system

Abstract

The system according to this embodiment aims to support dementia prevention by providing personalized health advice based on health data. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, a monitoring unit, and a support unit. The collection unit collects health data. The analysis unit analyzes the data collected by the collection unit. The provision unit provides personalized health advice based on the analysis results obtained by the analysis unit. The monitoring unit monitors changes in cognitive function based on the advice provided by the provision unit. The support unit provides support for dementia prevention, such as presenting questions and establishing habits, based on the changes in cognitive function monitored by the monitoring unit.

Description

Technical Field

[0004] ,

[0006] , , , ,

[0005] , , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, individualized health advice based on health data and support for dementia prevention have not been sufficiently provided, and there is room for improvement.

[0005] The system according to the embodiment aims to provide individualized health advice based on health data and support dementia prevention.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, a monitoring unit, and a support unit. The collection unit collects health data. The analysis unit analyzes the data collected by the collection unit. The provision unit provides personalized health advice based on the analysis results obtained by the analysis unit. The monitoring unit monitors changes in cognitive function based on the advice provided by the provision unit. The support unit provides support for dementia prevention, such as presenting questions and establishing habits, based on the changes in cognitive function monitored by the monitoring unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide personalized health advice based on health data and support dementia prevention. [Brief explanation of the drawing]

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

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

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

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

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

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server. <​​​​​The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The mind-body protection agent according to an embodiment of the present invention is a system that utilizes generative AI and IoT to support the daily health maintenance and dementia prevention of middle-aged and elderly people, as well as families caring for elderly individuals. This mind-body protection agent collects health data from wearable devices and IoT sensors, and the generative AI analyzes this data. The generative AI predicts health status and risks and provides personalized health advice. Furthermore, if an abnormality is detected, it quickly sends an alert and coordinates with medical institutions. In addition, the generative AI monitors changes in cognitive function in real time and supports the creation of questions and habits for dementia prevention. This enables elderly people to maintain independent living and reduces the burden on families and caregivers. For example, the mind-body protection agent records vital signs such as heart rate, blood pressure, body temperature, and steps in real time from wearable devices and IoT sensors. This allows for a detailed understanding of daily health status. The generative AI analyzes the collected data and can detect abnormal fluctuations in heart rate and blood pressure, and predict the risk of heart disease and hypertension. This allows for early detection of risks and the provision of appropriate advice. The generative AI provides advice on diet and exercise based on the user's health status and lifestyle. For example, the generative AI provides quizzes and games to train memory and attention. This can help prevent cognitive decline and aid in dementia prevention. If an abnormality is detected, it quickly sends an alert and coordinates with medical institutions. For example, if an abnormality in heart rate or blood pressure is detected, it sends an alert to the user and their family and contacts a medical institution. This enables a quick response and minimizes health risks. The generative AI optimizes care plans and reduces the burden on families and caregivers. For example, it proposes an appropriate care plan according to the user's health condition and needs, and adjusts it as needed. This allows families and caregivers to provide care with peace of mind. In this way, the Mind and Body Protection Agent can utilize generative AI and IoT to support the daily health maintenance and dementia prevention of middle-aged and elderly people, support families caring for elderly people, and promote independent living.

[0029] The mind and body protection agent according to this embodiment comprises a data collection unit, an analysis unit, a data provision unit, a monitoring unit, and a support unit. The data collection unit collects health data. The data collection unit can collect health data using, for example, wearable devices or IoT sensors. The data collection unit can record vital signs such as heart rate, blood pressure, body temperature, and steps in real time. The data collection unit can measure heart rate using, for example, a wearable device and measure blood pressure using IoT sensors. The data collection unit can also measure body temperature using a body temperature sensor and measure steps using a pedometer. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can analyze the collected data in real time using, for example, generative AI. The analysis unit can detect abnormal fluctuations in heart rate and blood pressure and predict the risk of heart disease and hypertension. The analysis unit can analyze fluctuations in heart rate using, for example, generative AI and predict the risk of heart disease. The analysis unit can also analyze fluctuations in blood pressure and predict the risk of hypertension. The analysis unit can also analyze fluctuations in body temperature and predict the risk of fever. The service provider provides personalized health advice based on the analysis results obtained by the analysis provider. For example, the service provider can use generative AI to provide dietary and exercise advice based on the user's health status and lifestyle. For example, the service provider can use generative AI to analyze the user's diet and propose a healthy meal plan. The service provider can also analyze the user's exercise habits and propose an appropriate exercise program. The service provider can also analyze the user's sleep patterns and provide advice to promote quality sleep. The monitoring service provider monitors changes in cognitive function based on the advice provided by the service provider. For example, the monitoring service provider can use generative AI to monitor changes in cognitive function in real time. The monitoring service provider can monitor changes in memory and attention and detect cognitive decline early. For example, the monitoring service provider can use generative AI to analyze changes in memory and predict cognitive decline. The monitoring service provider can also analyze changes in attention and predict cognitive decline.The monitoring unit can analyze changes in problem-solving ability and predict cognitive decline. The support unit provides support for dementia prevention through questions and habit formation based on the changes in cognitive function monitored by the monitoring unit. For example, the support unit can provide quizzes and games for dementia prevention using generative AI. The support unit can provide quizzes and games to train memory and attention, thereby preventing cognitive decline. For example, the support unit can provide quizzes to train memory using generative AI. The support unit can also provide games to train attention. The support unit can also provide puzzles to train problem-solving ability. As a result, the mind-body protection agent according to this embodiment can collect, analyze, and provide advice on health data, monitor cognitive function, and support dementia prevention.

[0030] The data collection unit collects health data. The data collection unit can collect health data using, for example, wearable devices and IoT sensors. Specifically, wearable devices include smartwatches and fitness trackers. These devices can record vital signs such as heart rate, blood pressure, body temperature, and steps in real time. For example, a smartwatch can measure heart rate using an optical heart rate sensor and blood pressure using a pressure sensor. It also has a built-in body temperature sensor, allowing it to measure skin temperature and record body temperature fluctuations in real time. Furthermore, it can measure steps using an accelerometer and gyroscope to understand the user's activity level. IoT sensors include health monitoring devices installed in the home. For example, a sensor placed in a bed can measure the user's heart rate and respiratory rate during sleep to evaluate sleep quality. A sensor placed in a toilet can analyze urine components to monitor health status. This data is centrally managed by the data collection unit and transmitted to a cloud server. The data is securely stored on the cloud server and can be accessed by the analysis and provisioning units as needed. The data collection unit can adjust the frequency and accuracy of data collection, allowing for flexible responses to specific situations and conditions. For example, increasing the data collection frequency during specific time periods, such as during exercise or sleep, enables a more detailed understanding of health status. This allows the data collection unit to efficiently and effectively collect health data, improving the overall system performance.

[0031] The analysis department analyzes the data collected by the data collection department. For example, the analysis department can analyze the collected data in real time using generative AI. Generative AI has the ability to process vast amounts of data quickly and accurately, making it suitable for detecting abnormal patterns and risks. Specifically, it can detect abnormal fluctuations in heart rate and blood pressure and predict the risk of heart disease and hypertension. For example, generative AI can analyze fluctuations in heart rate and detect abnormal fluctuations that exceed the normal range. This allows for early detection of a potential increased risk of heart disease. It can also analyze fluctuations in blood pressure and predict the risk of hypertension. If blood pressure rises sharply or remains high for a long period, the generative AI will determine this to be abnormal and issue a warning to the user. Furthermore, it can analyze fluctuations in body temperature and predict the risk of fever. If body temperature rises sharply or remains high above the normal range, the generative AI will determine this to be abnormal and alert the user. The analysis department can comprehensively analyze this data and understand the user's health status in real time. In addition, it can utilize historical data and statistical information to perform long-term health risk assessments and trend analyses. For example, based on past heart rate data, it can predict risk fluctuations during specific time periods or situations and formulate future countermeasures. Furthermore, anomaly detection algorithms can detect unusual patterns or abnormal data, enabling early warnings. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term health management and anomaly detection, improving the overall reliability and safety of the system.

[0032] The service provider provides personalized health advice based on the analysis results obtained by the analysis provider. For example, the service provider can use generative AI to provide diet and exercise advice based on the user's health condition and lifestyle. Specifically, the generative AI analyzes the user's diet and proposes a nutritionally balanced and healthy meal plan. For example, it analyzes the calories and nutrients in the meals the user has consumed and suggests ingredients and recipes to supplement any deficient nutrients. It can also analyze the user's exercise habits and propose an appropriate exercise program. For example, based on the user's exercise volume and heart rate data, it suggests effective types and frequencies of exercise to prevent inactivity or excessive exercise. Furthermore, it can analyze the user's sleep patterns and provide advice to promote quality sleep. For example, it analyzes the quality and duration of sleep and suggests appropriate sleep environments and relaxation methods. The service provider provides this advice to users in an easy-to-understand manner, supporting them in achieving a healthy lifestyle. In addition, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the advice. For example, by collecting the results of the advice the user has implemented and having the generative AI evaluate its effectiveness, the service provider can provide more effective advice. Furthermore, the service provider can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only smartphone notifications but also email and voice calls. This allows the service provider to quickly and reliably provide personalized health advice to users and support their health management.

[0033] The monitoring unit monitors changes in cognitive function based on advice provided by the service provider. The monitoring unit can, for example, use generative AI to monitor changes in cognitive function in real time. Specifically, the generative AI analyzes changes in the user's memory and attention to detect cognitive decline early. For example, the generative AI analyzes the results of a memory test taken by the user and detects declines exceeding the normal range. It can also analyze changes in attention to detect signs of decreased concentration or distractibility. Furthermore, it can analyze changes in problem-solving ability to predict cognitive decline. For example, it analyzes the results of a problem-solving test taken by the user and detects declines exceeding the normal range. The monitoring unit comprehensively analyzes this data to grasp changes in the user's cognitive function in real time. Furthermore, it can utilize past data and statistical information to evaluate long-term fluctuations in cognitive function and predict future risks. For example, based on past memory data, it can predict risk fluctuations in specific time periods or situations and formulate future countermeasures. Additionally, it can use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings. This allows the monitoring unit to not only grasp the situation in real time, but also to manage cognitive function over the long term and detect anomalies, thereby improving the reliability and safety of the entire system.

[0034] The support department assists with dementia prevention by providing questions and habit-forming activities based on changes in cognitive function monitored by the monitoring department. For example, the support department can provide quizzes and games for dementia prevention using generative AI. Specifically, the generative AI generates and provides quizzes and games to train users' memory and attention. For example, as a quiz to train memory, it can present users with a series of words or numbers and ask them to reproduce them after a certain period of time. As a game to train attention, it can provide a game where users quickly tap specific objects displayed on the screen. Furthermore, as a puzzle to train problem-solving ability, it can present users with complex problems and encourage them to solve them. Through these quizzes and games, the support department can continuously train users' cognitive functions. In addition, the support department can collect user feedback and continuously improve the content of quizzes and games. For example, by collecting the results of users playing quizzes and games and having the generative AI evaluate their effectiveness, it can provide more effective content. The support department can also reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only smartphone notifications but also email and voice calls. This allows the support department to provide users with prompt and reliable support for dementia prevention and prevent cognitive decline.

[0035] The data collection unit can collect health data using wearable devices and IoT sensors. For example, the data collection unit can measure heart rate using a wearable device and blood pressure using an IoT sensor. The data collection unit can also measure body temperature using a body temperature sensor and step count using a pedometer. For example, the data collection unit can record heart rate in real time using a smartwatch. The data collection unit can also record step count using a fitness tracker. The data collection unit can also measure indoor temperature and humidity using environmental sensors. This makes the collection of health data more efficient by using wearable devices and IoT sensors. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from a wearable device into a generating AI, which can analyze the data and evaluate the health status.

[0036] The analysis unit can analyze collected data in real time and predict health status and risks. For example, the analysis unit can analyze collected data in real time using generative AI. The analysis unit can detect abnormal fluctuations in heart rate and blood pressure and predict the risk of heart disease and hypertension. For example, the analysis unit can analyze fluctuations in heart rate using generative AI and predict the risk of heart disease. The analysis unit can also analyze fluctuations in blood pressure and predict the risk of hypertension. The analysis unit can also analyze fluctuations in body temperature and predict the risk of fever. This enables early detection of health status and risks through real-time data analysis. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected data into generative AI, which can analyze the data and predict health status and risks.

[0037] The service provider can provide dietary and exercise advice based on the user's health condition and lifestyle. For example, the service provider can use generative AI to provide dietary and exercise advice based on the user's health condition and lifestyle. For example, the service provider can use generative AI to analyze the user's diet and propose a healthy meal plan. The service provider can also analyze the user's exercise habits and propose an appropriate exercise program. The service provider can also analyze the user's sleep patterns and provide advice to promote quality sleep. This improves the user's health management through personalized advice. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's health data into generative AI, which can then analyze the data and provide personalized advice.

[0038] The monitoring unit can monitor changes in cognitive function in real time. The monitoring unit can monitor changes in cognitive function in real time, for example, using generative AI. The monitoring unit can monitor changes in memory and attention and detect cognitive decline early. The monitoring unit can analyze changes in memory using generative AI and predict cognitive decline. The monitoring unit can also analyze changes in attention and predict cognitive decline. The monitoring unit can also analyze changes in problem-solving ability and predict cognitive decline. This allows for early detection of changes in cognitive function through real-time monitoring. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input collected data into a generative AI, which can analyze the data and monitor changes in cognitive function.

[0039] The support department can provide quizzes and games for dementia prevention. For example, the support department can use generative AI to provide quizzes and games for dementia prevention. The support department can provide quizzes and games to train memory and attention, thereby preventing cognitive decline. For example, the support department can use generative AI to provide quizzes to train memory. The support department can also provide games to train attention. The support department can also provide puzzles to train problem-solving abilities. In this way, dementia prevention can be supported through quizzes and games. Some or all of the above-described processes in the support department may be performed using AI, for example, or without AI. For example, the support department can use generative AI to generate quiz and game content and provide it to users.

[0040] The service provider can quickly send alerts when an abnormality is detected and coordinate with medical institutions. The service provider can, for example, use generative AI to detect abnormalities and quickly send alerts. If abnormalities in heart rate or blood pressure are detected, the service provider can send alerts to the user or their family and contact medical institutions. The service provider can, for example, use generative AI to detect abnormal heart rate and send alerts. The service provider can also detect abnormal blood pressure and send alerts. The service provider can also detect abnormal body temperature and send alerts. This enables a rapid response when an abnormality is detected, minimizing health risks. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can build a system that uses generative AI to detect abnormalities and send alerts.

[0041] The support department can propose appropriate care plans according to the user's health condition and needs, and make adjustments as necessary. For example, the support department can use generative AI to propose appropriate care plans according to the user's health condition and needs. The support department can also adjust care plans based on the user's health condition and needs. For example, the support department can use generative AI to analyze the user's health condition and propose appropriate care plans. The support department can also adjust care plans according to the user's needs. The support department can also monitor the implementation status of care plans and make adjustments as necessary. This reduces the burden on families and caregivers by proposing and adjusting appropriate care plans. Some or all of the above processes in the support department may be performed using AI, for example, or without AI. For example, the support department can build a system that proposes and adjusts care plans using generative AI.

[0042] The data collection unit can analyze the user's past health data and select the optimal collection method. For example, the data collection unit can analyze the user's past health data using generative AI. The data collection unit can select the most effective collection method from the user's past data. The data collection unit can adjust the collection frequency based on the user's past data. The data collection unit can analyze the user's past data and concentrate collection during specific time periods. This enables efficient data collection by selecting the optimal collection method based on past data. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can be configured to analyze the user's past health data using generative AI and select the optimal collection method.

[0043] The data collection unit can filter health data based on the user's current lifestyle and activity level. For example, the data collection unit can analyze the user's current lifestyle and activity level using generative AI. If the user is exercising, the data collection unit can prioritize collecting exercise data. If the user is resting, the data collection unit can prioritize collecting relaxation data. If the user is working, the data collection unit can prioritize collecting stress levels. By filtering the data based on lifestyle and activity level, more relevant data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can build a system that analyzes the user's lifestyle and activity level using generative AI and filters the data.

[0044] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting health data. For example, the data collection unit can analyze the user's geographical location using generative AI. If the user is at high altitude, the data collection unit can prioritize the collection of altitude-related data. If the user is in an urban area, the data collection unit can prioritize the collection of data related to environmental pollution. If the user is indoors, the data collection unit can prioritize the collection of data related to the indoor environment. In this way, by considering geographical location, highly relevant data can be prioritized. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can be configured to analyze the user's geographical location using generative AI and determine the priority of the data.

[0045] The data collection unit can analyze a user's social media activity and collect relevant data when collecting health data. For example, the data collection unit can use generative AI to analyze a user's social media activity. If a user is experiencing stress on social media, the data collection unit can collect stress-related data. If a user is relaxing on social media, the data collection unit can collect relaxation-related data. If a user is active on social media, the data collection unit can collect activity-related data. This allows for the collection of relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can build a system that uses generative AI to analyze a user's social media activity and determine data priorities.

[0046] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during the analysis. For example, the analysis unit can use generative AI to evaluate the importance of health data. The analysis unit can perform detailed analysis on important health data. The analysis unit can perform simplified analysis on less important health data. The analysis unit can determine the priority of the analysis according to the importance of the health data. This enables efficient data analysis by adjusting the level of detail of the analysis based on the importance of the health data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can build a system that uses generative AI to evaluate the importance of health data and adjust the level of detail of the analysis.

[0047] The analysis unit can apply different analysis algorithms depending on the category of health data during analysis. For example, the analysis unit can classify health data categories using generative AI. For heart rate data, the analysis unit can apply an algorithm to predict the risk of heart disease. For blood pressure data, the analysis unit can apply an algorithm to predict the risk of hypertension. For step count data, the analysis unit can apply an algorithm to predict the risk of lack of exercise. By applying different analysis algorithms depending on the category of health data, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can be built to classify health data categories using generative AI and apply an appropriate analysis algorithm.

[0048] The analysis unit can determine the priority of analysis based on the timing of health data collection during the analysis process. For example, the analysis unit can use generative AI to evaluate the timing of health data collection. The analysis unit can prioritize the analysis of recently collected data. The analysis unit can analyze current data while referring to historical data. The analysis unit can determine the priority of analysis based on data collected during a specific period. This enables efficient data analysis by prioritizing analysis based on the timing of health data collection. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can build a system that uses generative AI to evaluate the timing of health data collection and determine the priority of analysis.

[0049] The analysis unit can adjust the order of analysis based on the relevance of health data during the analysis process. For example, the analysis unit can use generative AI to evaluate the relevance of health data. The analysis unit can prioritize the analysis of highly relevant data. The analysis unit can postpone the analysis of less relevant data. The analysis unit can determine the order of analysis according to the relevance of health data. This allows for prioritizing the analysis of important data by adjusting the order of analysis based on the relevance of health data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can build a system that uses generative AI to evaluate the relevance of health data and determine the order of analysis.

[0050] The service provider can adjust the level of detail in advice based on the importance of the health condition when providing advice. For example, the service provider can use generative AI to assess the importance of the health condition. For important health conditions, the service provider can provide detailed advice. For less important health conditions, the service provider can provide simplified advice. The service provider can determine the priority of advice according to the importance of the health condition. This enables efficient advice provision by adjusting the level of detail of advice based on the importance of the health condition. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can build a system that uses generative AI to assess the importance of the health condition and adjust the level of detail of the advice.

[0051] The service provider can apply different advice algorithms depending on the health condition category when providing advice. For example, the service provider can classify health condition categories using generative AI. For those at risk of heart disease, the service provider can provide advice on preventing heart disease. For those at risk of hypertension, the service provider can provide advice on preventing hypertension. For those at risk of lack of exercise, the service provider can provide advice on exercise habits. By applying different advice algorithms depending on the health condition category, more accurate advice becomes possible. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can build a system that classifies health condition categories using generative AI and applies an appropriate advice algorithm.

[0052] The service provider can prioritize advice based on changes in health status when providing advice. For example, the service provider can use generative AI to evaluate changes in health status. If health status changes rapidly, the service provider can prioritize providing advice. If health status is stable, the service provider can provide advice regularly. The service provider can prioritize advice according to changes in health status. This allows for the priority of important advice by prioritizing it based on changes in health status. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can build a system that uses generative AI to evaluate changes in health status and determine the priority of advice.

[0053] The service provider can adjust the order of advice based on the relevance of health conditions when providing advice. The service provider can, for example, use generative AI to evaluate the relevance of health conditions. The service provider can prioritize providing advice for highly relevant health conditions. The service provider can postpone providing advice for less relevant health conditions. The service provider can determine the order of advice according to the relevance of health conditions. This allows important advice to be prioritized by adjusting the order of advice based on the relevance of health conditions. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can build a system that uses generative AI to evaluate the relevance of health conditions and determine the order of advice.

[0054] The monitoring unit can record changes in cognitive function in detail during monitoring and analyze the patterns of change. For example, the monitoring unit can use generative AI to record changes in cognitive function in detail. The monitoring unit can record changes in cognitive function daily and analyze long-term patterns. The monitoring unit can record changes in cognitive function on a weekly basis and analyze short-term patterns. The monitoring unit can record changes in cognitive function on a monthly basis and analyze seasonal patterns. This allows for early detection of changes in cognitive function by recording changes in detail and analyzing the patterns. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can be built to record changes in cognitive function and analyze patterns using generative AI.

[0055] The monitoring unit can apply different monitoring methods depending on changes in cognitive function during monitoring. For example, the monitoring unit can use generative AI to evaluate changes in cognitive function. If a decline in cognitive function is observed, the monitoring unit can apply a detailed monitoring method. If cognitive function is stable, the monitoring unit can apply a simplified monitoring method. The monitoring unit can adjust the monitoring method according to changes in cognitive function. This allows for more accurate monitoring by applying different monitoring methods according to changes in cognitive function. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can be configured to evaluate changes in cognitive function using generative AI and apply an appropriate monitoring method.

[0056] The monitoring unit can perform monitoring while considering the geographical distribution of changes in cognitive function. For example, the monitoring unit can evaluate the geographical distribution of changes in cognitive function using generative AI. If the user is in a specific region, the monitoring unit can prioritize monitoring data related to that region. If the user is on the move, the monitoring unit can monitor data related to the destination region. If the user is in multiple regions, the monitoring unit can integrate and monitor data from each region. This makes it possible to perform monitoring tailored to the characteristics of each region by considering the geographical distribution of changes in cognitive function. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can be built to evaluate and monitor the geographical distribution of changes in cognitive function using generative AI.

[0057] The monitoring unit can improve the accuracy of monitoring by referring to literature related to changes in cognitive function during monitoring. The monitoring unit can, for example, use generative AI to refer to literature related to changes in cognitive function. The monitoring unit can improve monitoring methods by referring to the latest research literature. The monitoring unit can improve the accuracy of monitoring by referring to past literature on changes in cognitive function. The monitoring unit can continuously improve the accuracy of monitoring by utilizing literature databases. This allows for improved monitoring accuracy by referring to literature related to changes in cognitive function. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can build a system that uses generative AI to refer to literature and improve the accuracy of monitoring.

[0058] The support department can record changes in cognitive function in detail during support and analyze the effectiveness of the support. For example, the support department can use generative AI to record changes in cognitive function in detail. The support department can record changes in cognitive function before and after support and analyze the effectiveness. The support department can record changes in cognitive function during the support period and analyze the effectiveness. The support department can record changes in cognitive function after the support period and analyze the long-term effects. In this way, the effectiveness of the support can be evaluated by recording changes in cognitive function in detail and analyzing the effectiveness of the support. Some or all of the above processes in the support department may be performed using AI, for example, or without AI. For example, the support department can build a system that uses generative AI to record changes in cognitive function and analyze the effectiveness of the support.

[0059] The support unit can apply different support methods depending on changes in cognitive function during support. For example, the support unit can use generative AI to evaluate changes in cognitive function. If a decline in cognitive function is observed, the support unit can apply a detailed support method. If cognitive function is stable, the support unit can apply a simplified support method. The support unit can adjust the support method according to changes in cognitive function. This makes it possible to provide more effective support by applying different support methods according to changes in cognitive function. Some or all of the above processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can build a system that uses generative AI to evaluate changes in cognitive function and apply an appropriate support method.

[0060] The support unit can provide support while considering the geographical distribution of changes in cognitive function. For example, the support unit can use generative AI to evaluate the geographical distribution of changes in cognitive function. If the user is in a specific region, the support unit can provide support relevant to that region. If the user is on the move, the support unit can provide support relevant to the destination region. If the user is in multiple regions, the support unit can integrate data from each region to provide support. This makes it possible to provide support tailored to the characteristics of each region by considering the geographical distribution of changes in cognitive function. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can build a system that uses generative AI to evaluate the geographical distribution of changes in cognitive function and provide support.

[0061] The support unit can improve the accuracy of its support by referring to literature related to changes in cognitive function during the support process. For example, the support unit can use generative AI to refer to literature related to changes in cognitive function. The support unit can refer to the latest research literature to improve its support methods. The support unit can refer to past literature on changes in cognitive function to improve the accuracy of its support. The support unit can utilize literature databases to continuously improve the accuracy of its support. This allows the support unit to improve the accuracy of its support by referring to literature related to changes in cognitive function. Some or all of the above processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can build a system that uses generative AI to refer to literature and improve the accuracy of its support.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] The data collection unit can adjust the collection frequency by referring to the user's past health data when collecting user health data. For example, if past data shows a tendency for health to deteriorate during a specific time period, the collection frequency can be increased during that time period. Conversely, if past data shows that health is stable on a specific day of the week, the collection frequency can be decreased on that day. Furthermore, if past data shows that health fluctuates during a specific season, the collection frequency can be adjusted for that season. This enables efficient data collection by referring to the user's past health data.

[0064] The service provider can adjust the frequency of health advice based on the user's health status. For example, if the user's health is stable, the frequency of advice can be reduced. Conversely, if the user's health is fluctuating, the frequency can be increased. Furthermore, if the user's health rapidly deteriorates, advice can be provided quickly. In this way, by adjusting the frequency of advice according to the user's health status, advice can be provided at the appropriate time.

[0065] The support department can adjust the level of support provided based on the user's health condition. For example, if the user's health is stable, mild support can be provided. If the user's health is fluctuating, moderate support can be provided. Furthermore, if the user's health deteriorates rapidly, rapid and advanced support can be provided. In this way, appropriate support can be provided by adjusting the level of support according to the user's health condition.

[0066] The analytics department can prioritize user health data analysis based on when the data was collected. For example, it can prioritize the analysis of recently collected data. It can also analyze current data while referring to past data. Furthermore, it can prioritize analysis based on data collected during a specific period. This enables efficient data analysis by prioritizing analysis based on when the health data was collected.

[0067] The monitoring unit can adjust the level of detail of user health data based on its importance. For example, it can perform detailed monitoring of important health data, while simplifying monitoring of less important data. Furthermore, it can determine the monitoring priority based on the importance of the health data. This allows for efficient data monitoring by adjusting the level of detail based on the importance of the health data.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The data collection unit collects health data. The data collection unit can collect health data using, for example, wearable devices or IoT sensors. The data collection unit can record vital signs such as heart rate, blood pressure, body temperature, and steps in real time. For example, the data collection unit can measure heart rate using a wearable device and measure blood pressure using an IoT sensor. The data collection unit can also measure body temperature using a body temperature sensor and measure steps using a pedometer. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the collected data in real time, for example, using generative AI. The analysis unit can detect abnormal fluctuations in heart rate and blood pressure and predict the risk of heart disease and hypertension. For example, the analysis unit can analyze heart rate fluctuations using generative AI and predict the risk of heart disease. The analysis unit can also analyze blood pressure fluctuations and predict the risk of hypertension. The analysis unit can also analyze body temperature fluctuations and predict the risk of fever. Step 3: The service provider provides personalized health advice based on the analysis results obtained by the analysis provider. For example, the service provider can use generative AI to provide diet and exercise advice based on the user's health condition and lifestyle. For example, the service provider can use generative AI to analyze the user's diet and propose a healthy meal plan. The service provider can also analyze the user's exercise habits and propose an appropriate exercise program. The service provider can also analyze the user's sleep patterns and provide advice to promote quality sleep. Step 4: The monitoring unit monitors changes in cognitive function based on the advice provided by the service provider. The monitoring unit can, for example, use generative AI to monitor changes in cognitive function in real time. The monitoring unit can monitor changes in memory and attention and detect cognitive decline early. The monitoring unit can, for example, use generative AI to analyze changes in memory and predict cognitive decline. The monitoring unit can also analyze changes in attention and predict cognitive decline. The monitoring unit can also analyze changes in problem-solving ability and predict cognitive decline. Step 5: The support department assists with dementia prevention by providing questions and habit-forming activities based on changes in cognitive function monitored by the monitoring department. The support department can, for example, provide quizzes and games for dementia prevention using generative AI. The support department can provide quizzes and games to train memory and attention, thereby preventing cognitive decline. The support department can, for example, provide quizzes to train memory using generative AI. The support department can also provide games to train attention. The support department can also provide puzzles to train problem-solving skills.

[0070] (Example of form 2) The mind-body protection agent according to an embodiment of the present invention is a system that utilizes generative AI and IoT to support the daily health maintenance and dementia prevention of middle-aged and elderly people, as well as families caring for elderly individuals. This mind-body protection agent collects health data from wearable devices and IoT sensors, and the generative AI analyzes this data. The generative AI predicts health status and risks and provides personalized health advice. Furthermore, if an abnormality is detected, it quickly sends an alert and coordinates with medical institutions. In addition, the generative AI monitors changes in cognitive function in real time and supports the creation of questions and habits for dementia prevention. This enables elderly people to maintain independent living and reduces the burden on families and caregivers. For example, the mind-body protection agent records vital signs such as heart rate, blood pressure, body temperature, and steps in real time from wearable devices and IoT sensors. This allows for a detailed understanding of daily health status. The generative AI analyzes the collected data and can detect abnormal fluctuations in heart rate and blood pressure, and predict the risk of heart disease and hypertension. This allows for early detection of risks and the provision of appropriate advice. The generative AI provides advice on diet and exercise based on the user's health status and lifestyle. For example, the generative AI provides quizzes and games to train memory and attention. This can help prevent cognitive decline and aid in dementia prevention. If an abnormality is detected, it quickly sends an alert and coordinates with medical institutions. For example, if an abnormality in heart rate or blood pressure is detected, it sends an alert to the user and their family and contacts a medical institution. This enables a quick response and minimizes health risks. The generative AI optimizes care plans and reduces the burden on families and caregivers. For example, it proposes an appropriate care plan according to the user's health condition and needs, and adjusts it as needed. This allows families and caregivers to provide care with peace of mind. In this way, the Mind and Body Protection Agent can utilize generative AI and IoT to support the daily health maintenance and dementia prevention of middle-aged and elderly people, support families caring for elderly people, and promote independent living.

[0071] The mind and body protection agent according to this embodiment comprises a data collection unit, an analysis unit, a data provision unit, a monitoring unit, and a support unit. The data collection unit collects health data. The data collection unit can collect health data using, for example, wearable devices or IoT sensors. The data collection unit can record vital signs such as heart rate, blood pressure, body temperature, and steps in real time. The data collection unit can measure heart rate using, for example, a wearable device and measure blood pressure using IoT sensors. The data collection unit can also measure body temperature using a body temperature sensor and measure steps using a pedometer. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can analyze the collected data in real time using, for example, generative AI. The analysis unit can detect abnormal fluctuations in heart rate and blood pressure and predict the risk of heart disease and hypertension. The analysis unit can analyze fluctuations in heart rate using, for example, generative AI and predict the risk of heart disease. The analysis unit can also analyze fluctuations in blood pressure and predict the risk of hypertension. The analysis unit can also analyze fluctuations in body temperature and predict the risk of fever. The service provider provides personalized health advice based on the analysis results obtained by the analysis provider. For example, the service provider can use generative AI to provide dietary and exercise advice based on the user's health status and lifestyle. For example, the service provider can use generative AI to analyze the user's diet and propose a healthy meal plan. The service provider can also analyze the user's exercise habits and propose an appropriate exercise program. The service provider can also analyze the user's sleep patterns and provide advice to promote quality sleep. The monitoring service provider monitors changes in cognitive function based on the advice provided by the service provider. For example, the monitoring service provider can use generative AI to monitor changes in cognitive function in real time. The monitoring service provider can monitor changes in memory and attention and detect cognitive decline early. For example, the monitoring service provider can use generative AI to analyze changes in memory and predict cognitive decline. The monitoring service provider can also analyze changes in attention and predict cognitive decline.The monitoring unit can analyze changes in problem-solving ability and predict cognitive decline. The support unit provides support for dementia prevention through questions and habit formation based on the changes in cognitive function monitored by the monitoring unit. For example, the support unit can provide quizzes and games for dementia prevention using generative AI. The support unit can provide quizzes and games to train memory and attention, thereby preventing cognitive decline. For example, the support unit can provide quizzes to train memory using generative AI. The support unit can also provide games to train attention. The support unit can also provide puzzles to train problem-solving ability. As a result, the mind-body protection agent according to this embodiment can collect, analyze, and provide advice on health data, monitor cognitive function, and support dementia prevention.

[0072] The data collection unit collects health data. The data collection unit can collect health data using, for example, wearable devices and IoT sensors. Specifically, wearable devices include smartwatches and fitness trackers. These devices can record vital signs such as heart rate, blood pressure, body temperature, and steps in real time. For example, a smartwatch can measure heart rate using an optical heart rate sensor and blood pressure using a pressure sensor. It also has a built-in body temperature sensor, allowing it to measure skin temperature and record body temperature fluctuations in real time. Furthermore, it can measure steps using an accelerometer and gyroscope to understand the user's activity level. IoT sensors include health monitoring devices installed in the home. For example, a sensor placed in a bed can measure the user's heart rate and respiratory rate during sleep to evaluate sleep quality. A sensor placed in a toilet can analyze urine components to monitor health status. This data is centrally managed by the data collection unit and transmitted to a cloud server. The data is securely stored on the cloud server and can be accessed by the analysis and provisioning units as needed. The data collection unit can adjust the frequency and accuracy of data collection, allowing for flexible responses to specific situations and conditions. For example, increasing the data collection frequency during specific time periods, such as during exercise or sleep, enables a more detailed understanding of health status. This allows the data collection unit to efficiently and effectively collect health data, improving the overall system performance.

[0073] The analysis department analyzes the data collected by the data collection department. For example, the analysis department can analyze the collected data in real time using generative AI. Generative AI has the ability to process vast amounts of data quickly and accurately, making it suitable for detecting abnormal patterns and risks. Specifically, it can detect abnormal fluctuations in heart rate and blood pressure and predict the risk of heart disease and hypertension. For example, generative AI can analyze fluctuations in heart rate and detect abnormal fluctuations that exceed the normal range. This allows for early detection of a potential increased risk of heart disease. It can also analyze fluctuations in blood pressure and predict the risk of hypertension. If blood pressure rises sharply or remains high for a long period, the generative AI will determine this to be abnormal and issue a warning to the user. Furthermore, it can analyze fluctuations in body temperature and predict the risk of fever. If body temperature rises sharply or remains high above the normal range, the generative AI will determine this to be abnormal and alert the user. The analysis department can comprehensively analyze this data and understand the user's health status in real time. In addition, it can utilize historical data and statistical information to perform long-term health risk assessments and trend analyses. For example, based on past heart rate data, it can predict risk fluctuations during specific time periods or situations and formulate future countermeasures. Furthermore, anomaly detection algorithms can detect unusual patterns or abnormal data, enabling early warnings. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term health management and anomaly detection, improving the overall reliability and safety of the system.

[0074] The service provider provides personalized health advice based on the analysis results obtained by the analysis provider. For example, the service provider can use generative AI to provide diet and exercise advice based on the user's health condition and lifestyle. Specifically, the generative AI analyzes the user's diet and proposes a nutritionally balanced and healthy meal plan. For example, it analyzes the calories and nutrients in the meals the user has consumed and suggests ingredients and recipes to supplement any deficient nutrients. It can also analyze the user's exercise habits and propose an appropriate exercise program. For example, based on the user's exercise volume and heart rate data, it suggests effective types and frequencies of exercise to prevent inactivity or excessive exercise. Furthermore, it can analyze the user's sleep patterns and provide advice to promote quality sleep. For example, it analyzes the quality and duration of sleep and suggests appropriate sleep environments and relaxation methods. The service provider provides this advice to users in an easy-to-understand manner, supporting them in achieving a healthy lifestyle. In addition, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the advice. For example, by collecting the results of the advice the user has implemented and having the generative AI evaluate its effectiveness, the service provider can provide more effective advice. Furthermore, the service provider can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only smartphone notifications but also email and voice calls. This allows the service provider to quickly and reliably provide personalized health advice to users and support their health management.

[0075] The monitoring unit monitors changes in cognitive function based on advice provided by the service provider. The monitoring unit can, for example, use generative AI to monitor changes in cognitive function in real time. Specifically, the generative AI analyzes changes in the user's memory and attention to detect cognitive decline early. For example, the generative AI analyzes the results of a memory test taken by the user and detects declines exceeding the normal range. It can also analyze changes in attention to detect signs of decreased concentration or distractibility. Furthermore, it can analyze changes in problem-solving ability to predict cognitive decline. For example, it analyzes the results of a problem-solving test taken by the user and detects declines exceeding the normal range. The monitoring unit comprehensively analyzes this data to grasp changes in the user's cognitive function in real time. Furthermore, it can utilize past data and statistical information to evaluate long-term fluctuations in cognitive function and predict future risks. For example, based on past memory data, it can predict risk fluctuations in specific time periods or situations and formulate future countermeasures. Additionally, it can use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings. This allows the monitoring unit to not only grasp the situation in real time, but also to manage cognitive function over the long term and detect anomalies, thereby improving the reliability and safety of the entire system.

[0076] The support department assists with dementia prevention by providing questions and habit-forming activities based on changes in cognitive function monitored by the monitoring department. For example, the support department can provide quizzes and games for dementia prevention using generative AI. Specifically, the generative AI generates and provides quizzes and games to train users' memory and attention. For example, as a quiz to train memory, it can present users with a series of words or numbers and ask them to reproduce them after a certain period of time. As a game to train attention, it can provide a game where users quickly tap specific objects displayed on the screen. Furthermore, as a puzzle to train problem-solving ability, it can present users with complex problems and encourage them to solve them. Through these quizzes and games, the support department can continuously train users' cognitive functions. In addition, the support department can collect user feedback and continuously improve the content of quizzes and games. For example, by collecting the results of users playing quizzes and games and having the generative AI evaluate their effectiveness, it can provide more effective content. The support department can also reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only smartphone notifications but also email and voice calls. This allows the support department to provide users with prompt and reliable support for dementia prevention and prevent cognitive decline.

[0077] The data collection unit can collect health data using wearable devices and IoT sensors. For example, the data collection unit can measure heart rate using a wearable device and blood pressure using an IoT sensor. The data collection unit can also measure body temperature using a body temperature sensor and step count using a pedometer. For example, the data collection unit can record heart rate in real time using a smartwatch. The data collection unit can also record step count using a fitness tracker. The data collection unit can also measure indoor temperature and humidity using environmental sensors. This makes the collection of health data more efficient by using wearable devices and IoT sensors. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from a wearable device into a generating AI, which can analyze the data and evaluate the health status.

[0078] The analysis unit can analyze collected data in real time and predict health status and risks. For example, the analysis unit can analyze collected data in real time using generative AI. The analysis unit can detect abnormal fluctuations in heart rate and blood pressure and predict the risk of heart disease and hypertension. For example, the analysis unit can analyze fluctuations in heart rate using generative AI and predict the risk of heart disease. The analysis unit can also analyze fluctuations in blood pressure and predict the risk of hypertension. The analysis unit can also analyze fluctuations in body temperature and predict the risk of fever. This enables early detection of health status and risks through real-time data analysis. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected data into generative AI, which can analyze the data and predict health status and risks.

[0079] The service provider can provide dietary and exercise advice based on the user's health condition and lifestyle. For example, the service provider can use generative AI to provide dietary and exercise advice based on the user's health condition and lifestyle. For example, the service provider can use generative AI to analyze the user's diet and propose a healthy meal plan. The service provider can also analyze the user's exercise habits and propose an appropriate exercise program. The service provider can also analyze the user's sleep patterns and provide advice to promote quality sleep. This improves the user's health management through personalized advice. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's health data into generative AI, which can then analyze the data and provide personalized advice.

[0080] The monitoring unit can monitor changes in cognitive function in real time. The monitoring unit can monitor changes in cognitive function in real time, for example, using generative AI. The monitoring unit can monitor changes in memory and attention and detect cognitive decline early. The monitoring unit can analyze changes in memory using generative AI and predict cognitive decline. The monitoring unit can also analyze changes in attention and predict cognitive decline. The monitoring unit can also analyze changes in problem-solving ability and predict cognitive decline. This allows for early detection of changes in cognitive function through real-time monitoring. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input collected data into a generative AI, which can analyze the data and monitor changes in cognitive function.

[0081] The support department can provide quizzes and games for dementia prevention. For example, the support department can use generative AI to provide quizzes and games for dementia prevention. The support department can provide quizzes and games to train memory and attention, thereby preventing cognitive decline. For example, the support department can use generative AI to provide quizzes to train memory. The support department can also provide games to train attention. The support department can also provide puzzles to train problem-solving abilities. In this way, dementia prevention can be supported through quizzes and games. Some or all of the above-described processes in the support department may be performed using AI, for example, or without AI. For example, the support department can use generative AI to generate quiz and game content and provide it to users.

[0082] The service provider can quickly send alerts when an abnormality is detected and coordinate with medical institutions. The service provider can, for example, use generative AI to detect abnormalities and quickly send alerts. If abnormalities in heart rate or blood pressure are detected, the service provider can send alerts to the user or their family and contact medical institutions. The service provider can, for example, use generative AI to detect abnormal heart rate and send alerts. The service provider can also detect abnormal blood pressure and send alerts. The service provider can also detect abnormal body temperature and send alerts. This enables a rapid response when an abnormality is detected, minimizing health risks. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can build a system that uses generative AI to detect abnormalities and send alerts.

[0083] The support department can propose appropriate care plans according to the user's health condition and needs, and make adjustments as necessary. For example, the support department can use generative AI to propose appropriate care plans according to the user's health condition and needs. The support department can also adjust care plans based on the user's health condition and needs. For example, the support department can use generative AI to analyze the user's health condition and propose appropriate care plans. The support department can also adjust care plans according to the user's needs. The support department can also monitor the implementation status of care plans and make adjustments as necessary. This reduces the burden on families and caregivers by proposing and adjusting appropriate care plans. Some or all of the above processes in the support department may be performed using AI, for example, or without AI. For example, the support department can build a system that proposes and adjusts care plans using generative AI.

[0084] The data collection unit can estimate the user's emotions and adjust the timing of health data collection based on the estimated emotions. The data collection unit can estimate the user's emotions using, for example, an emotion engine or generative AI. If the user is stressed, the data collection unit can reduce the collection timing to alleviate the user's burden. If the user is relaxed, the data collection unit can increase the collection timing to collect more detailed data. If the user is in a hurry, the data collection unit can shorten the collection timing to quickly acquire data. In this way, the user's burden can be reduced by adjusting the collection timing according to the user's emotions. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can be built to estimate the user's emotions using generative AI and adjust the collection timing.

[0085] The data collection unit can analyze the user's past health data and select the optimal collection method. For example, the data collection unit can analyze the user's past health data using generative AI. The data collection unit can select the most effective collection method from the user's past data. The data collection unit can adjust the collection frequency based on the user's past data. The data collection unit can analyze the user's past data and concentrate collection during specific time periods. This enables efficient data collection by selecting the optimal collection method based on past data. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can be configured to analyze the user's past health data using generative AI and select the optimal collection method.

[0086] The data collection unit can filter health data based on the user's current lifestyle and activity level. For example, the data collection unit can analyze the user's current lifestyle and activity level using generative AI. If the user is exercising, the data collection unit can prioritize collecting exercise data. If the user is resting, the data collection unit can prioritize collecting relaxation data. If the user is working, the data collection unit can prioritize collecting stress levels. By filtering the data based on lifestyle and activity level, more relevant data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can build a system that analyzes the user's lifestyle and activity level using generative AI and filters the data.

[0087] The data collection unit can estimate the user's emotions and determine the priority of health data to collect based on the estimated user emotions. The data collection unit can estimate the user's emotions using, for example, an emotion engine or generative AI. If the user is stressed, the data collection unit can prioritize the collection of stress-related data. If the user is relaxed, the data collection unit can prioritize the collection of relaxation-related data. If the user is in a hurry, the data collection unit can prioritize the collection of data that can be collected quickly. In this way, important data can be collected preferentially by determining the priority of data according to the user's emotions. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can be built to estimate the user's emotions and determine the priority of data using generative AI.

[0088] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting health data. For example, the data collection unit can analyze the user's geographical location using generative AI. If the user is at high altitude, the data collection unit can prioritize the collection of altitude-related data. If the user is in an urban area, the data collection unit can prioritize the collection of data related to environmental pollution. If the user is indoors, the data collection unit can prioritize the collection of data related to the indoor environment. In this way, by considering geographical location, highly relevant data can be prioritized. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can be configured to analyze the user's geographical location using generative AI and determine the priority of the data.

[0089] The data collection unit can analyze a user's social media activity and collect relevant data when collecting health data. For example, the data collection unit can use generative AI to analyze a user's social media activity. If a user is experiencing stress on social media, the data collection unit can collect stress-related data. If a user is relaxing on social media, the data collection unit can collect relaxation-related data. If a user is active on social media, the data collection unit can collect activity-related data. This allows for the collection of relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can build a system that uses generative AI to analyze a user's social media activity and determine data priorities.

[0090] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. The analysis unit can estimate the user's emotions using, for example, an emotion engine or generative AI. If the user is stressed, the analysis unit can provide a simple, visual presentation. If the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can provide concise, to-the-point analysis results. This allows for the provision of analysis results that are easy for the user to understand by adjusting the presentation of the analysis according to the user's emotions. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not. For example, the analysis unit can be built to estimate the user's emotions using generative AI and adjust the presentation of the analysis.

[0091] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during the analysis. For example, the analysis unit can use generative AI to evaluate the importance of health data. The analysis unit can perform detailed analysis on important health data. The analysis unit can perform simplified analysis on less important health data. The analysis unit can determine the priority of the analysis according to the importance of the health data. This enables efficient data analysis by adjusting the level of detail of the analysis based on the importance of the health data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can build a system that uses generative AI to evaluate the importance of health data and adjust the level of detail of the analysis.

[0092] The analysis unit can apply different analysis algorithms depending on the category of health data during analysis. For example, the analysis unit can classify health data categories using generative AI. For heart rate data, the analysis unit can apply an algorithm to predict the risk of heart disease. For blood pressure data, the analysis unit can apply an algorithm to predict the risk of hypertension. For step count data, the analysis unit can apply an algorithm to predict the risk of lack of exercise. By applying different analysis algorithms depending on the category of health data, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can be built to classify health data categories using generative AI and apply an appropriate analysis algorithm.

[0093] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. The analysis unit can estimate the user's emotions using, for example, an emotion engine or generative AI. If the user is stressed, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. If the user is in a hurry, the analysis unit can provide a quick analysis result. By adjusting the length of the analysis according to the user's emotions, the analysis unit can provide an analysis result of an appropriate length for the user. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can be built to estimate the user's emotions using generative AI and adjust the length of the analysis.

[0094] The analysis unit can determine the priority of analysis based on the timing of health data collection during the analysis process. For example, the analysis unit can use generative AI to evaluate the timing of health data collection. The analysis unit can prioritize the analysis of recently collected data. The analysis unit can analyze current data while referring to historical data. The analysis unit can determine the priority of analysis based on data collected during a specific period. This enables efficient data analysis by prioritizing analysis based on the timing of health data collection. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can build a system that uses generative AI to evaluate the timing of health data collection and determine the priority of analysis.

[0095] The analysis unit can adjust the order of analysis based on the relevance of health data during the analysis process. For example, the analysis unit can use generative AI to evaluate the relevance of health data. The analysis unit can prioritize the analysis of highly relevant data. The analysis unit can postpone the analysis of less relevant data. The analysis unit can determine the order of analysis according to the relevance of health data. This allows for prioritizing the analysis of important data by adjusting the order of analysis based on the relevance of health data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can build a system that uses generative AI to evaluate the relevance of health data and determine the order of analysis.

[0096] The service provider can estimate the user's emotions and adjust the way advice is presented based on those emotions. For example, the service provider can estimate the user's emotions using an emotion engine or generative AI. If the user is stressed, the service provider can provide simple, visual advice. If the user is relaxed, the service provider can provide detailed advice. If the user is in a hurry, the service provider can provide concise, to-the-point advice. This allows the service provider to provide advice that is easy for the user to understand by adjusting the presentation of the advice according to the user's emotions. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can build a system that uses generative AI to estimate the user's emotions and adjust the presentation of the advice.

[0097] The service provider can adjust the level of detail in advice based on the importance of the health condition when providing advice. For example, the service provider can use generative AI to assess the importance of the health condition. For important health conditions, the service provider can provide detailed advice. For less important health conditions, the service provider can provide simplified advice. The service provider can determine the priority of advice according to the importance of the health condition. This enables efficient advice provision by adjusting the level of detail of advice based on the importance of the health condition. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can build a system that uses generative AI to assess the importance of the health condition and adjust the level of detail of the advice.

[0098] The service provider can apply different advice algorithms depending on the health condition category when providing advice. For example, the service provider can classify health condition categories using generative AI. For those at risk of heart disease, the service provider can provide advice on preventing heart disease. For those at risk of hypertension, the service provider can provide advice on preventing hypertension. For those at risk of lack of exercise, the service provider can provide advice on exercise habits. By applying different advice algorithms depending on the health condition category, more accurate advice becomes possible. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can build a system that classifies health condition categories using generative AI and applies an appropriate advice algorithm.

[0099] The service provider can estimate the user's emotions and adjust the length of the advice based on the estimated emotions. The service provider can estimate the user's emotions using, for example, an emotion engine or generative AI. If the user is stressed, the service provider can provide short, to-the-point advice. If the user is relaxed, the service provider can provide detailed advice. If the user is in a hurry, the service provider can provide advice quickly. By adjusting the length of the advice according to the user's emotions, the service provider can provide advice of an appropriate length for the user. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can build a system that estimates the user's emotions using generative AI and adjusts the length of the advice.

[0100] The service provider can prioritize advice based on changes in health status when providing advice. For example, the service provider can use generative AI to evaluate changes in health status. If health status changes rapidly, the service provider can prioritize providing advice. If health status is stable, the service provider can provide advice regularly. The service provider can prioritize advice according to changes in health status. This allows for the priority of important advice by prioritizing it based on changes in health status. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can build a system that uses generative AI to evaluate changes in health status and determine the priority of advice.

[0101] The service provider can adjust the order of advice based on the relevance of health conditions when providing advice. The service provider can, for example, use generative AI to evaluate the relevance of health conditions. The service provider can prioritize providing advice for highly relevant health conditions. The service provider can postpone providing advice for less relevant health conditions. The service provider can determine the order of advice according to the relevance of health conditions. This allows important advice to be prioritized by adjusting the order of advice based on the relevance of health conditions. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can build a system that uses generative AI to evaluate the relevance of health conditions and determine the order of advice.

[0102] The monitoring unit can estimate the user's emotions and adjust the monitoring criteria based on the estimated emotions. The monitoring unit can estimate the user's emotions using, for example, an emotion engine or generative AI. If the user is stressed, the monitoring unit can reduce the frequency of monitoring to alleviate the user's burden. If the user is relaxed, the monitoring unit can increase the frequency of monitoring to collect more detailed data. If the user is in a hurry, the monitoring unit can simplify the monitoring criteria to quickly acquire data. This reduces the user's burden by adjusting the monitoring criteria according to the user's emotions. Some or all of the above processing in the monitoring unit may be performed using, for example, AI, or not using AI. For example, the monitoring unit can be built to estimate the user's emotions using generative AI and adjust the monitoring criteria.

[0103] The monitoring unit can record changes in cognitive function in detail during monitoring and analyze the patterns of change. For example, the monitoring unit can use generative AI to record changes in cognitive function in detail. The monitoring unit can record changes in cognitive function daily and analyze long-term patterns. The monitoring unit can record changes in cognitive function on a weekly basis and analyze short-term patterns. The monitoring unit can record changes in cognitive function on a monthly basis and analyze seasonal patterns. This allows for early detection of changes in cognitive function by recording changes in detail and analyzing the patterns. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can be built to record changes in cognitive function and analyze patterns using generative AI.

[0104] The monitoring unit can apply different monitoring methods depending on changes in cognitive function during monitoring. For example, the monitoring unit can use generative AI to evaluate changes in cognitive function. If a decline in cognitive function is observed, the monitoring unit can apply a detailed monitoring method. If cognitive function is stable, the monitoring unit can apply a simplified monitoring method. The monitoring unit can adjust the monitoring method according to changes in cognitive function. This allows for more accurate monitoring by applying different monitoring methods according to changes in cognitive function. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can be configured to evaluate changes in cognitive function using generative AI and apply an appropriate monitoring method.

[0105] The monitoring unit can estimate the user's emotions and adjust the order in which the monitoring results are displayed based on the estimated emotions. The monitoring unit can estimate the user's emotions using, for example, an emotion engine or generative AI. If the user is feeling stressed, the monitoring unit can prioritize displaying important results. If the user is relaxed, the monitoring unit can display detailed results. If the user is in a hurry, the monitoring unit can display concise results. By adjusting the order in which the monitoring results are displayed according to the user's emotions, the system can provide results that are easy for the user to understand. Some or all of the above processing in the monitoring unit may be performed using, for example, AI, or not using AI. For example, the monitoring unit can be configured to estimate the user's emotions using generative AI and adjust the order in which the monitoring results are displayed.

[0106] The monitoring unit can perform monitoring while considering the geographical distribution of changes in cognitive function. For example, the monitoring unit can evaluate the geographical distribution of changes in cognitive function using generative AI. If the user is in a specific region, the monitoring unit can prioritize monitoring data related to that region. If the user is on the move, the monitoring unit can monitor data related to the destination region. If the user is in multiple regions, the monitoring unit can integrate and monitor data from each region. This makes it possible to perform monitoring tailored to the characteristics of each region by considering the geographical distribution of changes in cognitive function. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can be built to evaluate and monitor the geographical distribution of changes in cognitive function using generative AI.

[0107] The monitoring unit can improve the accuracy of monitoring by referring to literature related to changes in cognitive function during monitoring. The monitoring unit can, for example, use generative AI to refer to literature related to changes in cognitive function. The monitoring unit can improve monitoring methods by referring to the latest research literature. The monitoring unit can improve the accuracy of monitoring by referring to past literature on changes in cognitive function. The monitoring unit can continuously improve the accuracy of monitoring by utilizing literature databases. This allows for improved monitoring accuracy by referring to literature related to changes in cognitive function. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can build a system that uses generative AI to refer to literature and improve the accuracy of monitoring.

[0108] The support unit can estimate the user's emotions and adjust its support methods based on those emotions. For example, the support unit can estimate the user's emotions using an emotion engine or generative AI. If the user is stressed, the support unit can provide relaxing support methods. If the user is relaxed, the support unit can provide proactive support methods. If the user is in a hurry, the support unit can provide quick support methods. This allows the support unit to provide appropriate support to the user by adjusting its methods according to their emotions. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can be built to estimate the user's emotions using generative AI and adjust its support methods accordingly.

[0109] The support department can record changes in cognitive function in detail during support and analyze the effectiveness of the support. For example, the support department can use generative AI to record changes in cognitive function in detail. The support department can record changes in cognitive function before and after support and analyze the effectiveness. The support department can record changes in cognitive function during the support period and analyze the effectiveness. The support department can record changes in cognitive function after the support period and analyze the long-term effects. In this way, the effectiveness of the support can be evaluated by recording changes in cognitive function in detail and analyzing the effectiveness of the support. Some or all of the above processes in the support department may be performed using AI, for example, or without AI. For example, the support department can build a system that uses generative AI to record changes in cognitive function and analyze the effectiveness of the support.

[0110] The support unit can apply different support methods depending on changes in cognitive function during support. For example, the support unit can use generative AI to evaluate changes in cognitive function. If a decline in cognitive function is observed, the support unit can apply a detailed support method. If cognitive function is stable, the support unit can apply a simplified support method. The support unit can adjust the support method according to changes in cognitive function. This makes it possible to provide more effective support by applying different support methods according to changes in cognitive function. Some or all of the above processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can build a system that uses generative AI to evaluate changes in cognitive function and apply an appropriate support method.

[0111] The support unit can estimate the user's emotions and determine the priority of support based on the estimated emotions. The support unit can estimate the user's emotions using, for example, an emotion engine or generative AI. If the user is feeling stressed, the support unit can prioritize stress reduction support. If the user is relaxed, the support unit can prioritize support to maintain relaxation. If the user is in a hurry, the support unit can prioritize rapid support. This allows important support to be provided preferentially by determining the priority of support according to the user's emotions. Some or all of the above processing in the support unit may be performed using, for example, AI, or not using AI. For example, the support unit can build a system that estimates the user's emotions and determines the priority of support using generative AI.

[0112] The support unit can provide support while considering the geographical distribution of changes in cognitive function. For example, the support unit can use generative AI to evaluate the geographical distribution of changes in cognitive function. If the user is in a specific region, the support unit can provide support relevant to that region. If the user is on the move, the support unit can provide support relevant to the destination region. If the user is in multiple regions, the support unit can integrate data from each region to provide support. This makes it possible to provide support tailored to the characteristics of each region by considering the geographical distribution of changes in cognitive function. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can build a system that uses generative AI to evaluate the geographical distribution of changes in cognitive function and provide support.

[0113] The support unit can improve the accuracy of its support by referring to literature related to changes in cognitive function during the support process. For example, the support unit can use generative AI to refer to literature related to changes in cognitive function. The support unit can refer to the latest research literature to improve its support methods. The support unit can refer to past literature on changes in cognitive function to improve the accuracy of its support. The support unit can utilize literature databases to continuously improve the accuracy of its support. This allows the support unit to improve the accuracy of its support by referring to literature related to changes in cognitive function. Some or all of the above processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can build a system that uses generative AI to refer to literature and improve the accuracy of its support.

[0114] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0115] The data collection unit can adjust the collection frequency by referring to the user's past health data when collecting user health data. For example, if past data shows a tendency for health to deteriorate during a specific time period, the collection frequency can be increased during that time period. Conversely, if past data shows that health is stable on a specific day of the week, the collection frequency can be decreased on that day. Furthermore, if past data shows that health fluctuates during a specific season, the collection frequency can be adjusted for that season. This enables efficient data collection by referring to the user's past health data.

[0116] The analytics department can estimate the user's emotions when analyzing user health data and adjust the level of detail of the analysis based on those emotions. For example, if the user is stressed, a concise analysis can be provided. If the user is relaxed, a detailed analysis can be provided. Furthermore, if the user is in a hurry, a concise analysis focusing on the key points can be provided. In this way, by adjusting the level of detail of the analysis according to the user's emotions, the analysis can be provided in a way that is easy for the user to understand.

[0117] The service provider can adjust the frequency of health advice based on the user's health status. For example, if the user's health is stable, the frequency of advice can be reduced. Conversely, if the user's health is fluctuating, the frequency can be increased. Furthermore, if the user's health rapidly deteriorates, advice can be provided quickly. In this way, by adjusting the frequency of advice according to the user's health status, advice can be provided at the appropriate time.

[0118] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated emotions. For example, if the user is stressed, the monitoring frequency can be reduced to lessen the user's burden. Conversely, if the user is relaxed, the monitoring frequency can be increased to collect more detailed data. Furthermore, if the user is in a hurry, the monitoring frequency can be shortened to quickly acquire data. In this way, by adjusting the monitoring frequency according to the user's emotions, the user's burden can be reduced.

[0119] The support department can adjust the level of support provided based on the user's health condition. For example, if the user's health is stable, mild support can be provided. If the user's health is fluctuating, moderate support can be provided. Furthermore, if the user's health deteriorates rapidly, rapid and advanced support can be provided. In this way, appropriate support can be provided by adjusting the level of support according to the user's health condition.

[0120] The data collection unit can estimate the user's emotions and adjust the types of data collected based on those estimates. For example, if the user is stressed, stress-related data can be prioritized. If the user is relaxed, relaxation-related data can be prioritized. Furthermore, if the user is in a hurry, data that can be collected quickly can be prioritized. In this way, by adjusting the types of data collected according to the user's emotions, important data can be prioritized.

[0121] The analytics department can prioritize user health data analysis based on when the data was collected. For example, it can prioritize the analysis of recently collected data. It can also analyze current data while referring to past data. Furthermore, it can prioritize analysis based on data collected during a specific period. This enables efficient data analysis by prioritizing analysis based on when the health data was collected.

[0122] The service provider can estimate the user's emotions and adjust the way advice is presented based on those emotions. For example, if the user is stressed, simple, visual advice can be provided. If the user is relaxed, detailed advice can be provided. Furthermore, if the user is in a hurry, concise, to-the-point advice can be provided. By adjusting the way advice is presented according to the user's emotions, the service can provide advice that is easy for the user to understand.

[0123] The monitoring unit can adjust the level of detail of user health data based on its importance. For example, it can perform detailed monitoring of important health data, while simplifying monitoring of less important data. Furthermore, it can determine the monitoring priority based on the importance of the health data. This allows for efficient data monitoring by adjusting the level of detail based on the importance of the health data.

[0124] The support unit can estimate the user's emotions and determine the priority of support based on those emotions. For example, if the user is stressed, stress reduction support can be prioritized. If the user is relaxed, support to maintain that relaxation can be prioritized. Furthermore, if the user is in a hurry, rapid support can be prioritized. In this way, by determining the priority of support according to the user's emotions, important support can be provided preferentially.

[0125] The following briefly describes the processing flow for example form 2.

[0126] Step 1: The data collection unit collects health data. The data collection unit can collect health data using, for example, wearable devices or IoT sensors. The data collection unit can record vital signs such as heart rate, blood pressure, body temperature, and steps in real time. For example, the data collection unit can measure heart rate using a wearable device and measure blood pressure using an IoT sensor. The data collection unit can also measure body temperature using a body temperature sensor and measure steps using a pedometer. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the collected data in real time, for example, using generative AI. The analysis unit can detect abnormal fluctuations in heart rate and blood pressure and predict the risk of heart disease and hypertension. For example, the analysis unit can analyze heart rate fluctuations using generative AI and predict the risk of heart disease. The analysis unit can also analyze blood pressure fluctuations and predict the risk of hypertension. The analysis unit can also analyze body temperature fluctuations and predict the risk of fever. Step 3: The service provider provides personalized health advice based on the analysis results obtained by the analysis provider. For example, the service provider can use generative AI to provide diet and exercise advice based on the user's health condition and lifestyle. For example, the service provider can use generative AI to analyze the user's diet and propose a healthy meal plan. The service provider can also analyze the user's exercise habits and propose an appropriate exercise program. The service provider can also analyze the user's sleep patterns and provide advice to promote quality sleep. Step 4: The monitoring unit monitors changes in cognitive function based on the advice provided by the service provider. The monitoring unit can, for example, use generative AI to monitor changes in cognitive function in real time. The monitoring unit can monitor changes in memory and attention and detect cognitive decline early. The monitoring unit can, for example, use generative AI to analyze changes in memory and predict cognitive decline. The monitoring unit can also analyze changes in attention and predict cognitive decline. The monitoring unit can also analyze changes in problem-solving ability and predict cognitive decline. Step 5: The support department assists with dementia prevention by providing questions and habit-forming activities based on changes in cognitive function monitored by the monitoring department. The support department can, for example, provide quizzes and games for dementia prevention using generative AI. The support department can provide quizzes and games to train memory and attention, thereby preventing cognitive decline. The support department can, for example, provide quizzes to train memory using generative AI. The support department can also provide games to train attention. The support department can also provide puzzles to train problem-solving skills.

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0128] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0129] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the data collection unit, analysis unit, provision unit, monitoring unit, and support unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit is implemented by the computer 36 of the smart device 14 and collects health data from wearable devices and IoT sensors. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data in real time. The provision unit is implemented by the control unit 46A of the smart device 14 and provides personalized health advice. The monitoring unit is implemented by the specific processing unit 290 of the data processing unit 12 and monitors changes in cognitive function in real time. The support unit is implemented by the control unit 46A of the smart device 14 and supports the creation of questions and habits for dementia prevention. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0131] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0132] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the data collection unit, analysis unit, provision unit, monitoring unit, and support unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the computer 36 of the smart glasses 214 and collects health data from wearable devices and IoT sensors. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data in real time. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides personalized health advice. The monitoring unit is implemented by the specific processing unit 290 of the data processing unit 12 and monitors changes in cognitive function in real time. The support unit is implemented by the control unit 46A of the smart glasses 214 and assists in providing questions and establishing habits for dementia prevention. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0147] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0148] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0155] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, monitoring unit, and support unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the headset terminal 314 and collects health data from wearable devices and IoT sensors. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data in real time. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides personalized health advice. The monitoring unit is implemented by the specific processing unit 290 of the data processing unit 12 and monitors changes in cognitive function in real time. The support unit is implemented by the control unit 46A of the headset terminal 314 and supports the creation of questions and habits for dementia prevention. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0163] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0164] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0165] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0166] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0167] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0168] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0169] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0170] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0171] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0172] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0173] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0174] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0175] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0176] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0177] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0178] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0179] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, monitoring unit, and support unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the robot 414 and collects health data from wearable devices and IoT sensors. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data in real time. The provision unit is implemented by the control unit 46A of the robot 414 and provides personalized health advice. The monitoring unit is implemented by the specific processing unit 290 of the data processing unit 12 and monitors changes in cognitive function in real time. The support unit is implemented by the control unit 46A of the robot 414 and assists in creating questions and habits for dementia prevention. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0180] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0181] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0182] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0183] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0184] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0185] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0186] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0187] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0188] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0189] 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.

[0190] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0191] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0192] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0193] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0194] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0195] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0196] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0197] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0198] (Note 1) A data collection unit that collects health data, An analysis unit analyzes the data collected by the aforementioned collection unit, A provision unit that provides personalized health advice based on the analysis results obtained by the aforementioned analysis unit, A monitoring unit that monitors changes in cognitive function based on the advice provided by the aforementioned provision unit, The system includes a support unit that provides support for dementia prevention, such as creating questions and establishing habits, based on changes in cognitive function monitored by the aforementioned monitoring unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect health data using wearable devices and IoT sensors. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is The collected data is analyzed in real time to predict health status and risks. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, It provides diet and exercise advice based on the user's health status and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 5) The monitoring unit, Monitor changes in cognitive function in real time. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned support unit, We offer quizzes and games to help prevent dementia. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, If an abnormality is detected, an alert will be sent quickly, and the system will collaborate with medical institutions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned support unit, We propose an appropriate care plan based on the user's health condition and needs, and make adjustments as necessary. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of health data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Analyze the user's past health data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting health data, filtering is performed based on the user's current lifestyle and activity level. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is It estimates the user's emotions and prioritizes the health data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting health data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When collecting health data, we analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the health data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the category of health data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the health data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of health data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing advice, adjust the level of detail based on the importance of the health condition. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing advice, different advice algorithms are applied depending on the health condition category. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the advice based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing advice, prioritize the advice based on changes in your health condition. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing advice, adjust the order of advice based on the relevance of the health condition. The system described in Appendix 1, characterized by the features described herein. (Note 27) The monitoring unit, The system estimates user sentiment and adjusts monitoring criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The monitoring unit, During monitoring, changes in cognitive function are recorded in detail, and patterns of change are analyzed. The system described in Appendix 1, characterized by the features described herein. (Note 29) The monitoring unit, During monitoring, different monitoring methods are applied depending on changes in cognitive function. The system described in Appendix 1, characterized by the features described herein. (Note 30) The monitoring unit, It estimates the user's emotions and adjusts the order in which monitoring results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The monitoring unit, During monitoring, consider the geographical distribution of changes in cognitive function. The system described in Appendix 1, characterized by the features described herein. (Note 32) The monitoring unit, During monitoring, refer to literature related to changes in cognitive function to improve the accuracy of monitoring. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned support unit, It estimates the user's emotions and adjusts the support method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned support unit, During support, we meticulously record changes in cognitive function and analyze the effectiveness of the support. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned support unit, When providing support, different support methods are applied according to changes in cognitive function. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned support unit, It estimates the user's emotions and determines the priority of support based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned support unit, When providing support, consider the geographical distribution of changes in cognitive function. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned support unit, When providing support, we refer to literature related to changes in cognitive function to improve the accuracy of the support. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0199] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects health data, An analysis unit analyzes the data collected by the aforementioned collection unit, A provision unit that provides personalized health advice based on the analysis results obtained by the aforementioned analysis unit, A monitoring unit that monitors changes in cognitive function based on the advice provided by the aforementioned provision unit, The system includes a support unit that provides support for dementia prevention, such as creating questions and establishing habits, based on changes in cognitive function monitored by the aforementioned monitoring unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect health data using wearable devices and IoT sensors. The system according to feature 1.

3. The aforementioned analysis unit is The collected data is analyzed in real time to predict health status and risks. The system according to feature 1.

4. The aforementioned supply unit is, It provides diet and exercise advice based on the user's health status and lifestyle. The system according to feature 1.

5. The monitoring unit, Monitor changes in cognitive function in real time. The system according to feature 1.

6. The aforementioned support unit, We offer quizzes and games to help prevent dementia. The system according to feature 1.

7. The aforementioned supply unit is, If an abnormality is detected, an alert will be sent quickly, and the system will collaborate with medical institutions. The system according to feature 1.

8. The aforementioned support unit, We propose an appropriate care plan based on the user's health condition and needs, and make adjustments as necessary. The system according to feature 1.

9. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of health data collection based on those estimated emotions. The system according to feature 1.

10. The aforementioned collection unit is Analyze the user's past health data and select the optimal data collection method. The system according to feature 1.

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