system

Abstract

The system according to the embodiment aims to comprehensively manage the health condition and living environment of a user and provide optimal advice and reminders. [Solution] A system according to an embodiment includes a health data acquisition unit, a monitoring unit, an environmental data acquisition unit, an environment management unit, a voice analysis unit, a reminder setting unit, and a lifestyle analysis unit. The health data acquisition unit acquires health data. The monitoring unit monitors the user's health condition based on the data acquired by the health data acquisition unit. The environmental data acquisition unit acquires environmental data. The environment management unit manages the user's living environment based on the data acquired by the environmental data acquisition unit. The voice analysis unit analyzes voice. The reminder setting unit sets reminders based on voice commands analyzed by the voice analysis unit. The lifestyle analysis unit analyzes the user's lifestyle.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately manage a user's health condition and living environment comprehensively, and there is room for improvement.

[0005] The system according to the embodiment aims to comprehensively manage the health condition and living environment of a user and provide optimal advice and reminders. [Means for solving the problem]

[0006] The system according to the embodiment includes a health data acquisition unit, a monitoring unit, an environmental data acquisition unit, an environment management unit, a voice analysis unit, a reminder setting unit, and a lifestyle analysis unit. The health data acquisition unit acquires health data. The monitoring unit monitors the user's health condition based on the data acquired by the health data acquisition unit. The environmental data acquisition unit acquires environmental data. The environment management unit manages the user's living environment based on the data acquired by the environmental data acquisition unit. The voice analysis unit analyzes voice. The reminder setting unit sets a reminder based on the voice command analyzed by the voice analysis unit. The lifestyle analysis unit analyzes the user's lifestyle. [Effects of the Invention]

[0007] The system according to the embodiment can comprehensively manage the health condition and living environment of the user and provide optimal advice and reminders. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The smart secretary system according to an embodiment of the present invention utilizes the features of a wristwatch to provide optimal advice, reminders, and management. Since 94% of people own wristwatches, this smart secretary system can be used by many people. Because wristwatches are worn on the skin, they can acquire health data such as body temperature and pulse rate. They can also acquire environmental data such as room temperature and humidity. Furthermore, if the wristwatch is equipped with a microphone, it can output and input voice data, functioning as a voice assistant. Because the wristwatch is worn every day, it can track lifestyle habits and location information. For example, the wristwatch acquires health data such as body temperature and pulse rate. This data can be used to monitor the user's health and issue an alert if an abnormality is detected. For example, if the body temperature becomes high, an alert can be issued to warn of a possible fever. Next, the wristwatch acquires environmental data such as room temperature and humidity. This data is used to maintain a comfortable living environment for the user. For example, if the room temperature becomes high, an alert can be issued to encourage the use of the air conditioner. Furthermore, if the wristwatch is equipped with a microphone, it can output and input voice data. This allows it to function as a voice assistant, answering the user's questions and setting reminders. For example, a meeting reminder can be set in response to a voice command such as "Remind me about tomorrow's meeting." Furthermore, since a watch is worn every day, it can track the user's lifestyle and location information. This allows it to analyze the user's behavioral patterns and provide optimal advice. For example, for a user who jogs every morning, the optimal jogging time can be suggested based on the weather forecast. In this way, by utilizing the features of a watch, a multi-functional smart secretary system can be provided, including health management, environmental management, voice assistance, and lifestyle analysis. This allows the smart secretary system to monitor the user's health status and issue an alert if an abnormality is detected. Furthermore, to maintain a comfortable living environment for the user, it can acquire environmental data such as room temperature and humidity and issue an alert as necessary.It also functions as a voice assistant, answering users' questions and setting reminders, and can provide optimal advice based on the user's lifestyle and location.

[0029] The smart secretary system according to the embodiment includes a health data acquisition unit, a monitoring unit, an environmental data acquisition unit, an environment management unit, a voice analysis unit, a reminder setting unit, and a lifestyle analysis unit. The health data acquisition unit acquires health data such as a user's body temperature and pulse rate. For example, the health data acquisition unit can measure the user's body temperature and pulse rate using a body temperature sensor and a pulse sensor. The monitoring unit monitors the user's health condition based on the data acquired by the health data acquisition unit. For example, the monitoring unit can issue an alert notifying the user of a possible fever if the user's body temperature becomes high. The environmental data acquisition unit acquires environmental data such as room temperature and humidity. For example, the environmental data acquisition unit can measure room temperature and humidity using a room temperature sensor and a humidity sensor. The environment management unit manages the user's living environment based on the data acquired by the environmental data acquisition unit. For example, the environment management unit can issue an alert to encourage the user to use an air conditioner if the room temperature becomes high. The voice analysis unit analyzes voice commands using voice recognition technology. For example, the voice analysis unit can analyze a user's voice commands and set reminders or answer questions. The reminder setting unit can set reminders based on the voice commands analyzed by the voice analysis unit. For example, the reminder setting unit can set a meeting reminder in response to a voice command such as "Remind me about tomorrow's meeting." The lifestyle habit analysis unit can analyze a user's lifestyle and provide optimal advice. For example, for a user who jogs every morning, the lifestyle habit analysis unit can suggest the optimal jogging time based on the weather forecast. This allows the smart secretary system according to the embodiment to monitor the user's health status and issue an alert if an abnormality is detected. Furthermore, to maintain a comfortable living environment for the user, the smart secretary system can acquire environmental data such as room temperature and humidity and issue an alert as necessary. Furthermore, the smart secretary system can function as a voice assistant, answering the user's questions and setting reminders. This allows the smart secretary system to understand the user's lifestyle and location information and provide optimal advice.

[0030] The health data acquisition unit may include a body temperature sensor and a pulse sensor. Body temperature sensors include, for example, infrared sensors and contact sensors. Infrared sensors can measure body temperature without contact, while contact sensors can measure body temperature by contacting the skin. Pulse sensors include, for example, photoelectric sensors and pressure sensors. Photoelectric sensors can measure pulse using light, while pressure sensors can measure pulse using changes in pressure. This allows accurate acquisition of body temperature and pulse data. Some or all of the above-described processing in the health data acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the health data acquisition unit may input data acquired from the body temperature sensor and pulse sensor into a generation AI, which then analyzes the data and evaluates the health condition.

[0031] The environmental data acquisition unit may include a room temperature sensor and a humidity sensor. Examples of room temperature sensors include a thermistor and an infrared sensor. A thermistor is a sensor whose resistance value changes in response to temperature changes, and an infrared sensor can measure temperature using infrared rays. Examples of humidity sensors include a capacitance sensor and a resistance sensor. A capacitance sensor is a sensor whose capacitance changes in response to humidity changes, and a resistance sensor is a sensor whose resistance value changes in response to humidity changes. This allows room temperature and humidity data to be accurately acquired. Some or all of the above-described processing in the environmental data acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the environmental data acquisition unit may input data acquired from the room temperature sensor and humidity sensor into a generation AI, which may analyze the data and evaluate the environmental state.

[0032] The voice analysis unit can analyze voice commands using voice recognition technology. Voice recognition technology includes, for example, deep learning-based voice recognition and template matching. Deep learning-based voice recognition is a technology that achieves highly accurate voice recognition by learning from a large amount of voice data, and template matching is a technology that determines whether a voice command matches a pre-registered voice pattern. This allows for accurate analysis of the voice command. Some or all of the above-mentioned processing in the voice analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice analysis unit can input a user's voice command into a generation AI, which can analyze the voice command and set a reminder or answer a question.

[0033] The reminder setting unit can set reminders based on voice commands. Reminders include, for example, taking medicine or starting exercise. The reminder setting unit can set these reminders based on the user's voice commands. For example, the reminder setting unit can set a meeting reminder in response to a voice command such as "Remind me about tomorrow's meeting." This allows reminders to be set based on voice commands. Some or all of the above-described processing in the reminder setting unit may be performed using, for example, AI, or may be performed without using AI. For example, the reminder setting unit can input the voice command analyzed by the voice analysis unit into a generation AI, which then sets the reminder.

[0034] The lifestyle habit analysis unit can analyze the user's behavioral patterns and provide optimal advice. The lifestyle habit analysis unit can analyze the user's behavioral patterns, such as the user's movement patterns and meal timings. For example, if the user jogs every morning, the lifestyle habit analysis unit can suggest the optimal time for jogging based on the weather forecast. The lifestyle habit analysis unit can also analyze the user's dietary data and suggest healthy eating habits. Furthermore, the lifestyle habit analysis unit can analyze the user's sleep data and provide advice for improving sleep quality. This makes it possible to analyze the user's behavioral patterns and provide optimal advice. Some or all of the above-mentioned processing in the lifestyle habit analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the lifestyle habit analysis unit can input the user's behavioral pattern data into a generation AI, which can analyze the data and provide optimal advice.

[0035] When acquiring body temperature and pulse data, the health data acquisition unit can improve the accuracy of the data by taking into account the user's activity level. For example, when the user is exercising, the health data acquisition unit can acquire body temperature and pulse data frequently to provide highly accurate data according to the user's activity level. When the user is resting, the health data acquisition unit can acquire body temperature and pulse data less frequently to provide accurate data for the user at rest. Furthermore, when the user is sleeping, the health data acquisition unit can acquire body temperature and pulse data at regular intervals to evaluate the quality of sleep. This improves the accuracy of the data according to the user's activity level. Some or all of the above-described processing in the health data acquisition unit may be performed using, for example, AI, or may be performed without AI. For example, the health data acquisition unit can input the user's activity level data into a generation AI, which can analyze the data and adjust the frequency of body temperature and pulse acquisition.

[0036] When acquiring health data, the health data acquisition unit can detect abnormal values ​​by referring to the user's past health data. For example, the health data acquisition unit can reference the user's past body temperature data and issue an alert if the user's current body temperature is abnormally high. The health data acquisition unit can also reference the user's past pulse data and issue an alert if the user's current pulse is abnormally low. Furthermore, the health data acquisition unit can comprehensively analyze the user's past health data and encourage the user to visit a medical institution if an abnormal value is detected. This makes it possible to detect abnormal values ​​by referring to the past health data. Some or all of the above-mentioned processing in the health data acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the health data acquisition unit can input the user's past health data into the generation AI, which can analyze the data and detect abnormal values.

[0037] The health data acquisition unit can acquire the user's dietary data when acquiring health data, thereby grasping the user's overall health condition. For example, the health data acquisition unit can acquire body temperature data immediately after the user eats a meal to evaluate the impact of the meal. The health data acquisition unit can also acquire pulse data immediately after the user eats a meal to evaluate the impact of the meal. Furthermore, the health data can be comprehensively analyzed with the user's dietary data and body temperature / pulse data to evaluate the health condition. In this way, by acquiring the dietary data together, the overall health condition can be grasped. Some or all of the above-described processing in the health data acquisition unit can be performed, for example, using AI, or can be performed without using AI. For example, the health data acquisition unit can input the user's dietary data into a generation AI, which can analyze the data and evaluate the overall health condition.

[0038] The health data acquisition unit can acquire the user's sleep data when acquiring health data, thereby grasping the user's overall health condition. For example, the health data acquisition unit can acquire body temperature data while the user is sleeping and evaluate the quality of the sleep. It can also acquire pulse data while the user is sleeping and evaluate the quality of the sleep. Furthermore, the health data acquisition unit can comprehensively analyze the user's sleep data and body temperature / pulse data to evaluate the health condition. In this way, by acquiring the sleep data together, the overall health condition can be grasped. Some or all of the above-mentioned processing in the health data acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the health data acquisition unit can input the user's sleep data into a generation AI, which can analyze the data and evaluate the overall health condition.

[0039] During monitoring, the monitoring unit can detect abnormal values ​​by referring to the user's past health data. For example, the monitoring unit can refer to the user's past body temperature data and issue an alert if the user's current body temperature is abnormally high. The monitoring unit can also refer to the user's past pulse data and issue an alert if the user's current pulse is abnormally low. Furthermore, the monitoring unit can comprehensively analyze the user's past health data and encourage the user to visit a medical institution if an abnormal value is detected. This makes it possible to detect abnormal values ​​by referring to the past health data. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's past health data into a generation AI, which can analyze the data and detect abnormal values.

[0040] During monitoring, the monitoring unit can also analyze the user's lifestyle habit data to comprehensively evaluate the health condition. For example, the monitoring unit can comprehensively analyze the user's exercise data and body temperature / pulse data to evaluate the health condition. The monitoring unit can also comprehensively analyze the user's diet data and body temperature / pulse data to evaluate the health condition. Furthermore, the monitoring unit can comprehensively analyze the user's sleep data and body temperature / pulse data to evaluate the health condition. In this way, the health condition can be comprehensively evaluated by analyzing the lifestyle habit data together. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's lifestyle habit data into a generation AI, which then analyzes the data to comprehensively evaluate the health condition.

[0041] During monitoring, the monitoring unit can also analyze the user's exercise data to comprehensively evaluate the health condition. For example, the monitoring unit can comprehensively analyze the user's exercise data, body temperature, and pulse data to evaluate the health condition. The monitoring unit can also comprehensively analyze the user's exercise data and dietary data to evaluate the health condition. Furthermore, the monitoring unit can comprehensively analyze the user's exercise data and sleep data to evaluate the health condition. In this way, the health condition can be comprehensively evaluated by analyzing the exercise data together. Some or all of the above-mentioned processing in the monitoring unit can be performed, for example, using AI, or can be performed without using AI. For example, the monitoring unit can input the user's exercise data into a generation AI, which can analyze the data and comprehensively evaluate the health condition.

[0042] During monitoring, the monitoring unit can also analyze the user's stress level and comprehensively evaluate the health state. The monitoring unit can, for example, comprehensively analyze the user's stress level and body temperature / pulse data to evaluate the health state. The monitoring unit can also comprehensively analyze the user's stress level and dietary data to evaluate the health state. Furthermore, the monitoring unit can comprehensively analyze the user's stress level and sleep data to evaluate the health state. In this way, by analyzing the stress level together, the health state can be comprehensively evaluated. Some or all of the above-mentioned processing in the monitoring unit can be performed, for example, using AI or without AI. For example, the monitoring unit can input the user's stress level data to a generation AI, which can analyze the data and comprehensively evaluate the health state.

[0043] When acquiring room temperature and humidity data, the environmental data acquisition unit can improve the accuracy of the data by taking into account the user's activity level. For example, when the user is exercising, the environmental data acquisition unit can acquire room temperature and humidity data frequently to provide highly accurate data according to the user's activity level. Furthermore, when the user is resting, the environmental data acquisition unit can acquire room temperature and humidity data less frequently to provide accurate data for the user's resting state. Furthermore, when the user is sleeping, the environmental data acquisition unit can acquire room temperature and humidity data at regular intervals to evaluate the quality of sleep. This can improve the accuracy of the data according to the user's activity level. Some or all of the above-described processing in the environmental data acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the environmental data acquisition unit can input the user's activity level data to a generation AI, which can analyze the data and adjust the frequency of room temperature and humidity acquisition.

[0044] When acquiring environmental data, the environmental data acquisition unit can detect abnormal values ​​by referring to the user's past environmental data. For example, the environmental data acquisition unit can reference the user's past room temperature data and issue an alert if the current room temperature is abnormally high. The environmental data acquisition unit can also reference the user's past humidity data and issue an alert if the current humidity is abnormally low. Furthermore, the user's past environmental data can be comprehensively analyzed, and appropriate countermeasures can be proposed if an abnormal value is detected. This makes it possible to detect abnormal values ​​by referring to past environmental data. Some or all of the above-described processing in the environmental data acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the environmental data acquisition unit can input the user's past environmental data into a generation AI, which can analyze the data and detect abnormal values.

[0045] When acquiring environmental data, the environmental data acquisition unit may also acquire weather data for the user's residential area, thereby enabling the overall environmental condition to be grasped. For example, the environmental data acquisition unit may also acquire temperature data for the user's residential area and comprehensively analyze it together with the room temperature data. The environmental data acquisition unit may also acquire humidity data for the user's residential area and comprehensively analyze the indoor and outdoor humidity. Furthermore, weather forecast data for the user's residential area may also be acquired, which can be used to adjust the indoor environment. In this way, by also acquiring weather data for the residential area, the overall environmental condition can be grasped. Some or all of the above-described processing in the environmental data acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the environmental data acquisition unit may input weather data for the user's residential area to the generation AI, which may analyze the data to grasp the overall environmental condition.

[0046] The environmental data acquisition unit can acquire the user's indoor activity data together with the environmental data to grasp the overall environmental condition. The environmental data acquisition unit can, for example, acquire the user's indoor activity data and room temperature data together to provide a comfortable environment according to the activity. The environmental data acquisition unit can also acquire the user's indoor activity data and humidity data together to provide a comfortable environment according to the activity. Furthermore, the user's indoor activity data and weather data can be acquired together to grasp the overall environmental condition. In this way, by acquiring the indoor activity data together, the overall environmental condition can be grasped. Some or all of the above-mentioned processing in the environmental data acquisition unit can be performed, for example, using AI or without AI. For example, the environmental data acquisition unit can input the user's indoor activity data to a generation AI, which can analyze the data to grasp the overall environmental condition.

[0047] During environmental management, the environmental management unit can select the optimal management method by referring to the user's past environmental data. For example, the environmental management unit can set the optimal room temperature by referring to the user's past room temperature data. It can also set the optimal humidity by referring to the user's past humidity data. Furthermore, it can comprehensively analyze the user's past environmental data and select the optimal environmental management method. This makes it possible to select the optimal management method by referring to the past environmental data. Some or all of the above-mentioned processing in the environmental management unit can be performed, for example, using AI or without AI. For example, the environmental management unit can input the user's past environmental data into a generation AI, which can analyze the data and select the optimal management method.

[0048] The environmental management unit can analyze the user's lifestyle data together during environmental management and provide an optimal environmental management method. The environmental management unit, for example, can analyze the user's lifestyle data and room temperature data together to set an optimal room temperature. The environmental management unit can also analyze the user's lifestyle data and humidity data together to set an optimal humidity. Furthermore, the user's lifestyle data and environmental data can be analyzed comprehensively to provide an optimal environmental management method. In this way, by analyzing the lifestyle data together, an optimal environmental management method can be provided. Some or all of the above-mentioned processing in the environmental management unit can be performed, for example, using AI, or can be performed without using AI. For example, the environmental management unit can input the user's lifestyle data into a generation AI, which can analyze the data and provide an optimal environmental management method.

[0049] The environmental management unit can analyze the user's exercise data during environmental management and provide an optimal environmental management method. For example, the environmental management unit can analyze the user's exercise data and room temperature data together to set a room temperature suitable for exercise. The environmental management unit can also analyze the user's exercise data and humidity data together to set a humidity suitable for exercise. Furthermore, the user's exercise data and environmental data can be analyzed comprehensively to provide an optimal environmental management method. In this way, by analyzing the exercise data together, an optimal environmental management method can be provided. Some or all of the above-mentioned processing in the environmental management unit can be performed using, for example, AI, or can be performed without using AI. For example, the environmental management unit can input the user's exercise data into a generation AI, which can analyze the data and provide an optimal environmental management method.

[0050] The environment management unit can also analyze the user's stress level during environment management and provide an optimal environment management method. The environment management unit, for example, can analyze the user's stress level and room temperature data together to set a room temperature suitable for stress reduction. It can also analyze the user's stress level and humidity data together to set a humidity suitable for stress reduction. Furthermore, it can comprehensively analyze the user's stress level and environmental data to provide an optimal environment management method. By analyzing the stress level together, it is possible to provide an optimal environment management method. Some or all of the above-described processing in the environment management unit can be performed, for example, using AI, or can be performed without using AI. For example, the environment management unit can input the user's stress level data into a generation AI, which analyzes the data and provides an optimal environment management method.

[0051] During voice analysis, the voice analysis unit can improve analysis accuracy by referring to the user's past voice command history. For example, the voice analysis unit can refer to the user's past voice command history and quickly respond to similar commands. The voice analysis unit can also improve analysis accuracy for frequently used commands by referring to the user's past voice command history. Furthermore, the user's past voice command history can be comprehensively analyzed to improve analysis accuracy. This allows analysis accuracy to be improved by referring to the past voice command history. Some or all of the above-mentioned processing in the voice analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice analysis unit can input the user's past voice command history into a generation AI, which then analyzes the data to improve analysis accuracy.

[0052] The voice analysis unit can analyze the user's lifestyle data together with the voice analysis to provide an optimal voice command analysis method. The voice analysis unit can, for example, analyze the user's lifestyle data and voice command history together to provide an optimal analysis method. Furthermore, the voice analysis unit can analyze the user's lifestyle data and voice command history together to improve analysis accuracy for frequently used commands. Furthermore, the user's lifestyle data and voice command history can be analyzed comprehensively to improve analysis accuracy. This allows for the optimal voice command analysis method to be provided by analyzing the lifestyle data together. Some or all of the above-described processing in the voice analysis unit can be performed, for example, using AI, or can be performed without using AI. For example, the voice analysis unit can input the user's lifestyle data into a generation AI, which then analyzes the data to provide an optimal voice command analysis method.

[0053] The voice analysis unit can improve the analysis accuracy by also analyzing the user's language data during voice analysis. The voice analysis unit can improve the analysis accuracy by, for example, analyzing the user's language data and voice command history together. The voice analysis unit can also improve the analysis accuracy for frequently used commands by analyzing the user's language data and voice command history together. Furthermore, the user's language data and voice command history can be analyzed comprehensively to improve the analysis accuracy. In this way, the analysis accuracy can be improved by analyzing the language data together. Some or all of the above-mentioned processing in the voice analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the voice analysis unit can input the user's language data to a generation AI, which can analyze the data to improve the analysis accuracy.

[0054] The voice analysis unit can improve the accuracy of the analysis by also analyzing the user's speech patterns during voice analysis. The voice analysis unit can improve the accuracy of the analysis by, for example, analyzing the user's speech patterns and voice command history together. The voice analysis unit can also improve the accuracy of the analysis for frequently used commands by analyzing the user's speech patterns and voice command history together. Furthermore, the user's speech patterns and voice command history can be comprehensively analyzed to improve the accuracy of the analysis. In this way, the analysis accuracy can be improved by analyzing the speech patterns together. Some or all of the above-described processing in the voice analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the voice analysis unit can input the user's speech pattern data to a generation AI, which can analyze the data to improve the accuracy of the analysis.

[0055] When setting a reminder, the reminder setting unit can select the optimal setting method by referring to the user's past reminder history. For example, the reminder setting unit can refer to the user's past reminder history and quickly set similar reminders. The reminder setting unit can also refer to the user's past reminder history and provide frequently used reminder setting methods. Furthermore, the reminder setting unit can comprehensively analyze the user's past reminder history and provide the optimal reminder setting method. This makes it possible to select the optimal setting method by referring to the past reminder history. Some or all of the above-mentioned processing in the reminder setting unit can be performed using, for example, AI, or can be performed without using AI. For example, the reminder setting unit can input the user's past reminder history into a generation AI, which can analyze the data and select the optimal setting method.

[0056] The reminder setting unit can analyze the user's lifestyle habit data together when setting a reminder, and provide an optimal reminder setting method. The reminder setting unit can, for example, analyze the user's lifestyle habit data and reminder history together to provide an optimal reminder setting method. It can also analyze the user's lifestyle habit data and reminder history together to provide a frequently used reminder setting method. Furthermore, it can comprehensively analyze the user's lifestyle habit data and reminder history to provide an optimal reminder setting method. By analyzing the lifestyle habit data together, it is possible to provide an optimal reminder setting method. Some or all of the above-described processing in the reminder setting unit can be performed, for example, using AI, or can be performed without using AI. For example, the reminder setting unit can input the user's lifestyle habit data to a generation AI, which analyzes the data and provides an optimal reminder setting method.

[0057] The reminder setting unit can provide an optimal reminder by also setting the user's schedule data when setting a reminder. The reminder setting unit can, for example, analyze the user's schedule data and reminder history together to provide an optimal reminder. The reminder setting unit can also analyze the user's schedule data and reminder history together to provide a frequently used reminder setting method. Furthermore, the reminder setting unit can comprehensively analyze the user's schedule data and reminder history to provide an optimal reminder. This allows the optimal reminder to be provided by setting the schedule data together. Some or all of the above-described processing in the reminder setting unit can be performed, for example, using AI or without AI. For example, the reminder setting unit can input the user's schedule data to a generation AI, which can analyze the data and provide an optimal reminder.

[0058] The reminder setting unit can provide an optimal reminder by also setting the user's activity data when setting a reminder. The reminder setting unit can, for example, analyze the user's activity data and reminder history together to provide an optimal reminder. The reminder setting unit can also analyze the user's activity data and reminder history together to provide a frequently used reminder setting method. Furthermore, the reminder setting unit can comprehensively analyze the user's activity data and reminder history to provide an optimal reminder. As a result, the optimal reminder can be provided by setting the activity data together. Some or all of the above-described processing in the reminder setting unit can be performed, for example, using AI or without AI. For example, the reminder setting unit can input the user's activity data to a generation AI, which can analyze the data and provide an optimal reminder.

[0059] During lifestyle analysis, the lifestyle analysis unit can select the optimal analysis method by referring to the user's past lifestyle data. For example, the lifestyle analysis unit can refer to the user's past lifestyle data and provide the optimal analysis method for similar patterns. It can also refer to the user's past lifestyle data and provide the optimal analysis method for frequently occurring patterns. Furthermore, it can comprehensively analyze the user's past lifestyle data and provide the optimal analysis method. This allows the optimal analysis method to be selected by referring to the past lifestyle data. Some or all of the above-described processing in the lifestyle analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the lifestyle analysis unit can input the user's past lifestyle data into a generation AI, which then analyzes the data and selects the optimal analysis method.

[0060] The lifestyle habit analysis unit can analyze the user's health data together during lifestyle habit analysis and provide an optimal lifestyle habit analysis method. The lifestyle habit analysis unit can, for example, analyze the user's health data and lifestyle habit data together and provide an optimal lifestyle habit analysis method. It can also analyze the user's health data and lifestyle habit data together and provide an optimal analysis method for frequently observed patterns. It can also comprehensively analyze the user's health data and lifestyle habit data and provide an optimal lifestyle habit analysis method. In this way, it is possible to provide an optimal lifestyle habit analysis method by analyzing the health data together. Some or all of the above-mentioned processing in the lifestyle habit analysis unit can be performed, for example, using AI or without AI. For example, the lifestyle habit analysis unit can input the user's health data into a generation AI, which analyzes the data and provides an optimal lifestyle habit analysis method.

[0061] The lifestyle habit analysis unit can also analyze the user's dietary data when analyzing lifestyle habits and suggest optimal lifestyle habits. The lifestyle habit analysis unit can, for example, analyze the user's dietary data and lifestyle data together to suggest optimal dietary habits. The lifestyle habit analysis unit can also analyze the user's dietary data and lifestyle data together to suggest healthy dietary habits. Furthermore, the user's dietary data and lifestyle data can be analyzed comprehensively to suggest optimal lifestyle habits. In this way, optimal lifestyle habits can be suggested by analyzing the dietary data together. Some or all of the above-mentioned processing in the lifestyle habit analysis unit can be performed, for example, using AI or without AI. For example, the lifestyle habit analysis unit can input the user's dietary data into a generation AI, which can analyze the data and suggest optimal lifestyle habits.

[0062] The lifestyle habit analysis unit can also analyze the user's exercise data when analyzing lifestyle habits and suggest optimal lifestyle habits. The lifestyle habit analysis unit can, for example, analyze the user's exercise data and lifestyle data together to suggest optimal exercise habits. The lifestyle habit analysis unit can also analyze the user's exercise data and lifestyle data together to suggest healthy exercise habits. Furthermore, the user's exercise data and lifestyle data can be analyzed comprehensively to suggest optimal lifestyle habits. In this way, optimal lifestyle habits can be suggested by analyzing the exercise data together. Some or all of the above-mentioned processing in the lifestyle habit analysis unit can be performed, for example, using AI or without AI. For example, the lifestyle habit analysis unit can input the user's exercise data into a generation AI, which can analyze the data and suggest optimal lifestyle habits.

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

[0064] The smart secretary system can also obtain user preference data and provide personalized advice. For example, if a user likes a particular music genre, music of that genre can be played when the user is relaxing. If a user likes a particular food, recipes related to that food can be suggested. Furthermore, if a user likes a particular exercise, a training plan related to that exercise can be provided. This allows personalized advice to be provided based on the user's preferences.

[0065] The health data acquisition unit can also monitor the user's skin condition and provide skin care advice. For example, it can detect the dryness of the skin and recommend the use of moisturizing cream. It can also detect the amount of oil on the skin and suggest an appropriate face washing method. It can also detect UV damage to the skin and recommend the use of sunscreen. This allows it to provide advice to maintain the user's skin health.

[0066] The environmental data acquisition unit can also acquire air quality data for the user's residential area and encourage the use of an air purifier. For example, if the PM2.5 concentration is high, the use of an air purifier can be recommended. Also, if there is a lot of pollen in the air, an alert can be issued to close the windows. Furthermore, if the carbon dioxide concentration is high, ventilation can be encouraged. This makes it possible to provide advice on improving the air quality in the user's residential environment.

[0067] The reminder setting unit can further refer to the user's schedule data and suggest the optimal timing for the reminder. For example, the user can set a reminder just before a meeting. Alternatively, the user can set a reminder before exercise. Furthermore, the user can set a reminder at the time when they take their medicine. In this way, the optimal timing for the reminder can be suggested based on the user's schedule.

[0068] The health data acquisition unit can further acquire the user's dietary data and evaluate the nutritional balance. For example, it can record the calories and nutrients the user has ingested and evaluate whether they are in excess or deficiency. If the user is consuming too much of a particular nutrient, it can advise the user to reduce that nutrient. Furthermore, if the user is deficient in a particular nutrient, it can suggest ingredients that will supplement that nutrient. This makes it possible to evaluate the nutritional balance based on the user's dietary data.

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

[0070] Step 1: The health data acquisition unit acquires health data such as the user's body temperature, pulse rate, etc. For example, the health data acquisition unit measures the user's body temperature and pulse rate using a body temperature sensor and a pulse rate sensor. Step 2: The monitoring unit monitors the user's health condition based on the data acquired by the health data acquisition unit. For example, if the user's body temperature rises, it issues an alert to notify the user of the possibility of fever. Step 3: The environmental data acquisition unit acquires environmental data such as room temperature, humidity, etc. For example, the room temperature and humidity are measured using a room temperature sensor and a humidity sensor. Step 4: The environment management unit manages the user's living environment based on the data acquired by the environment data acquisition unit. For example, if the room temperature becomes high, it issues an alert to encourage the use of the air conditioner. Step 5: The voice analysis unit uses voice recognition technology to analyze the voice command. For example, it analyzes the user's voice command and sets reminders or answers questions. Step 6: The reminder setting unit sets a reminder based on the voice command analyzed by the voice analysis unit. For example, a reminder for a meeting is set in response to a voice command such as "Remind me about tomorrow's meeting." Step 7: The lifestyle analysis unit analyzes the user's lifestyle and provides optimal advice. For example, for a user who jogs every morning, the unit suggests the best time to jog based on the weather forecast.

[0071] (Example 2) The smart secretary system according to an embodiment of the present invention utilizes the features of a wristwatch to provide optimal advice, reminders, and management. Since 94% of people own wristwatches, this smart secretary system can be used by many people. Because wristwatches are worn on the skin, they can acquire health data such as body temperature and pulse rate. They can also acquire environmental data such as room temperature and humidity. Furthermore, if the wristwatch is equipped with a microphone, it can output and input voice data, functioning as a voice assistant. Because the wristwatch is worn every day, it can track lifestyle habits and location information. For example, the wristwatch acquires health data such as body temperature and pulse rate. This data can be used to monitor the user's health and issue an alert if an abnormality is detected. For example, if the body temperature becomes high, an alert can be issued to warn of a possible fever. Next, the wristwatch acquires environmental data such as room temperature and humidity. This data is used to maintain a comfortable living environment for the user. For example, if the room temperature becomes high, an alert can be issued to encourage the use of the air conditioner. Furthermore, if the wristwatch is equipped with a microphone, it can output and input voice data. This allows it to function as a voice assistant, answering the user's questions and setting reminders. For example, a meeting reminder can be set in response to a voice command such as "Remind me about tomorrow's meeting." Furthermore, since a watch is worn every day, it can track the user's lifestyle and location information. This allows it to analyze the user's behavioral patterns and provide optimal advice. For example, for a user who jogs every morning, the optimal jogging time can be suggested based on the weather forecast. In this way, by utilizing the features of a watch, a multi-functional smart secretary system can be provided, including health management, environmental management, voice assistance, and lifestyle analysis. This allows the smart secretary system to monitor the user's health status and issue an alert if an abnormality is detected. Furthermore, to maintain a comfortable living environment for the user, it can acquire environmental data such as room temperature and humidity and issue an alert as necessary.It also functions as a voice assistant, answering users' questions and setting reminders, and can provide optimal advice based on the user's lifestyle and location.

[0072] The smart secretary system according to the embodiment includes a health data acquisition unit, a monitoring unit, an environmental data acquisition unit, an environment management unit, a voice analysis unit, a reminder setting unit, and a lifestyle analysis unit. The health data acquisition unit acquires health data such as a user's body temperature and pulse rate. For example, the health data acquisition unit can measure the user's body temperature and pulse rate using a body temperature sensor and a pulse sensor. The monitoring unit monitors the user's health condition based on the data acquired by the health data acquisition unit. For example, the monitoring unit can issue an alert notifying the user of a possible fever if the user's body temperature becomes high. The environmental data acquisition unit acquires environmental data such as room temperature and humidity. For example, the environmental data acquisition unit can measure room temperature and humidity using a room temperature sensor and a humidity sensor. The environment management unit manages the user's living environment based on the data acquired by the environmental data acquisition unit. For example, the environment management unit can issue an alert to encourage the user to use an air conditioner if the room temperature becomes high. The voice analysis unit analyzes voice commands using voice recognition technology. For example, the voice analysis unit can analyze a user's voice commands and set reminders or answer questions. The reminder setting unit can set reminders based on the voice commands analyzed by the voice analysis unit. For example, the reminder setting unit can set a meeting reminder in response to a voice command such as "Remind me about tomorrow's meeting." The lifestyle habit analysis unit can analyze a user's lifestyle and provide optimal advice. For example, for a user who jogs every morning, the lifestyle habit analysis unit can suggest the optimal jogging time based on the weather forecast. This allows the smart secretary system according to the embodiment to monitor the user's health status and issue an alert if an abnormality is detected. Furthermore, to maintain a comfortable living environment for the user, the smart secretary system can acquire environmental data such as room temperature and humidity and issue an alert as necessary. Furthermore, the smart secretary system can function as a voice assistant, answering the user's questions and setting reminders. This allows the smart secretary system to understand the user's lifestyle and location information and provide optimal advice.

[0073] The health data acquisition unit may include a body temperature sensor and a pulse sensor. Body temperature sensors include, for example, infrared sensors and contact sensors. Infrared sensors can measure body temperature without contact, while contact sensors can measure body temperature by contacting the skin. Pulse sensors include, for example, photoelectric sensors and pressure sensors. Photoelectric sensors can measure pulse using light, while pressure sensors can measure pulse using changes in pressure. This allows accurate acquisition of body temperature and pulse data. Some or all of the above-described processing in the health data acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the health data acquisition unit may input data acquired from the body temperature sensor and pulse sensor into a generation AI, which then analyzes the data and evaluates the health condition.

[0074] The environmental data acquisition unit may include a room temperature sensor and a humidity sensor. Examples of room temperature sensors include a thermistor and an infrared sensor. A thermistor is a sensor whose resistance value changes in response to temperature changes, and an infrared sensor can measure temperature using infrared rays. Examples of humidity sensors include a capacitance sensor and a resistance sensor. A capacitance sensor is a sensor whose capacitance changes in response to humidity changes, and a resistance sensor is a sensor whose resistance value changes in response to humidity changes. This allows room temperature and humidity data to be accurately acquired. Some or all of the above-described processing in the environmental data acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the environmental data acquisition unit may input data acquired from the room temperature sensor and humidity sensor into a generation AI, which may analyze the data and evaluate the environmental state.

[0075] The voice analysis unit can analyze voice commands using voice recognition technology. Voice recognition technology includes, for example, deep learning-based voice recognition and template matching. Deep learning-based voice recognition is a technology that achieves highly accurate voice recognition by learning from a large amount of voice data, and template matching is a technology that determines whether a voice command matches a pre-registered voice pattern. This allows for accurate analysis of the voice command. Some or all of the above-mentioned processing in the voice analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice analysis unit can input a user's voice command into a generation AI, which can analyze the voice command and set a reminder or answer a question.

[0076] The reminder setting unit can set reminders based on voice commands. Reminders include, for example, taking medicine or starting exercise. The reminder setting unit can set these reminders based on the user's voice commands. For example, the reminder setting unit can set a meeting reminder in response to a voice command such as "Remind me about tomorrow's meeting." This allows reminders to be set based on voice commands. Some or all of the above-described processing in the reminder setting unit may be performed using, for example, AI, or may be performed without using AI. For example, the reminder setting unit can input the voice command analyzed by the voice analysis unit into a generation AI, which then sets the reminder.

[0077] The lifestyle habit analysis unit can analyze the user's behavioral patterns and provide optimal advice. The lifestyle habit analysis unit can analyze the user's behavioral patterns, such as the user's movement patterns and meal timings. For example, if the user jogs every morning, the lifestyle habit analysis unit can suggest the optimal time for jogging based on the weather forecast. The lifestyle habit analysis unit can also analyze the user's dietary data and suggest healthy eating habits. Furthermore, the lifestyle habit analysis unit can analyze the user's sleep data and provide advice for improving sleep quality. This makes it possible to analyze the user's behavioral patterns and provide optimal advice. Some or all of the above-mentioned processing in the lifestyle habit analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the lifestyle habit analysis unit can input the user's behavioral pattern data into a generation AI, which can analyze the data and provide optimal advice.

[0078] The health data acquisition unit can estimate the user's emotions and adjust the frequency of acquiring body temperature and pulse rate based on the estimated user emotions. For example, if the user is feeling stressed, the health data acquisition unit can increase the frequency of acquiring body temperature and pulse rate to detect abnormalities early. Also, if the user is relaxed, the health data acquisition unit can decrease the frequency of acquiring body temperature and pulse rate to reduce the burden of data acquisition. Furthermore, if the user is exercising, the health data acquisition unit can increase the frequency of acquiring body temperature and pulse rate to accurately grasp the effects of exercise.

[0079] When acquiring body temperature and pulse data, the health data acquisition unit can improve the accuracy of the data by taking into account the user's activity level. For example, when the user is exercising, the health data acquisition unit can acquire body temperature and pulse data frequently to provide highly accurate data according to the user's activity level. When the user is resting, the health data acquisition unit can acquire body temperature and pulse data less frequently to provide accurate data for the user at rest. Furthermore, when the user is sleeping, the health data acquisition unit can acquire body temperature and pulse data at regular intervals to evaluate the quality of sleep. This improves the accuracy of the data according to the user's activity level. Some or all of the above-described processing in the health data acquisition unit may be performed using, for example, AI, or may be performed without AI. For example, the health data acquisition unit can input the user's activity level data into a generation AI, which can analyze the data and adjust the frequency of body temperature and pulse acquisition.

[0080] When acquiring health data, the health data acquisition unit can detect abnormal values ​​by referring to the user's past health data. For example, the health data acquisition unit can reference the user's past body temperature data and issue an alert if the user's current body temperature is abnormally high. The health data acquisition unit can also reference the user's past pulse data and issue an alert if the user's current pulse is abnormally low. Furthermore, the health data acquisition unit can comprehensively analyze the user's past health data and encourage the user to visit a medical institution if an abnormal value is detected. This makes it possible to detect abnormal values ​​by referring to the past health data. Some or all of the above-mentioned processing in the health data acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the health data acquisition unit can input the user's past health data into the generation AI, which can analyze the data and detect abnormal values.

[0081] The health data acquisition unit can estimate the user's emotions and determine the priority of health data to be acquired based on the estimated user's emotions. For example, if the user is feeling stressed, the health data acquisition unit can prioritize acquiring pulse data and evaluate the stress level. Furthermore, if the user is relaxed, the health data acquisition unit can prioritize acquiring body temperature data and evaluate the relaxed state. Furthermore, if the user is exercising, the health data acquisition unit can prioritize acquiring pulse data and evaluate the effects of the exercise. This allows the priority of health data to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the health data acquisition unit can be performed using, for example, AI, or without AI. For example, the health data acquisition unit can input the user's emotion data into the generation AI, which can analyze the data and determine the priority of the health data.

[0082] The health data acquisition unit can acquire the user's dietary data when acquiring health data, thereby grasping the user's overall health condition. For example, the health data acquisition unit can acquire body temperature data immediately after the user eats a meal to evaluate the impact of the meal. The health data acquisition unit can also acquire pulse data immediately after the user eats a meal to evaluate the impact of the meal. Furthermore, the health data can be comprehensively analyzed with the user's dietary data and body temperature / pulse data to evaluate the health condition. In this way, by acquiring the dietary data together, the overall health condition can be grasped. Some or all of the above-described processing in the health data acquisition unit can be performed, for example, using AI, or can be performed without using AI. For example, the health data acquisition unit can input the user's dietary data into a generation AI, which can analyze the data and evaluate the overall health condition.

[0083] The health data acquisition unit can acquire the user's sleep data when acquiring health data, thereby grasping the user's overall health condition. For example, the health data acquisition unit can acquire body temperature data while the user is sleeping and evaluate the quality of the sleep. It can also acquire pulse data while the user is sleeping and evaluate the quality of the sleep. Furthermore, the health data acquisition unit can comprehensively analyze the user's sleep data and body temperature / pulse data to evaluate the health condition. In this way, by acquiring the sleep data together, the overall health condition can be grasped. Some or all of the above-mentioned processing in the health data acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the health data acquisition unit can input the user's sleep data into a generation AI, which can analyze the data and evaluate the overall health condition.

[0084] The monitoring unit can estimate the user's emotions and adjust the health condition monitoring method based on the estimated user emotions. For example, if the user is feeling stressed, the monitoring unit can increase the monitoring frequency of pulse data and evaluate the stress level. Furthermore, if the user is relaxed, the monitoring unit can decrease the monitoring frequency of body temperature data and evaluate the relaxed state. Furthermore, if the user is exercising, the monitoring unit can increase the monitoring frequency of pulse data and evaluate the effects of the exercise. This allows the health condition monitoring method to be adjusted according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the monitoring unit can be performed using, for example, AI, or without AI. For example, the monitoring unit can input the user's emotion data into the generation AI, which can analyze the data and adjust the health condition monitoring method.

[0085] During monitoring, the monitoring unit can detect abnormal values ​​by referring to the user's past health data. For example, the monitoring unit can refer to the user's past body temperature data and issue an alert if the user's current body temperature is abnormally high. The monitoring unit can also refer to the user's past pulse data and issue an alert if the user's current pulse is abnormally low. Furthermore, the monitoring unit can comprehensively analyze the user's past health data and encourage the user to visit a medical institution if an abnormal value is detected. This makes it possible to detect abnormal values ​​by referring to the past health data. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's past health data into a generation AI, which can analyze the data and detect abnormal values.

[0086] During monitoring, the monitoring unit can also analyze the user's lifestyle habit data to comprehensively evaluate the health condition. For example, the monitoring unit can comprehensively analyze the user's exercise data and body temperature / pulse data to evaluate the health condition. The monitoring unit can also comprehensively analyze the user's diet data and body temperature / pulse data to evaluate the health condition. Furthermore, the monitoring unit can comprehensively analyze the user's sleep data and body temperature / pulse data to evaluate the health condition. In this way, the health condition can be comprehensively evaluated by analyzing the lifestyle habit data together. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's lifestyle habit data into a generation AI, which then analyzes the data to comprehensively evaluate the health condition.

[0087] The monitoring unit can estimate the user's emotions and adjust the display method of the monitoring results based on the estimated user emotions. For example, if the user is nervous, the monitoring unit can provide a simple, highly visible display method. If the user is relaxed, the monitoring unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the monitoring unit can provide a display method that focuses on the main points. This makes it possible to adjust the display method of the monitoring results according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. Generation AI can be a text generation AI (for example, LLM) or a multimodal generation AI.

[0088] During monitoring, the monitoring unit can also analyze the user's exercise data to comprehensively evaluate the health condition. For example, the monitoring unit can comprehensively analyze the user's exercise data, body temperature, and pulse data to evaluate the health condition. The monitoring unit can also comprehensively analyze the user's exercise data and dietary data to evaluate the health condition. Furthermore, the monitoring unit can comprehensively analyze the user's exercise data and sleep data to evaluate the health condition. In this way, the health condition can be comprehensively evaluated by analyzing the exercise data together. Some or all of the above-mentioned processing in the monitoring unit can be performed, for example, using AI, or can be performed without using AI. For example, the monitoring unit can input the user's exercise data into a generation AI, which can analyze the data and comprehensively evaluate the health condition.

[0089] During monitoring, the monitoring unit can also analyze the user's stress level and comprehensively evaluate the health state. The monitoring unit can, for example, comprehensively analyze the user's stress level and body temperature / pulse data to evaluate the health state. The monitoring unit can also comprehensively analyze the user's stress level and dietary data to evaluate the health state. Furthermore, the monitoring unit can comprehensively analyze the user's stress level and sleep data to evaluate the health state. In this way, by analyzing the stress level together, the health state can be comprehensively evaluated. Some or all of the above-mentioned processing in the monitoring unit can be performed, for example, using AI or without AI. For example, the monitoring unit can input the user's stress level data to a generation AI, which can analyze the data and comprehensively evaluate the health state.

[0090] The environmental data acquisition unit can estimate the user's emotions and adjust the frequency of acquiring room temperature and humidity based on the estimated user emotions. For example, if the user is feeling stressed, the environmental data acquisition unit can increase the frequency of acquiring room temperature and humidity to maintain a comfortable environment. Furthermore, if the user is relaxed, the environmental data acquisition unit can reduce the frequency of acquiring room temperature and humidity to reduce the burden of data acquisition. Furthermore, if the user is exercising, the environmental data acquisition unit can increase the frequency of acquiring room temperature and humidity to accurately grasp the effects of the exercise. This allows the frequency of acquiring room temperature and humidity to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the environmental data acquisition unit can be performed using, for example, AI, or without AI. For example, the environmental data acquisition unit can input the user's emotion data into the generation AI, which can analyze the data and adjust the frequency of acquiring room temperature and humidity.

[0091] When acquiring room temperature and humidity data, the environmental data acquisition unit can improve the accuracy of the data by taking into account the user's activity level. For example, when the user is exercising, the environmental data acquisition unit can acquire room temperature and humidity data frequently to provide highly accurate data according to the user's activity level. Furthermore, when the user is resting, the environmental data acquisition unit can acquire room temperature and humidity data less frequently to provide accurate data for the user's resting state. Furthermore, when the user is sleeping, the environmental data acquisition unit can acquire room temperature and humidity data at regular intervals to evaluate the quality of sleep. This can improve the accuracy of the data according to the user's activity level. Some or all of the above-described processing in the environmental data acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the environmental data acquisition unit can input the user's activity level data to a generation AI, which can analyze the data and adjust the frequency of room temperature and humidity acquisition.

[0092] When acquiring environmental data, the environmental data acquisition unit can detect abnormal values ​​by referring to the user's past environmental data. For example, the environmental data acquisition unit can reference the user's past room temperature data and issue an alert if the current room temperature is abnormally high. The environmental data acquisition unit can also reference the user's past humidity data and issue an alert if the current humidity is abnormally low. Furthermore, the user's past environmental data can be comprehensively analyzed, and appropriate countermeasures can be proposed if an abnormal value is detected. This makes it possible to detect abnormal values ​​by referring to past environmental data. Some or all of the above-described processing in the environmental data acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the environmental data acquisition unit can input the user's past environmental data into a generation AI, which can analyze the data and detect abnormal values.

[0093] The environmental data acquisition unit can estimate the user's emotions and determine the priority of the environmental data to be acquired based on the estimated user's emotions. For example, if the user is feeling stressed, the environmental data acquisition unit can prioritize acquiring room temperature data to maintain a comfortable environment. Furthermore, if the user is relaxed, the environmental data acquisition unit can prioritize acquiring humidity data to maintain a relaxed state. Furthermore, if the user is exercising, the environmental data acquisition unit can prioritize acquiring room temperature data to evaluate the effects of the exercise. This allows the priority of environmental data to be determined according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the environmental data acquisition unit can be performed using, for example, an AI, or without an AI. For example, the environmental data acquisition unit can input the user's emotion data into the generation AI, which can analyze the data and determine the priority of the environmental data.

[0094] When acquiring environmental data, the environmental data acquisition unit may also acquire weather data for the user's residential area, thereby enabling the overall environmental condition to be grasped. For example, the environmental data acquisition unit may also acquire temperature data for the user's residential area and comprehensively analyze it together with the room temperature data. The environmental data acquisition unit may also acquire humidity data for the user's residential area and comprehensively analyze the indoor and outdoor humidity. Furthermore, weather forecast data for the user's residential area may also be acquired, which can be used to adjust the indoor environment. In this way, by also acquiring weather data for the residential area, the overall environmental condition can be grasped. Some or all of the above-described processing in the environmental data acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the environmental data acquisition unit may input weather data for the user's residential area to the generation AI, which may analyze the data to grasp the overall environmental condition.

[0095] The environmental data acquisition unit can acquire the user's indoor activity data together with the environmental data to grasp the overall environmental condition. The environmental data acquisition unit can, for example, acquire the user's indoor activity data and room temperature data together to provide a comfortable environment according to the activity. The environmental data acquisition unit can also acquire the user's indoor activity data and humidity data together to provide a comfortable environment according to the activity. Furthermore, the user's indoor activity data and weather data can be acquired together to grasp the overall environmental condition. In this way, by acquiring the indoor activity data together, the overall environmental condition can be grasped. Some or all of the above-mentioned processing in the environmental data acquisition unit can be performed, for example, using AI or without AI. For example, the environmental data acquisition unit can input the user's indoor activity data to a generation AI, which can analyze the data to grasp the overall environmental condition.

[0096] The environment management unit can estimate the user's emotions and adjust the environment management method based on the estimated user emotions. For example, if the user is feeling stressed, the environment management unit can adjust the room temperature to a comfortable temperature and provide a relaxing environment. Also, if the user is relaxing, the environment management unit can appropriately adjust the humidity to maintain a comfortable environment. Furthermore, if the user is exercising, the environment management unit can appropriately adjust the room temperature and provide an environment suitable for exercise. This makes it possible to adjust the environment management method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. Generation AI

[0097] During environmental management, the environmental management unit can select the optimal management method by referring to the user's past environmental data. For example, the environmental management unit can set the optimal room temperature by referring to the user's past room temperature data. It can also set the optimal humidity by referring to the user's past humidity data. Furthermore, it can comprehensively analyze the user's past environmental data and select the optimal environmental management method. This makes it possible to select the optimal management method by referring to the past environmental data. Some or all of the above-mentioned processing in the environmental management unit can be performed, for example, using AI or without AI. For example, the environmental management unit can input the user's past environmental data into a generation AI, which can analyze the data and select the optimal management method.

[0098] The environmental management unit can analyze the user's lifestyle data together during environmental management and provide an optimal environmental management method. The environmental management unit, for example, can analyze the user's lifestyle data and room temperature data together to set an optimal room temperature. The environmental management unit can also analyze the user's lifestyle data and humidity data together to set an optimal humidity. Furthermore, the user's lifestyle data and environmental data can be analyzed comprehensively to provide an optimal environmental management method. In this way, by analyzing the lifestyle data together, an optimal environmental management method can be provided. Some or all of the above-mentioned processing in the environmental management unit can be performed, for example, using AI, or can be performed without using AI. For example, the environmental management unit can input the user's lifestyle data into a generation AI, which can analyze the data and provide an optimal environmental management method.

[0099] The environment management unit can estimate the user's emotions and adjust the display method of the environment management results based on the estimated user emotions. For example, if the user is nervous, the environment management unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the environment management unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the environment management unit can provide a display method that focuses on the main points. This allows the display method of the environment management results to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the environment management unit can be performed using, for example, AI, or without AI. For example, the environment management unit can input the user's emotion data into the generation AI, which can analyze the data and adjust the display method of the environment management results.

[0100] The environmental management unit can analyze the user's exercise data during environmental management and provide an optimal environmental management method. For example, the environmental management unit can analyze the user's exercise data and room temperature data together to set a room temperature suitable for exercise. The environmental management unit can also analyze the user's exercise data and humidity data together to set a humidity suitable for exercise. Furthermore, the user's exercise data and environmental data can be analyzed comprehensively to provide an optimal environmental management method. In this way, by analyzing the exercise data together, an optimal environmental management method can be provided. Some or all of the above-mentioned processing in the environmental management unit can be performed using, for example, AI, or can be performed without using AI. For example, the environmental management unit can input the user's exercise data into a generation AI, which can analyze the data and provide an optimal environmental management method.

[0101] The environment management unit can also analyze the user's stress level during environment management and provide an optimal environment management method. The environment management unit, for example, can analyze the user's stress level and room temperature data together to set a room temperature suitable for stress reduction. It can also analyze the user's stress level and humidity data together to set a humidity suitable for stress reduction. Furthermore, it can comprehensively analyze the user's stress level and environmental data to provide an optimal environment management method. By analyzing the stress level together, it is possible to provide an optimal environment management method. Some or all of the above-described processing in the environment management unit can be performed, for example, using AI, or can be performed without using AI. For example, the environment management unit can input the user's stress level data into a generation AI, which analyzes the data and provides an optimal environment management method.

[0102] The voice analysis unit can estimate the user's emotions and adjust the analysis method of the voice command based on the estimated user's emotions. For example, if the user is stressed, the voice analysis unit can increase the analysis accuracy of the voice command and respond quickly. Also, if the user is relaxed, the voice analysis unit can decrease the analysis accuracy of the voice command and respond in a relaxed state. Furthermore, if the user is in a hurry, the voice analysis unit can increase the analysis accuracy of the voice command and respond quickly. This makes it possible to adjust the analysis method of the voice command according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the voice analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice analysis unit can input user's emotion data into the generation AI, which can analyze the data and adjust the analysis method of the voice command.

[0103] During voice analysis, the voice analysis unit can improve analysis accuracy by referring to the user's past voice command history. For example, the voice analysis unit can refer to the user's past voice command history and quickly respond to similar commands. The voice analysis unit can also improve analysis accuracy for frequently used commands by referring to the user's past voice command history. Furthermore, the user's past voice command history can be comprehensively analyzed to improve analysis accuracy. This allows analysis accuracy to be improved by referring to the past voice command history. Some or all of the above-mentioned processing in the voice analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice analysis unit can input the user's past voice command history into a generation AI, which then analyzes the data to improve analysis accuracy.

[0104] The voice analysis unit can analyze the user's lifestyle data together with the voice analysis to provide an optimal voice command analysis method. The voice analysis unit can, for example, analyze the user's lifestyle data and voice command history together to provide an optimal analysis method. Furthermore, the voice analysis unit can analyze the user's lifestyle data and voice command history together to improve analysis accuracy for frequently used commands. Furthermore, the user's lifestyle data and voice command history can be analyzed comprehensively to improve analysis accuracy. This allows for the optimal voice command analysis method to be provided by analyzing the lifestyle data together. Some or all of the above-described processing in the voice analysis unit can be performed, for example, using AI, or can be performed without using AI. For example, the voice analysis unit can input the user's lifestyle data into a generation AI, which then analyzes the data to provide an optimal voice command analysis method.

[0105] The voice analysis unit can estimate the user's emotions and determine the priority of voice commands based on the estimated user emotions. For example, if the user is feeling stressed, the voice analysis unit can prioritize analysis of important voice commands. Also, if the user is relaxed, the voice analysis unit can prioritize analysis of voice commands that can be responded to in a relaxed state. Furthermore, if the user is in a hurry, the voice analysis unit can prioritize analysis of voice commands that require a quick response. This makes it possible to determine the priority of voice commands according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the voice analysis unit may be performed using AI, for example, and the AI

[0106] The voice analysis unit can improve the analysis accuracy by also analyzing the user's language data during voice analysis. The voice analysis unit can improve the analysis accuracy by, for example, analyzing the user's language data and voice command history together. The voice analysis unit can also improve the analysis accuracy for frequently used commands by analyzing the user's language data and voice command history together. Furthermore, the user's language data and voice command history can be analyzed comprehensively to improve the analysis accuracy. In this way, the analysis accuracy can be improved by analyzing the language data together. Some or all of the above-mentioned processing in the voice analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the voice analysis unit can input the user's language data to a generation AI, which can analyze the data to improve the analysis accuracy.

[0107] The voice analysis unit can improve the accuracy of the analysis by also analyzing the user's speech patterns during voice analysis. The voice analysis unit can improve the accuracy of the analysis by, for example, analyzing the user's speech patterns and voice command history together. The voice analysis unit can also improve the accuracy of the analysis for frequently used commands by analyzing the user's speech patterns and voice command history together. Furthermore, the user's speech patterns and voice command history can be comprehensively analyzed to improve the accuracy of the analysis. In this way, the analysis accuracy can be improved by analyzing the speech patterns together. Some or all of the above-described processing in the voice analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the voice analysis unit can input the user's speech pattern data to a generation AI, which can analyze the data to improve the accuracy of the analysis.

[0108] The reminder setting unit can estimate the user's emotions and adjust the reminder setting method based on the estimated user emotions. For example, if the user is stressed, the reminder setting unit can provide a simple reminder setting method. If the user is relaxed, the reminder setting unit can provide detailed reminder setting options. Furthermore, if the user is in a hurry, the reminder setting unit can provide a method for quickly setting a reminder. This allows the reminder setting method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the reminder setting unit can be performed using, for example, AI, or without AI. For example, the reminder setting unit can input the user's emotion data into the generation AI, which can analyze the data and adjust the reminder setting method.

[0109] When setting a reminder, the reminder setting unit can select the optimal setting method by referring to the user's past reminder history. For example, the reminder setting unit can refer to the user's past reminder history and quickly set similar reminders. The reminder setting unit can also refer to the user's past reminder history and provide frequently used reminder setting methods. Furthermore, the reminder setting unit can comprehensively analyze the user's past reminder history and provide the optimal reminder setting method. This makes it possible to select the optimal setting method by referring to the past reminder history. Some or all of the above-mentioned processing in the reminder setting unit can be performed using, for example, AI, or can be performed without using AI. For example, the reminder setting unit can input the user's past reminder history into a generation AI, which can analyze the data and select the optimal setting method.

[0110] The reminder setting unit can analyze the user's lifestyle habit data together when setting a reminder, and provide an optimal reminder setting method. The reminder setting unit can, for example, analyze the user's lifestyle habit data and reminder history together to provide an optimal reminder setting method. It can also analyze the user's lifestyle habit data and reminder history together to provide a frequently used reminder setting method. Furthermore, it can comprehensively analyze the user's lifestyle habit data and reminder history to provide an optimal reminder setting method. By analyzing the lifestyle habit data together, it is possible to provide an optimal reminder setting method. Some or all of the above-described processing in the reminder setting unit can be performed, for example, using AI, or can be performed without using AI. For example, the reminder setting unit can input the user's lifestyle habit data to a generation AI, which analyzes the data and provides an optimal reminder setting method.

[0111] The reminder setting unit can estimate the user's emotions and determine the priority of reminders based on the estimated user emotions. For example, if the user is feeling stressed, the reminder setting unit can prioritize important reminders. Furthermore, if the user is relaxed, the reminder setting unit can prioritize reminders that can be addressed in a relaxed state. Furthermore, if the user is in a hurry, the reminder setting unit can prioritize reminders that require a quick response. This allows the priority of reminders to be determined according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reminder setting unit can be performed using, for example, AI, or without AI. For example, the reminder setting unit can input the user's emotion data into the generation AI, which can analyze the data and determine the priority of reminders.

[0112] The reminder setting unit can provide an optimal reminder by also setting the user's schedule data when setting a reminder. The reminder setting unit can, for example, analyze the user's schedule data and reminder history together to provide an optimal reminder. The reminder setting unit can also analyze the user's schedule data and reminder history together to provide a frequently used reminder setting method. Furthermore, the reminder setting unit can comprehensively analyze the user's schedule data and reminder history to provide an optimal reminder. This allows the optimal reminder to be provided by setting the schedule data together. Some or all of the above-described processing in the reminder setting unit can be performed, for example, using AI or without AI. For example, the reminder setting unit can input the user's schedule data to a generation AI, which can analyze the data and provide an optimal reminder.

[0113] The reminder setting unit can provide an optimal reminder by also setting the user's activity data when setting a reminder. The reminder setting unit can, for example, analyze the user's activity data and reminder history together to provide an optimal reminder. The reminder setting unit can also analyze the user's activity data and reminder history together to provide a frequently used reminder setting method. Furthermore, the reminder setting unit can comprehensively analyze the user's activity data and reminder history to provide an optimal reminder. As a result, the optimal reminder can be provided by setting the activity data together. Some or all of the above-described processing in the reminder setting unit can be performed, for example, using AI or without AI. For example, the reminder setting unit can input the user's activity data to a generation AI, which can analyze the data and provide an optimal reminder.

[0114] The lifestyle habit analysis unit can estimate the user's emotions and adjust the lifestyle habit analysis method based on the estimated user's emotions. For example, if the user is feeling stressed, the lifestyle habit analysis unit prioritizes the analysis of lifestyle habits that are useful for stress reduction. Also, if the user is relaxed, the lifestyle habit analysis unit can analyze lifestyle habits to help maintain a relaxed state. Furthermore, if the user is in a hurry, the lifestyle habit analysis unit can analyze lifestyle habits that allow for a quick response. This makes it possible to adjust the lifestyle habit analysis method according to the user's emotions. The estimation of emotions can be performed, for example, by an emotion engine or

[0115] During lifestyle analysis, the lifestyle analysis unit can select the optimal analysis method by referring to the user's past lifestyle data. For example, the lifestyle analysis unit can refer to the user's past lifestyle data and provide the optimal analysis method for similar patterns. It can also refer to the user's past lifestyle data and provide the optimal analysis method for frequently occurring patterns. Furthermore, it can comprehensively analyze the user's past lifestyle data and provide the optimal analysis method. This allows the optimal analysis method to be selected by referring to the past lifestyle data. Some or all of the above-described processing in the lifestyle analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the lifestyle analysis unit can input the user's past lifestyle data into a generation AI, which then analyzes the data and selects the optimal analysis method.

[0116] The lifestyle habit analysis unit can analyze the user's health data together during lifestyle habit analysis and provide an optimal lifestyle habit analysis method. The lifestyle habit analysis unit can, for example, analyze the user's health data and lifestyle habit data together and provide an optimal lifestyle habit analysis method. It can also analyze the user's health data and lifestyle habit data together and provide an optimal analysis method for frequently observed patterns. It can also comprehensively analyze the user's health data and lifestyle habit data and provide an optimal lifestyle habit analysis method. In this way, it is possible to provide an optimal lifestyle habit analysis method by analyzing the health data together. Some or all of the above-mentioned processing in the lifestyle habit analysis unit can be performed, for example, using AI or without AI. For example, the lifestyle habit analysis unit can input the user's health data into a generation AI, which analyzes the data and provides an optimal lifestyle habit analysis method.

[0117] The lifestyle habit analysis unit can estimate the user's emotions and prioritize lifestyle habits based on the estimated user emotions. For example, if the user is feeling stressed, the lifestyle habit analysis unit can prioritize lifestyle habits that help reduce stress. Furthermore, if the user is relaxed, the lifestyle habit analysis unit can prioritize lifestyle habits that help maintain a relaxed state. Furthermore, if the user is in a hurry, the lifestyle habit analysis unit can prioritize lifestyle habits that allow for quick responses. This allows lifestyle habits to be prioritized according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the lifestyle habit analysis unit can be performed using, for example, AI, or without AI. For example, the lifestyle habit analysis unit can input the user's emotion data into the generation AI, which can analyze the data and prioritize lifestyle habits.

[0118] The lifestyle habit analysis unit can also analyze the user's dietary data when analyzing lifestyle habits and suggest optimal lifestyle habits. The lifestyle habit analysis unit can, for example, analyze the user's dietary data and lifestyle data together to suggest optimal dietary habits. The lifestyle habit analysis unit can also analyze the user's dietary data and lifestyle data together to suggest healthy dietary habits. Furthermore, the user's dietary data and lifestyle data can be analyzed comprehensively to suggest optimal lifestyle habits. In this way, optimal lifestyle habits can be suggested by analyzing the dietary data together. Some or all of the above-mentioned processing in the lifestyle habit analysis unit can be performed, for example, using AI or without AI. For example, the lifestyle habit analysis unit can input the user's dietary data into a generation AI, which can analyze the data and suggest optimal lifestyle habits.

[0119] The lifestyle habit analysis unit can also analyze the user's exercise data when analyzing lifestyle habits and suggest optimal lifestyle habits. The lifestyle habit analysis unit can, for example, analyze the user's exercise data and lifestyle data together to suggest optimal exercise habits. The lifestyle habit analysis unit can also analyze the user's exercise data and lifestyle data together to suggest healthy exercise habits. Furthermore, the user's exercise data and lifestyle data can be analyzed comprehensively to suggest optimal lifestyle habits. In this way, optimal lifestyle habits can be suggested by analyzing the exercise data together. Some or all of the above-mentioned processing in the lifestyle habit analysis unit can be performed, for example, using AI or without AI. For example, the lifestyle habit analysis unit can input the user's exercise data into a generation AI, which can analyze the data and suggest optimal lifestyle habits. === Hard Collateral 1-1 === Each of the multiple elements, including the health data acquisition unit, monitoring unit, environmental data acquisition unit, environment management unit, voice analysis unit, reminder setting unit, and lifestyle habit analysis unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the health data acquisition unit is realized using a body temperature sensor and a pulse sensor of the smart device 14. The monitoring unit is realized by the specific processing unit 290 of the data processing device 12 and monitors the user's health condition based on the health data. The environmental data acquisition unit is realized using a room temperature sensor and a humidity sensor of the smart device 14. The environment management unit is realized by the specific processing unit 290 of the data processing device 12 and manages the user's living environment based on the environmental data. The voice analysis unit analyzes voice commands using the microphone of the smart device 14. The reminder setting unit is realized by the specific processing unit 290 of the data processing device 12 and sets reminders based on the voice commands. The lifestyle habit analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's lifestyle habits and provides optimal advice. === Hard Collateral 1-2 === Each of the multiple elements, including the health data acquisition unit, monitoring unit, environmental data acquisition unit, environment management unit, voice analysis unit, reminder setting unit, and lifestyle habit analysis unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the health data acquisition unit is realized using a body temperature sensor and a pulse sensor of the smart glasses 214. The monitoring unit is realized by the specific processing unit 290 of the data processing device 12 and monitors the user's health condition based on the health data. The environmental data acquisition unit is realized using a room temperature sensor and a humidity sensor of the smart glasses 214. The environment management unit is realized by the specific processing unit 290 of the data processing device 12 and manages the user's living environment based on the environmental data. The voice analysis unit analyzes voice commands using the microphone of the smart glasses 214. The reminder setting unit is realized by the specific processing unit 290 of the data processing device 12 and sets reminders based on the voice commands. The lifestyle habit analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's lifestyle habits and provides optimal advice. === Hard Collateral 1-3 === Each of the multiple elements, including the health data acquisition unit, monitoring unit, environmental data acquisition unit, environment management unit, voice analysis unit, reminder setting unit, and lifestyle habit analysis unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the health data acquisition unit is realized using a body temperature sensor and a pulse sensor of the headset-type terminal 314. The monitoring unit is realized by the specific processing unit 290 of the data processing device 12 and monitors the user's health condition based on the health data. The environmental data acquisition unit is realized using a room temperature sensor and a humidity sensor of the headset-type terminal 314. The environment management unit is realized by the specific processing unit 290 of the data processing device 12 and manages the user's living environment based on the environmental data. The voice analysis unit analyzes voice commands using the microphone of the headset-type terminal 314. The reminder setting unit is realized by the specific processing unit 290 of the data processing device 12 and sets reminders based on the voice commands. The lifestyle analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the lifestyle of the user and provides optimal advice. === Hard Collateral 1-4 === Each of the multiple elements, including the health data acquisition unit, monitoring unit, environmental data acquisition unit, environment management unit, voice analysis unit, reminder setting unit, and lifestyle habit analysis unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the health data acquisition unit is realized using a body temperature sensor and a pulse sensor of the robot 414. The monitoring unit is realized by the specific processing unit 290 of the data processing device 12 and monitors the user's health condition based on the health data. The environmental data acquisition unit is realized using a room temperature sensor and a humidity sensor of the robot 414. The environment management unit is realized by the specific processing unit 290 of the data processing device 12 and manages the user's living environment based on the environmental data. The voice analysis unit analyzes voice commands using the microphone of the robot 414. The reminder setting unit is realized by the specific processing unit 290 of the data processing device 12 and sets reminders based on the voice commands. The lifestyle habit analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's lifestyle habits and provides optimal advice.

[0120] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0121] The smart secretary system can also obtain user preference data and provide personalized advice. For example, if a user likes a particular music genre, music of that genre can be played when the user is relaxing. If a user likes a particular food, recipes related to that food can be suggested. Furthermore, if a user likes a particular exercise, a training plan related to that exercise can be provided. This allows personalized advice to be provided based on the user's preferences.

[0122] The health data acquisition unit can also monitor the user's skin condition and provide skin care advice. For example, it can detect the dryness of the skin and recommend the use of moisturizing cream. It can also detect the amount of oil on the skin and suggest an appropriate face washing method. It can also detect UV damage to the skin and recommend the use of sunscreen. This allows it to provide advice to maintain the user's skin health.

[0123] The environmental data acquisition unit can also acquire air quality data for the user's residential area and encourage the use of an air purifier. For example, if the PM2.5 concentration is high, the use of an air purifier can be recommended. Also, if there is a lot of pollen in the air, an alert can be issued to close the windows. Furthermore, if the carbon dioxide concentration is high, ventilation can be encouraged. This makes it possible to provide advice on improving the air quality in the user's residential environment.

[0124] The voice analysis unit can further analyze the tone and speed of the user's voice to estimate the emotional state. For example, if the voice tone is high and the speed is fast, it can be estimated that the user is nervous. If the voice tone is low and the speed is slow, it can be estimated that the user is relaxed. Furthermore, if the voice tone is unstable, it can be estimated that the user is feeling stressed. In this way, the emotional state can be estimated based on the characteristics of the user's voice.

[0125] The reminder setting unit can further refer to the user's schedule data and suggest the optimal timing for the reminder. For example, the user can set a reminder just before a meeting. Alternatively, the user can set a reminder before exercise. Furthermore, the user can set a reminder at the time when they take their medicine. In this way, the optimal timing for the reminder can be suggested based on the user's schedule.

[0126] The lifestyle analysis unit can further estimate the user's emotions and suggest ways to improve lifestyle habits based on the estimated emotions. For example, if the user is feeling stressed, it can suggest yoga or meditation to help them relax. If the user is tired, it can provide advice on how to get enough sleep. If the user is feeling energetic, it can suggest taking up a new hobby or activity. In this way, it is possible to suggest ways to improve lifestyle habits based on the user's emotions.

[0127] The health data acquisition unit can further acquire the user's dietary data and evaluate the nutritional balance. For example, it can record the calories and nutrients the user has ingested and evaluate whether they are in excess or deficiency. If the user is consuming too much of a particular nutrient, it can advise the user to reduce that nutrient. Furthermore, if the user is deficient in a particular nutrient, it can suggest ingredients that will supplement that nutrient. This makes it possible to evaluate the nutritional balance based on the user's dietary data.

[0128] The environmental data acquisition unit can further estimate the user's emotions and adjust the frequency of environmental data acquisition based on the estimated emotions. For example, if the user is feeling stressed, room temperature and humidity data can be acquired more frequently to maintain a comfortable environment. If the user is relaxed, data acquisition can be reduced to reduce the burden of data acquisition. Furthermore, if the user is exercising, room temperature and humidity data can be acquired more frequently to accurately grasp the effects of the exercise. This makes it possible to adjust the frequency of environmental data acquisition according to the user's emotions.

[0129] The voice analysis unit can further estimate the user's emotions and adjust the analysis method of the voice command based on the estimated emotions. For example, if the user is feeling stressed, the accuracy of the analysis of the voice command can be increased, allowing for a quick response. Alternatively, if the user is relaxed, the accuracy of the analysis of the voice command can be decreased, allowing for a relaxed response. Furthermore, if the user is in a hurry, the accuracy of the analysis of the voice command can be increased, allowing for a quick response. This makes it possible to adjust the analysis method of the voice command according to the user's emotions.

[0130] The reminder setting unit can further estimate the user's emotions and determine the priority of reminders based on the estimated emotions. For example, if the user is feeling stressed, important reminders can be prioritized. If the user is relaxed, reminders that can be handled in a relaxed state can be prioritized. If the user is in a hurry, reminders that require a quick response can be prioritized. In this way, the priority of reminders can be determined according to the user's emotions.

[0131] The processing flow of the second embodiment will be briefly explained below.

[0132] Step 1: The health data acquisition unit acquires health data such as the user's body temperature, pulse rate, etc. For example, the health data acquisition unit measures the user's body temperature and pulse rate using a body temperature sensor and a pulse rate sensor. Step 2: The monitoring unit monitors the user's health condition based on the data acquired by the health data acquisition unit. For example, if the user's body temperature rises, it issues an alert to notify the user of the possibility of fever. Step 3: The environmental data acquisition unit acquires environmental data such as room temperature, humidity, etc. For example, the room temperature and humidity are measured using a room temperature sensor and a humidity sensor. Step 4: The environment management unit manages the user's living environment based on the data acquired by the environment data acquisition unit. For example, if the room temperature becomes high, it issues an alert to encourage the use of the air conditioner. Step 5: The voice analysis unit uses voice recognition technology to analyze the voice command. For example, it analyzes the user's voice command and sets reminders or answers questions. Step 6: The reminder setting unit sets a reminder based on the voice command analyzed by the voice analysis unit. For example, a reminder for a meeting is set in response to a voice command such as "Remind me about tomorrow's meeting." Step 7: The lifestyle analysis unit analyzes the user's lifestyle and provides optimal advice. For example, for a user who jogs every morning, the unit suggests the best time to jog based on the weather forecast.

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0135] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0138] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0151] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0154] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0155] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0160] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0161] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0162] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0163] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0164] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0165] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0166] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0167] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0170] 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.

[0171] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0172] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0173] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0174] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0175] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0176] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0177] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0178] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0179] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0180] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0181] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0182] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0183] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0184] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0185] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0186] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0187] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0188] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0189] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0190] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0191] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0192] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0193] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0194] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0195] 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.

[0196] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0197] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0198] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0199] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0200] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0201] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0202] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0203] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0204] [Explanation of symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A system comprising: a health data acquisition unit that acquires health data; a monitoring unit that monitors a user's health condition based on the data acquired by the health data acquisition unit; an environmental data acquisition unit that acquires environmental data; an environment management unit that manages the user's living environment based on the data acquired by the environmental data acquisition unit; a voice analysis unit that analyzes voice; a reminder setting unit that sets reminders based on voice commands analyzed by the voice analysis unit; and a lifestyle analysis unit that analyzes the user's lifestyle habits.

2. The health data acquisition unit: Equipped with a body temperature sensor and a pulse sensor The system of claim 1 .

3. The environmental data acquisition unit Equipped with room temperature and humidity sensors The system of claim 1 .

4. The voice analysis unit Analyze voice commands using voice recognition technology The system of claim 1 .

5. The reminder setting unit Set reminders based on voice commands The system of claim 1 .

6. 2. The system according to claim 1, wherein the lifestyle analysis unit analyzes the user's behavioral patterns and provides appropriate advice.

7. The health data acquisition unit: Estimate the user's emotions and adjust the frequency of body temperature and pulse rate acquisition based on the estimated user emotions. The system of claim 1 .

8. The health data acquisition unit: When collecting body temperature and pulse data, the accuracy of the data is improved by taking into account the user's activity level. The system of claim 1 .

9. The health data acquisition unit: When acquiring health data, the system references the user's past health data to detect abnormal values. The system of claim 1 .

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