system

Abstract

The system according to this embodiment aims to predict the progression of aging based on an individual's health data and provide a customized health management plan. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, a monitoring unit, and a notification unit. The collection unit collects individual health data. The analysis unit analyzes the data collected by the collection unit and predicts the progression of aging. The provision unit provides a customized health management plan based on the progression of aging predicted by the analysis unit. The monitoring unit monitors the health status in real time based on the health management plan provided by the provision unit. The notification unit detects abnormalities in the health status monitored by the monitoring unit and notifies nursing homes and medical institutions.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, predicting the progress of aging based on personal health data and providing an appropriate health management plan have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to predict the progress of aging based on personal health data and provide a customized health management plan.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a data provision unit, a monitoring unit, and a notification unit. The data collection unit collects individual health data. The analysis unit analyzes the data collected by the data collection unit and predicts the progression of aging. The data provision unit provides a customized health management plan based on the aging progression predicted by the analysis unit. The monitoring unit monitors the health status in real time based on the health management plan provided by the data provision unit. The notification unit detects abnormalities in the health status monitored by the monitoring unit and notifies nursing homes and medical institutions. [Effects of the Invention]

[0007] The system according to this embodiment can predict the progression of aging based on an individual's health data and provide a customized health management plan. [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 manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 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) An aging prediction agent system according to an embodiment of the present invention is a system that utilizes AI technology to predict the progression of aging from an individual's health data and provides a customized health management plan. This aging prediction agent system collaborates with nursing homes and medical institutions to develop highly reliable care support services. For example, the aging prediction agent system collects an individual's health data. This data includes daily health status, medical records, and lifestyle habits. Next, the aging prediction agent system uses AI to analyze the collected data and predict the progression of aging. The AI ​​uses machine learning algorithms to generate an aging prediction model based on the individual's health data. Based on the generated aging prediction model, the aging prediction agent system provides a customized health management plan. This plan includes advice on diet and exercise, and suggestions for regular health checkups. The aging prediction agent system also provides real-time monitoring of health status and care support. For example, if an abnormality in health status is detected, the aging prediction agent system notifies nursing homes and medical institutions to encourage a prompt response. Furthermore, the aging prediction agent system enhances encryption technology to protect privacy. Individual health data is stored encrypted to prevent unauthorized access by third parties. This system is expected to reduce care costs, extend healthy life expectancy, and decrease the number of hospital visits. For example, it is predicted to reduce care costs by up to 20% annually, extend healthy life expectancy by an average of more than two years, and reduce hospital visits by 30% annually. This aging prediction agent system will be an extremely useful tool for the elderly and their families, care facilities, and medical institutions. With the progression of an aging society and technological advancements, demand is expected to increase even further in the future. As a result, the aging prediction agent system can predict the progression of aging and provide customized health management plans by collecting, analyzing, providing, monitoring, and notifying individuals about their health data.

[0029] The aging prediction agent system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, a monitoring unit, and a notification unit. The collection unit collects personal health data. Personal health data includes, but is not limited to, vital signs, exercise data, and dietary data. The collection unit collects vital signs using, for example, a wearable device. The collection unit can also collect exercise data using a smartphone application. Furthermore, the collection unit can collect dietary data using a food logging application. For example, the collection unit collects vital signs such as heart rate and blood pressure in real time from a wearable device. The smartphone application records the user's exercise volume and step count and collects it as exercise data. The food logging application records the contents of meals consumed by the user and collects it as dietary data. The analysis unit analyzes the data collected by the collection unit and predicts the progression of aging. The analysis unit generates an aging prediction model based on individual health data using, for example, a machine learning algorithm. For example, the analysis unit predicts the progression of aging from health data using a decision tree algorithm. Furthermore, the analysis unit can generate aging prediction models using neural networks. Additionally, the analysis unit can predict the progression of aging using support vector machines. For example, the analysis unit uses a decision tree algorithm to extract features from health data and predict the progression of aging. The neural network learns from a large amount of health data and generates a highly accurate aging prediction model. The support vector machine classifies health data and predicts the progression of aging. The service provider provides a customized health management plan based on the aging progression predicted by the analysis unit. The service provider provides a health management plan that includes, for example, advice on diet and exercise, and suggestions for regular health checkups. For example, the service provider provides a balanced meal plan according to the user's health condition. The service provider can also provide an exercise plan tailored to the user's fitness level. Furthermore, the service provider can suggest regular health checkups. For example, the service provider suggests a nutritionally balanced meal plan based on the user's health condition. The exercise plan suggests appropriate exercises according to the user's physical fitness and health condition.Regular health checks monitor the user's health status and provide suggestions for early detection of abnormalities. The monitoring unit monitors the user's health status in real time based on the health management plan provided by the service provider. The monitoring unit monitors vital signs in real time, for example, using wearable devices. For example, the monitoring unit monitors heart rate and blood pressure in real time and detects abnormalities. The monitoring unit can also monitor exercise data in real time. Furthermore, the monitoring unit can monitor dietary data in real time. For example, the monitoring unit monitors heart rate and blood pressure data acquired from wearable devices in real time and detects abnormalities. Exercise data monitors the user's exercise volume and step count in real time and detects abnormalities. Dietary data monitors the contents of meals consumed by the user in real time and detects abnormalities. The notification unit detects abnormalities in the health status monitored by the monitoring unit and notifies care facilities and medical institutions. For example, the notification unit sends a notification to care facilities and medical institutions when an abnormality is detected. For example, the notification unit can send notifications to nursing care facilities via email or SMS when it detects an abnormality. It can also notify medical institutions by phone. Furthermore, the notification unit can send notifications via an app when it detects an abnormality. For example, when it detects an abnormality, it sends an email notification to nursing care facilities. SMS notifications are effective when a quick response is required. Phone notifications are used as a means of direct contact in emergencies. App notifications are used as a means of providing real-time notifications to users, nursing care facilities, and medical institutions. As a result, the aging prediction agent system according to this embodiment can predict the progression of aging and provide customized health management plans by collecting, analyzing, providing, monitoring, and notifying individuals about their health data.

[0030] The data collection unit collects personal health data. Personal health data includes, but is not limited to, vital signs, exercise data, and dietary data. For example, the data collection unit collects vital signs using a wearable device. Specifically, the wearable device monitors vital signs such as heart rate, blood pressure, body temperature, and oxygen saturation in real time and collects the data. This allows the user's health status to be constantly monitored. The data collection unit can also collect exercise data using a smartphone app. The smartphone app records and collects exercise data such as the user's steps, distance traveled, calories burned, and exercise time. Furthermore, the data collection unit can also collect dietary data using a food logging app. The food logging app records the contents, calories, and nutrients of the meals consumed by the user and collects the data. For example, the data collection unit collects vital signs such as heart rate and blood pressure from a wearable device in real time. The smartphone app records the user's exercise volume and steps and collects them as exercise data. The food logging app records the contents of the meals consumed by the user and collects them as dietary data. This allows the data collection unit to collect diverse data for a comprehensive understanding of an individual's health status. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and provisioning units. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. As a result, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes the data collected by the collection unit and predicts the progression of aging. For example, the analysis unit uses machine learning algorithms to generate aging prediction models based on individual health data. Specifically, the analysis unit uses a decision tree algorithm to predict the progression of aging from health data. The decision tree algorithm extracts features from the data and generates rules for predicting the progression of aging. The analysis unit can also generate aging prediction models using neural networks. Neural networks learn from large amounts of health data and generate highly accurate aging prediction models. Furthermore, the analysis unit can also predict the progression of aging using support vector machines. Support vector machines classify health data and predict the progression of aging. For example, the analysis unit uses a decision tree algorithm to extract features from health data and predict the progression of aging. Neural networks learn from large amounts of health data and generate highly accurate aging prediction models. Support vector machines classify health data and predict the progression of aging. This allows the analysis unit to quickly and accurately analyze collected data and predict the progression of aging. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, it can predict the progression of aging in specific age groups and lifestyles based on past health data, and formulate future countermeasures. The analysis unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The service provider offers a customized health management plan based on the aging progression predicted by the analysis department. This plan includes, for example, advice on diet and exercise, and suggestions for regular health checkups. Specifically, the service provider offers a balanced meal plan tailored to the user's health condition. The meal plan is designed to ensure the user receives the necessary nutrients in an appropriate amount, taking into account their nutritional balance. The service provider can also offer an exercise plan tailored to the user's fitness level. The exercise plan suggests appropriate exercises based on the user's physical strength and health condition. For example, it offers exercise plans tailored to the user's needs, such as walking, jogging, or strength training. Furthermore, the service provider can also suggest regular health checkups. Regular health checkups monitor the user's health condition and offer suggestions for early detection of abnormalities. For example, the service provider suggests a nutritionally balanced meal plan based on the user's health condition. The exercise plan suggests appropriate exercises based on the user's physical strength and health condition. Regular health checkups monitor the user's health condition and offer suggestions for early detection of abnormalities. This allows the service provider to offer customized health management plans tailored to each user's health condition, thereby preventing the progression of aging. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the health management plans. For example, based on user feedback, meal plans and exercise plans can be revised to provide more effective health management plans. In addition, the service provider can incorporate the latest medical information and research findings to constantly update the health management plans. This enables the service provider to provide users with the optimal health management plan and prevent the progression of aging.

[0033] The monitoring unit monitors the user's health status in real time based on the health management plan provided by the service provider. For example, the monitoring unit monitors vital signs in real time using wearable devices. Specifically, it monitors heart rate and blood pressure in real time and detects abnormalities. Wearable devices are attached to the user's body, allowing for constant monitoring of vital signs. The monitoring unit can also monitor exercise data in real time. Exercise data monitors the user's activity level and steps in real time and detects abnormalities. Furthermore, the monitoring unit can also monitor dietary data in real time. Dietary data monitors the contents of meals consumed by the user in real time and detects abnormalities. For example, the monitoring unit monitors heart rate and blood pressure data obtained from wearable devices in real time and detects abnormalities. Exercise data monitors the user's activity level and steps in real time and detects abnormalities. Dietary data monitors the contents of meals consumed by the user in real time and detects abnormalities. This allows the monitoring unit to constantly understand the user's health status and respond quickly if an abnormality occurs. Furthermore, the monitoring unit can analyze the collected data and monitor long-term changes in health status. For example, it can analyze trends in users' health status based on past data and predict future risks. The monitoring unit can also use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings. As a result, the monitoring unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0034] The notification unit detects abnormalities in health conditions monitored by the monitoring unit and notifies nursing homes and medical institutions. For example, when the notification unit detects an abnormality, it sends a notification to the nursing home or medical institution. Specifically, when the notification unit detects an abnormality, it sends a notification to the nursing home via email or SMS. Email notifications are used to provide detailed information, while SMS notifications are effective when a quick response is required. The notification unit can also notify medical institutions by telephone. Telephone notifications are used as a means of direct contact in emergencies. Furthermore, when the notification unit detects an abnormality, it can also send notifications through an app. App notifications are used as a means of providing real-time notifications to users, nursing homes, and medical institutions. For example, when the notification unit detects an abnormality, it sends an email notification to the nursing home. SMS notifications are effective when a quick response is required. Telephone notifications are used as a means of direct contact in emergencies. App notifications are used as a means of providing real-time notifications to users, nursing homes, and medical institutions. This allows the notification unit to quickly and reliably transmit information when an abnormality occurs and support appropriate responses. Furthermore, the notification unit can continuously improve the accuracy and effectiveness of its notifications. For example, it can review notification content and methods based on feedback from nursing homes and medical institutions that receive notifications, providing more effective notifications. The notification unit can also reliably transmit information using multiple communication methods. For instance, it can reliably deliver important information using not only smartphone notifications but also voice calls, SMS, and email. This allows the notification unit to provide users with prompt and reliable instructions, minimizing the risk of disaster.

[0035] The data collection unit can collect data such as daily health status, medical records, and lifestyle habits. For example, the data collection unit can collect sleep data, exercise data, and dietary data as part of daily health status. For example, the data collection unit can collect sleep data using a wearable device. The data collection unit can also collect exercise data using a smartphone app. Furthermore, the data collection unit can collect dietary data using a food logging app. For example, the data collection unit can collect sleep quality and duration from a wearable device. A smartphone app records the user's exercise volume and steps and collects it as exercise data. A food logging app records the contents of meals consumed by the user and collects it as dietary data. The data collection unit can collect medical records such as diagnosis results, prescriptions, and treatment history. For example, the data collection unit can obtain diagnosis results from a medical institution and collect them as medical records. The data collection unit can also collect prescription data. Furthermore, the data collection unit can also collect treatment history. For example, the data collection unit can electronically obtain diagnosis results from a medical institution and store them as medical records. Prescription data is obtained from pharmacies and collected as medical records. Treatment history data is collected from medical institutions and stored as medical records. The data collection unit can collect lifestyle habits such as smoking habits, drinking habits, and exercise habits. For example, the data collection unit can collect a user's smoking habits in the form of a questionnaire. The data collection unit can also collect data using an app that records drinking habits. Furthermore, the data collection unit can also collect data using an app that records exercise habits. For example, the data collection unit collects a user's smoking habits in the form of a questionnaire and stores it in a database. Drinking habits are collected using an app that records what the user drinks. Exercise habits are collected using an app that records what the user does for exercise. By collecting data on daily health status, medical records, and lifestyle habits, more accurate aging prediction becomes possible. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the user's health data into a generating AI and have the generating AI perform the data collection.

[0036] The analysis unit can generate aging prediction models based on individual health data using machine learning algorithms. For example, the analysis unit can predict the progression of aging from health data using a decision tree algorithm. For example, the analysis unit can extract features from health data using a decision tree algorithm and predict the progression of aging. The analysis unit can also generate aging prediction models using neural networks. For example, the analysis unit can use a neural network to learn from a large amount of health data and generate a highly accurate aging prediction model. Furthermore, the analysis unit can also predict the progression of aging using support vector machines. For example, the analysis unit can use a support vector machine to classify health data and predict the progression of aging. In this way, by using machine learning algorithms, aging prediction models based on individual health data can be generated. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without using generative AI. For example, the analysis unit can input user health data into a generative AI and have the generative AI generate an aging prediction model.

[0037] The service provider can offer customized health management plans, including advice on diet and exercise, and suggestions for regular health checkups. For example, the service provider can provide a balanced meal plan tailored to the user's health condition. For example, the service provider can propose a nutritionally balanced meal plan based on the user's health condition. The service provider can also provide an exercise plan tailored to the user's physical ability. For example, the service provider can suggest appropriate exercises based on the user's physical strength and health condition. Furthermore, the service provider can also suggest regular health checkups. For example, the service provider can monitor the user's health condition and suggest ways to detect abnormalities early. By providing customized health management plans, it becomes possible to provide appropriate advice and suggestions tailored to individual health conditions. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can input the user's health data into a generative AI and have the generative AI generate a health management plan.

[0038] The monitoring unit can monitor health status in real time. For example, the monitoring unit can monitor vital signs in real time using a wearable device. For example, the monitoring unit can monitor heart rate and blood pressure in real time and detect abnormalities. The monitoring unit can also monitor exercise data in real time. For example, the monitoring unit can monitor the user's exercise volume and step count in real time and detect abnormalities. Furthermore, the monitoring unit can also monitor dietary data in real time. For example, the monitoring unit can monitor the contents of the meals consumed by the user in real time and detect abnormalities. This allows for early detection of abnormalities and prompt response by monitoring health status in real time. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input data acquired from a wearable device into a generative AI and have the generative AI perform real-time monitoring.

[0039] The notification unit can notify nursing homes and medical institutions if it detects an abnormality in a person's health condition. For example, the notification unit can send a notification to nursing homes and medical institutions when it detects an abnormality. For example, the notification unit can send a notification to nursing homes via email or SMS when it detects an abnormality. The notification unit can also notify medical institutions by phone. Furthermore, the notification unit can also send notifications through an app when it detects an abnormality. For example, the notification unit can send an email notification to nursing homes when it detects an abnormality. SMS notifications are effective when a quick response is required. Phone notifications are used as a means of making direct contact in emergencies. App notifications are used as a means of notifying users, nursing homes, and medical institutions in real time. This enables a quick response by notifying when an abnormality in a person's health condition is detected. Some or all of the above-described processes in the notification unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the notification unit can input the data on the detected abnormality into a generation AI and have the generation AI generate the notification.

[0040] The aging prediction agent system according to this embodiment includes an encryption unit to enhance encryption technology for privacy protection. The encryption unit can enhance encryption technology for privacy protection. For example, the encryption unit encrypts data using AES (Advanced Encryption Standard). For example, the encryption unit encrypts personal health data using AES to prevent unauthorized access by third parties. The encryption unit can also encrypt data using RSA (Rivest-Shamir-Adleman). For example, the encryption unit uses RSA to encrypt data when it is transmitted to ensure data security. Furthermore, the encryption unit can also verify data integrity using SHA (Secure Hash Algorithm). For example, the encryption unit uses SHA to generate a hash value of the data and detect data tampering. By enhancing encryption technology in this way, the privacy protection of personal health data is improved. Some or all of the above-described processes in the encryption unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the encryption unit can have a generative AI perform data encryption.

[0041] The aging prediction agent system according to this embodiment has a data collection unit that analyzes the user's past health data and selects the optimal data collection method. The data collection unit can analyze the user's past health data and select the optimal data collection method. The data collection unit can, for example, identify the most effective data collection timing from the user's past health data. For example, the data collection unit analyzes the user's past health data and identifies the optimal data collection timing. The data collection unit can also select the type of data to collect based on the user's past health data. For example, the data collection unit analyzes the user's past health data and selects the type of data to collect. Furthermore, the data collection unit can analyze the user's past health data and adjust the data collection frequency. For example, the data collection unit analyzes the user's past health data and adjusts the data collection frequency. This allows for the selection of the optimal data collection method by analyzing past health data. Some or all of the above-described processes in the data collection unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the data collection unit can input the user's past health data into a generative AI and have the generative AI select the optimal data collection method.

[0042] The aging prediction agent system according to this embodiment has a data collection unit that filters the health data based on the user's current lifestyle and areas of interest when collecting it. The data collection unit can filter the health data based on the user's current lifestyle and areas of interest when collecting it. For example, the data collection unit can filter the types of data to collect based on the user's current lifestyle. The data collection unit can also determine the priority of the data to collect based on the user's areas of interest. For example, the data collection unit can determine the priority of the data to collect based on the user's areas of interest. Furthermore, the data collection unit can adjust the level of detail of the data to collect according to the user's lifestyle and areas of interest. For example, the data collection unit can adjust the level of detail of the data to collect according to the user's lifestyle and areas of interest. This allows for the collection of more relevant data by filtering the data based on the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using, for example, generative AI, or without using generative AI. For example, the data collection unit can input data on the user's lifestyle and areas of interest into a generating AI, and have the generating AI perform filtering.

[0043] The aging prediction agent system according to the embodiment prioritizes the collection of highly relevant data when the collection unit collects health data, taking into account the user's geographical location information. The collection unit can prioritize the collection of highly relevant data when collecting health data, taking into account the user's geographical location information. For example, if the user is in a specific region, the collection unit prioritizes the collection of health data related to that region. For example, if the user is in a specific region, the collection unit prioritizes the collection of vital signs related to that region. The collection unit can also collect health data related to environmental factors based on the user's geographical location information. For example, the collection unit collects exercise data related to environmental factors based on the user's geographical location information. Furthermore, the collection unit can also collect highly relevant health data by taking into account the user's travel history. For example, the collection unit collects highly relevant dietary data by taking into account the user's travel history. This allows for the priority collection of highly relevant data by considering geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, generative AI, or without using generative AI. For example, the data collection unit can input the user's geographical location information into the generating AI, allowing the generating AI to perform data collection.

[0044] The aging prediction agent system according to this embodiment analyzes the user's social media activity and collects relevant data when collecting health data. The collection unit can analyze the user's social media activity and collect relevant data when collecting health data. For example, the collection unit analyzes health-related posts from the user's social media activity and collects relevant vital signs. The collection unit can also collect health data while considering the user's social media friendships. For example, the collection unit collects relevant exercise data while considering the user's social media friendships. Furthermore, the collection unit can identify areas of health interest from the user's social media activity and collect relevant data. For example, the collection unit identifies areas of health interest from the user's social media activity and collects relevant dietary data. This allows for the collection of relevant health data by analyzing social media activity. Some or all of the above-described processing in the collection unit may be performed using, for example, generative AI, or without using generative AI. For example, the data collection unit can input data on the user's social media activity into a generating AI, allowing the generating AI to perform data collection.

[0045] The aging prediction agent system according to this embodiment adjusts the level of detail of the analysis based on the importance of the health data during analysis. The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during analysis. For example, the analysis unit performs a detailed analysis on important health data. For example, the analysis unit performs a detailed analysis on important vital signs. The analysis unit can also perform a simplified analysis on general health data. For example, the analysis unit performs a simplified analysis on general exercise data. Furthermore, the analysis unit can also determine the priority of the analysis according to the importance of the health data. For example, the analysis unit determines the priority of the analysis according to the importance of the health data. This makes it possible to perform a detailed analysis on important data by adjusting the level of detail of the analysis based on the importance of the health data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the importance of the health data into the generative AI and have the generative AI perform the adjustment of the level of detail of the analysis.

[0046] The aging prediction agent system according to the embodiment applies different analysis algorithms to the health data category during analysis. The analysis unit can apply different analysis algorithms to the health data category during analysis. For example, the analysis unit selects the optimal analysis algorithm according to the health data category. For example, the analysis unit selects the optimal analysis algorithm for the vital signs category. The analysis unit can also apply different analysis methods to each health data category. For example, the analysis unit applies different analysis methods to the exercise data category. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the health data category. For example, the analysis unit adjusts the level of detail of the analysis based on the diet data category. This improves the accuracy of the analysis by applying the optimal analysis algorithm according to the health data category. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the health data category into the generative AI and have the generative AI execute the application of the analysis algorithm.

[0047] In the aging prediction agent system according to this embodiment, the analysis unit determines the priority of analysis based on the timing of health data collection during analysis. The analysis unit can determine the priority of analysis based on the timing of health data collection during analysis. For example, the analysis unit prioritizes the analysis of recently collected health data. For example, the analysis unit prioritizes the analysis of recently collected vital signs. The analysis unit can also determine the priority of analysis by referring to past health data. For example, the analysis unit determines the priority of analysis by referring to past exercise data. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the timing of health data collection. For example, the analysis unit adjusts the level of detail of the analysis of dietary data based on the timing of health data collection. This allows for prioritizing the analysis of the latest data by determining the priority of analysis based on the timing of health data collection. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the timing of health data collection to the generative AI and have the generative AI perform the determination of the analysis priority.

[0048] The aging prediction agent system according to this embodiment has an analysis unit that adjusts the order of analysis based on the relevance of health data during analysis. The analysis unit can adjust the order of analysis based on the relevance of health data during analysis. For example, the analysis unit prioritizes the analysis of highly relevant health data. For example, the analysis unit prioritizes the analysis of highly relevant vital signs. The analysis unit can also determine the order of analysis based on the relevance of health data. For example, the analysis unit determines the order of analysis of exercise data based on the relevance of health data. Furthermore, the analysis unit can adjust the level of detail of the analysis according to the relevance of health data. For example, the analysis unit adjusts the level of detail of the analysis of diet data according to the relevance of health data. This allows for prioritizing the analysis of more relevant data by adjusting the order of analysis based on the relevance of health data. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the relevance of health data into a generative AI and have the generative AI perform the adjustment of the order of analysis.

[0049] The aging prediction agent system according to this embodiment adjusts the level of detail provided based on the importance of the health management plan at the time of provision. The provision unit can adjust the level of detail provided based on the importance of the health management plan at the time of provision. For example, the provision unit provides detailed information for important health management plans. For example, the provision unit provides detailed information for important dietary plans. The provision unit can also provide simplified information for general health management plans. For example, the provision unit provides simplified information for general exercise plans. Furthermore, the provision unit can also determine the priority of provision according to the importance of the health management plan. For example, the provision unit determines the priority of provision according to the importance of the health management plan. This allows for the provision of detailed information for important plans by adjusting the level of detail provided based on the importance of the health management plan. Some or all of the above processing in the provision unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the provision unit can input the importance of the health management plan into the generative AI and have the generative AI perform the adjustment of the level of detail provided.

[0050] The aging prediction agent system according to this embodiment applies different provisioning algorithms depending on the category of the health management plan when providing the service. The provisioning unit can apply different provisioning algorithms depending on the category of the health management plan when providing the service. For example, the provisioning unit selects the optimal provisioning algorithm depending on the category of the health management plan. For example, the provisioning unit selects the optimal provisioning algorithm for the meal plan category. The provisioning unit can also apply different provisioning methods for each category of health management plan. For example, the provisioning unit applies different provisioning methods for the exercise plan category. Furthermore, the provisioning unit can adjust the level of detail of the service based on the category of the health management plan. For example, the provisioning unit adjusts the level of detail of the service based on the regular health check category. This improves the accuracy of the service by applying the optimal provisioning algorithm depending on the category of the health management plan. Some or all of the above processing in the provisioning unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the provisioning unit can input the categories of the health management plan into a generative AI and have the generative AI execute the application of the provisioning algorithm.

[0051] The aging prediction agent system according to this embodiment has a provisioning unit that determines the priority of provision based on the submission timing of health management plans at the time of provision. The provisioning unit can determine the priority of provision based on the submission timing of health management plans at the time of provision. For example, the provisioning unit may prioritize providing health management plans that are of high urgency. For example, the provisioning unit may prioritize providing meal plans that are of high urgency. The provisioning unit can also determine the priority of provision based on the submission timing. For example, the provisioning unit may prioritize providing exercise plans based on the submission timing. Furthermore, the provisioning unit may adjust the level of detail of the provision depending on the submission timing. For example, the provisioning unit may adjust the level of detail of the proposal for regular health checks depending on the submission timing. This allows for the provision of plans that are of high urgency by determining the priority of provision based on the submission timing of health management plans. Some or all of the above processing in the provisioning unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the provisioning unit may input the submission timing of health management plans into a generative AI and have the generative AI perform the determination of the priority of provision.

[0052] The aging prediction agent system according to this embodiment adjusts the order of provision based on the relevance of health management plans at the time of provision. The provision unit can adjust the order of provision based on the relevance of health management plans at the time of provision. For example, the provision unit may prioritize providing health management plans that are highly relevant. For example, the provision unit may prioritize providing meal plans that are highly relevant. The provision unit can also determine the order of provision based on the relevance of health management plans. For example, the provision unit may determine the order of provision of exercise plans based on the relevance of health management plans. Furthermore, the provision unit may adjust the level of detail of provision according to the relevance of health management plans. For example, the provision unit may adjust the level of detail of suggestions for regular health checks according to the relevance of health management plans. This allows for the provision of more relevant plans by adjusting the order of provision based on the relevance of health management plans. Some or all of the above processing in the provision unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the provision unit may input the relevance of health management plans into a generative AI and have the generative AI perform the adjustment of the order of provision.

[0053] The aging prediction agent system according to this embodiment has a monitoring unit that, when monitoring, selects the optimal monitoring method by referring to the user's past health data. The monitoring unit can select the optimal monitoring method by referring to the user's past health data when monitoring. For example, the monitoring unit selects the optimal monitoring method based on the user's past health data. For example, the monitoring unit selects the optimal monitoring method based on the user's past vital signs. The monitoring unit can also adjust the level of detail of monitoring by referring to the user's past health data. For example, the monitoring unit adjusts the level of detail of monitoring by referring to the user's past exercise data. Furthermore, the monitoring unit can also determine the frequency of monitoring based on the user's past health data. For example, the monitoring unit determines the frequency of monitoring based on the user's past dietary data. This allows the optimal monitoring method to be selected by referring to past health data. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the monitoring unit can input the user's past health data into a generative AI and have the generative AI select the optimal monitoring method.

[0054] The aging prediction agent system according to the embodiment allows the monitoring unit to customize the monitoring means based on the user's current lifestyle during monitoring. The monitoring unit can customize the monitoring means based on the user's current lifestyle during monitoring. For example, the monitoring unit customizes the monitoring means for vital signs based on the user's current lifestyle. The monitoring unit can also adjust the level of detail of monitoring according to the user's lifestyle. For example, the monitoring unit adjusts the level of detail of monitoring exercise data according to the user's lifestyle. Furthermore, the monitoring unit can also determine the frequency of monitoring based on the user's lifestyle. For example, the monitoring unit determines the frequency of monitoring dietary data based on the user's lifestyle. This enables more appropriate monitoring by customizing the monitoring means based on the current lifestyle. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the monitoring unit can input data on the user's lifestyle into a generative AI and have the generative AI perform the customization of the monitoring means.

[0055] The aging prediction agent system according to the embodiment has a monitoring unit that, when monitoring, selects the optimal monitoring method considering the user's geographical location information. The monitoring unit can select the optimal monitoring method considering the user's geographical location information when monitoring. For example, if the user is in a specific area, the monitoring unit will prioritize monitoring health data related to that area. For example, if the user is in a specific area, the monitoring unit will prioritize monitoring vital signs related to that area. The monitoring unit can also monitor health data related to environmental factors based on the user's geographical location information. For example, the monitoring unit will monitor exercise data related to environmental factors based on the user's geographical location information. Furthermore, the monitoring unit can also monitor highly relevant health data considering the user's travel history. For example, the monitoring unit will monitor highly relevant dietary data considering the user's travel history. This allows for the selection of the optimal monitoring method by considering geographical location information. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the monitoring unit can input the user's geographical location information into a generative AI and have the generative AI select the monitoring method.

[0056] The aging prediction agent system according to this embodiment has a monitoring unit that analyzes the user's social media activity and proposes monitoring methods during monitoring. The monitoring unit can analyze the user's social media activity and propose monitoring methods during monitoring. For example, the monitoring unit analyzes health-related posts from the user's social media activity and monitors related data. For example, the monitoring unit analyzes health-related posts from the user's social media activity and monitors related vital signs. The monitoring unit can also monitor health data while considering the user's social media friendships. For example, the monitoring unit monitors related exercise data while considering the user's social media friendships. Furthermore, the monitoring unit can identify areas of health interest from the user's social media activity and monitor related data. For example, the monitoring unit identifies areas of health interest from the user's social media activity and monitors related diet data. This allows for the monitoring of related health data by analyzing social media activity. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the monitoring unit can input data on the user's social media activity into a generative AI and have the generative AI execute the proposal of monitoring methods.

[0057] The aging prediction agent system according to the embodiment selects the optimal notification method by referring to the user's past health data when a notification is made. The notification unit can select the optimal notification method by referring to the user's past health data when a notification is made. For example, the notification unit selects the optimal notification method based on the user's past health data. For example, the notification unit selects the optimal notification method based on the user's past vital signs. The notification unit can also adjust the level of detail of the notification by referring to the user's past health data. For example, the notification unit adjusts the level of detail of the notification by referring to the user's past exercise data. Furthermore, the notification unit can also determine the frequency of notifications based on the user's past health data. For example, the notification unit determines the frequency of notifications based on the user's past dietary data. This allows the system to select the optimal notification method by referring to past health data. Some or all of the above-described processing in the notification unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the notification unit can input the user's past health data into a generative AI and have the generative AI select the optimal notification method.

[0058] The aging prediction agent system according to this embodiment has a notification unit that, when it sends a notification, customizes the notification means based on the user's current lifestyle. The notification unit can customize the notification means based on the user's current lifestyle when it sends a notification. For example, the notification unit customizes the notification means for vital signs based on the user's current lifestyle. The notification unit can also adjust the level of detail of the notification according to the user's lifestyle. For example, the notification unit adjusts the level of detail of the exercise data notification according to the user's lifestyle. Furthermore, the notification unit can also determine the frequency of the notification based on the user's lifestyle. For example, the notification unit determines the frequency of the meal data notification based on the user's lifestyle. This makes it possible to send more appropriate notifications by customizing the notification means based on the current lifestyle. Some or all of the above processing in the notification unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the notification unit can input data on the user's lifestyle into a generative AI and have the generative AI perform the customization of the notification means.

[0059] The aging prediction agent system according to the embodiment selects the optimal notification method when a notification is made, taking into account the user's geographical location information. The notification unit can select the optimal notification method when a notification is made, taking into account the user's geographical location information. For example, if the user is in a specific region, the notification unit will prioritize notifying health data related to that region. For example, if the user is in a specific region, the notification unit will prioritize notifying vital signs related to that region. The notification unit can also notify health data related to environmental factors based on the user's geographical location information. For example, the notification unit will notify exercise data related to environmental factors based on the user's geographical location information. Furthermore, the notification unit can also notify health data that is highly relevant, taking into account the user's travel history. For example, the notification unit will notify dietary data that is highly relevant, taking into account the user's travel history. This allows the system to select the optimal notification method by taking into account geographical location information. Some or all of the above processing in the notification unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the notification unit can input the user's geographical location information into a generative AI and have the generative AI select the notification method.

[0060] The aging prediction agent system according to this embodiment has a notification unit that, when notifying, analyzes the user's social media activity and proposes a means of notification. The notification unit can analyze the user's social media activity and propose a means of notification when notifying. For example, the notification unit analyzes health-related posts from the user's social media activity and notifies the user of relevant vital signs. The notification unit can also notify the user of health data while considering the user's social media friendships. For example, the notification unit notifies the user of relevant exercise data while considering the user's social media friendships. Furthermore, the notification unit can identify areas of health interest from the user's social media activity and notify the user of relevant data. For example, the notification unit identifies areas of health interest from the user's social media activity and notifies the user of relevant dietary data. In this way, relevant health data can be notified by analyzing social media activity. Some or all of the above-described processing in the notification unit may be performed using, for example, generative AI, or without using generative AI. For example, the notification unit can input data on the user's social media activity into a generating AI and have the generating AI suggest notification methods.

[0061] The aging prediction agent system according to this embodiment has an encryption unit that, when encrypting data, selects the optimal encryption method by referring to the user's past data. The encryption unit can select the optimal encryption method by referring to the user's past data when encrypting data. For example, the encryption unit selects the optimal encryption method based on the user's past data. For example, the encryption unit selects the optimal encryption method based on the user's past vital signs. The encryption unit can also adjust the level of detail of the encryption by referring to the user's past data. For example, the encryption unit adjusts the level of detail of the encryption by referring to the user's past exercise data. Furthermore, the encryption unit can also determine the frequency of encryption based on the user's past data. For example, the encryption unit determines the frequency of encryption based on the user's past dietary data. This allows the system to select the optimal encryption method by referring to past data. Some or all of the above-described processes in the encryption unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the encryption unit can input the user's past data into a generative AI and have the generative AI select the optimal encryption method.

[0062] The aging prediction agent system according to this embodiment has an encryption unit that, when encrypting data, selects the optimal encryption method considering the user's geographical location information. The encryption unit can select the optimal encryption method considering the user's geographical location information when encrypting data. For example, if the user is in a specific region, the encryption unit prioritizes encrypting data related to that region. For example, if the user is in a specific region, the encryption unit prioritizes encrypting vital signs related to that region. The encryption unit can also encrypt data related to environmental factors based on the user's geographical location information. For example, the encryption unit encrypts exercise data related to environmental factors based on the user's geographical location information. Furthermore, the encryption unit can also encrypt highly relevant data considering the user's travel history. For example, the encryption unit encrypts highly relevant dietary data considering the user's travel history. This allows for the selection of the optimal encryption method by considering geographical location information. Some or all of the above-described processing in the encryption unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the encryption unit can input the user's geographical location information into a generative AI and have the generative AI select the encryption method.

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

[0064] The data collection unit can analyze the user's past health data and select the optimal collection method. For example, it can identify the most effective timing for data collection based on the user's past health data. It can also select the types of data to collect based on the user's past health data. Furthermore, it can adjust the collection frequency by analyzing the user's past health data. In this way, the optimal collection method can be selected by analyzing past health data.

[0065] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data. For example, it can perform detailed analysis on important health data, while performing simplified analysis on general health data. Furthermore, it can determine the priority of the analysis according to the importance of the health data. This allows for detailed analysis of important data by adjusting the level of detail based on the importance of the health data.

[0066] The delivery unit can apply different delivery algorithms depending on the category of the health management plan. For example, it can select the optimal delivery algorithm depending on the category of the health management plan. It can also apply different delivery methods to each category of health management plan. Furthermore, it can adjust the level of detail of the delivery based on the category of the health management plan. As a result, the accuracy of the delivery is improved by applying the optimal delivery algorithm according to the category of the health management plan.

[0067] The monitoring unit can customize monitoring methods based on the user's current living situation. For example, it can customize monitoring methods based on the user's current living situation. It can also adjust the level of detail of monitoring according to the user's living situation. Furthermore, it can determine the frequency of monitoring based on the user's living situation. This allows for more appropriate monitoring by customizing monitoring methods based on the current living situation.

[0068] The notification unit can select the optimal notification method by considering the user's geographical location. For example, if the user is in a specific region, it can prioritize notifying them of health data relevant to that region. It can also notify them of health data related to environmental factors based on the user's geographical location. Furthermore, it can notify them of highly relevant health data by considering the user's travel history. In this way, the optimal notification method can be selected by considering geographical location.

[0069] The following briefly describes the processing flow for example form 1.

[0070] Step 1: The data collection unit collects personal health data. This data includes vital signs, exercise data, and dietary data. The data collection unit collects vital signs using a wearable device, exercise data using a smartphone app, and dietary data using a food logging app. For example, the data collection unit collects vital signs such as heart rate and blood pressure in real time from a wearable device, records the user's exercise level and steps using a smartphone app, and records the contents of the meals the user has eaten using a food logging app. Step 2: The analysis unit analyzes the data collected by the collection unit and predicts the progression of aging. The analysis unit uses machine learning algorithms to generate an aging prediction model based on individual health data. For example, the analysis unit uses decision tree algorithms, neural networks, and support vector machines to predict the progression of aging. Step 3: The service provider provides a customized health management plan based on the aging progression predicted by the analysis unit. The service provider provides a health management plan that includes diet and exercise advice, and suggestions for regular health checkups. For example, the service provider will suggest a balanced diet plan, exercise plan, and regular health checkups according to the user's health condition. Step 4: The monitoring unit monitors the health status in real time based on the health management plan provided by the service provider. The monitoring unit uses wearable devices to monitor vital signs in real time, as well as exercise and dietary data. For example, the monitoring unit monitors heart rate, blood pressure, exercise volume, steps taken, and the contents of meals consumed in real time and detects any abnormalities. Step 5: The notification unit detects abnormalities in the health status monitored by the monitoring unit and notifies the care facility or medical institution. When an abnormality is detected, the notification unit notifies the care facility or medical institution via email, SMS, phone, or app.

[0071] (Example of form 2) An aging prediction agent system according to an embodiment of the present invention is a system that utilizes AI technology to predict the progression of aging from an individual's health data and provides a customized health management plan. This aging prediction agent system collaborates with nursing homes and medical institutions to develop highly reliable care support services. For example, the aging prediction agent system collects an individual's health data. This data includes daily health status, medical records, and lifestyle habits. Next, the aging prediction agent system uses AI to analyze the collected data and predict the progression of aging. The AI ​​uses machine learning algorithms to generate an aging prediction model based on the individual's health data. Based on the generated aging prediction model, the aging prediction agent system provides a customized health management plan. This plan includes advice on diet and exercise, and suggestions for regular health checkups. The aging prediction agent system also provides real-time monitoring of health status and care support. For example, if an abnormality in health status is detected, the aging prediction agent system notifies nursing homes and medical institutions to encourage a prompt response. Furthermore, the aging prediction agent system enhances encryption technology to protect privacy. Individual health data is stored encrypted to prevent unauthorized access by third parties. This system is expected to reduce care costs, extend healthy life expectancy, and decrease the number of hospital visits. For example, it is predicted to reduce care costs by up to 20% annually, extend healthy life expectancy by an average of more than two years, and reduce hospital visits by 30% annually. This aging prediction agent system will be an extremely useful tool for the elderly and their families, care facilities, and medical institutions. With the progression of an aging society and technological advancements, demand is expected to increase even further in the future. As a result, the aging prediction agent system can predict the progression of aging and provide customized health management plans by collecting, analyzing, providing, monitoring, and notifying individuals about their health data.

[0072] The aging prediction agent system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, a monitoring unit, and a notification unit. The collection unit collects personal health data. Personal health data includes, but is not limited to, vital signs, exercise data, and dietary data. The collection unit collects vital signs using, for example, a wearable device. The collection unit can also collect exercise data using a smartphone application. Furthermore, the collection unit can collect dietary data using a food logging application. For example, the collection unit collects vital signs such as heart rate and blood pressure in real time from a wearable device. The smartphone application records the user's exercise volume and step count and collects it as exercise data. The food logging application records the contents of meals consumed by the user and collects it as dietary data. The analysis unit analyzes the data collected by the collection unit and predicts the progression of aging. The analysis unit generates an aging prediction model based on individual health data using, for example, a machine learning algorithm. For example, the analysis unit predicts the progression of aging from health data using a decision tree algorithm. Furthermore, the analysis unit can generate aging prediction models using neural networks. Additionally, the analysis unit can predict the progression of aging using support vector machines. For example, the analysis unit uses a decision tree algorithm to extract features from health data and predict the progression of aging. The neural network learns from a large amount of health data and generates a highly accurate aging prediction model. The support vector machine classifies health data and predicts the progression of aging. The service provider provides a customized health management plan based on the aging progression predicted by the analysis unit. The service provider provides a health management plan that includes, for example, advice on diet and exercise, and suggestions for regular health checkups. For example, the service provider provides a balanced meal plan according to the user's health condition. The service provider can also provide an exercise plan tailored to the user's fitness level. Furthermore, the service provider can suggest regular health checkups. For example, the service provider suggests a nutritionally balanced meal plan based on the user's health condition. The exercise plan suggests appropriate exercises according to the user's physical fitness and health condition.Regular health checks monitor the user's health status and provide suggestions for early detection of abnormalities. The monitoring unit monitors the user's health status in real time based on the health management plan provided by the service provider. The monitoring unit monitors vital signs in real time, for example, using wearable devices. For example, the monitoring unit monitors heart rate and blood pressure in real time and detects abnormalities. The monitoring unit can also monitor exercise data in real time. Furthermore, the monitoring unit can monitor dietary data in real time. For example, the monitoring unit monitors heart rate and blood pressure data acquired from wearable devices in real time and detects abnormalities. Exercise data monitors the user's exercise volume and step count in real time and detects abnormalities. Dietary data monitors the contents of meals consumed by the user in real time and detects abnormalities. The notification unit detects abnormalities in the health status monitored by the monitoring unit and notifies care facilities and medical institutions. For example, the notification unit sends a notification to care facilities and medical institutions when an abnormality is detected. For example, the notification unit can send notifications to nursing care facilities via email or SMS when it detects an abnormality. It can also notify medical institutions by phone. Furthermore, the notification unit can send notifications via an app when it detects an abnormality. For example, when it detects an abnormality, it sends an email notification to nursing care facilities. SMS notifications are effective when a quick response is required. Phone notifications are used as a means of direct contact in emergencies. App notifications are used as a means of providing real-time notifications to users, nursing care facilities, and medical institutions. As a result, the aging prediction agent system according to this embodiment can predict the progression of aging and provide customized health management plans by collecting, analyzing, providing, monitoring, and notifying individuals about their health data.

[0073] The data collection unit collects personal health data. Personal health data includes, but is not limited to, vital signs, exercise data, and dietary data. For example, the data collection unit collects vital signs using a wearable device. Specifically, the wearable device monitors vital signs such as heart rate, blood pressure, body temperature, and oxygen saturation in real time and collects the data. This allows the user's health status to be constantly monitored. The data collection unit can also collect exercise data using a smartphone app. The smartphone app records and collects exercise data such as the user's steps, distance traveled, calories burned, and exercise time. Furthermore, the data collection unit can also collect dietary data using a food logging app. The food logging app records the contents, calories, and nutrients of the meals consumed by the user and collects the data. For example, the data collection unit collects vital signs such as heart rate and blood pressure from a wearable device in real time. The smartphone app records the user's exercise volume and steps and collects them as exercise data. The food logging app records the contents of the meals consumed by the user and collects them as dietary data. This allows the data collection unit to collect diverse data for a comprehensive understanding of an individual's health status. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and provisioning units. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. As a result, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0074] The analysis unit analyzes the data collected by the collection unit and predicts the progression of aging. For example, the analysis unit uses machine learning algorithms to generate aging prediction models based on individual health data. Specifically, the analysis unit uses a decision tree algorithm to predict the progression of aging from health data. The decision tree algorithm extracts features from the data and generates rules for predicting the progression of aging. The analysis unit can also generate aging prediction models using neural networks. Neural networks learn from large amounts of health data and generate highly accurate aging prediction models. Furthermore, the analysis unit can also predict the progression of aging using support vector machines. Support vector machines classify health data and predict the progression of aging. For example, the analysis unit uses a decision tree algorithm to extract features from health data and predict the progression of aging. Neural networks learn from large amounts of health data and generate highly accurate aging prediction models. Support vector machines classify health data and predict the progression of aging. This allows the analysis unit to quickly and accurately analyze collected data and predict the progression of aging. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, it can predict the progression of aging in specific age groups and lifestyles based on past health data, and formulate future countermeasures. The analysis unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0075] The service provider offers a customized health management plan based on the aging progression predicted by the analysis department. This plan includes, for example, advice on diet and exercise, and suggestions for regular health checkups. Specifically, the service provider offers a balanced meal plan tailored to the user's health condition. The meal plan is designed to ensure the user receives the necessary nutrients in an appropriate amount, taking into account their nutritional balance. The service provider can also offer an exercise plan tailored to the user's fitness level. The exercise plan suggests appropriate exercises based on the user's physical strength and health condition. For example, it offers exercise plans tailored to the user's needs, such as walking, jogging, or strength training. Furthermore, the service provider can also suggest regular health checkups. Regular health checkups monitor the user's health condition and offer suggestions for early detection of abnormalities. For example, the service provider suggests a nutritionally balanced meal plan based on the user's health condition. The exercise plan suggests appropriate exercises based on the user's physical strength and health condition. Regular health checkups monitor the user's health condition and offer suggestions for early detection of abnormalities. This allows the service provider to offer customized health management plans tailored to each user's health condition, thereby preventing the progression of aging. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the health management plans. For example, based on user feedback, meal plans and exercise plans can be revised to provide more effective health management plans. In addition, the service provider can incorporate the latest medical information and research findings to constantly update the health management plans. This enables the service provider to provide users with the optimal health management plan and prevent the progression of aging.

[0076] The monitoring unit monitors the user's health status in real time based on the health management plan provided by the service provider. For example, the monitoring unit monitors vital signs in real time using wearable devices. Specifically, it monitors heart rate and blood pressure in real time and detects abnormalities. Wearable devices are attached to the user's body, allowing for constant monitoring of vital signs. The monitoring unit can also monitor exercise data in real time. Exercise data monitors the user's activity level and steps in real time and detects abnormalities. Furthermore, the monitoring unit can also monitor dietary data in real time. Dietary data monitors the contents of meals consumed by the user in real time and detects abnormalities. For example, the monitoring unit monitors heart rate and blood pressure data obtained from wearable devices in real time and detects abnormalities. Exercise data monitors the user's activity level and steps in real time and detects abnormalities. Dietary data monitors the contents of meals consumed by the user in real time and detects abnormalities. This allows the monitoring unit to constantly understand the user's health status and respond quickly if an abnormality occurs. Furthermore, the monitoring unit can analyze the collected data and monitor long-term changes in health status. For example, it can analyze trends in users' health status based on past data and predict future risks. The monitoring unit can also use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings. As a result, the monitoring unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0077] The notification unit detects abnormalities in health conditions monitored by the monitoring unit and notifies nursing homes and medical institutions. For example, when the notification unit detects an abnormality, it sends a notification to the nursing home or medical institution. Specifically, when the notification unit detects an abnormality, it sends a notification to the nursing home via email or SMS. Email notifications are used to provide detailed information, while SMS notifications are effective when a quick response is required. The notification unit can also notify medical institutions by telephone. Telephone notifications are used as a means of direct contact in emergencies. Furthermore, when the notification unit detects an abnormality, it can also send notifications through an app. App notifications are used as a means of providing real-time notifications to users, nursing homes, and medical institutions. For example, when the notification unit detects an abnormality, it sends an email notification to the nursing home. SMS notifications are effective when a quick response is required. Telephone notifications are used as a means of direct contact in emergencies. App notifications are used as a means of providing real-time notifications to users, nursing homes, and medical institutions. This allows the notification unit to quickly and reliably transmit information when an abnormality occurs and support appropriate responses. Furthermore, the notification unit can continuously improve the accuracy and effectiveness of its notifications. For example, it can review notification content and methods based on feedback from nursing homes and medical institutions that receive notifications, providing more effective notifications. The notification unit can also reliably transmit information using multiple communication methods. For instance, it can reliably deliver important information using not only smartphone notifications but also voice calls, SMS, and email. This allows the notification unit to provide users with prompt and reliable instructions, minimizing the risk of disaster.

[0078] The data collection unit can collect data such as daily health status, medical records, and lifestyle habits. For example, the data collection unit can collect sleep data, exercise data, and dietary data as part of daily health status. For example, the data collection unit can collect sleep data using a wearable device. The data collection unit can also collect exercise data using a smartphone app. Furthermore, the data collection unit can collect dietary data using a food logging app. For example, the data collection unit can collect sleep quality and duration from a wearable device. A smartphone app records the user's exercise volume and steps and collects it as exercise data. A food logging app records the contents of meals consumed by the user and collects it as dietary data. The data collection unit can collect medical records such as diagnosis results, prescriptions, and treatment history. For example, the data collection unit can obtain diagnosis results from a medical institution and collect them as medical records. The data collection unit can also collect prescription data. Furthermore, the data collection unit can also collect treatment history. For example, the data collection unit can electronically obtain diagnosis results from a medical institution and store them as medical records. Prescription data is obtained from pharmacies and collected as medical records. Treatment history data is collected from medical institutions and stored as medical records. The data collection unit can collect lifestyle habits such as smoking habits, drinking habits, and exercise habits. For example, the data collection unit can collect a user's smoking habits in the form of a questionnaire. The data collection unit can also collect data using an app that records drinking habits. Furthermore, the data collection unit can also collect data using an app that records exercise habits. For example, the data collection unit collects a user's smoking habits in the form of a questionnaire and stores it in a database. Drinking habits are collected using an app that records what the user drinks. Exercise habits are collected using an app that records what the user does for exercise. By collecting data on daily health status, medical records, and lifestyle habits, more accurate aging prediction becomes possible. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the user's health data into a generating AI and have the generating AI perform the data collection.

[0079] The analysis unit can generate aging prediction models based on individual health data using machine learning algorithms. For example, the analysis unit can predict the progression of aging from health data using a decision tree algorithm. For example, the analysis unit can extract features from health data using a decision tree algorithm and predict the progression of aging. The analysis unit can also generate aging prediction models using neural networks. For example, the analysis unit can use a neural network to learn from a large amount of health data and generate a highly accurate aging prediction model. Furthermore, the analysis unit can also predict the progression of aging using support vector machines. For example, the analysis unit can use a support vector machine to classify health data and predict the progression of aging. In this way, by using machine learning algorithms, aging prediction models based on individual health data can be generated. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without using generative AI. For example, the analysis unit can input user health data into a generative AI and have the generative AI generate an aging prediction model.

[0080] The service provider can offer customized health management plans, including advice on diet and exercise, and suggestions for regular health checkups. For example, the service provider can provide a balanced meal plan tailored to the user's health condition. For example, the service provider can propose a nutritionally balanced meal plan based on the user's health condition. The service provider can also provide an exercise plan tailored to the user's physical ability. For example, the service provider can suggest appropriate exercises based on the user's physical strength and health condition. Furthermore, the service provider can also suggest regular health checkups. For example, the service provider can monitor the user's health condition and suggest ways to detect abnormalities early. By providing customized health management plans, it becomes possible to provide appropriate advice and suggestions tailored to individual health conditions. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can input the user's health data into a generative AI and have the generative AI generate a health management plan.

[0081] The monitoring unit can monitor health status in real time. For example, the monitoring unit can monitor vital signs in real time using a wearable device. For example, the monitoring unit can monitor heart rate and blood pressure in real time and detect abnormalities. The monitoring unit can also monitor exercise data in real time. For example, the monitoring unit can monitor the user's exercise volume and step count in real time and detect abnormalities. Furthermore, the monitoring unit can also monitor dietary data in real time. For example, the monitoring unit can monitor the contents of the meals consumed by the user in real time and detect abnormalities. This allows for early detection of abnormalities and prompt response by monitoring health status in real time. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input data acquired from a wearable device into a generative AI and have the generative AI perform real-time monitoring.

[0082] The notification unit can notify nursing homes and medical institutions if it detects an abnormality in a person's health condition. For example, the notification unit can send a notification to nursing homes and medical institutions when it detects an abnormality. For example, the notification unit can send a notification to nursing homes via email or SMS when it detects an abnormality. The notification unit can also notify medical institutions by phone. Furthermore, the notification unit can also send notifications through an app when it detects an abnormality. For example, the notification unit can send an email notification to nursing homes when it detects an abnormality. SMS notifications are effective when a quick response is required. Phone notifications are used as a means of making direct contact in emergencies. App notifications are used as a means of notifying users, nursing homes, and medical institutions in real time. This enables a quick response by notifying when an abnormality in a person's health condition is detected. Some or all of the above-described processes in the notification unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the notification unit can input the data on the detected abnormality into a generation AI and have the generation AI generate the notification.

[0083] The aging prediction agent system according to this embodiment includes an encryption unit to enhance encryption technology for privacy protection. The encryption unit can enhance encryption technology for privacy protection. For example, the encryption unit encrypts data using AES (Advanced Encryption Standard). For example, the encryption unit encrypts personal health data using AES to prevent unauthorized access by third parties. The encryption unit can also encrypt data using RSA (Rivest-Shamir-Adleman). For example, the encryption unit uses RSA to encrypt data when it is transmitted to ensure data security. Furthermore, the encryption unit can also verify data integrity using SHA (Secure Hash Algorithm). For example, the encryption unit uses SHA to generate a hash value of the data and detect data tampering. By enhancing encryption technology in this way, the privacy protection of personal health data is improved. Some or all of the above-described processes in the encryption unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the encryption unit can have a generative AI perform data encryption.

[0084] The aging prediction agent system according to this embodiment has a data collection unit that estimates the user's emotions and adjusts the timing of health data collection based on the estimated user's emotions. The data collection unit can estimate the user's emotions and adjust the timing of health data collection based on the estimated user's emotions. For example, if the user is feeling stressed, the data collection unit will collect health data when the user is relaxed. For example, if the user is feeling stressed, the data collection unit will collect vital signs when the user is relaxed. The data collection unit can also collect detailed health data when the user is relaxed. For example, if the user is relaxed, the data collection unit will collect detailed exercise data. Furthermore, if the user is in a hurry, the data collection unit can collect simplified health data. For example, if the user is in a hurry, the data collection unit will collect simplified meal data. This allows for the collection of health data at a more appropriate time by adjusting the collection timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the processing described above in the data collection unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of data collection.

[0085] The aging prediction agent system according to this embodiment has a data collection unit that analyzes the user's past health data and selects the optimal data collection method. The data collection unit can analyze the user's past health data and select the optimal data collection method. The data collection unit can, for example, identify the most effective data collection timing from the user's past health data. For example, the data collection unit analyzes the user's past health data and identifies the optimal data collection timing. The data collection unit can also select the type of data to collect based on the user's past health data. For example, the data collection unit analyzes the user's past health data and selects the type of data to collect. Furthermore, the data collection unit can analyze the user's past health data and adjust the data collection frequency. For example, the data collection unit analyzes the user's past health data and adjusts the data collection frequency. This allows for the selection of the optimal data collection method by analyzing past health data. Some or all of the above-described processes in the data collection unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the data collection unit can input the user's past health data into a generative AI and have the generative AI select the optimal data collection method.

[0086] The aging prediction agent system according to this embodiment has a data collection unit that filters the health data based on the user's current lifestyle and areas of interest when collecting it. The data collection unit can filter the health data based on the user's current lifestyle and areas of interest when collecting it. For example, the data collection unit can filter the types of data to collect based on the user's current lifestyle. The data collection unit can also determine the priority of the data to collect based on the user's areas of interest. For example, the data collection unit can determine the priority of the data to collect based on the user's areas of interest. Furthermore, the data collection unit can adjust the level of detail of the data to collect according to the user's lifestyle and areas of interest. For example, the data collection unit can adjust the level of detail of the data to collect according to the user's lifestyle and areas of interest. This allows for the collection of more relevant data by filtering the data based on the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using, for example, generative AI, or without using generative AI. For example, the data collection unit can input data on the user's lifestyle and areas of interest into a generating AI, and have the generating AI perform filtering.

[0087] The aging prediction agent system according to this embodiment has a data collection unit that estimates the user's emotions and determines the priority of health data to collect based on the estimated user emotions. 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. For example, if the user is stressed, the data collection unit will prioritize collecting stress-related health data. For example, if the user is stressed, the data collection unit will prioritize collecting stress-related vital signs. The data collection unit can also collect general health data if the user is relaxed. For example, if the user is relaxed, the data collection unit will collect general exercise data. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting only important health data. For example, if the user is in a hurry, the data collection unit will prioritize collecting only important dietary data. This allows for the priority collection of important data by determining the data priority based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of the data.

[0088] The aging prediction agent system according to the embodiment prioritizes the collection of highly relevant data when the collection unit collects health data, taking into account the user's geographical location information. The collection unit can prioritize the collection of highly relevant data when collecting health data, taking into account the user's geographical location information. For example, if the user is in a specific region, the collection unit prioritizes the collection of health data related to that region. For example, if the user is in a specific region, the collection unit prioritizes the collection of vital signs related to that region. The collection unit can also collect health data related to environmental factors based on the user's geographical location information. For example, the collection unit collects exercise data related to environmental factors based on the user's geographical location information. Furthermore, the collection unit can also collect highly relevant health data by taking into account the user's travel history. For example, the collection unit collects highly relevant dietary data by taking into account the user's travel history. This allows for the priority collection of highly relevant data by considering geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, generative AI, or without using generative AI. For example, the data collection unit can input the user's geographical location information into the generating AI, allowing the generating AI to perform data collection.

[0089] The aging prediction agent system according to this embodiment analyzes the user's social media activity and collects relevant data when collecting health data. The collection unit can analyze the user's social media activity and collect relevant data when collecting health data. For example, the collection unit analyzes health-related posts from the user's social media activity and collects relevant vital signs. The collection unit can also collect health data while considering the user's social media friendships. For example, the collection unit collects relevant exercise data while considering the user's social media friendships. Furthermore, the collection unit can identify areas of health interest from the user's social media activity and collect relevant data. For example, the collection unit identifies areas of health interest from the user's social media activity and collects relevant dietary data. This allows for the collection of relevant health data by analyzing social media activity. Some or all of the above-described processing in the collection unit may be performed using, for example, generative AI, or without using generative AI. For example, the data collection unit can input data on the user's social media activity into a generating AI, allowing the generating AI to perform data collection.

[0090] The aging prediction agent system according to this embodiment has an analysis unit that estimates the user's emotions and adjusts the method of expressing the analysis based on the estimated user's emotions. The analysis unit can estimate the user's emotions and adjust the method of expressing the analysis based on the estimated user's emotions. For example, if the user is tense, the analysis unit provides simple and highly visual analysis results. For example, if the user is tense, the analysis unit provides simple and highly visual analysis results of vital signs. The analysis unit can also provide detailed analysis results if the user is relaxed. For example, if the user is relaxed, the analysis unit provides detailed analysis results of exercise data. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. For example, if the user is in a hurry, the analysis unit provides concise analysis results of diet data. By adjusting the method of expressing the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the method of expressing the analysis.

[0091] The aging prediction agent system according to this embodiment adjusts the level of detail of the analysis based on the importance of the health data during analysis. The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during analysis. For example, the analysis unit performs a detailed analysis on important health data. For example, the analysis unit performs a detailed analysis on important vital signs. The analysis unit can also perform a simplified analysis on general health data. For example, the analysis unit performs a simplified analysis on general exercise data. Furthermore, the analysis unit can also determine the priority of the analysis according to the importance of the health data. For example, the analysis unit determines the priority of the analysis according to the importance of the health data. This makes it possible to perform a detailed analysis on important data by adjusting the level of detail of the analysis based on the importance of the health data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the importance of the health data into the generative AI and have the generative AI perform the adjustment of the level of detail of the analysis.

[0092] The aging prediction agent system according to the embodiment applies different analysis algorithms to the health data category during analysis. The analysis unit can apply different analysis algorithms to the health data category during analysis. For example, the analysis unit selects the optimal analysis algorithm according to the health data category. For example, the analysis unit selects the optimal analysis algorithm for the vital signs category. The analysis unit can also apply different analysis methods to each health data category. For example, the analysis unit applies different analysis methods to the exercise data category. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the health data category. For example, the analysis unit adjusts the level of detail of the analysis based on the diet data category. This improves the accuracy of the analysis by applying the optimal analysis algorithm according to the health data category. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the health data category into the generative AI and have the generative AI execute the application of the analysis algorithm.

[0093] The aging prediction agent system according to this embodiment has an analysis unit that estimates the user's emotions and adjusts the length of the analysis based on the estimated user's emotions. The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. For example, if the user is in a hurry, the analysis unit provides a short and concise analysis result. For example, if the user is in a hurry, the analysis unit provides a short and concise analysis result of vital signs. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, if the user is relaxed, the analysis unit provides a detailed analysis result of exercise data. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis result. For example, if the user is excited, the analysis unit provides a visually stimulating analysis result of diet data. By adjusting the length of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the length of the analysis.

[0094] In the aging prediction agent system according to this embodiment, the analysis unit determines the priority of analysis based on the timing of health data collection during analysis. The analysis unit can determine the priority of analysis based on the timing of health data collection during analysis. For example, the analysis unit prioritizes the analysis of recently collected health data. For example, the analysis unit prioritizes the analysis of recently collected vital signs. The analysis unit can also determine the priority of analysis by referring to past health data. For example, the analysis unit determines the priority of analysis by referring to past exercise data. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the timing of health data collection. For example, the analysis unit adjusts the level of detail of the analysis of dietary data based on the timing of health data collection. This allows for prioritizing the analysis of the latest data by determining the priority of analysis based on the timing of health data collection. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the timing of health data collection to the generative AI and have the generative AI perform the determination of the analysis priority.

[0095] The aging prediction agent system according to this embodiment has an analysis unit that adjusts the order of analysis based on the relevance of health data during analysis. The analysis unit can adjust the order of analysis based on the relevance of health data during analysis. For example, the analysis unit prioritizes the analysis of highly relevant health data. For example, the analysis unit prioritizes the analysis of highly relevant vital signs. The analysis unit can also determine the order of analysis based on the relevance of health data. For example, the analysis unit determines the order of analysis of exercise data based on the relevance of health data. Furthermore, the analysis unit can adjust the level of detail of the analysis according to the relevance of health data. For example, the analysis unit adjusts the level of detail of the analysis of diet data according to the relevance of health data. This allows for prioritizing the analysis of more relevant data by adjusting the order of analysis based on the relevance of health data. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the relevance of health data into a generative AI and have the generative AI perform the adjustment of the order of analysis.

[0096] The aging prediction agent system according to this embodiment estimates the user's emotions and adjusts the way the health management plan is presented based on the estimated emotions. The provider can estimate the user's emotions and adjust the way the health management plan is presented based on the estimated emotions. For example, if the user is stressed, the provider can provide a simple and easy-to-understand health management plan. For example, if the user is stressed, the provider can provide a simple and easy-to-understand meal plan. The provider can also provide a detailed health management plan if the user is relaxed. For example, if the user is relaxed, the provider can provide a detailed exercise plan. Furthermore, if the user is in a hurry, the provider can provide a concise health management plan. For example, if the user is in a hurry, the provider can provide a concise suggestion for regular health checks. By adjusting the way the health management plan is presented based on the user's emotions, a more appropriate plan can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the processing described above in the service provider may be performed using a generative AI, or not using a generative AI. For example, the service provider may input user emotion data into a generative AI and have the generative AI adjust the way the health management plan is presented.

[0097] The aging prediction agent system according to this embodiment adjusts the level of detail provided based on the importance of the health management plan at the time of provision. The provision unit can adjust the level of detail provided based on the importance of the health management plan at the time of provision. For example, the provision unit provides detailed information for important health management plans. For example, the provision unit provides detailed information for important dietary plans. The provision unit can also provide simplified information for general health management plans. For example, the provision unit provides simplified information for general exercise plans. Furthermore, the provision unit can also determine the priority of provision according to the importance of the health management plan. For example, the provision unit determines the priority of provision according to the importance of the health management plan. This allows for the provision of detailed information for important plans by adjusting the level of detail provided based on the importance of the health management plan. Some or all of the above processing in the provision unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the provision unit can input the importance of the health management plan into the generative AI and have the generative AI perform the adjustment of the level of detail provided.

[0098] The aging prediction agent system according to this embodiment applies different provisioning algorithms depending on the category of the health management plan when providing the service. The provisioning unit can apply different provisioning algorithms depending on the category of the health management plan when providing the service. For example, the provisioning unit selects the optimal provisioning algorithm depending on the category of the health management plan. For example, the provisioning unit selects the optimal provisioning algorithm for the meal plan category. The provisioning unit can also apply different provisioning methods for each category of health management plan. For example, the provisioning unit applies different provisioning methods for the exercise plan category. Furthermore, the provisioning unit can adjust the level of detail of the service based on the category of the health management plan. For example, the provisioning unit adjusts the level of detail of the service based on the regular health check category. This improves the accuracy of the service by applying the optimal provisioning algorithm depending on the category of the health management plan. Some or all of the above processing in the provisioning unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the provisioning unit can input the categories of the health management plan into a generative AI and have the generative AI execute the application of the provisioning algorithm.

[0099] The aging prediction agent system according to this embodiment estimates the user's emotions and adjusts the length of the health management plan provided based on the estimated emotions. The provider can estimate the user's emotions and adjust the length of the health management plan provided based on the estimated emotions. For example, if the user is in a hurry, the provider can provide a short, concise health management plan. For example, if the user is in a hurry, the provider can provide a short, concise meal plan. The provider can also provide a detailed health management plan if the user is relaxed. For example, if the user is relaxed, the provider can provide a detailed exercise plan. Furthermore, if the user is excited, the provider can provide a visually stimulating health management plan. For example, if the user is excited, the provider can provide a visually stimulating suggestion for regular health checks. This allows for the provision of a more appropriate plan by adjusting the length of the health management plan based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the processing described above in the service provider may be performed using a generative AI, or not using a generative AI. For example, the service provider may input user emotion data into a generative AI and have the generative AI adjust the length of the health management plan.

[0100] The aging prediction agent system according to this embodiment has a provisioning unit that determines the priority of provision based on the submission timing of health management plans at the time of provision. The provisioning unit can determine the priority of provision based on the submission timing of health management plans at the time of provision. For example, the provisioning unit may prioritize providing health management plans that are of high urgency. For example, the provisioning unit may prioritize providing meal plans that are of high urgency. The provisioning unit can also determine the priority of provision based on the submission timing. For example, the provisioning unit may prioritize providing exercise plans based on the submission timing. Furthermore, the provisioning unit may adjust the level of detail of the provision depending on the submission timing. For example, the provisioning unit may adjust the level of detail of the proposal for regular health checks depending on the submission timing. This allows for the provision of plans that are of high urgency by determining the priority of provision based on the submission timing of health management plans. Some or all of the above processing in the provisioning unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the provisioning unit may input the submission timing of health management plans into a generative AI and have the generative AI perform the determination of the priority of provision.

[0101] The aging prediction agent system according to this embodiment adjusts the order of provision based on the relevance of health management plans at the time of provision. The provision unit can adjust the order of provision based on the relevance of health management plans at the time of provision. For example, the provision unit may prioritize providing health management plans that are highly relevant. For example, the provision unit may prioritize providing meal plans that are highly relevant. The provision unit can also determine the order of provision based on the relevance of health management plans. For example, the provision unit may determine the order of provision of exercise plans based on the relevance of health management plans. Furthermore, the provision unit may adjust the level of detail of provision according to the relevance of health management plans. For example, the provision unit may adjust the level of detail of suggestions for regular health checks according to the relevance of health management plans. This allows for the provision of more relevant plans by adjusting the order of provision based on the relevance of health management plans. Some or all of the above processing in the provision unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the provision unit may input the relevance of health management plans into a generative AI and have the generative AI perform the adjustment of the order of provision.

[0102] The aging prediction agent system according to this embodiment has a monitoring unit that estimates the user's emotions and adjusts the monitoring method based on the estimated user's emotions. The monitoring unit can estimate the user's emotions and adjust the monitoring method based on the estimated user's emotions. For example, if the user is tense, the monitoring unit provides a simple and highly visible monitoring method. For example, if the user is tense, the monitoring unit provides a simple and highly visible monitoring method for vital signs. The monitoring unit can also provide a detailed monitoring method if the user is relaxed. For example, if the user is relaxed, the monitoring unit provides a detailed monitoring method for exercise data. Furthermore, if the user is in a hurry, the monitoring unit can provide a concise monitoring method. For example, if the user is in a hurry, the monitoring unit provides a concise monitoring method for meal data. This makes it possible to perform more appropriate monitoring by adjusting the monitoring method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processes in the monitoring unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI adjust the monitoring method.

[0103] The aging prediction agent system according to this embodiment has a monitoring unit that, when monitoring, selects the optimal monitoring method by referring to the user's past health data. The monitoring unit can select the optimal monitoring method by referring to the user's past health data when monitoring. For example, the monitoring unit selects the optimal monitoring method based on the user's past health data. For example, the monitoring unit selects the optimal monitoring method based on the user's past vital signs. The monitoring unit can also adjust the level of detail of monitoring by referring to the user's past health data. For example, the monitoring unit adjusts the level of detail of monitoring by referring to the user's past exercise data. Furthermore, the monitoring unit can also determine the frequency of monitoring based on the user's past health data. For example, the monitoring unit determines the frequency of monitoring based on the user's past dietary data. This allows the optimal monitoring method to be selected by referring to past health data. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the monitoring unit can input the user's past health data into a generative AI and have the generative AI select the optimal monitoring method.

[0104] The aging prediction agent system according to the embodiment allows the monitoring unit to customize the monitoring means based on the user's current lifestyle during monitoring. The monitoring unit can customize the monitoring means based on the user's current lifestyle during monitoring. For example, the monitoring unit customizes the monitoring means for vital signs based on the user's current lifestyle. The monitoring unit can also adjust the level of detail of monitoring according to the user's lifestyle. For example, the monitoring unit adjusts the level of detail of monitoring exercise data according to the user's lifestyle. Furthermore, the monitoring unit can also determine the frequency of monitoring based on the user's lifestyle. For example, the monitoring unit determines the frequency of monitoring dietary data based on the user's lifestyle. This enables more appropriate monitoring by customizing the monitoring means based on the current lifestyle. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the monitoring unit can input data on the user's lifestyle into a generative AI and have the generative AI perform the customization of the monitoring means.

[0105] The aging prediction agent system according to this embodiment has a monitoring unit that estimates the user's emotions and determines monitoring priorities based on the estimated user's emotions. The monitoring unit can estimate the user's emotions and determine monitoring priorities based on the estimated user's emotions. For example, if the user is stressed, the monitoring unit will prioritize monitoring stress-related health data. For example, if the user is stressed, the monitoring unit will prioritize monitoring stress-related vital signs. The monitoring unit can also monitor general health data if the user is relaxed. For example, if the user is relaxed, the monitoring unit will monitor general exercise data. Furthermore, if the user is in a hurry, the monitoring unit can prioritize monitoring only important health data. For example, if the user is in a hurry, the monitoring unit will prioritize monitoring only important dietary data. This allows for prioritizing monitoring of important data by determining monitoring priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the monitoring unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI determine the monitoring priorities.

[0106] The aging prediction agent system according to the embodiment has a monitoring unit that, when monitoring, selects the optimal monitoring method considering the user's geographical location information. The monitoring unit can select the optimal monitoring method considering the user's geographical location information when monitoring. For example, if the user is in a specific area, the monitoring unit will prioritize monitoring health data related to that area. For example, if the user is in a specific area, the monitoring unit will prioritize monitoring vital signs related to that area. The monitoring unit can also monitor health data related to environmental factors based on the user's geographical location information. For example, the monitoring unit will monitor exercise data related to environmental factors based on the user's geographical location information. Furthermore, the monitoring unit can also monitor highly relevant health data considering the user's travel history. For example, the monitoring unit will monitor highly relevant dietary data considering the user's travel history. This allows for the selection of the optimal monitoring method by considering geographical location information. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the monitoring unit can input the user's geographical location information into a generative AI and have the generative AI select the monitoring method.

[0107] The aging prediction agent system according to this embodiment has a monitoring unit that analyzes the user's social media activity and proposes monitoring methods during monitoring. The monitoring unit can analyze the user's social media activity and propose monitoring methods during monitoring. For example, the monitoring unit analyzes health-related posts from the user's social media activity and monitors related data. For example, the monitoring unit analyzes health-related posts from the user's social media activity and monitors related vital signs. The monitoring unit can also monitor health data while considering the user's social media friendships. For example, the monitoring unit monitors related exercise data while considering the user's social media friendships. Furthermore, the monitoring unit can identify areas of health interest from the user's social media activity and monitor related data. For example, the monitoring unit identifies areas of health interest from the user's social media activity and monitors related diet data. This allows for the monitoring of related health data by analyzing social media activity. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the monitoring unit can input data on the user's social media activity into a generative AI and have the generative AI execute the proposal of monitoring methods.

[0108] The aging prediction agent system according to this embodiment has a notification unit that estimates the user's emotions and adjusts the notification method based on the estimated user's emotions. The notification unit can estimate the user's emotions and adjust the notification method based on the estimated user's emotions. For example, if the user is tense, the notification unit provides a simple and highly visible notification method. For example, if the user is tense, the notification unit provides a simple and highly visible notification method for vital signs. The notification unit can also provide a detailed notification method if the user is relaxed. For example, if the user is relaxed, the notification unit provides a notification method for detailed exercise data. Furthermore, if the user is in a hurry, the notification unit can provide a concise notification method. For example, if the user is in a hurry, the notification unit provides a notification method for concise meal data. By adjusting the notification method based on the user's emotions, more appropriate notifications become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the notification unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the notification unit can input user emotion data into a generative AI and have the generative AI adjust the notification method.

[0109] The aging prediction agent system according to the embodiment selects the optimal notification method by referring to the user's past health data when a notification is made. The notification unit can select the optimal notification method by referring to the user's past health data when a notification is made. For example, the notification unit selects the optimal notification method based on the user's past health data. For example, the notification unit selects the optimal notification method based on the user's past vital signs. The notification unit can also adjust the level of detail of the notification by referring to the user's past health data. For example, the notification unit adjusts the level of detail of the notification by referring to the user's past exercise data. Furthermore, the notification unit can also determine the frequency of notifications based on the user's past health data. For example, the notification unit determines the frequency of notifications based on the user's past dietary data. This allows the system to select the optimal notification method by referring to past health data. Some or all of the above-described processing in the notification unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the notification unit can input the user's past health data into a generative AI and have the generative AI select the optimal notification method.

[0110] The aging prediction agent system according to this embodiment has a notification unit that, when it sends a notification, customizes the notification means based on the user's current lifestyle. The notification unit can customize the notification means based on the user's current lifestyle when it sends a notification. For example, the notification unit customizes the notification means for vital signs based on the user's current lifestyle. The notification unit can also adjust the level of detail of the notification according to the user's lifestyle. For example, the notification unit adjusts the level of detail of the exercise data notification according to the user's lifestyle. Furthermore, the notification unit can also determine the frequency of the notification based on the user's lifestyle. For example, the notification unit determines the frequency of the meal data notification based on the user's lifestyle. This makes it possible to send more appropriate notifications by customizing the notification means based on the current lifestyle. Some or all of the above processing in the notification unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the notification unit can input data on the user's lifestyle into a generative AI and have the generative AI perform the customization of the notification means.

[0111] The aging prediction agent system according to this embodiment has a notification unit that estimates the user's emotions and determines the priority of notifications based on the estimated user's emotions. The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated user's emotions. For example, if the user is feeling stressed, the notification unit will prioritize notifying stress-related health data. For example, if the user is feeling stressed, the notification unit will prioritize notifying stress-related vital signs. The notification unit can also notify general health data if the user is relaxed. For example, if the user is relaxed, the notification unit will notify general exercise data. Furthermore, if the user is in a hurry, the notification unit can prioritize notifying only important health data. For example, if the user is in a hurry, the notification unit will prioritize notifying only important dietary data. In this way, by determining the priority of notifications based on the user's emotions, important data can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the processing described above in the notification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the notification unit can input user emotion data into a generative AI and have the generative AI determine the priority of notifications.

[0112] The aging prediction agent system according to the embodiment selects the optimal notification method when a notification is made, taking into account the user's geographical location information. The notification unit can select the optimal notification method when a notification is made, taking into account the user's geographical location information. For example, if the user is in a specific region, the notification unit will prioritize notifying health data related to that region. For example, if the user is in a specific region, the notification unit will prioritize notifying vital signs related to that region. The notification unit can also notify health data related to environmental factors based on the user's geographical location information. For example, the notification unit will notify exercise data related to environmental factors based on the user's geographical location information. Furthermore, the notification unit can also notify health data that is highly relevant, taking into account the user's travel history. For example, the notification unit will notify dietary data that is highly relevant, taking into account the user's travel history. This allows the system to select the optimal notification method by taking into account geographical location information. Some or all of the above processing in the notification unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the notification unit can input the user's geographical location information into a generative AI and have the generative AI select the notification method.

[0113] The aging prediction agent system according to this embodiment has a notification unit that, when notifying, analyzes the user's social media activity and proposes a means of notification. The notification unit can analyze the user's social media activity and propose a means of notification when notifying. For example, the notification unit analyzes health-related posts from the user's social media activity and notifies the user of relevant vital signs. The notification unit can also notify the user of health data while considering the user's social media friendships. For example, the notification unit notifies the user of relevant exercise data while considering the user's social media friendships. Furthermore, the notification unit can identify areas of health interest from the user's social media activity and notify the user of relevant data. For example, the notification unit identifies areas of health interest from the user's social media activity and notifies the user of relevant dietary data. In this way, relevant health data can be notified by analyzing social media activity. Some or all of the above-described processing in the notification unit may be performed using, for example, generative AI, or without using generative AI. For example, the notification unit can input data on the user's social media activity into a generating AI and have the generating AI suggest notification methods.

[0114] The aging prediction agent system according to this embodiment has an encryption unit that estimates the user's emotions and adjusts the encryption method based on the estimated user's emotions. The encryption unit can estimate the user's emotions and adjust the encryption method based on the estimated user's emotions. For example, if the user is tense, the encryption unit provides a simple and highly visible encryption method. For example, if the user is tense, the encryption unit provides a simple and highly visible encryption method for vital signs. The encryption unit can also provide a detailed encryption method if the user is relaxed. For example, if the user is relaxed, the encryption unit provides a detailed encryption method for exercise data. Furthermore, if the user is in a hurry, the encryption unit can provide a concise encryption method. For example, if the user is in a hurry, the encryption unit provides a concise encryption method for meal data. By adjusting the encryption method based on the user's emotions, more appropriate encryption becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the encryption unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the encryption unit can input user emotion data into a generative AI and have the generative AI adjust the encryption method.

[0115] The aging prediction agent system according to this embodiment has an encryption unit that, when encrypting data, selects the optimal encryption method by referring to the user's past data. The encryption unit can select the optimal encryption method by referring to the user's past data when encrypting data. For example, the encryption unit selects the optimal encryption method based on the user's past data. For example, the encryption unit selects the optimal encryption method based on the user's past vital signs. The encryption unit can also adjust the level of detail of the encryption by referring to the user's past data. For example, the encryption unit adjusts the level of detail of the encryption by referring to the user's past exercise data. Furthermore, the encryption unit can also determine the frequency of encryption based on the user's past data. For example, the encryption unit determines the frequency of encryption based on the user's past dietary data. This allows the system to select the optimal encryption method by referring to past data. Some or all of the above-described processes in the encryption unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the encryption unit can input the user's past data into a generative AI and have the generative AI select the optimal encryption method.

[0116] The aging prediction agent system according to this embodiment has an encryption unit that estimates the user's emotions and determines encryption priorities based on the estimated user's emotions. The encryption unit can estimate the user's emotions and determine encryption priorities based on the estimated user's emotions. For example, if the user is stressed, the encryption unit will prioritize encrypting stress-related data. For example, if the user is stressed, the encryption unit will prioritize encrypting stress-related vital signs. The encryption unit can also encrypt general data if the user is relaxed. For example, if the user is relaxed, the encryption unit will encrypt general exercise data. Furthermore, if the user is in a hurry, the encryption unit can prioritize encrypting only important data. For example, if the user is in a hurry, the encryption unit will prioritize encrypting only important meal data. In this way, important data can be prioritized by determining encryption priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processes in the encryption unit may be performed using, for example, a generative AI, or without a generative AI. For example, the encryption unit can input user emotion data into a generative AI and have the generative AI determine the encryption priority.

[0117] The aging prediction agent system according to this embodiment has an encryption unit that, when encrypting data, selects the optimal encryption method considering the user's geographical location information. The encryption unit can select the optimal encryption method considering the user's geographical location information when encrypting data. For example, if the user is in a specific region, the encryption unit prioritizes encrypting data related to that region. For example, if the user is in a specific region, the encryption unit prioritizes encrypting vital signs related to that region. The encryption unit can also encrypt data related to environmental factors based on the user's geographical location information. For example, the encryption unit encrypts exercise data related to environmental factors based on the user's geographical location information. Furthermore, the encryption unit can also encrypt highly relevant data considering the user's travel history. For example, the encryption unit encrypts highly relevant dietary data considering the user's travel history. This allows for the selection of the optimal encryption method by considering geographical location information. Some or all of the above-described processing in the encryption unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the encryption unit can input the user's geographical location information into a generative AI and have the generative AI select the encryption method.

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

[0119] The analysis unit can estimate the user's emotions and determine the priority of analysis based on those emotions. For example, if the user is stressed, stress-related health data can be prioritized for analysis. If the user is relaxed, general health data can be analyzed. Furthermore, if the user is in a hurry, only important health data can be prioritized for analysis. In this way, by determining the priority of analysis based on the user's emotions, important data can be analyzed preferentially.

[0120] The service provider can estimate the user's emotions and adjust the level of detail in the health management plan based on those emotions. For example, if the user is stressed, a simple and easy-to-understand health management plan can be provided. If the user is relaxed, a more detailed health management plan can be provided. Furthermore, if the user is in a hurry, a concise health management plan can be provided. By adjusting the level of detail in the health management plan based on the user's emotions, a more appropriate plan can be provided.

[0121] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on those emotions. For example, if the user is stressed, health data can be monitored more frequently. Conversely, if the user is relaxed, health data can be monitored at a normal frequency. Furthermore, if the user is in a hurry, only important health data can be prioritized for monitoring. This allows for prioritizing the monitoring of important data by adjusting the monitoring frequency based on the user's emotions.

[0122] The notification unit can estimate the user's emotions and adjust the notification content based on those emotions. For example, if the user is stressed, it can provide a simple and highly visible notification. If the user is relaxed, it can provide a more detailed notification. Furthermore, if the user is in a hurry, it can provide a concise notification. By adjusting the notification content based on the user's emotions, more appropriate notifications can be provided.

[0123] The encryption unit can estimate the user's emotions and adjust the encryption strength based on those emotions. For example, if the user is stressed, strong encryption can be provided. If the user is relaxed, normal encryption can be provided. Furthermore, if the user is in a hurry, simplified encryption can be provided. This allows for more appropriate encryption by adjusting the encryption strength based on the user's emotions.

[0124] The data collection unit can analyze the user's past health data and select the optimal collection method. For example, it can identify the most effective timing for data collection based on the user's past health data. It can also select the types of data to collect based on the user's past health data. Furthermore, it can adjust the collection frequency by analyzing the user's past health data. In this way, the optimal collection method can be selected by analyzing past health data.

[0125] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data. For example, it can perform detailed analysis on important health data, while performing simplified analysis on general health data. Furthermore, it can determine the priority of the analysis according to the importance of the health data. This allows for detailed analysis of important data by adjusting the level of detail based on the importance of the health data.

[0126] The delivery unit can apply different delivery algorithms depending on the category of the health management plan. For example, it can select the optimal delivery algorithm depending on the category of the health management plan. It can also apply different delivery methods to each category of health management plan. Furthermore, it can adjust the level of detail of the delivery based on the category of the health management plan. As a result, the accuracy of the delivery is improved by applying the optimal delivery algorithm according to the category of the health management plan.

[0127] The monitoring unit can customize monitoring methods based on the user's current living situation. For example, it can customize monitoring methods based on the user's current living situation. It can also adjust the level of detail of monitoring according to the user's living situation. Furthermore, it can determine the frequency of monitoring based on the user's living situation. This allows for more appropriate monitoring by customizing monitoring methods based on the current living situation.

[0128] The notification unit can select the optimal notification method by considering the user's geographical location. For example, if the user is in a specific region, it can prioritize notifying them of health data relevant to that region. It can also notify them of health data related to environmental factors based on the user's geographical location. Furthermore, it can notify them of highly relevant health data by considering the user's travel history. In this way, the optimal notification method can be selected by considering geographical location.

[0129] The following briefly describes the processing flow for example form 2.

[0130] Step 1: The data collection unit collects personal health data. This data includes vital signs, exercise data, and dietary data. The data collection unit collects vital signs using a wearable device, exercise data using a smartphone app, and dietary data using a food logging app. For example, the data collection unit collects vital signs such as heart rate and blood pressure in real time from a wearable device, records the user's exercise level and steps using a smartphone app, and records the contents of the meals the user has eaten using a food logging app. Step 2: The analysis unit analyzes the data collected by the collection unit and predicts the progression of aging. The analysis unit uses machine learning algorithms to generate an aging prediction model based on individual health data. For example, the analysis unit uses decision tree algorithms, neural networks, and support vector machines to predict the progression of aging. Step 3: The service provider provides a customized health management plan based on the aging progression predicted by the analysis unit. The service provider provides a health management plan that includes diet and exercise advice, and suggestions for regular health checkups. For example, the service provider will suggest a balanced diet plan, exercise plan, and regular health checkups according to the user's health condition. Step 4: The monitoring unit monitors the health status in real time based on the health management plan provided by the service provider. The monitoring unit uses wearable devices to monitor vital signs in real time, as well as exercise and dietary data. For example, the monitoring unit monitors heart rate, blood pressure, exercise volume, steps taken, and the contents of meals consumed in real time and detects any abnormalities. Step 5: The notification unit detects abnormalities in the health status monitored by the monitoring unit and notifies the care facility or medical institution. When an abnormality is detected, the notification unit notifies the care facility or medical institution via email, SMS, phone, or app.

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

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, monitoring unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects health data using the smart device 14's wearable device or smartphone application. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and predicts the progression of aging. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides a customized health management plan. The monitoring unit is implemented by the control unit 46A of the smart device 14, which monitors the health status in real time. The notification unit is implemented by the specific processing unit 290 of the data processing unit 12, which notifies nursing homes and medical institutions when an abnormality is detected. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

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

[0148] 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.

[0149] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0150] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, monitoring unit, and notification unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects health data using the camera and microphone of the smart glasses 214. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and predicts the progression of aging. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides a customized health management plan. The monitoring unit is implemented by the control unit 46A of the smart glasses 214, which monitors the health status in real time. The notification unit is implemented by the specific processing unit 290 of the data processing unit 12, which notifies nursing homes and medical institutions when an abnormality is detected. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.).

[0163] 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.

[0164] 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.

[0165] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0166] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, monitoring unit, and notification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects health data using the camera and microphone of the headset terminal 314. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and predicts the progression of aging. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides a customized health management plan. The monitoring unit is implemented by the control unit 46A of the headset terminal 314, which monitors the health status in real time. The notification unit is implemented by the specific processing unit 290 of the data processing unit 12, which notifies nursing homes and medical institutions when an abnormality is detected. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.).

[0180] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0181] 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.

[0182] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0183] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, monitoring unit, and notification unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit collects health data using the camera and microphone of the robot 414. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and predicts the progression of aging. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides a customized health management plan. The monitoring unit is implemented by the control unit 46A of the robot 414, which monitors the health status in real time. The notification unit is implemented by the specific processing unit 290 of the data processing unit 12, which notifies nursing homes and medical institutions when an abnormality is detected. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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."

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] (Note 1) A data collection unit that collects personal health data, An analysis unit analyzes the data collected by the aforementioned collection unit and predicts the progression of aging, Based on the aging progression predicted by the aforementioned analysis unit, a provision unit provides a customized health management plan. Based on the health management plan provided by the aforementioned provision unit, a monitoring unit monitors the health status in real time, The system includes a notification unit that detects abnormalities in health conditions monitored by the aforementioned monitoring unit and notifies nursing care facilities and medical institutions. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data on daily health status, medical records, and lifestyle habits. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Using machine learning algorithms, generate aging prediction models based on individual health data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, We offer customized health management plans that include advice on diet and exercise, and suggestions for regular health checkups. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned monitoring unit, Real-time monitoring of health status The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned notification unit, If an abnormality in health is detected, the system will notify nursing homes and medical institutions. The system described in Appendix 1, characterized by the features described herein. (Note 7) To protect privacy, it is equipped with an encryption unit that enhances encryption technology. The system described in Appendix 1, characterized by the features described herein. (Note 8) 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 9) 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 10) The aforementioned collection unit is When collecting health data, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) 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 12) 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 13) 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 14) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the health data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of health data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, 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 18) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the health data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the health data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, We estimate the user's emotions and adjust how the health management plan is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing the service, adjust the level of detail based on the importance of the health management plan. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing the service, a different delivery algorithm will be applied depending on the category of the health management plan. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the health management plan provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, Prioritizing services based on when the health management plan was submitted will be determined at the time of provision. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing services, adjust the order of delivery based on their relevance to your health management plan. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned monitoring unit, It estimates user sentiment and adjusts monitoring methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned monitoring unit, During monitoring, the system selects the optimal monitoring method by referring to the user's past health data. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned monitoring unit, During monitoring, the monitoring methods are customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned monitoring unit, It estimates user sentiment and determines monitoring priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned monitoring unit, During monitoring, the optimal monitoring method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned monitoring unit, During monitoring, we analyze users' social media activity and propose monitoring methods. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned notification unit, It estimates the user's emotions and adjusts the notification method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned notification unit, When sending a notification, the system will refer to the user's past health data to select the most suitable notification method. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned notification unit, When sending notifications, customize the notification method based on the user's current life situation. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned notification unit, When sending notifications, the system will select the most suitable notification method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned notification unit, When sending notifications, we analyze the user's social media activity and suggest notification methods. The system described in Appendix 1, characterized by the features described herein. (Note 38) The encryption unit is It estimates the user's emotions and adjusts the encryption method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The encryption unit is During encryption, the system selects the optimal encryption method by referring to the user's past data. The system described in Appendix 1, characterized by the features described herein. (Note 40) The encryption unit is It estimates the user's emotions and determines encryption priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 41) The encryption unit is During encryption, the system selects the optimal encryption method while considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0203] 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 personal health data, An analysis unit analyzes the data collected by the aforementioned collection unit and predicts the progression of aging, Based on the aging progression predicted by the aforementioned analysis unit, a provision unit provides a customized health management plan. Based on the health management plan provided by the aforementioned provision unit, a monitoring unit monitors the health status in real time, The system includes a notification unit that detects abnormalities in health conditions monitored by the aforementioned monitoring unit and notifies nursing care facilities and medical institutions. A system characterized by the following features.

2. The aforementioned collection unit is We collect data on daily health status, medical records, and lifestyle habits. The system according to feature 1.

3. The aforementioned analysis unit, Using machine learning algorithms, generate aging prediction models based on individual health data. The system according to feature 1.

4. The aforementioned supply unit is, We provide customized health management plans that include advice on diet and exercise, and suggestions for regular health checkups. The system according to feature 1.

5. The aforementioned monitoring unit, Real-time monitoring of health status The system according to feature 1.

6. The aforementioned notification unit, If an abnormality in health is detected, the system will notify nursing homes and medical institutions. The system according to feature 1.

7. To protect privacy, it is equipped with an encryption unit that enhances encryption technology. The system according to feature 1.

8. 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.

9. 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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