user's emotions, more appropriate relaxation can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, or not using AI. For example, the service provider can input the user's emotion data into the generative AI, which can then adjust the relaxation instructions based on the emotion.

[0079] The service provider can adjust the level of detail in the instructions based on the importance of relaxation at the time of delivery. For example, if relaxation is important, the service provider will provide detailed instructions. For example, if relaxation is not so important, the service provider will provide simplified instructions. For example, the service provider will adjust the level of detail in the instructions in stages according to the importance of relaxation. This allows for the provision of appropriate instructions by adjusting the level of detail based on the importance of relaxation. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the importance of relaxation into a generating AI, and the generating AI can adjust the level of detail in the instructions.

[0080] The service provider can apply different instruction algorithms depending on the relaxation category at the time of service provision. For example, the service provider applies a meditation instruction algorithm for meditation. For example, the service provider applies a stretching instruction algorithm for stretching. For example, the service provider applies a deep breathing instruction algorithm for deep breathing. By applying an analysis algorithm according to the relaxation category, more accurate analysis results can be obtained. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the relaxation category into a generating AI, and the generating AI can apply an instruction algorithm according to the category.

[0081] The service provider can estimate the user's emotions and adjust the length of relaxation instructions based on the estimated emotions. For example, if the user is stressed, the service provider will provide longer relaxation instructions. For example, if the user is relaxed, the service provider will provide shorter relaxation instructions. For example, if the user is in a hurry, the service provider will provide short and effective relaxation instructions. By adjusting the length of relaxation instructions according to the user's emotions, more appropriate relaxation can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI, and the generative AI can adjust the length of relaxation instructions based on the emotions.

[0082] The service provider can determine the priority of instructions based on the timing of relaxation at the time of provision. For example, the service provider will provide instructions preferentially when relaxation is needed. For example, the service provider will adjust the priority of instructions according to the timing of relaxation. For example, the service provider will adjust the content of the instructions based on the timing of relaxation. By determining the priority of instructions based on the timing of relaxation, relaxation can be provided at the appropriate time. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the timing of relaxation into a generating AI, and the generating AI can determine the priority of instructions based on the timing.

[0083] The service provider can adjust the order of instructions based on their relevance to relaxation during delivery. For example, the service provider prioritizes providing instructions with a high relevance to relaxation. For example, the service provider adjusts the order of instructions according to their relevance to relaxation. For example, the service provider adjusts the content of the instructions based on their relevance to relaxation. By adjusting the order of instructions based on their relevance to relaxation, more effective relaxation can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the relevance to relaxation into a generating AI, which can then adjust the order of instructions based on the relevance.

[0084] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0085] The acquisition unit acquires the user's sleep data, and the analysis unit analyzes the acquired sleep data to evaluate the user's sleep quality. For example, the acquisition unit monitors the user's heart rate and movement during sleep and records the depth of sleep and the frequency of interruptions. The analysis unit analyzes this data to evaluate the user's sleep quality and identify areas for improvement. Based on the analysis results, the provision unit can provide the user with specific advice to improve their sleep quality. For example, it can instruct them on relaxation methods before bedtime and how to create an appropriate sleep environment. This allows the user to get better quality sleep and contributes to an improvement in their overall health.

[0086] The system can estimate the user's emotions and select relaxation music based on those estimates. For example, if the user is stressed, it can provide classical music with a relaxing effect. If the user is relaxed, it can provide calming ambient music to maintain that mood. If the user has just exercised, it can provide music with a slow tempo to help them cool down. By providing music that matches the user's emotions, more effective relaxation can be achieved.

[0087] The analysis unit can acquire and analyze the user's dietary data to evaluate its impact on their health. For example, the acquisition unit records the content and calories of the meals the user consumes. The analysis unit analyzes this data to evaluate the user's nutritional balance and the quality of their diet. Based on the analysis results, the provision unit can provide the user with advice on healthy eating. For example, it can provide advice on ingredients to increase the intake of specific nutrients or on the timing of meals. This allows the user to lead a healthier lifestyle.

[0088] The system can estimate the user's emotions and, based on those estimates, suggest aromatherapy for relaxation. For example, if the user is stressed, it might suggest relaxing aromas such as lavender or chamomile. If the user is relaxed, it might suggest citrus aromas to maintain that mood. If the user has just exercised, it might suggest peppermint or eucalyptus aromas to relieve muscle tension. By providing aromatherapy tailored to the user's emotions, more effective relaxation can be achieved.

[0089] The acquisition unit acquires user activity data, and the analysis unit analyzes the acquired activity data to evaluate the user's exercise habits. For example, the acquisition unit records the user's steps and exercise time. The analysis unit analyzes this data to evaluate the user's exercise volume and quality. Based on the analysis results, the provision unit can point out areas for improvement in the user's exercise habits and provide a specific exercise plan. For example, it can provide advice on adjusting the frequency and intensity of exercise, or recommend specific exercises. This allows the user to develop more effective exercise habits.

[0090] The system can estimate the user's emotions and, based on those estimates, provide meditation guidance for relaxation. For example, if the user is feeling stressed, it can provide guidance on deep breathing or mindfulness meditation. If the user is relaxed, it can provide gentle meditation guidance to maintain that relaxation. If the user has just exercised, it can provide guidance on body scan meditation to help with cooling down. By providing meditation guidance tailored to the user's emotions, more effective relaxation can be achieved.

[0091] The analysis unit estimates the user's stress level, and the service provider can propose specific actions to reduce stress based on the estimated stress level. For example, the analysis unit analyzes heart rate and environmental data to evaluate the user's stress level. If the stress level is high, the service provider suggests deep breathing or light exercise. If the stress level is moderate, it suggests listening to relaxing music or taking a short break. If the stress level is low, it suggests light stretching or meditation to maintain relaxation. In this way, stress reduction can be supported by providing specific actions tailored to the user's stress level.

[0092] The acquisition unit acquires the user's water intake data, and the analysis unit analyzes the acquired water intake data to evaluate the user's hydration status. For example, the acquisition unit records the amount of water the user has consumed. The analysis unit analyzes this data to evaluate the user's hydration status and calculate the required amount of water intake. Based on the analysis results, the provision unit can provide the user with appropriate hydration advice. For example, it can provide specific instructions on the timing and amount of water to hydrate after exercise or on hot days. This enables the user to hydrate appropriately and contributes to maintaining good health.

[0093] The service provider can estimate the user's emotions and, based on those emotions, provide visual content for relaxation. For example, if the user is stressed, it can provide images of natural scenery or calm oceans. If the user is relaxed, it can provide images of beautiful scenery or art to maintain that relaxation. If the user has just exercised, it can provide images with a relaxing atmosphere to help them cool down. By providing visual content tailored to the user's emotions, more effective relaxation can be achieved.

[0094] The analysis unit can monitor users' health data over the long term and analyze trends in their health status. For example, the acquisition unit records the user's heart rate, exercise data, and sleep data over a long period. The analysis unit analyzes this data to evaluate trends in the user's health status and predict health risks. Based on the analysis results, the provision unit can provide users with specific advice to mitigate health risks. For example, it may recommend improving exercise habits, reviewing diet, and undergoing regular health checkups. This allows users to manage their health over the long term and reduce health risks.

[0095] The following briefly describes the processing flow for example form 2.

[0096] Step 1: The acquisition unit acquires heart rate and environmental data in real time. For example, a smartwatch can be used to acquire heart rate and environmental data in real time. An optical heart rate sensor can be used to measure heart rate, and a temperature sensor or humidity sensor can be used to acquire environmental data. Data is acquired every second. Step 2: The analysis unit analyzes the data acquired by the acquisition unit. For example, it integrates and analyzes heart rate, exercise data, stress indicators, and environmental data. A heart rate variability analysis algorithm can be used to analyze heart rate data, and temperature and humidity fluctuations can be analyzed to analyze environmental data. An algorithm for evaluating stress levels can be used to analyze stress indicators. Step 3: The service unit provides relaxation instructions based on the data analyzed by the analysis unit. For example, it provides specific instructions on how long to use the sauna, when to use the cold bath, and how long to spend in the fresh air. Audio guides, smartphone apps, and fitness facility displays can be used to provide relaxation instructions.

[0097] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0098] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0099] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0100] Each of the multiple elements described above, including the acquisition unit, analysis unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit acquires heart rate and environmental data in real time using the optical heart rate sensor, temperature sensor, and humidity sensor of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and integrates and analyzes heart rate, exercise data, stress indicators, and environmental data. The provision unit is implemented in the control unit 46A of the smart device 14 and specifically instructs the user on the sauna usage time, the timing of using the cold bath, and the time for outdoor air bathing. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0101] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0102] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0103] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0104] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0105] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0107] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0108] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0109] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0110] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0111] In the smart glasses 214, 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0112] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0115] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed 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 necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements described above, including the acquisition unit, analysis unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit acquires heart rate and environmental data in real time using the optical heart rate sensor, temperature sensor, and humidity sensor of the smart glasses 214. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and integrates and analyzes heart rate, exercise data, stress indicators, and environmental data. The provision unit is implemented in the control unit 46A of the smart glasses 214 and specifically instructs the user on the sauna usage time, the timing of using the cold bath, and the time for outdoor air bathing. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0117] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0118] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the headset terminal 314, 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 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the acquisition unit, analysis unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit acquires heart rate and environmental data in real time using the optical heart rate sensor, temperature sensor, and humidity sensor of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and integrates and analyzes heart rate, exercise data, stress indicators, and environmental data. The provision unit is implemented in the control unit 46A of the headset terminal 314 and specifically instructs the user on the sauna usage time, the timing of using the cold bath, and the time for outdoor air bathing. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0133] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0134] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0141] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed 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 necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the acquisition unit, analysis unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit acquires heart rate and environmental data in real time using the robot 414's optical heart rate sensor, temperature sensor, and humidity sensor. The analysis unit is implemented in the data processing unit 12, for example, by the specific processing unit 290, which integrates and analyzes heart rate, exercise data, stress indicators, and environmental data. The provision unit is implemented in the robot 414, for example, by the control unit 46A, which specifically instructs the user on the sauna usage time, the timing of using the cold bath, and the time for outdoor air bathing. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0150] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0151] Figure 9 shows the emotion map 400, in 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 the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0152] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0153] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0154] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0155] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it 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 emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0156] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0157] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0158] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0159] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0160] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0161] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0162] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0163] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0164] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0165] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0166] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0167] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0168] (Note 1) An acquisition unit that acquires heart rate and environmental data in real time, An analysis unit analyzes the data acquired by the acquisition unit, A providing unit that provides relaxation instructions based on the data analyzed by the aforementioned analysis unit, Equipped with A system characterized by the following features. (Note 2) The aforementioned supply unit is, Provide specific instructions regarding sauna usage time, cold bath usage timing, and outdoor air time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The acquisition unit is, Use a smartwatch to acquire heart rate and environmental data in real time. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, Integrate and analyze heart rate, exercise data, stress indicators, and environmental data. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Providing additional value to fitness facilities The system described in Appendix 1, characterized by the features described herein. (Note 6) The acquisition unit is, The system estimates the user's emotions and adjusts the timing of heart rate and environmental data acquisition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, Analyze the user's past health data and select the optimal data acquisition method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, When acquiring data, filtering is performed based on the user's current activity status and environmental conditions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, It estimates the user's emotions and determines the priority of data to acquire based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, When acquiring data, the system prioritizes the acquisition of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, When acquiring data, the system analyzes the user's social media activity and retrieves relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of heart rate and environmental data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was acquired. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, It estimates the user's emotions and adjusts the relaxation instructions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing the service, adjust the level of detail in the instructions based on the importance of relaxation. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing the service, different instruction algorithms are applied depending on the relaxation category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of relaxation instructions based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing the service, prioritize instructions based on when the relaxation is to be performed. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing the service, adjust the order of instructions based on their relevance to relaxation. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An acquisition unit that acquires heart rate and environmental data in real time, An analysis unit analyzes the data acquired by the acquisition unit, A providing unit that provides relaxation instructions based on the data analyzed by the aforementioned analysis unit, Equipped with A system characterized by the following features.

2. The aforementioned supply unit is, Provide specific instructions regarding sauna usage time, cold bath usage timing, and outdoor air time. The system according to feature 1.

3. The acquisition unit is, Use a smartwatch to acquire heart rate and environmental data in real time. The system according to feature 1.

4. The aforementioned analysis unit, Integrate and analyze heart rate, exercise data, stress indicators, and environmental data. The system according to feature 1.

5. The aforementioned supply unit is, Providing additional value to fitness facilities The system according to feature 1.

6. The acquisition unit is, The system estimates the user's emotions and adjusts the timing of heart rate and environmental data acquisition based on the estimated emotions. The system according to feature 1.

7. The acquisition unit is, Analyze the user's past health data and select the optimal data acquisition method. The system according to feature 1.

8. The acquisition unit is, When acquiring data, filtering is performed based on the user's current activity status and environmental conditions. The system according to feature 1.

9. The acquisition unit is, It estimates the user's emotions and determines the priority of data to acquire based on the estimated user emotions. The system according to feature 1.

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