System

Abstract

An object of the system according to the embodiment is to check a health condition by comprehensively analyzing daily health data and video data.SOLUTION: A system includes a data storage part, a health condition check part, a video recording part, and a motion analysis part. The data storage unit stores data of a meal amount, an exercise amount, and excretion. The health condition check unit checks a health condition using the data accumulated by the data accumulation unit. The video recording unit records daily video data. The motion analysis unit analyzes the video data recorded by the video recording unit to detect a change in motion or an injury.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including 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 analyze daily health data and video data comprehensively to check health status, and there is room for improvement.

[0005] The system according to the embodiment aims to check the health status by comprehensively analyzing daily health data and video data. [Means for solving the problem]

[0006] The system according to the embodiment includes a data storage unit, a health condition check unit, a video recording unit, and a movement analysis unit. The data storage unit stores data on food intake, exercise volume, and excretion. The health condition check unit checks the health condition using the data stored by the data storage unit. The video recording unit records daily video data. The movement analysis unit analyzes the video data recorded by the video recording unit to detect changes in movement and injuries. [Effects of the Invention]

[0007] The system according to the embodiment can check the health status by comprehensively analyzing daily health data and video data. [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 health management system according to an embodiment of the present invention accumulates data on the amount of food eaten, the amount of exercise, and excretion, and uses a generating AI to check the health status, records daily video data, and detects changes in movement and injuries early on. This allows the health management system to efficiently manage the user's health status and detect health problems early on.

[0029] A health management system according to an embodiment includes a data storage unit, a health status check unit, a video recording unit, and a movement analysis unit. The data storage unit stores data on the amount of food eaten, the amount of exercise, and excretion. For example, it records the amount of food eaten and the amount of water consumed, and the amount of exercise as steps taken and calories burned. It can also record the color and amount of excretion. The health status check unit uses the data stored by the data storage unit to check the health status. For example, a generation AI analyzes the data on the amount of food eaten, the amount of exercise, and excretion, and issues an alert if there are any health concerns. The video recording unit records daily video data. For example, indoor and outdoor activities are recorded with a camera and saved as video data. The movement analysis unit analyzes the video data recorded by the video recording unit to detect changes in movement and injuries. For example, the generation AI analyzes the video data to detect movement patterns and abnormal movements. This allows the health management system according to an embodiment to efficiently manage the user's health status and detect health problems early. For example, the AI ​​can analyze data on food intake, exercise, and excretion, and issue an alert if there are any health concerns. It can also analyze video data to quickly detect changes in movement or injuries, allowing for prompt and appropriate responses.

[0030] The data storage unit can record changes in walking speed, food intake, water intake, and the color and amount of excrement. The data storage unit, for example, records walking speed. For example, it measures walking speed using a GPS device and stores the data. It also records food intake and water intake. For example, it records food intake every day and measures water intake. It also records changes in the color and amount of excrement. For example, it records the color of excrement every day and measures the amount. This makes it possible to record detailed health data.

[0031] The motion analysis unit can analyze footage of movements and sleep when the user is away from home. The motion analysis unit, for example, analyzes movements when the user is away from home. For example, a camera is used to record movements when the user is away from home, and the generation AI analyzes the data. It also analyzes footage of sleep. For example, a camera is used to record movements while the user is sleeping, and the generation AI analyzes the data. This makes it possible to analyze movements and sleep when the user is away from home.

[0032] The health status check section records the details of meals, the types of nutrients, and nutritional balance, and the generation AI can analyze nutritional imbalances and assess health risks. The health status check section, for example, records the details of meals. For example, nutrients such as protein, carbohydrates, lipids, vitamins, and minerals are recorded individually, and the generation AI evaluates their balance. The contents of meals can also be recorded in photographs, and the generation AI performs image analysis to automatically extract the types and amounts of nutrients. Furthermore, the contents of meals can be recorded via voice input, and the generation AI analyzes the voice data to extract nutrient information. This makes it possible to analyze nutritional imbalances and assess health risks.

[0033] The health status check unit records the type of exercise, aerobic exercise, and strength training in addition to data on the amount of exercise, and the generation AI can analyze the effects of each exercise. The health status check unit, for example, records the type of exercise in detail. For example, aerobic exercise, strength training, stretching, and other exercises are recorded separately, and the generation AI evaluates their effects. The type of exercise is also recorded as a video, and the generation AI analyzes the video to automatically extract the type and intensity of the exercise. Furthermore, the type of exercise is recorded via audio input, and the generation AI analyzes the audio data to extract exercise information. This makes it possible to analyze the type and effects of exercise.

[0034] The health status check unit integrates data on food intake and exercise volume with other health data, sleep data, and stress levels, allowing the generation AI to analyze the overall health status. The health status check unit, for example, integrates data on food intake and exercise volume with sleep data, allowing the generation AI to analyze the overall health status. For example, sleep time and sleep quality are recorded, and the generation AI analyzes the data. The food intake and exercise volume data is also integrated with stress levels, allowing the generation AI to analyze the overall health status. For example, data is recorded using a device that measures stress levels, and the generation AI analyzes the data. Furthermore, the food intake and exercise volume data is integrated with biometric data such as heart rate and blood pressure, allowing the generation AI to analyze the overall health status. For example, biometric data is recorded using a wearable device, and the generation AI analyzes the data. This allows the overall health status to be analyzed.

[0035] The data accumulation unit allows the user to easily record data using voice input, and the generation AI can analyze the voice data. The data accumulation unit, for example, uses voice input to build a system that allows the user to easily record data on the amount of food eaten and the amount of exercise. For example, voice recognition technology is used to convert the user's voice into text data. Furthermore, using voice input, the user records the details of their meals and exercise by voice, and the generation AI analyzes the voice data. For example, the user inputs the details of their meals by voice, and the generation AI analyzes the voice data. Furthermore, using voice input, the user records the color and amount of excrement by voice, and the generation AI analyzes the voice data. For example, the user inputs information about excrement by voice, and the generation AI analyzes the voice data. This makes it easy to record data using voice input.

[0036] The video recording unit records the audio data in addition to the video data, and the generation AI can analyze both the audio and the video to detect the abnormality. The video recording unit, for example, records audio data in addition to the video data, and the generation AI analyzes both the audio and the video to detect abnormalities. For example, the user's movements and audio are recorded simultaneously, and the generation AI analyzes these data. The audio data is also analyzed to detect changes in the tone and rhythm of the user's voice. For example, if the user's voice is different from usual, the generation AI detects an abnormality. Furthermore, the video data and audio data are integrated, and the generation AI comprehensively detects abnormalities. For example, the user's movements and changes in voice are analyzed simultaneously to detect abnormalities. This makes it possible to detect abnormalities by analyzing both audio and video.

[0037] The motion analysis unit analyzes the video data in detail, analyzing the movement patterns, walking rhythm, and posture, allowing the generation AI to detect the subtle changes. The motion analysis unit, for example, analyzes the video data and analyzes the user's movement patterns in detail. For example, it records changes in walking rhythm and posture, and the generation AI analyzes the data. To analyze the movement patterns, the video data is divided into frames, and the generation AI analyzes the movement of each frame. For example, it analyzes changes in walking rhythm and posture for each frame. Furthermore, it analyzes the video data and reproduces the user's movement patterns as a 3D model. For example, it displays the user's movements as a 3D model, and the generation AI analyzes the model. This allows the movement patterns to be analyzed in detail and subtle changes to be detected.

[0038] The video recording unit simultaneously records video data from different viewpoints and multiple cameras, and the generation AI can analyze the video data from multiple angles. The video recording unit, for example, uses multiple cameras to simultaneously record video data from different viewpoints, and the generation AI analyzes the video data from multiple angles. For example, indoor and outdoor cameras are used simultaneously. Furthermore, video data from different viewpoints is integrated and analyzed comprehensively by the generation AI. For example, the video from multiple cameras is analyzed as a single data set. Furthermore, the user's movements are reproduced as a 3D model using multiple cameras, and the generation AI analyzes the model. For example, a 3D model is created based on video from different viewpoints. This allows for multifaceted analysis of video data from different viewpoints.

[0039] The motion analysis unit integrates the analysis results of the video data with other health data, heart rate, and blood pressure, allowing the generation AI to assess the overall health risk. The motion analysis unit, for example, integrates the analysis results of the video data with biometric data such as heart rate and blood pressure, allowing the generation AI to assess the overall health risk. For example, biometric data is recorded using a wearable device. The analysis results of the video data are also integrated with sleep data and stress levels, allowing the generation AI to assess the overall health risk. For example, sleep time and stress levels are recorded, and the generation AI analyzes these data. The analysis results of the video data are further integrated with data on food intake and exercise volume, allowing the generation AI to assess the overall health risk. For example, food intake and exercise volume are recorded, and the generation AI analyzes these data. This allows the overall health risk to be assessed.

[0040] The data accumulation unit can record the walking speed, and the generation AI can analyze the data. For example, the data accumulation unit records the walking speed, and the generation AI analyzes the data. For example, the walking speed is measured using a GPS device, and the generation AI analyzes the data. In addition, the walking speed is recorded in real time, and the generation AI issues an alert if it is slower than the normal speed. For example, the speed is recorded using a smartphone app. Furthermore, walking speed data is accumulated over a long period of time, and the generation AI analyzes the data to evaluate the health status. For example, the daily walking speed is recorded, and the generation AI analyzes the data. In this way, the walking speed data can be analyzed.

[0041] The data accumulation unit integrates the walking speed data with other health data, heart rate, and blood pressure, allowing the generation AI to evaluate the overall health risk. The data accumulation unit, for example, integrates the walking speed data with biometric data such as heart rate and blood pressure, allowing the generation AI to evaluate the overall health risk. For example, biometric data is recorded using a wearable device. The walking speed data is also integrated with sleep data and stress level, allowing the generation AI to evaluate the overall health risk. For example, sleep time and stress level are recorded, and the generation AI analyzes these data. The walking speed data is further integrated with data on food intake and exercise volume, allowing the generation AI to evaluate the overall health risk. For example, food intake and exercise volume are recorded, and the generation AI analyzes these data. This allows the overall health risk to be evaluated.

[0042] The data accumulation unit simultaneously records walking speed data from different viewpoints and multiple devices, and the generation AI can analyze the walking speed data from multiple angles. For example, the data accumulation unit simultaneously records walking speed data from different viewpoints using multiple devices, and the generation AI analyzes the data from multiple angles. For example, a smartphone and a wearable device are used simultaneously. Furthermore, walking speed data from different viewpoints is integrated and analyzed comprehensively by the generation AI. For example, data from multiple devices is analyzed as a single data set. Furthermore, the user's walking speed is reproduced as a 3D model using multiple devices, and the generation AI analyzes the model. For example, a 3D model is created based on data from different viewpoints. This allows walking speed data from different viewpoints to be analyzed from multiple angles.

[0043] The data accumulation unit integrates the analysis results of the walking speed data with other health data, heart rate, and blood pressure, allowing the generation AI to assess the overall health risk. The data accumulation unit, for example, integrates the analysis results of the walking speed data with biometric data such as heart rate and blood pressure, allowing the generation AI to assess the overall health risk. For example, biometric data is recorded using a wearable device. The analysis results of the walking speed data are also integrated with sleep data and stress level, allowing the generation AI to assess the overall health risk. For example, sleep time and stress level are recorded, and the generation AI analyzes these data. The analysis results of the walking speed data are also integrated with data on food intake and exercise volume, allowing the generation AI to assess the overall health risk. For example, food intake and exercise volume are recorded, and the generation AI analyzes these data. This allows the overall health risk to be assessed.

[0044] The data accumulation unit records the amount of food eaten and the amount of water intake, and the generation AI can analyze the data. The data accumulation unit, for example, records the amount of food eaten and the amount of water intake, and the generation AI analyzes the data. For example, the amount of food eaten is recorded daily, and the generation AI analyzes the data. In addition, the amount of food eaten and the amount of water intake are recorded in real time, and the generation AI issues an alert if the amount is reduced compared to normal amounts. For example, the amount of food eaten is recorded using a smartphone app. Furthermore, data on the amount of food eaten and the amount of water intake is accumulated over a long period of time, and the generation AI analyzes the data to evaluate the health status. For example, the amount of food eaten daily is recorded, and the generation AI analyzes the data. This makes it possible to analyze the data on the amount of food eaten and the amount of water intake.

[0045] The data accumulation unit integrates the data on food amount and water intake with other health data, heart rate, and blood pressure, allowing the generation AI to evaluate the overall health risk. The data accumulation unit, for example, integrates the data on food amount and water intake with biometric data such as heart rate and blood pressure, allowing the generation AI to evaluate the overall health risk. For example, biometric data is recorded using a wearable device. The data on food amount and water intake is also integrated with sleep data and stress level, allowing the generation AI to evaluate the overall health risk. For example, sleep time and stress level are recorded, and the generation AI analyzes the data. The data on food amount and water intake is also integrated with exercise amount data, allowing the generation AI to evaluate the overall health risk. For example, the amount of exercise is recorded, and the generation AI analyzes the data. This allows the overall health risk to be evaluated.

[0046] The data storage unit simultaneously records data on food amount and water intake from different perspectives and multiple devices, and the generation AI can analyze the data on food amount and water intake from multiple perspectives. For example, the data storage unit simultaneously records data on food amount and water intake from different perspectives using multiple devices, and the generation AI analyzes the data from multiple perspectives. For example, a smartphone and a wearable device are used simultaneously. Furthermore, data on food amount and water intake from different perspectives is integrated and comprehensively analyzed by the generation AI. For example, data from multiple devices is analyzed as a single data set. Furthermore, the user's food amount and water intake are reproduced as a 3D model using multiple devices, and the generation AI analyzes the model. For example, a 3D model is created based on data from different perspectives. This allows data on food amount and water intake from different perspectives to be analyzed from multiple perspectives.

[0047] The data accumulation unit integrates the analysis results of the data on food amount and water intake with other health data, heart rate, and blood pressure, allowing the generation AI to evaluate the overall health risk. The data accumulation unit, for example, integrates the analysis results of the data on food amount and water intake with biological data such as heart rate and blood pressure, allowing the generation AI to evaluate the overall health risk. For example, biological data is recorded using a wearable device. Furthermore, the analysis results of the data on food amount and water intake are integrated with sleep data and stress level, allowing the generation AI to evaluate the overall health risk. For example, sleep time and stress level are recorded, and the generation AI analyzes these data. Furthermore, the analysis results of the data on food amount and water intake are integrated with data on exercise amount, allowing the generation AI to evaluate the overall health risk. For example, the amount of exercise is recorded, and the generation AI analyzes these data. This allows the overall health risk to be evaluated.

[0048] The data accumulation unit records the color and amount of excrement, and the generation AI can analyze the data. The data accumulation unit, for example, records the color and amount of excrement, and the generation AI analyzes the data. For example, the color of excrement is recorded daily, and the generation AI analyzes the data. The color and amount of excrement are also recorded in real time, and the generation AI issues an alert if there is an abnormality compared to the normal color or amount. For example, the color and amount of excrement are recorded using a smartphone app. Furthermore, data on the color and amount of excrement is accumulated over a long period of time, and the generation AI analyzes the data to evaluate the health status. For example, the color and amount of excrement are recorded daily, and the generation AI analyzes the data. This makes it possible to analyze the data on the color and amount of excrement.

[0049] The data accumulation unit integrates data on the color and amount of excrement with other health data, heart rate, and blood pressure, allowing the generation AI to assess the overall health risk. The data accumulation unit, for example, integrates data on the color and amount of excrement with biometric data such as heart rate and blood pressure, allowing the generation AI to assess the overall health risk. For example, biometric data is recorded using a wearable device. Furthermore, data on the color and amount of excrement is integrated with sleep data and stress level, allowing the generation AI to assess the overall health risk. For example, sleep time and stress level are recorded, and the generation AI analyzes the data. Furthermore, data on the color and amount of excrement is integrated with data on food intake and exercise volume, allowing the generation AI to assess the overall health risk. For example, food intake and exercise volume are recorded, and the generation AI analyzes the data. This allows the overall health risk to be assessed.

[0050] The data storage unit simultaneously records data on the color and amount of excrement from different perspectives and from multiple devices, and the generation AI can analyze the data on the color and amount of excrement from multiple angles. For example, the data storage unit simultaneously records data on the color and amount of excrement from different perspectives using multiple devices, and the generation AI analyzes the data from multiple angles. For example, a smartphone and a wearable device are used simultaneously. Furthermore, data on the color and amount of excrement from different perspectives is integrated and comprehensively analyzed by the generation AI. For example, data from multiple devices is analyzed as a single data set. Furthermore, the color and amount of the user's excrement is reproduced as a 3D model using multiple devices, and the generation AI analyzes the model. For example, a 3D model is created based on data from different perspectives. This allows data on the color and amount of excrement from different perspectives to be analyzed from multiple angles.

[0051] The data accumulation unit integrates the analysis results of the data on the color and amount of excrement with other health data, heart rate, and blood pressure, allowing the generation AI to assess the overall health risk. The data accumulation unit, for example, integrates the analysis results of the data on the color and amount of excrement with biometric data such as heart rate and blood pressure, allowing the generation AI to assess the overall health risk. For example, biometric data is recorded using a wearable device. The analysis results of the data on the color and amount of excrement are also integrated with sleep data and stress level, allowing the generation AI to assess the overall health risk. For example, sleep time and stress level are recorded, and the generation AI analyzes these data. The analysis results of the data on the color and amount of excrement are also integrated with data on food intake and exercise volume, allowing the generation AI to assess the overall health risk. For example, the amount of food intake and exercise volume are recorded, and the generation AI analyzes these data. This allows the overall health risk to be assessed.

[0052] The motion analysis unit can record video of the movements when the user is away and the sleep, and the generation AI can analyze the data. The motion analysis unit, for example, records the movements when the user is away, and the generation AI analyzes the data. For example, a camera can be used to record the movements when the user is away, and the generation AI can analyze the data. Also, video of the sleep can be recorded, and the generation AI can analyze the data. For example, a camera can be used to record the movements while sleeping, and the generation AI can analyze the data. This makes it possible to analyze data of the movements and sleep when the user is away.

[0053] The movement analysis unit integrates the video data of movements and sleep when the user is away from home with other health data, heart rate, and blood pressure, allowing the generation AI to evaluate the overall health risk. For example, the movement analysis unit integrates the video data of movements and sleep when the user is away from home with biometric data such as heart rate and blood pressure, allowing the generation AI to evaluate the overall health risk. For example, biometric data is recorded using a wearable device. Furthermore, the video data of movements and sleep when the user is away from home is integrated with sleep data and stress level, allowing the generation AI to evaluate the overall health risk. For example, sleep time and stress level are recorded, and the generation AI analyzes the data. Furthermore, the video data of movements and sleep when the user is away from home is integrated with data on food intake and exercise volume, allowing the generation AI to evaluate the overall health risk. For example, food intake and exercise volume are recorded, and the generation AI analyzes the data. This allows the overall health risk to be evaluated.

[0054] The motion analysis unit simultaneously records video data of the user's movements and sleep during absence from different viewpoints and from multiple cameras, and the generation AI can analyze the video data of the user's movements and sleep during absence from multiple angles. The motion analysis unit, for example, uses multiple cameras to simultaneously record video data of the user's movements and sleep during absence from different viewpoints, and the generation AI analyzes the video data from multiple angles. For example, indoor and outdoor cameras are used simultaneously. Furthermore, video data from different viewpoints is integrated and comprehensively analyzed by the generation AI. For example, the video from multiple cameras is analyzed as a single data set. Furthermore, the user's movements and sleep are reproduced as a 3D model using multiple cameras, and the generation AI analyzes the model. For example, a 3D model is created based on video from different viewpoints. This allows the video data of the user's movements and sleep during absence from different viewpoints to be analyzed from multiple angles.

[0055] The movement analysis unit integrates the analysis results of the video data of movements and sleep when the user is away from home with other health data, heart rate, and blood pressure, allowing the generation AI to assess the overall health risk. For example, the movement analysis unit integrates the analysis results of the video data of movements and sleep when the user is away from home with biometric data such as heart rate and blood pressure, allowing the generation AI to assess the overall health risk. For example, biometric data is recorded using a wearable device. Furthermore, the analysis results of the video data of movements and sleep when the user is away from home are integrated with sleep data and stress level, allowing the generation AI to assess the overall health risk. For example, sleep time and stress level are recorded, and the generation AI analyzes these data. Furthermore, the analysis results of the video data of movements and sleep when the user is away from home are integrated with data on food intake and exercise volume, allowing the generation AI to assess the overall health risk. For example, food intake and exercise volume are recorded, and the generation AI analyzes these data. This allows the overall health risk to be assessed.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The health management system may further include a voice assistant unit. The voice assistant unit allows the user to input health data by voice. For example, the user inputs the contents of their meals and the type of exercise they do by voice, and the voice assistant unit converts the data into text and sends it to the data storage unit. The voice assistant unit may also provide health advice to the user by voice. For example, if the user asks, "How was your exercise today?" the voice assistant unit may reply, "You achieved your exercise goal today." The voice assistant unit may also set reminders based on the user's health data. For example, it may issue a voice reminder such as, "Your water intake is low, so please drink another glass of water."

[0058] The health management system may further include a biometric authentication unit. The biometric authentication unit may use a user's fingerprint or face authentication to restrict access to the system. For example, fingerprint authentication may be performed when a user logs into the system, and data may only be accessed if authentication is successful. The biometric authentication unit may also encrypt data to protect the user's health data. For example, the user's health data may be encrypted to prevent third parties from accessing the data. The biometric authentication unit may also periodically back up the user's health data. For example, data may be automatically backed up every week to prevent data loss.

[0059] The health management system may further include an environmental data acquisition unit. The environmental data acquisition unit can acquire environmental data around the user and analyze factors that affect the user's health status. For example, data such as temperature, humidity, and air quality is acquired, and the generation AI analyzes this data. The environmental data acquisition unit can also make suggestions for improving the user's living environment. For example, it may suggest using a humidifier if the humidity indoors is low. Furthermore, the environmental data acquisition unit can acquire environmental data when the user goes out and evaluate health risks. For example, it may recommend wearing a mask if the air quality outside the home is poor.

[0060] The health management system may further include a social support unit. The social support unit enables a user to share health information with other users and receive support. For example, a user may share his or her health data with family and friends and receive encouraging messages. The social support unit may also support a user to participate in a health community. For example, the social support unit may provide a forum for users with the same health goals to exchange information. The social support unit may also enable a user to receive advice from experts. For example, a nutritionist or trainer may provide advice based on the user's health data.

[0061] The health management system may further include a gamification section. The gamification section may incorporate game elements to help users continue managing their health in a fun way. For example, users may earn points each time they achieve their exercise or dietary goals and use the points to purchase virtual items. The gamification section may also provide a function for users to compete with each other. For example, users may compete against their friends in terms of the amount of exercise they can do, and a ranking may be displayed. Furthermore, the gamification section may enable users to participate in quizzes and challenges related to health management. For example, users may earn points by answering quizzes related to nutrition.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The data storage unit stores data on food intake, exercise, and excretion. For example, it records the amount of food and water intake, and the amount of exercise as steps taken and calories burned. It can also record the color and amount of excrement. Step 2: The health status check unit checks the health status using the data accumulated by the data accumulation unit. For example, the generation AI analyzes data on food intake, exercise, and excretion, and issues an alert if there are any health concerns. Step 3: The video recording unit records daily video data. For example, indoor and outdoor activities are recorded by a camera and saved as video data. Step 4: The motion analysis unit analyzes the video data recorded by the video recording unit to detect changes in movement and injuries. For example, the generative AI analyzes the video data to detect movement patterns and abnormal movements.

[0064] (Example 2) The health management system according to an embodiment of the present invention accumulates data on the amount of food eaten, the amount of exercise, and excretion, and uses a generating AI to check the health status, records daily video data, and detects changes in movement and injuries early on. This allows the health management system to efficiently manage the user's health status and detect health problems early on.

[0065] A health management system according to an embodiment includes a data storage unit, a health status check unit, a video recording unit, and a movement analysis unit. The data storage unit stores data on the amount of food eaten, the amount of exercise, and excretion. For example, it records the amount of food eaten and the amount of water consumed, and the amount of exercise as steps taken and calories burned. It can also record the color and amount of excretion. The health status check unit uses the data stored by the data storage unit to check the health status. For example, a generation AI analyzes the data on the amount of food eaten, the amount of exercise, and excretion, and issues an alert if there are any health concerns. The video recording unit records daily video data. For example, indoor and outdoor activities are recorded with a camera and saved as video data. The movement analysis unit analyzes the video data recorded by the video recording unit to detect changes in movement and injuries. For example, the generation AI analyzes the video data to detect movement patterns and abnormal movements. This allows the health management system according to an embodiment to efficiently manage the user's health status and detect health problems early. For example, the AI ​​can analyze data on food intake, exercise, and excretion, and issue an alert if there are any health concerns. It can also analyze video data to quickly detect changes in movement or injuries, allowing for prompt and appropriate responses.

[0066] The data storage unit can record changes in walking speed, food intake, water intake, and the color and amount of excrement. The data storage unit, for example, records walking speed. For example, it measures walking speed using a GPS device and stores the data. It also records food intake and water intake. For example, it records food intake every day and measures water intake. It also records changes in the color and amount of excrement. For example, it records the color of excrement every day and measures the amount. This makes it possible to record detailed health data.

[0067] The motion analysis unit can analyze footage of movements and sleep when the user is away from home. The motion analysis unit, for example, analyzes movements when the user is away from home. For example, a camera is used to record movements when the user is away from home, and the generation AI analyzes the data. It also analyzes footage of sleep. For example, a camera is used to record movements while the user is sleeping, and the generation AI analyzes the data. This makes it possible to analyze movements and sleep when the user is away from home.

[0068] The health status check section records the details of meals, the types of nutrients, and nutritional balance, and the generation AI can analyze nutritional imbalances and assess health risks. The health status check section, for example, records the details of meals. For example, nutrients such as protein, carbohydrates, lipids, vitamins, and minerals are recorded individually, and the generation AI evaluates their balance. The contents of meals can also be recorded in photographs, and the generation AI performs image analysis to automatically extract the types and amounts of nutrients. Furthermore, the contents of meals can be recorded via voice input, and the generation AI analyzes the voice data to extract nutrient information. This makes it possible to analyze nutritional imbalances and assess health risks.

[0069] The health status check unit records the type of exercise, aerobic exercise, and strength training in addition to data on the amount of exercise, and the generation AI can analyze the effects of each exercise. The health status check unit, for example, records the type of exercise in detail. For example, aerobic exercise, strength training, stretching, and other exercises are recorded separately, and the generation AI evaluates their effects. The type of exercise is also recorded as a video, and the generation AI analyzes the video to automatically extract the type and intensity of the exercise. Furthermore, the type of exercise is recorded via audio input, and the generation AI analyzes the audio data to extract exercise information. This makes it possible to analyze the type and effects of exercise.

[0070] The health status check unit uses the emotion estimation function to record the user's emotions while eating or exercising, and the generation AI can analyze the relationship between emotions and health status. The health status check unit records the user's emotions while eating or exercising, for example. For example, emotions while eating or exercising are recorded, and the generation AI analyzes this data. The emotion estimation function also analyzes the user's facial expressions and voice to automatically record emotions while eating or exercising. Furthermore, the emotion estimation function is used to accumulate user emotion data, and the generation AI analyzes the relationship between long-term changes in emotions and health status. This makes it possible to analyze the relationship between emotions and health status.

[0071] The health status check unit integrates data on food intake and exercise volume with other health data, sleep data, and stress levels, allowing the generation AI to analyze the overall health status. The health status check unit, for example, integrates data on food intake and exercise volume with sleep data, allowing the generation AI to analyze the overall health status. For example, sleep time and sleep quality are recorded, and the generation AI analyzes the data. The food intake and exercise volume data is also integrated with stress levels, allowing the generation AI to analyze the overall health status. For example, data is recorded using a device that measures stress levels, and the generation AI analyzes the data. Furthermore, the food intake and exercise volume data is integrated with biometric data such as heart rate and blood pressure, allowing the generation AI to analyze the overall health status. For example, biometric data is recorded using a wearable device, and the generation AI analyzes the data. This allows the overall health status to be analyzed.

[0072] The data accumulation unit allows the user to easily record data using voice input, and the generation AI can analyze the voice data. The data accumulation unit, for example, uses voice input to build a system that allows the user to easily record data on the amount of food eaten and the amount of exercise. For example, voice recognition technology is used to convert the user's voice into text data. Furthermore, using voice input, the user records the details of their meals and exercise by voice, and the generation AI analyzes the voice data. For example, the user inputs the details of their meals by voice, and the generation AI analyzes the voice data. Furthermore, using voice input, the user records the color and amount of excrement by voice, and the generation AI analyzes the voice data. For example, the user inputs information about excrement by voice, and the generation AI analyzes the voice data. This makes it easy to record data using voice input.

[0073] The health status check unit can use the emotion estimation function to analyze the user's emotions in real time when eating or exercising, and make suggestions to elicit positive emotions. The health status check unit can, for example, use the emotion estimation function to analyze the user's emotions in real time when eating or exercising, and make suggestions to elicit positive emotions. For example, it can display an encouraging message based on the user's emotion data. It can also use the emotion estimation function to analyze the user's emotions and suggest music or videos to elicit positive emotions. For example, it can play relaxing music that matches the user's emotions. It can also use the emotion estimation function to provide advice on diet and exercise based on the user's emotion data. For example, it can suggest a meal menu or exercise plan that matches the user's emotions. This makes it possible to make suggestions to elicit positive emotions.

[0074] The video recording unit records the audio data in addition to the video data, and the generation AI can analyze both the audio and the video to detect the abnormality. The video recording unit, for example, records audio data in addition to the video data, and the generation AI analyzes both the audio and the video to detect abnormalities. For example, the user's movements and audio are recorded simultaneously, and the generation AI analyzes these data. The audio data is also analyzed to detect changes in the tone and rhythm of the user's voice. For example, if the user's voice is different from usual, the generation AI detects an abnormality. Furthermore, the video data and audio data are integrated, and the generation AI comprehensively detects abnormalities. For example, the user's movements and changes in voice are analyzed simultaneously to detect abnormalities. This makes it possible to detect abnormalities by analyzing both audio and video.

[0075] The motion analysis unit analyzes the video data in detail, analyzing the movement patterns, walking rhythm, and posture, allowing the generation AI to detect the subtle changes. The motion analysis unit, for example, analyzes the video data and analyzes the user's movement patterns in detail. For example, it records changes in walking rhythm and posture, and the generation AI analyzes the data. To analyze the movement patterns, the video data is divided into frames, and the generation AI analyzes the movement of each frame. For example, it analyzes changes in walking rhythm and posture for each frame. Furthermore, it analyzes the video data and reproduces the user's movement patterns as a 3D model. For example, it displays the user's movements as a 3D model, and the generation AI analyzes the model. This allows the movement patterns to be analyzed in detail and subtle changes to be detected.

[0076] The motion analysis unit uses the emotion estimation function to analyze the user's emotions from video data and evaluate the impact of emotional changes on the health condition. The motion analysis unit, for example, analyzes the user's emotions from video data, and the generation AI evaluates the impact of emotional changes on the health condition. For example, it analyzes the user's facial expressions and movements to detect changes in emotions. The emotion estimation function also analyzes the user's emotions from video data in real time. For example, it uses a camera to analyze the user's facial expressions to detect changes in emotions. Furthermore, it analyzes the video data to evaluate the relationship between changes in the user's emotions and the health condition over the long term. For example, daily video data is recorded, and the generation AI analyzes that data. This makes it possible to evaluate the impact of emotional changes on the health condition.

[0077] The video recording unit simultaneously records video data from different viewpoints and multiple cameras, and the generation AI can analyze the video data from multiple angles. The video recording unit, for example, uses multiple cameras to simultaneously record video data from different viewpoints, and the generation AI analyzes the video data from multiple angles. For example, indoor and outdoor cameras are used simultaneously. Furthermore, video data from different viewpoints is integrated and analyzed comprehensively by the generation AI. For example, the video from multiple cameras is analyzed as a single data set. Furthermore, the user's movements are reproduced as a 3D model using multiple cameras, and the generation AI analyzes the model. For example, a 3D model is created based on video from different viewpoints. This allows for multifaceted analysis of video data from different viewpoints.

[0078] The motion analysis unit integrates the analysis results of the video data with other health data, heart rate, and blood pressure, allowing the generation AI to assess the overall health risk. The motion analysis unit, for example, integrates the analysis results of the video data with biometric data such as heart rate and blood pressure, allowing the generation AI to assess the overall health risk. For example, biometric data is recorded using a wearable device. The analysis results of the video data are also integrated with sleep data and stress levels, allowing the generation AI to assess the overall health risk. For example, sleep time and stress levels are recorded, and the generation AI analyzes these data. The analysis results of the video data are further integrated with data on food intake and exercise volume, allowing the generation AI to assess the overall health risk. For example, food intake and exercise volume are recorded, and the generation AI analyzes these data. This allows the overall health risk to be assessed.

[0079] The motion analysis unit can use the emotion estimation function to analyze the user's emotions from video data in real time and predict the health risk based on changes in the emotions. The motion analysis unit, for example, uses the emotion estimation function to analyze the user's emotions from video data in real time and predict the health risk based on changes in emotions. For example, it analyzes the user's facial expressions and movements to detect changes in emotions. It also analyzes the video data and evaluates the correlation between changes in the user's emotions and health risks. For example, it records video data over a long period of time and the generation AI analyzes the data. Furthermore, it uses the emotion estimation function to build a system that predicts health risks based on the user's emotion data. For example, it evaluates health risks based on the user's emotion scores. This makes it possible to predict health risks based on changes in emotions.

[0080] The data accumulation unit can record the walking speed, and the generation AI can analyze the data. For example, the data accumulation unit records the walking speed, and the generation AI analyzes the data. For example, the walking speed is measured using a GPS device, and the generation AI analyzes the data. In addition, the walking speed is recorded in real time, and the generation AI issues an alert if it is slower than the normal speed. For example, the speed is recorded using a smartphone app. Furthermore, walking speed data is accumulated over a long period of time, and the generation AI analyzes the data to evaluate the health status. For example, the daily walking speed is recorded, and the generation AI analyzes the data. In this way, the walking speed data can be analyzed.

[0081] The data accumulation unit integrates the walking speed data with other health data, heart rate, and blood pressure, allowing the generation AI to evaluate the overall health risk. The data accumulation unit, for example, integrates the walking speed data with biometric data such as heart rate and blood pressure, allowing the generation AI to evaluate the overall health risk. For example, biometric data is recorded using a wearable device. The walking speed data is also integrated with sleep data and stress level, allowing the generation AI to evaluate the overall health risk. For example, sleep time and stress level are recorded, and the generation AI analyzes these data. The walking speed data is further integrated with data on food intake and exercise volume, allowing the generation AI to evaluate the overall health risk. For example, food intake and exercise volume are recorded, and the generation AI analyzes these data. This allows the overall health risk to be evaluated.

[0082] The data accumulation unit uses an emotion estimation function to record the user's emotions during a walk, and the generation AI can analyze the association between the emotions and the health condition. The data accumulation unit, for example, records the user's emotions during a walk, and the generation AI analyzes the association between the emotions and the health condition. For example, emotions during a walk are recorded, and the generation AI analyzes the data. In addition, the emotion estimation function is used to analyze the user's facial expressions and voice and automatically record emotions during a walk. For example, a camera or microphone is used to analyze the user's emotions in real time. Furthermore, the emotion estimation function is used to accumulate the user's emotion data, and the generation AI analyzes the association between long-term changes in emotions and the health condition. For example, daily emotion data is recorded, and the generation AI analyzes the data. This makes it possible to analyze the association between emotions and the health condition.

[0083] The data accumulation unit simultaneously records walking speed data from different viewpoints and multiple devices, and the generation AI can analyze the walking speed data from multiple angles. For example, the data accumulation unit simultaneously records walking speed data from different viewpoints using multiple devices, and the generation AI analyzes the data from multiple angles. For example, a smartphone and a wearable device are used simultaneously. Furthermore, walking speed data from different viewpoints is integrated and analyzed comprehensively by the generation AI. For example, data from multiple devices is analyzed as a single data set. Furthermore, the user's walking speed is reproduced as a 3D model using multiple devices, and the generation AI analyzes the model. For example, a 3D model is created based on data from different viewpoints. This allows walking speed data from different viewpoints to be analyzed from multiple angles.

[0084] The data accumulation unit integrates the analysis results of the walking speed data with other health data, heart rate, and blood pressure, allowing the generation AI to assess the overall health risk. The data accumulation unit, for example, integrates the analysis results of the walking speed data with biometric data such as heart rate and blood pressure, allowing the generation AI to assess the overall health risk. For example, biometric data is recorded using a wearable device. The analysis results of the walking speed data are also integrated with sleep data and stress level, allowing the generation AI to assess the overall health risk. For example, sleep time and stress level are recorded, and the generation AI analyzes these data. The analysis results of the walking speed data are also integrated with data on food intake and exercise volume, allowing the generation AI to assess the overall health risk. For example, food intake and exercise volume are recorded, and the generation AI analyzes these data. This allows the overall health risk to be assessed.

[0085] The data accumulation unit can use the emotion estimation function to analyze the user's emotions during a walk in real time and predict the health risk based on changes in the emotions. The data accumulation unit, for example, uses the emotion estimation function to analyze the user's emotions during a walk in real time and predict the health risk based on changes in emotions. For example, it analyzes the user's facial expressions and movements to detect changes in emotions. It also analyzes emotional data during a walk, and the generation AI evaluates the correlation between emotional changes and health risks. For example, it records emotional data over a long period of time, and the generation AI analyzes the data. It also uses the emotion estimation function to build a system that predicts health risks based on the user's emotional data. For example, it evaluates health risks based on the user's emotion scores. This makes it possible to predict health risks based on changes in emotions.

[0086] The data accumulation unit records the amount of food eaten and the amount of water intake, and the generation AI can analyze the data. The data accumulation unit, for example, records the amount of food eaten and the amount of water intake, and the generation AI analyzes the data. For example, the amount of food eaten is recorded daily, and the generation AI analyzes the data. In addition, the amount of food eaten and the amount of water intake are recorded in real time, and the generation AI issues an alert if the amount is reduced compared to normal amounts. For example, the amount of food eaten is recorded using a smartphone app. Furthermore, data on the amount of food eaten and the amount of water intake is accumulated over a long period of time, and the generation AI analyzes the data to evaluate the health status. For example, the amount of food eaten daily is recorded, and the generation AI analyzes the data. This makes it possible to analyze the data on the amount of food eaten and the amount of water intake.

[0087] The data accumulation unit integrates the data on food amount and water intake with other health data, heart rate, and blood pressure, allowing the generation AI to evaluate the overall health risk. The data accumulation unit, for example, integrates the data on food amount and water intake with biometric data such as heart rate and blood pressure, allowing the generation AI to evaluate the overall health risk. For example, biometric data is recorded using a wearable device. The data on food amount and water intake is also integrated with sleep data and stress level, allowing the generation AI to evaluate the overall health risk. For example, sleep time and stress level are recorded, and the generation AI analyzes the data. The data on food amount and water intake is also integrated with exercise amount data, allowing the generation AI to evaluate the overall health risk. For example, the amount of exercise is recorded, and the generation AI analyzes the data. This allows the overall health risk to be evaluated.

[0088] The data accumulation unit uses an emotion estimation function to record the user's emotions while eating, and the generation AI can analyze the association between the emotions and the health condition. The data accumulation unit, for example, records the user's emotions while eating, and the generation AI analyzes the association between the emotions and the health condition. For example, emotions while eating are recorded, and the generation AI analyzes the data. Furthermore, the emotion estimation function is used to analyze the user's facial expressions and voice to automatically record emotions while eating. For example, the user's emotions are analyzed in real time using a camera or microphone. Furthermore, the emotion estimation function is used to accumulate the user's emotion data, and the generation AI analyzes the association between long-term changes in emotions and the health condition. For example, daily emotion data is recorded, and the generation AI analyzes the data. This makes it possible to analyze the association between emotions and the health condition.

[0089] The data storage unit simultaneously records data on food amount and water intake from different perspectives and multiple devices, and the generation AI can analyze the data on food amount and water intake from multiple perspectives. For example, the data storage unit simultaneously records data on food amount and water intake from different perspectives using multiple devices, and the generation AI analyzes the data from multiple perspectives. For example, a smartphone and a wearable device are used simultaneously. Furthermore, data on food amount and water intake from different perspectives is integrated and comprehensively analyzed by the generation AI. For example, data from multiple devices is analyzed as a single data set. Furthermore, the user's food amount and water intake are reproduced as a 3D model using multiple devices, and the generation AI analyzes the model. For example, a 3D model is created based on data from different perspectives. This allows data on food amount and water intake from different perspectives to be analyzed from multiple perspectives.

[0090] The data accumulation unit integrates the analysis results of the data on food amount and water intake with other health data, heart rate, and blood pressure, allowing the generation AI to evaluate the overall health risk. The data accumulation unit, for example, integrates the analysis results of the data on food amount and water intake with biological data such as heart rate and blood pressure, allowing the generation AI to evaluate the overall health risk. For example, biological data is recorded using a wearable device. Furthermore, the analysis results of the data on food amount and water intake are integrated with sleep data and stress level, allowing the generation AI to evaluate the overall health risk. For example, sleep time and stress level are recorded, and the generation AI analyzes these data. Furthermore, the analysis results of the data on food amount and water intake are integrated with data on exercise amount, allowing the generation AI to evaluate the overall health risk. For example, the amount of exercise is recorded, and the generation AI analyzes these data. This allows the overall health risk to be evaluated.

[0091] The data accumulation unit can use the emotion estimation function to analyze the user's emotions while eating in real time and predict the health risk based on the changes in the emotions. The data accumulation unit, for example, uses the emotion estimation function to analyze the user's emotions while eating in real time and predict the health risk based on the changes in emotions. For example, it analyzes the user's facial expressions and movements to detect changes in emotions. Furthermore, it analyzes the emotion data while eating, and the generation AI evaluates the correlation between the changes in emotions and the health risk. For example, it records emotion data over a long period of time, and the generation AI analyzes the data. Furthermore, it uses the emotion estimation function to build a system that predicts health risk based on the user's emotion data. For example, it evaluates health risk based on the user's emotion score. This makes it possible to predict health risk based on changes in emotions.

[0092] The data accumulation unit records the color and amount of excrement, and the generation AI can analyze the data. The data accumulation unit, for example, records the color and amount of excrement, and the generation AI analyzes the data. For example, the color of excrement is recorded daily, and the generation AI analyzes the data. The color and amount of excrement are also recorded in real time, and the generation AI issues an alert if there is an abnormality compared to the normal color or amount. For example, the color and amount of excrement are recorded using a smartphone app. Furthermore, data on the color and amount of excrement is accumulated over a long period of time, and the generation AI analyzes the data to evaluate the health status. For example, the color and amount of excrement are recorded daily, and the generation AI analyzes the data. This makes it possible to analyze the data on the color and amount of excrement.

[0093] The data accumulation unit integrates data on the color and amount of excrement with other health data, heart rate, and blood pressure, allowing the generation AI to assess the overall health risk. The data accumulation unit, for example, integrates data on the color and amount of excrement with biometric data such as heart rate and blood pressure, allowing the generation AI to assess the overall health risk. For example, biometric data is recorded using a wearable device. Furthermore, data on the color and amount of excrement is integrated with sleep data and stress level, allowing the generation AI to assess the overall health risk. For example, sleep time and stress level are recorded, and the generation AI analyzes the data. Furthermore, data on the color and amount of excrement is integrated with data on food intake and exercise volume, allowing the generation AI to assess the overall health risk. For example, food intake and exercise volume are recorded, and the generation AI analyzes the data. This allows the overall health risk to be assessed.

[0094] The data accumulation unit uses an emotion estimation function to record the user's emotions when excreting, and the generation AI can analyze the relationship between the emotions and the health condition. The data accumulation unit, for example, records the user's emotions when excreting, and the generation AI analyzes the relationship between the emotions and the health condition. For example, emotions during excretion are recorded, and the generation AI analyzes the data. In addition, the emotion estimation function is used to analyze the user's facial expressions and voice, and automatically record emotions when excreting. For example, a camera or microphone is used to analyze the user's emotions in real time. Furthermore, the emotion estimation function is used to accumulate the user's emotion data, and the generation AI analyzes the relationship between long-term changes in emotions and the health condition. For example, daily emotion data is recorded, and the generation AI analyzes the data. This makes it possible to analyze the relationship between emotions and the health condition.

[0095] The data storage unit simultaneously records data on the color and amount of excrement from different perspectives and from multiple devices, and the generation AI can analyze the data on the color and amount of excrement from multiple angles. For example, the data storage unit simultaneously records data on the color and amount of excrement from different perspectives using multiple devices, and the generation AI analyzes the data from multiple angles. For example, a smartphone and a wearable device are used simultaneously. Furthermore, data on the color and amount of excrement from different perspectives is integrated and comprehensively analyzed by the generation AI. For example, data from multiple devices is analyzed as a single data set. Furthermore, the color and amount of the user's excrement is reproduced as a 3D model using multiple devices, and the generation AI analyzes the model. For example, a 3D model is created based on data from different perspectives. This allows data on the color and amount of excrement from different perspectives to be analyzed from multiple angles.

[0096] The data accumulation unit integrates the analysis results of the data on the color and amount of excrement with other health data, heart rate, and blood pressure, allowing the generation AI to assess the overall health risk. The data accumulation unit, for example, integrates the analysis results of the data on the color and amount of excrement with biometric data such as heart rate and blood pressure, allowing the generation AI to assess the overall health risk. For example, biometric data is recorded using a wearable device. The analysis results of the data on the color and amount of excrement are also integrated with sleep data and stress level, allowing the generation AI to assess the overall health risk. For example, sleep time and stress level are recorded, and the generation AI analyzes these data. The analysis results of the data on the color and amount of excrement are also integrated with data on food intake and exercise volume, allowing the generation AI to assess the overall health risk. For example, the amount of food intake and exercise volume are recorded, and the generation AI analyzes these data. This allows the overall health risk to be assessed.

[0097] The data accumulation unit can use the emotion estimation function to analyze the user's emotions during excretion in real time and predict the health risk based on the change in emotion. The data accumulation unit, for example, uses the emotion estimation function to analyze the user's emotions during excretion in real time and predict the health risk based on the change in emotion. For example, the data accumulation unit analyzes the user's facial expressions and movements to detect changes in emotion. Furthermore, the emotion data during excretion is analyzed, and the generation AI evaluates the correlation between the change in emotion and the health risk. For example, emotion data over a long period of time is recorded, and the generation AI analyzes the data. Furthermore, the emotion estimation function is used to build a system that predicts health risk based on the user's emotion data. For example, health risk is evaluated based on the user's emotion score. This makes it possible to predict health risk based on changes in emotion.

[0098] The motion analysis unit can record video of the movements when the user is away and the sleep, and the generation AI can analyze the data. The motion analysis unit, for example, records the movements when the user is away, and the generation AI analyzes the data. For example, a camera can be used to record the movements when the user is away, and the generation AI can analyze the data. Also, video of the sleep can be recorded, and the generation AI can analyze the data. For example, a camera can be used to record the movements while sleeping, and the generation AI can analyze the data. This makes it possible to analyze data of the movements and sleep when the user is away.

[0099] The movement analysis unit integrates the video data of movements and sleep when the user is away from home with other health data, heart rate, and blood pressure, allowing the generation AI to evaluate the overall health risk. For example, the movement analysis unit integrates the video data of movements and sleep when the user is away from home with biometric data such as heart rate and blood pressure, allowing the generation AI to evaluate the overall health risk. For example, biometric data is recorded using a wearable device. Furthermore, the video data of movements and sleep when the user is away from home is integrated with sleep data and stress level, allowing the generation AI to evaluate the overall health risk. For example, sleep time and stress level are recorded, and the generation AI analyzes the data. Furthermore, the video data of movements and sleep when the user is away from home is integrated with data on food intake and exercise volume, allowing the generation AI to evaluate the overall health risk. For example, food intake and exercise volume are recorded, and the generation AI analyzes the data. This allows the overall health risk to be evaluated.

[0100] The movement analysis unit uses an emotion estimation function to record the user's movements when the user is away from home and the user's emotions while sleeping, and the generation AI can analyze the relationship between the emotions and the health condition. The movement analysis unit, for example, records the user's movements when the user is away from home and the user's emotions while sleeping, and the generation AI can analyze the relationship between the emotions and the health condition. For example, the user's emotions while the user is away are recorded, and the generation AI analyzes the data. Furthermore, the emotion estimation function is used to analyze the user's facial expressions and voice, and automatically record the user's movements when the user is away from home and the user's emotions while sleeping. For example, a camera or microphone is used to analyze the user's emotions in real time. Furthermore, the emotion estimation function is used to accumulate the user's emotion data, and the generation AI can analyze the relationship between long-term changes in emotion and the health condition. For example, daily emotion data is recorded, and the generation AI analyzes the data. This makes it possible to analyze the relationship between emotion and the health condition.

[0101] The motion analysis unit simultaneously records video data of the user's movements and sleep during absence from different viewpoints and from multiple cameras, and the generation AI can analyze the video data of the user's movements and sleep during absence from multiple angles. The motion analysis unit, for example, uses multiple cameras to simultaneously record video data of the user's movements and sleep during absence from different viewpoints, and the generation AI analyzes the video data from multiple angles. For example, indoor and outdoor cameras are used simultaneously. Furthermore, video data from different viewpoints is integrated and comprehensively analyzed by the generation AI. For example, the video from multiple cameras is analyzed as a single data set. Furthermore, the user's movements and sleep are reproduced as a 3D model using multiple cameras, and the generation AI analyzes the model. For example, a 3D model is created based on video from different viewpoints. This allows the video data of the user's movements and sleep during absence from different viewpoints to be analyzed from multiple angles.

[0102] The movement analysis unit integrates the analysis results of the video data of movements and sleep when the user is away from home with other health data, heart rate, and blood pressure, allowing the generation AI to assess the overall health risk. For example, the movement analysis unit integrates the analysis results of the video data of movements and sleep when the user is away from home with biometric data such as heart rate and blood pressure, allowing the generation AI to assess the overall health risk. For example, biometric data is recorded using a wearable device. Furthermore, the analysis results of the video data of movements and sleep when the user is away from home are integrated with sleep data and stress level, allowing the generation AI to assess the overall health risk. For example, sleep time and stress level are recorded, and the generation AI analyzes these data. Furthermore, the analysis results of the video data of movements and sleep when the user is away from home are integrated with data on food intake and exercise volume, allowing the generation AI to assess the overall health risk. For example, food intake and exercise volume are recorded, and the generation AI analyzes these data. This allows the overall health risk to be assessed.

[0103] The movement analysis unit can use the emotion estimation function to analyze the user's movements when the user is away from home or the user's emotions while sleeping in real time, and predict the health risk based on changes in the emotions. The movement analysis unit can, for example, use the emotion estimation function to analyze the user's movements when the user is away from home or the user's emotions while sleeping in real time, and predict the health risk based on changes in the emotions. For example, it analyzes the user's facial expressions and movements to detect changes in emotions. Furthermore, it analyzes emotion data when the user is away from home, and the generation AI evaluates the correlation between changes in emotions and health risks. For example, it records emotion data over a long period of time, and the generation AI analyzes the data. Furthermore, it uses the emotion estimation function to build a system that predicts health risks based on the user's emotion data. For example, it evaluates health risks based on the user's emotion scores. This makes it possible to predict health risks based on changes in emotions.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The health management system may further include a voice assistant unit. The voice assistant unit allows the user to input health data by voice. For example, the user inputs the contents of their meals and the type of exercise they do by voice, and the voice assistant unit converts the data into text and sends it to the data storage unit. The voice assistant unit may also provide health advice to the user by voice. For example, if the user asks, "How was your exercise today?" the voice assistant unit may reply, "You achieved your exercise goal today." The voice assistant unit may also set reminders based on the user's health data. For example, it may issue a voice reminder such as, "Your water intake is low, so please drink another glass of water."

[0106] The health management system may further include a biometric authentication unit. The biometric authentication unit may use a user's fingerprint or face authentication to restrict access to the system. For example, fingerprint authentication may be performed when a user logs into the system, and data may only be accessed if authentication is successful. The biometric authentication unit may also encrypt data to protect the user's health data. For example, the user's health data may be encrypted to prevent third parties from accessing the data. The biometric authentication unit may also periodically back up the user's health data. For example, data may be automatically backed up every week to prevent data loss.

[0107] The health management system may further include an environmental data acquisition unit. The environmental data acquisition unit can acquire environmental data around the user and analyze factors that affect the user's health status. For example, data such as temperature, humidity, and air quality is acquired, and the generation AI analyzes this data. The environmental data acquisition unit can also make suggestions for improving the user's living environment. For example, it may suggest using a humidifier if the humidity indoors is low. Furthermore, the environmental data acquisition unit can acquire environmental data when the user goes out and evaluate health risks. For example, it may recommend wearing a mask if the air quality outside the home is poor.

[0108] The health management system may further include a social support unit. The social support unit enables a user to share health information with other users and receive support. For example, a user may share his or her health data with family and friends and receive encouraging messages. The social support unit may also support a user to participate in a health community. For example, the social support unit may provide a forum for users with the same health goals to exchange information. The social support unit may also enable a user to receive advice from experts. For example, a nutritionist or trainer may provide advice based on the user's health data.

[0109] The health management system may further include a gamification section. The gamification section may incorporate game elements to help users continue managing their health in a fun way. For example, users may earn points each time they achieve their exercise or dietary goals and use the points to purchase virtual items. The gamification section may also provide a function for users to compete with each other. For example, users may compete against their friends in terms of the amount of exercise they can do, and a ranking may be displayed. Furthermore, the gamification section may enable users to participate in quizzes and challenges related to health management. For example, users may earn points by answering quizzes related to nutrition.

[0110] The health management system can further use the emotion estimation function to analyze the user's stress level in real time and make suggestions for stress reduction. For example, it can analyze the user's facial expressions and voice and suggest relaxing music if the stress level is high. The emotion estimation function can also be used to monitor the user's stress level over the long term and identify the cause of stress. For example, if the stress level rises at a specific time of day or in a specific situation, the cause can be analyzed. Furthermore, the emotion estimation function can be used to provide exercise and relaxation advice based on the user's stress level. For example, if stress is high, yoga or deep breathing exercises can be suggested.

[0111] The health management system can further use the emotion estimation function to suggest meals based on the user's emotions. For example, if the user is feeling stressed, it can suggest recipes using ingredients that have a relaxing effect. The emotion estimation function can also be used to suggest meal timings based on the user's emotions. For example, if the user is tired, it can suggest the best time to have a snack. The emotion estimation function can also be used to adjust the amount of food eaten based on the user's emotions. For example, if the user is feeling full, it can suggest reducing the amount of food eaten.

[0112] The health management system can further use the emotion estimation function to suggest an exercise plan based on the user's emotions. For example, if the user is feeling stressed, it can suggest yoga or stretching, which have a relaxing effect. The emotion estimation function can also be used to adjust the intensity of exercise according to the user's emotions. For example, if the user is tired, it can suggest light exercise. The emotion estimation function can also be used to suggest the timing of exercise based on the user's emotions. For example, it can suggest high-intensity exercise when the user is feeling energetic.

[0113] The health management system can further use the emotion estimation function to make suggestions for improving sleep based on the user's emotions. For example, if the user is feeling anxious, the system can play relaxing music. The emotion estimation function can also be used to suggest adjustments to the sleep environment based on the user's emotions. For example, if the user is feeling stressed, the system can suggest dimming the lights in the room. The emotion estimation function can also be used to suggest sleep timing based on the user's emotions. For example, if the user is tired, the system can suggest going to bed earlier.

[0114] The health management system can further use the emotion estimation function to suggest relaxation methods based on the user's emotions. For example, if the user is tense, deep breathing or meditation can be suggested. The emotion estimation function can also be used to suggest relaxation locations based on the user's emotions. For example, if the user is feeling stressed, a walk in nature can be suggested. The emotion estimation function can also be used to suggest relaxation times based on the user's emotions. For example, if the user is tired, a short nap can be suggested.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The data storage unit stores data on food intake, exercise, and excretion. For example, it records the amount of food and water intake, and the amount of exercise as steps taken and calories burned. It can also record the color and amount of excrement. Step 2: The health status check unit checks the health status using the data accumulated by the data accumulation unit. For example, the generation AI analyzes data on food intake, exercise, and excretion, and issues an alert if there are any health concerns. Step 3: The video recording unit records daily video data. For example, indoor and outdoor activities are recorded by a camera and saved as video data. Step 4: The motion analysis unit analyzes the video data recorded by the video recording unit to detect changes in movement and injuries. For example, the generative AI analyzes the video data to detect movement patterns and abnormal movements.

[0117] 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.

[0118] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] 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.

[0120] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0121] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0134] 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.

[0135] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

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

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

[0138] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0141] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0142] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0145] In the headset type terminal 314, the 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. 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 specific processing unit 290 using these models.

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

[0147] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0149] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0150] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

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

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

[0161] In the robot 414, 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 robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0163] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0164] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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. [Explanation of symbols]

[0184] 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 data storage unit that stores data on food intake, exercise, and excretion; a health condition check unit that checks a health condition using the data accumulated by the data accumulation unit; a video recording unit that records daily video data; a motion analysis unit that analyzes the video data recorded by the video recording unit to detect changes in motion and injuries. A system characterized by:

2. The data storage unit Record walking speed, food and water intake, and changes in the color and amount of feces 2. The system of claim 1.

3. The motion analysis unit Analyzing footage of movement and sleep while you're away 2. The system of claim 1.

4. The health condition check unit The contents of meals, types of nutrients, and nutritional balance are recorded in detail, and the AI ​​analyzes the imbalance in the nutritional balance to assess health risks.

2. The system of claim 1.

5. The health condition check unit In addition to the data on the amount of exercise, the type of exercise and aerobic exercise and strength training are recorded, and the generating AI analyzes the effects of each of the exercises.

2. The system of claim 1.

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