system

Abstract

The system according to this embodiment aims to reduce the burden on caregivers by appropriately understanding changes in a patient's physical condition and responding promptly. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a notification unit, a proposal unit, and an organization unit. The collection unit collects the patient's medical data and lifestyle data. The analysis unit analyzes the data collected by the collection unit and predicts the risk of deterioration of health. The notification unit notifies the patient of necessary precautions and countermeasures based on the analysis results obtained by the analysis unit. The proposal unit makes specific suggestions for meal, medication, and rest schedules based on the information notified by the notification unit. The organization unit automatically prioritizes tasks based on the information proposed by the proposal unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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, there is a problem that it is difficult to appropriately grasp the physical condition changes of patients and respond promptly, and the burden on caregivers is large.

[0005] The system according to the embodiment aims to reduce the burden on caregivers by appropriately grasping the physical condition changes of patients and responding promptly.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a notification unit, a proposal unit, and an organization unit. The data collection unit collects the patient's medical data and lifestyle data. The analysis unit analyzes the data collected by the data collection unit and predicts the risk of deterioration of health. The notification unit notifies the patient of necessary precautions and countermeasures based on the analysis results obtained by the analysis unit. The proposal unit makes specific suggestions for meal, medication, and rest schedules based on the information notified by the notification unit. The organization unit automatically prioritizes tasks based on the information proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can reduce the burden on caregivers by appropriately understanding changes in the patient's physical condition and responding quickly. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

[0018] The data processing device 包括 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) The caregiver support AI agent according to an embodiment of the present invention is a system designed to alleviate the burden caused by a shortage of caregivers and realize a society where everyone can enjoy their later years with peace of mind. The caregiver support AI agent uses AI to analyze the patient's medical and lifestyle data and predict the risk of deterioration in health. Next, the AI ​​notifies caregivers and family members of necessary precautions and countermeasures. Furthermore, the AI ​​makes specific suggestions for meal, medication, and rest schedules, streamlining caregiving tasks. The AI ​​also automatically prioritizes tasks for busy caregivers. This reduces stress on caregivers and improves the quality of life (QOL) for both patients and caregivers. For example, the caregiver support AI agent collects and analyzes the patient's medical and lifestyle data. In this process, it collects data such as the patient's body temperature, blood pressure, heart rate, diet, and exercise level. For example, the AI ​​can analyze the body temperature and blood pressure data that the patient records daily to understand changes in their health. This allows it to predict the risk of deterioration in health. Next, the AI ​​predicts the risk of deterioration in health and notifies caregivers and family members of necessary precautions and countermeasures. For example, if a patient's temperature rises, the AI ​​will notify them with a message such as, "Your temperature is rising. Please hydrate and rest." This allows caregivers and family members to take appropriate action quickly. Furthermore, the AI ​​provides specific suggestions for meal, medication, and rest schedules. For instance, it suggests meal menus, medication timings, and rest times tailored to the patient's physical condition and lifestyle. This streamlines caregiving tasks and improves patient health management. The AI ​​also automatically prioritizes tasks for busy caregivers. For example, it lists the tasks a caregiver needs to complete and prioritizes them according to their importance and urgency. This allows caregivers to work more efficiently. This system reduces caregiver stress and improves the quality of life (QOL) for both patients and caregivers. For example, with AI support, caregivers can take appropriate action quickly and improve patient health management. Also, the increased efficiency of caregiving tasks reduces the burden on caregivers. This contributes to creating a society where everyone can enjoy their later years with peace of mind. This allows the caregiver support AI agent to reduce the burden on caregivers and improve the quality of life for both patients and caregivers.

[0029] The caregiver support AI agent according to this embodiment comprises a collection unit, an analysis unit, a notification unit, a suggestion unit, and an organization unit. The collection unit collects the patient's medical data and lifestyle data. For example, the collection unit collects data such as the patient's body temperature, blood pressure, heart rate, diet, and exercise level. For example, the collection unit can collect data such as body temperature and blood pressure that the patient records daily. The collection unit can also collect data such as the patient's diet and exercise level. For example, the collection unit can collect data recorded by the patient using a smartphone or wearable device. The analysis unit analyzes the data collected by the collection unit and predicts the risk of deterioration of the patient's health. For example, the analysis unit analyzes the collected body temperature and blood pressure data to understand changes in the patient's health. For example, the analysis unit can use AI to analyze the data and predict the risk of deterioration of the patient's health. The analysis unit can also predict changes in the patient's health based on the collected data. The notification unit notifies the user of necessary precautions and countermeasures based on the analysis results obtained by the analysis unit. The notification unit, for example, sends a notification if the patient's temperature is elevated, such as "Your temperature is elevated. Please hydrate and rest." The notification unit can also send notifications based on analysis results using AI. Furthermore, the notification unit can notify patients and caregivers of appropriate countermeasures. The suggestion unit provides specific suggestions for meal, medication, and rest schedules based on the information provided by the notification unit. For example, the suggestion unit suggests meal menus, medication timing, and rest times tailored to the patient's physical condition and lifestyle. The suggestion unit can also use AI to make suggestions. Furthermore, the suggestion unit can also provide specific suggestions to improve the patient's health management. The organization unit automatically prioritizes tasks based on the information suggested by the suggestion unit. For example, the organization unit lists tasks that caregivers should perform and prioritizes them according to their importance and urgency. The organization unit can use AI to prioritize tasks. Furthermore, the organization unit can automatically prioritize tasks to streamline the caregiver's work. As a result, the caregiver support AI agent according to this embodiment can reduce the burden on caregivers and improve the quality of life (QOL) for both patients and caregivers.

[0030] The data collection unit collects patient medical and lifestyle data. Specifically, it collects data such as the patient's body temperature, blood pressure, heart rate, diet, and exercise level. For example, it can collect body temperature and blood pressure data that patients record daily. This data is recorded using smartphones or wearable devices and transmitted to the data collection unit. The data collection unit centrally manages this data and can update it in real time. Furthermore, the data collection unit can also collect data on the patient's diet and exercise level. For example, this data can be collected when patients record their meals using a smartphone app or measure their exercise level with a wearable device. The data collection unit stores this data on a cloud server and can link with other systems and departments as needed. This allows the data collection unit to comprehensively understand the patient's health status and build a foundation for providing appropriate care support. In addition, the data collection unit can flexibly respond to specific situations and conditions by adjusting the frequency and accuracy of data collection. For example, for patients whose health is deteriorating, increasing the frequency of data collection allows for a more detailed understanding of their health status. This enables the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis department analyzes data collected by the data collection department to predict the risk of health deterioration. Specifically, it analyzes collected body temperature and blood pressure data to understand changes in health. The analysis department can use AI to analyze data and predict the risk of health deterioration. For example, AI can compare past and current data to detect abnormal patterns and trends. This allows for early detection of changes in health and appropriate action to be taken. The analysis department can also predict changes in patients' health based on the collected data. For example, it can analyze body temperature and blood pressure data to identify periods and situations where health is likely to deteriorate. Furthermore, the analysis department can utilize past data and statistical information to conduct long-term risk assessments and trend analyses. For example, it can predict fluctuations in health during specific seasons or time periods based on past data and formulate future countermeasures. In addition, the analysis department can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue early warnings. This allows the analysis department to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The notification unit notifies users of necessary precautions and countermeasures based on the analysis results obtained by the analysis unit. Specifically, if a user's body temperature is elevated, it will send a notification such as, "Your body temperature is elevated. Please stay hydrated and rest." The notification unit can use AI to send notifications based on the analysis results. For example, the AI ​​analyzes the collected data and generates appropriate notifications when it detects abnormal patterns or risks. The notification unit can also notify patients and caregivers of appropriate countermeasures. For example, if there is a possibility of the user's condition worsening, it will notify them to see a doctor as soon as possible. Furthermore, the notification unit can customize the content of notifications. For example, it can adjust the content and timing of notifications to match the individual health condition and lifestyle of the patient. This allows the notification unit to provide more appropriate and effective notifications to patients and caregivers. In addition, the notification unit can reliably transmit information using multiple communication methods. For example, 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 information to patients and caregivers quickly and reliably and support appropriate responses.

[0033] The suggestion department makes specific suggestions for meals, medication, and rest schedules based on information notified by the notification department. Specifically, it suggests meal menus, medication timing, and rest periods tailored to the patient's physical condition and lifestyle. The suggestion department can use AI to make suggestions. For example, the AI ​​analyzes the patient's health status and lifestyle based on collected data to generate optimal meal menus and medication schedules. The suggestion department can also make specific suggestions to improve the patient's health management. For example, if the patient's condition is deteriorating, it may suggest meals rich in specific nutrients or suggest increasing rest time. Furthermore, the suggestion department can customize the suggestions. For example, it can adjust the content and timing of suggestions to match the patient's individual health condition and lifestyle. This allows the suggestion department to provide patients with more appropriate and effective suggestions. In addition, the suggestion department can monitor the effectiveness of the suggestions and modify them as needed. For example, it can monitor whether the suggested meal menus and medication schedules are effective and revise the suggestions if they are insufficient. This allows the suggestion department to continuously support the patient's health management and provide optimal suggestions.

[0034] The task prioritization department automatically prioritizes tasks based on information proposed by the proposal department. Specifically, it lists tasks that caregivers should perform and prioritizes them according to their importance and urgency. The task prioritization department can use AI to prioritize tasks. For example, the AI ​​can automatically list tasks that caregivers should perform based on collected data and proposals, and prioritize them according to their importance and urgency. The task prioritization department can also automatically prioritize tasks to streamline caregivers' work. For example, it can centrally manage tasks that caregivers should perform and prioritize important tasks to improve work efficiency. Furthermore, the task prioritization department can monitor the progress of tasks and adjust task priorities as needed. For example, if a high-urgency task arises, it will review the prioritization of existing tasks and prioritize the high-urgency task. The task prioritization department can also collect feedback from caregivers and continuously improve the accuracy and effectiveness of task prioritization. As a result, the task prioritization department can streamline caregivers' work and provide more appropriate care support to patients.

[0035] The data collection unit can collect data such as the patient's body temperature, blood pressure, heart rate, diet, and exercise level. For example, the data collection unit can measure the patient's body temperature and collect that data. The data collection unit can also measure the patient's blood pressure and collect that data. The data collection unit can also measure the patient's heart rate and collect that data. The data collection unit can also record the patient's diet and collect that data. The data collection unit can also record the patient's exercise level and collect that data. By collecting data such as the patient's body temperature, blood pressure, heart rate, diet, and exercise level, the patient's health status can be understood in detail. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the patient's body temperature data into a generating AI and have the generating AI perform the analysis of the body temperature data.

[0036] The analysis unit can analyze the collected data and understand changes in the patient's physical condition. For example, the analysis unit can analyze collected body temperature data to understand changes in physical condition. The analysis unit can also analyze collected blood pressure data to understand changes in physical condition. The analysis unit can also analyze collected heart rate data to understand changes in physical condition. The analysis unit can also analyze collected dietary data to understand changes in physical condition. The analysis unit can also analyze collected exercise data to understand changes in physical condition. This allows for a rapid understanding of changes in the patient's physical condition by analyzing the collected data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the analysis of changes in physical condition.

[0037] The notification unit can send a notification such as "Your body temperature is rising. Please stay hydrated and rest" if your body temperature is rising. The notification unit can, for example, send a notification such as "Your body temperature is rising. Please stay hydrated and rest" if your body temperature is rising. The notification unit can, for example, send a notification such as "Your body temperature is rising. Please stay hydrated and rest" if your body temperature is rising. The notification unit can, for example, send a notification such as "Your body temperature is rising. Please stay hydrated and rest" if your body temperature is rising. This allows for a quick response by providing an appropriate notification when your body temperature is rising. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input body temperature data into a generating AI and have the generating AI execute a notification of elevated body temperature.

[0038] The suggestion unit can propose meal menus, medication timing, and rest periods tailored to the patient's physical condition and lifestyle. For example, the suggestion unit can suggest meal menus tailored to the patient's physical condition. The suggestion unit can also suggest medication timing tailored to the patient's lifestyle. The suggestion unit can also suggest rest periods tailored to the patient's physical condition. For example, the suggestion unit can also suggest meal menus tailored to the patient's lifestyle. The suggestion unit can also suggest medication timing tailored to the patient's physical condition. The suggestion unit can also suggest rest periods tailored to the patient's lifestyle. This makes caregiving more efficient by providing suggestions tailored to the patient's physical condition and lifestyle. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input patient physical condition data into a generating AI and have the generating AI produce suggestions for meal menus, medication timing, and rest periods.

[0039] The sorting unit can list the tasks that caregivers need to perform and prioritize them according to their importance and urgency. For example, the sorting unit can list the tasks that caregivers need to perform. The sorting unit can also list the tasks that caregivers need to perform and prioritize them according to their importance. The sorting unit can also list the tasks that caregivers need to perform and prioritize them according to their urgency. The sorting unit can also list the tasks that caregivers need to perform and prioritize them according to their importance and urgency. This makes caregiving more efficient by listing and prioritizing the tasks that caregivers need to perform. Some or all of the above processing in the sorting unit may be performed using AI, for example, or without AI. For example, the sorting unit can input the tasks that caregivers need to perform into a generation AI and have the generation AI sort the tasks by prioritizing them.

[0040] The data collection unit can analyze a patient's past health data and select the optimal data collection method. For example, the data collection unit can select the most effective data collection method based on the patient's past health data. The data collection unit can also concentrate data collection during specific time periods based on the patient's past health data. For example, the data collection unit can analyze the patient's past health data and optimize the timing of data collection. This allows for the selection of the optimal data collection method by analyzing the patient's past health data. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the patient's past health data into a generating AI and have the generating AI select the optimal data collection method.

[0041] The data collection unit can filter data based on the patient's current living situation and environment during data collection. For example, the data collection unit can filter the types of data to be collected according to the patient's living situation. The data collection unit can also adjust the accuracy of the data to be collected based on the patient's environment. For example, the data collection unit can filter the timing of data collection according to the patient's daily rhythm. This improves the accuracy of the data collected by filtering the data based on the patient's living situation and environment. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input patient living situation data into a generating AI and have the generating AI perform data filtering.

[0042] The data collection unit can prioritize the collection of highly relevant data based on the patient's geographical location information during data collection. For example, the data collection unit can prioritize the collection of highly relevant data based on the patient's current location. The data collection unit can also prioritize the collection of highly relevant data by considering the patient's movement history. For example, the data collection unit can select the optimal data collection point based on the patient's geographical location information. This improves the efficiency of data collection by prioritizing the collection of highly relevant data based on the patient's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the patient's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0043] The data collection unit can analyze the patient's social media activity and collect relevant data during data collection. For example, the data collection unit can collect health-related data from the patient's social media activity. The data collection unit can also analyze the patient's social media activity and collect signs of changes in health. The data collection unit can also adjust the timing of data collection based on the patient's social media activity. This allows for the collection of relevant data by analyzing the patient's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the patient's social media activity data into a generating AI and have the generating AI collect relevant data.

[0044] The analysis unit can predict current changes in physical condition by referring to past data during analysis. For example, the analysis unit can predict current changes in body temperature by referring to past body temperature data. For example, the analysis unit can also predict current changes in blood pressure by referring to past blood pressure data. For example, the analysis unit can predict current changes in heart rate by referring to past heart rate data. In this way, current changes in physical condition can be predicted by referring to past data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past data into a generating AI and have the generating AI perform a prediction of current changes in physical condition.

[0045] The analysis unit can apply different analytical methods to each patient category during analysis. For example, the analysis unit can apply an analytical method for the elderly to predict changes in their physical condition. The analysis unit can also apply an analytical method for patients with chronic diseases to predict changes in their physical condition. The analysis unit can also apply an analytical method for healthy patients to predict changes in their physical condition. By applying different analytical methods to each patient category, it becomes possible to predict changes in physical condition more accurately. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input patient category data into a generating AI and have the generating AI execute the application of different analytical methods.

[0046] The analysis department can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis department can prioritize the analysis of the latest data to quickly grasp changes in physical condition. The analysis department can also, for example, refer to past data and determine the priority of analysis based on the submission date. The analysis department can also, for example, postpone the analysis of older data and prioritize the analysis of the latest data. This allows for the rapid analysis of the latest data by determining the priority of analysis based on the data submission date. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input the data submission date into a generating AI and have the generating AI perform the determination of the analysis priority.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data to quickly grasp changes in physical condition. The analysis unit can also prioritize the analysis of important data, for example, by postponing the analysis of less relevant data. The analysis unit can also optimize the order of analysis based on the relevance of the data. This allows for the prioritization of important data by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0048] The notification unit can adjust the level of detail of notifications based on the importance of the user's health condition. For example, if the user's health condition is deteriorating, the notification unit will provide a detailed notification. For example, if the user's health condition is stable, the notification unit can provide a concise notification. The notification unit can also adjust the level of detail of notifications according to the importance of the user's health condition. This allows important information to be prioritized and notified based on the importance of the user's health condition. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input health data into a generating AI and have the generating AI perform the adjustment of the level of detail of the notifications.

[0049] The notification unit can apply different notification algorithms depending on the health condition category when a notification is sent. For example, the notification unit can apply an appropriate notification algorithm to changes in body temperature. For example, the notification unit can also apply an appropriate notification algorithm to changes in blood pressure. For example, the notification unit can also apply an appropriate notification algorithm to changes in heart rate. By applying an appropriate notification algorithm according to the health condition category, more effective notifications become possible. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input health condition category data into a generating AI and have the generating AI execute the application of the notification algorithm.

[0050] The notification unit can determine the priority of notifications based on the submission date of health information. For example, the notification unit may prioritize notifications based on the most recent health data. The notification unit may also refer to past health data and determine the priority of notifications based on the submission date. For example, the notification unit may postpone notifications for older health data and prioritize notifications for the most recent health data. This allows for the rapid notification of the latest health data by determining the priority of notifications based on the submission date of health information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit may input the submission date of health data into a generating AI and have the generating AI determine the priority of notifications.

[0051] The notification unit can adjust the order of notifications based on the relevance of health information. For example, the notification unit may prioritize notifying users of highly relevant health data. The notification unit may also, for example, postpone notifying users of less relevant health data and prioritize notifying them of important health data. The notification unit may also optimize the order of notifications based on the relevance of health data. This allows for prioritizing notifications of important health data by adjusting the order of notifications based on the relevance of health information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit may input the relevance of health data into a generating AI and have the generating AI perform the adjustment of the notification order.

[0052] The proposal unit can adjust the level of detail in its proposals based on the importance of the user's health condition. For example, if the user's health condition is deteriorating, the proposal unit will provide detailed suggestions. If the user's health condition is stable, the proposal unit can also provide concise suggestions. The proposal unit can adjust the level of detail in its suggestions according to the importance of the user's health condition. This allows the proposal unit to prioritize important information by adjusting the level of detail based on the user's health condition. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input health data into a generating AI and have the generating AI adjust the level of detail in its suggestions.

[0053] The suggestion unit can apply different suggestion algorithms depending on the health condition category when making suggestions. For example, the suggestion unit can apply an appropriate suggestion algorithm to changes in body temperature. For example, the suggestion unit can also apply an appropriate suggestion algorithm to changes in blood pressure. For example, the suggestion unit can also apply an appropriate suggestion algorithm to changes in heart rate. This allows for more effective suggestions by applying an appropriate suggestion algorithm according to the health condition category. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input health condition category data into a generating AI and have the generating AI execute the application of the suggestion algorithm.

[0054] The proposal department can determine the priority of proposals based on the submission date of health data. For example, the proposal department may prioritize proposals based on the most recent health data. The proposal department may also determine the priority of proposals based on past health data and the submission date. For example, the proposal department may postpone older health data submissions and prioritize the most recent health data. This allows for the rapid proposal of the latest health data by determining the priority of proposals based on the submission date of health data. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department may input the submission date of health data into a generating AI and have the generating AI determine the priority of proposals.

[0055] The suggestion unit can adjust the order of suggestions based on the relevance of health conditions during the suggestion process. For example, the suggestion unit may prioritize suggesting health data with high relevance. The suggestion unit may also prioritize suggesting important health data, for example, by delaying suggesting less relevant health data. The suggestion unit can also optimize the order of suggestions based on the relevance of health data. This allows for the prioritization of important health data by adjusting the order of suggestions based on the relevance of health conditions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the relevance of health data into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0056] The task organization unit can select the optimal task organization method by referring to past task history during the organization process. For example, the task organization unit can select the most effective task organization method based on past task history. For example, the task organization unit can concentrate tasks in specific time periods based on past task history. For example, the task organization unit can analyze past task history and optimize the timing of task organization. This allows the optimal task organization method to be selected by referring to past task history. Some or all of the above processes in the task organization unit may be performed using AI, for example, or without AI. For example, the task organization unit can input past task history into a generating AI and have the generating AI select the optimal task organization method.

[0057] The sorting unit can adjust the order of tasks based on their importance and urgency during the sorting process. For example, the sorting unit can prioritize sorting tasks with high importance. The sorting unit can also prioritize sorting tasks with high urgency. The sorting unit can also optimize the order of tasks based on their importance and urgency. This allows important tasks to be prioritized by adjusting the order of tasks based on their importance and urgency. Some or all of the above processes in the sorting unit may be performed using AI, for example, or without AI. For example, the sorting unit can input task importance and urgency data into a generating AI and have the generating AI perform the task order adjustment.

[0058] The sorting unit can determine task priorities based on the submission date during sorting. For example, the sorting unit prioritizes the most recent tasks. The sorting unit can also refer to past tasks and determine task priorities based on the submission date. For example, the sorting unit can postpone older tasks and prioritize the most recent tasks. This allows for the rapid sorting of the most recent tasks by determining task priorities based on the submission date. Some or all of the above processes in the sorting unit may be performed using AI, for example, or without AI. For example, the sorting unit can input task submission date data into a generating AI and have the generating AI determine task priorities.

[0059] The sorting unit can adjust the order of tasks based on their relevance during sorting. For example, the sorting unit can prioritize sorting tasks that are highly relevant. The sorting unit can also prioritize sorting tasks that are less relevant and postpone sorting tasks that are less relevant. The sorting unit can also optimize the order of tasks based on their relevance. This allows for prioritizing important tasks by adjusting the order of tasks based on their relevance. Some or all of the above processes in the sorting unit may be performed using AI, for example, or without AI. For example, the sorting unit can input task relevance data into a generating AI and have the generating AI perform the task order adjustment.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The AI ​​caregiver support agent can propose an optimal care plan based on the patient's past health data. For example, it can analyze patterns of changes in the patient's condition from past data and suggest focused care during periods when their condition is likely to worsen. It can also understand the patient's eating and exercise tendencies from past data and propose specific meal menus and exercise plans for maintaining health. Furthermore, it can propose a medication schedule tailored to the patient's lifestyle based on past data. In this way, it can provide the most suitable care plan for the patient by utilizing past health data.

[0062] The caregiver support AI agent can optimize data collection methods based on the patient's living environment. For example, if the patient is at home, data can be collected using sensors within the home. If the patient is out, data can be collected using mobile devices or wearable devices. Furthermore, if the patient is in a hospital, data can be collected directly from medical equipment. This allows for the selection of the most appropriate data collection method according to the patient's living environment, resulting in more accurate data.

[0063] The caregiver support AI agent can suggest the most suitable care services based on the patient's geographical location. For example, if the patient is at home, it can suggest nearby home care services. If the patient is out, it can suggest nearby medical facilities and pharmacies. Furthermore, if the patient is traveling, it can suggest medical facilities and care services at their travel destination. This allows for the provision of optimal care services based on the patient's geographical location.

[0064] The caregiver support AI agent can analyze a patient's social media activity and detect changes in their health early. For example, it can detect signs of stress or anxiety from the content a patient posts on social media. It can also understand changes in a patient's physical condition from changes in the frequency and content of their posts. Furthermore, it can detect changes in lifestyle habits from photos and videos that patients share on social media. This allows for early detection of changes in a patient's health through their social media activity, enabling appropriate responses.

[0065] The caregiver support AI agent can optimize the timing of data collection based on the patient's daily rhythm. For example, if the patient has a morning routine, data collection can be concentrated in the morning hours. Similarly, if the patient has a night owl routine, data collection can be concentrated in the evening hours. Furthermore, the frequency of data collection can be adjusted to match the patient's daily rhythm. By optimizing the timing of data collection based on the patient's daily rhythm, more accurate data can be collected.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The data collection unit collects the patient's medical and lifestyle data. For example, it collects data such as the patient's body temperature, blood pressure, heart rate, diet, and exercise level. The data collection unit can collect data such as body temperature and blood pressure recorded by the patient daily, as well as data recorded using smartphones or wearable devices. Step 2: The analysis unit analyzes the data collected by the data collection unit and predicts the risk of health deterioration. For example, it analyzes collected body temperature and blood pressure data to understand changes in health. By using AI to analyze the data, it is possible to predict the risk of health deterioration. Step 3: The notification unit notifies the user of necessary precautions and countermeasures based on the analysis results obtained by the analysis unit. For example, if the user's body temperature is elevated, it will send a notification such as, "Your body temperature is elevated. Please stay hydrated and rest." AI can be used to send notifications based on the analysis results. Step 4: The suggestion unit makes specific suggestions for meals, medication, and rest schedules based on the information received by the notification unit. For example, it suggests meal menus, medication timing, and rest periods tailored to the patient's physical condition and lifestyle. AI can be used to make these suggestions. Step 5: The organization unit automatically prioritizes tasks based on the information proposed by the proposal unit. For example, it lists the tasks that a caregiver needs to perform and prioritizes them according to their importance and urgency. AI can be used to prioritize tasks.

[0068] (Example of form 2) The caregiver support AI agent according to an embodiment of the present invention is a system designed to alleviate the burden caused by a shortage of caregivers and realize a society where everyone can enjoy their later years with peace of mind. The caregiver support AI agent uses AI to analyze the patient's medical and lifestyle data and predict the risk of deterioration in health. Next, the AI ​​notifies caregivers and family members of necessary precautions and countermeasures. Furthermore, the AI ​​makes specific suggestions for meal, medication, and rest schedules, streamlining caregiving tasks. The AI ​​also automatically prioritizes tasks for busy caregivers. This reduces stress on caregivers and improves the quality of life (QOL) for both patients and caregivers. For example, the caregiver support AI agent collects and analyzes the patient's medical and lifestyle data. In this process, it collects data such as the patient's body temperature, blood pressure, heart rate, diet, and exercise level. For example, the AI ​​can analyze the body temperature and blood pressure data that the patient records daily to understand changes in their health. This allows it to predict the risk of deterioration in health. Next, the AI ​​predicts the risk of deterioration in health and notifies caregivers and family members of necessary precautions and countermeasures. For example, if a patient's temperature rises, the AI ​​will notify them with a message such as, "Your temperature is rising. Please hydrate and rest." This allows caregivers and family members to take appropriate action quickly. Furthermore, the AI ​​provides specific suggestions for meal, medication, and rest schedules. For instance, it suggests meal menus, medication timings, and rest times tailored to the patient's physical condition and lifestyle. This streamlines caregiving tasks and improves patient health management. The AI ​​also automatically prioritizes tasks for busy caregivers. For example, it lists the tasks a caregiver needs to complete and prioritizes them according to their importance and urgency. This allows caregivers to work more efficiently. This system reduces caregiver stress and improves the quality of life (QOL) for both patients and caregivers. For example, with AI support, caregivers can take appropriate action quickly and improve patient health management. Also, the increased efficiency of caregiving tasks reduces the burden on caregivers. This contributes to creating a society where everyone can enjoy their later years with peace of mind. This allows the caregiver support AI agent to reduce the burden on caregivers and improve the quality of life for both patients and caregivers.

[0069] The caregiver support AI agent according to this embodiment comprises a collection unit, an analysis unit, a notification unit, a suggestion unit, and an organization unit. The collection unit collects the patient's medical data and lifestyle data. For example, the collection unit collects data such as the patient's body temperature, blood pressure, heart rate, diet, and exercise level. For example, the collection unit can collect data such as body temperature and blood pressure that the patient records daily. The collection unit can also collect data such as the patient's diet and exercise level. For example, the collection unit can collect data recorded by the patient using a smartphone or wearable device. The analysis unit analyzes the data collected by the collection unit and predicts the risk of deterioration of the patient's health. For example, the analysis unit analyzes the collected body temperature and blood pressure data to understand changes in the patient's health. For example, the analysis unit can use AI to analyze the data and predict the risk of deterioration of the patient's health. The analysis unit can also predict changes in the patient's health based on the collected data. The notification unit notifies the user of necessary precautions and countermeasures based on the analysis results obtained by the analysis unit. The notification unit, for example, sends a notification if the patient's temperature is elevated, such as "Your temperature is elevated. Please hydrate and rest." The notification unit can also send notifications based on analysis results using AI. Furthermore, the notification unit can notify patients and caregivers of appropriate countermeasures. The suggestion unit provides specific suggestions for meal, medication, and rest schedules based on the information provided by the notification unit. For example, the suggestion unit suggests meal menus, medication timing, and rest times tailored to the patient's physical condition and lifestyle. The suggestion unit can also use AI to make suggestions. Furthermore, the suggestion unit can also provide specific suggestions to improve the patient's health management. The organization unit automatically prioritizes tasks based on the information suggested by the suggestion unit. For example, the organization unit lists tasks that caregivers should perform and prioritizes them according to their importance and urgency. The organization unit can use AI to prioritize tasks. Furthermore, the organization unit can automatically prioritize tasks to streamline the caregiver's work. As a result, the caregiver support AI agent according to this embodiment can reduce the burden on caregivers and improve the quality of life (QOL) for both patients and caregivers.

[0070] The data collection unit collects patient medical and lifestyle data. Specifically, it collects data such as the patient's body temperature, blood pressure, heart rate, diet, and exercise level. For example, it can collect body temperature and blood pressure data that patients record daily. This data is recorded using smartphones or wearable devices and transmitted to the data collection unit. The data collection unit centrally manages this data and can update it in real time. Furthermore, the data collection unit can also collect data on the patient's diet and exercise level. For example, this data can be collected when patients record their meals using a smartphone app or measure their exercise level with a wearable device. The data collection unit stores this data on a cloud server and can link with other systems and departments as needed. This allows the data collection unit to comprehensively understand the patient's health status and build a foundation for providing appropriate care support. In addition, the data collection unit can flexibly respond to specific situations and conditions by adjusting the frequency and accuracy of data collection. For example, for patients whose health is deteriorating, increasing the frequency of data collection allows for a more detailed understanding of their health status. This enables the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0071] The analysis department analyzes data collected by the data collection department to predict the risk of health deterioration. Specifically, it analyzes collected body temperature and blood pressure data to understand changes in health. The analysis department can use AI to analyze data and predict the risk of health deterioration. For example, AI can compare past and current data to detect abnormal patterns and trends. This allows for early detection of changes in health and appropriate action to be taken. The analysis department can also predict changes in patients' health based on the collected data. For example, it can analyze body temperature and blood pressure data to identify periods and situations where health is likely to deteriorate. Furthermore, the analysis department can utilize past data and statistical information to conduct long-term risk assessments and trend analyses. For example, it can predict fluctuations in health during specific seasons or time periods based on past data and formulate future countermeasures. In addition, the analysis department can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue early warnings. This allows the analysis department to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0072] The notification unit notifies users of necessary precautions and countermeasures based on the analysis results obtained by the analysis unit. Specifically, if a user's body temperature is elevated, it will send a notification such as, "Your body temperature is elevated. Please stay hydrated and rest." The notification unit can use AI to send notifications based on the analysis results. For example, the AI ​​analyzes the collected data and generates appropriate notifications when it detects abnormal patterns or risks. The notification unit can also notify patients and caregivers of appropriate countermeasures. For example, if there is a possibility of the user's condition worsening, it will notify them to see a doctor as soon as possible. Furthermore, the notification unit can customize the content of notifications. For example, it can adjust the content and timing of notifications to match the individual health condition and lifestyle of the patient. This allows the notification unit to provide more appropriate and effective notifications to patients and caregivers. In addition, the notification unit can reliably transmit information using multiple communication methods. For example, 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 information to patients and caregivers quickly and reliably and support appropriate responses.

[0073] The suggestion department makes specific suggestions for meals, medication, and rest schedules based on information notified by the notification department. Specifically, it suggests meal menus, medication timing, and rest periods tailored to the patient's physical condition and lifestyle. The suggestion department can use AI to make suggestions. For example, the AI ​​analyzes the patient's health status and lifestyle based on collected data to generate optimal meal menus and medication schedules. The suggestion department can also make specific suggestions to improve the patient's health management. For example, if the patient's condition is deteriorating, it may suggest meals rich in specific nutrients or suggest increasing rest time. Furthermore, the suggestion department can customize the suggestions. For example, it can adjust the content and timing of suggestions to match the patient's individual health condition and lifestyle. This allows the suggestion department to provide patients with more appropriate and effective suggestions. In addition, the suggestion department can monitor the effectiveness of the suggestions and modify them as needed. For example, it can monitor whether the suggested meal menus and medication schedules are effective and revise the suggestions if they are insufficient. This allows the suggestion department to continuously support the patient's health management and provide optimal suggestions.

[0074] The task prioritization department automatically prioritizes tasks based on information proposed by the proposal department. Specifically, it lists tasks that caregivers should perform and prioritizes them according to their importance and urgency. The task prioritization department can use AI to prioritize tasks. For example, the AI ​​can automatically list tasks that caregivers should perform based on collected data and proposals, and prioritize them according to their importance and urgency. The task prioritization department can also automatically prioritize tasks to streamline caregivers' work. For example, it can centrally manage tasks that caregivers should perform and prioritize important tasks to improve work efficiency. Furthermore, the task prioritization department can monitor the progress of tasks and adjust task priorities as needed. For example, if a high-urgency task arises, it will review the prioritization of existing tasks and prioritize the high-urgency task. The task prioritization department can also collect feedback from caregivers and continuously improve the accuracy and effectiveness of task prioritization. As a result, the task prioritization department can streamline caregivers' work and provide more appropriate care support to patients.

[0075] The data collection unit can collect data such as the patient's body temperature, blood pressure, heart rate, diet, and exercise level. For example, the data collection unit can measure the patient's body temperature and collect that data. The data collection unit can also measure the patient's blood pressure and collect that data. The data collection unit can also measure the patient's heart rate and collect that data. The data collection unit can also record the patient's diet and collect that data. The data collection unit can also record the patient's exercise level and collect that data. By collecting data such as the patient's body temperature, blood pressure, heart rate, diet, and exercise level, the patient's health status can be understood in detail. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the patient's body temperature data into a generating AI and have the generating AI perform the analysis of the body temperature data.

[0076] The analysis unit can analyze the collected data and understand changes in the patient's physical condition. For example, the analysis unit can analyze collected body temperature data to understand changes in physical condition. The analysis unit can also analyze collected blood pressure data to understand changes in physical condition. The analysis unit can also analyze collected heart rate data to understand changes in physical condition. The analysis unit can also analyze collected dietary data to understand changes in physical condition. The analysis unit can also analyze collected exercise data to understand changes in physical condition. This allows for a rapid understanding of changes in the patient's physical condition by analyzing the collected data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the analysis of changes in physical condition.

[0077] The notification unit can send a notification such as "Your body temperature is rising. Please stay hydrated and rest" if your body temperature is rising. The notification unit can, for example, send a notification such as "Your body temperature is rising. Please stay hydrated and rest" if your body temperature is rising. The notification unit can, for example, send a notification such as "Your body temperature is rising. Please stay hydrated and rest" if your body temperature is rising. The notification unit can, for example, send a notification such as "Your body temperature is rising. Please stay hydrated and rest" if your body temperature is rising. This allows for a quick response by providing an appropriate notification when your body temperature is rising. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input body temperature data into a generating AI and have the generating AI execute a notification of elevated body temperature.

[0078] The suggestion unit can propose meal menus, medication timing, and rest periods tailored to the patient's physical condition and lifestyle. For example, the suggestion unit can suggest meal menus tailored to the patient's physical condition. The suggestion unit can also suggest medication timing tailored to the patient's lifestyle. The suggestion unit can also suggest rest periods tailored to the patient's physical condition. For example, the suggestion unit can also suggest meal menus tailored to the patient's lifestyle. The suggestion unit can also suggest medication timing tailored to the patient's physical condition. The suggestion unit can also suggest rest periods tailored to the patient's lifestyle. This makes caregiving more efficient by providing suggestions tailored to the patient's physical condition and lifestyle. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input patient physical condition data into a generating AI and have the generating AI produce suggestions for meal menus, medication timing, and rest periods.

[0079] The sorting unit can list the tasks that caregivers need to perform and prioritize them according to their importance and urgency. For example, the sorting unit can list the tasks that caregivers need to perform. The sorting unit can also list the tasks that caregivers need to perform and prioritize them according to their importance. The sorting unit can also list the tasks that caregivers need to perform and prioritize them according to their urgency. The sorting unit can also list the tasks that caregivers need to perform and prioritize them according to their importance and urgency. This makes caregiving more efficient by listing and prioritizing the tasks that caregivers need to perform. Some or all of the above processing in the sorting unit may be performed using AI, for example, or without AI. For example, the sorting unit can input the tasks that caregivers need to perform into a generation AI and have the generation AI sort the tasks by prioritizing them.

[0080] The data collection unit can estimate the patient's emotions and adjust the frequency of data collection based on the estimated emotions. For example, if the patient is stressed, the data collection unit can reduce the frequency of data collection to alleviate the patient's burden. For example, if the patient is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. For example, if the patient is anxious, the data collection unit can appropriately adjust the frequency of data collection to provide reassurance. In this way, the burden on the patient can be reduced by adjusting the frequency of data collection based on the patient'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 processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the patient's emotion data into the generative AI and have the generative AI adjust the frequency of data collection.

[0081] The data collection unit can analyze a patient's past health data and select the optimal data collection method. For example, the data collection unit can select the most effective data collection method based on the patient's past health data. The data collection unit can also concentrate data collection during specific time periods based on the patient's past health data. For example, the data collection unit can analyze the patient's past health data and optimize the timing of data collection. This allows for the selection of the optimal data collection method by analyzing the patient's past health data. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the patient's past health data into a generating AI and have the generating AI select the optimal data collection method.

[0082] The data collection unit can filter data based on the patient's current living situation and environment during data collection. For example, the data collection unit can filter the types of data to be collected according to the patient's living situation. The data collection unit can also adjust the accuracy of the data to be collected based on the patient's environment. For example, the data collection unit can filter the timing of data collection according to the patient's daily rhythm. This improves the accuracy of the data collected by filtering the data based on the patient's living situation and environment. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input patient living situation data into a generating AI and have the generating AI perform data filtering.

[0083] The data collection unit can estimate the patient's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the patient is stressed, the data collection unit may prioritize collecting only important data. For example, if the patient is relaxed, the data collection unit may prioritize collecting detailed data. For example, if the patient is anxious, the data collection unit may prioritize collecting data that provides reassurance. This allows for the priority collection of important data by determining the priority of data to collect based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input patient emotion data into a generative AI and have the generative AI determine the priority of the data.

[0084] The data collection unit can prioritize the collection of highly relevant data based on the patient's geographical location information during data collection. For example, the data collection unit can prioritize the collection of highly relevant data based on the patient's current location. The data collection unit can also prioritize the collection of highly relevant data by considering the patient's movement history. For example, the data collection unit can select the optimal data collection point based on the patient's geographical location information. This improves the efficiency of data collection by prioritizing the collection of highly relevant data based on the patient's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the patient's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0085] The data collection unit can analyze the patient's social media activity and collect relevant data during data collection. For example, the data collection unit can collect health-related data from the patient's social media activity. The data collection unit can also analyze the patient's social media activity and collect signs of changes in health. The data collection unit can also adjust the timing of data collection based on the patient's social media activity. This allows for the collection of relevant data by analyzing the patient's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the patient's social media activity data into a generating AI and have the generating AI collect relevant data.

[0086] The analysis unit can estimate the patient's emotions and adjust the data analysis algorithm based on the estimated emotions. For example, if the patient is stressed, the analysis unit can apply an algorithm that focuses on stress reduction. For example, if the patient is relaxed, the analysis unit can also apply an algorithm that performs detailed data analysis. For example, if the patient is anxious, the analysis unit can also apply a data analysis algorithm that provides a sense of security. By adjusting the data analysis algorithm based on the patient's emotions, more appropriate analysis results can be obtained. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input patient emotion data into a generative AI and have the generative AI adjust the data analysis algorithm.

[0087] The analysis unit can predict current changes in physical condition by referring to past data during analysis. For example, the analysis unit can predict current changes in body temperature by referring to past body temperature data. For example, the analysis unit can also predict current changes in blood pressure by referring to past blood pressure data. For example, the analysis unit can predict current changes in heart rate by referring to past heart rate data. In this way, current changes in physical condition can be predicted by referring to past data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past data into a generating AI and have the generating AI perform a prediction of current changes in physical condition.

[0088] The analysis unit can apply different analytical methods to each patient category during analysis. For example, the analysis unit can apply an analytical method for the elderly to predict changes in their physical condition. The analysis unit can also apply an analytical method for patients with chronic diseases to predict changes in their physical condition. The analysis unit can also apply an analytical method for healthy patients to predict changes in their physical condition. By applying different analytical methods to each patient category, it becomes possible to predict changes in physical condition more accurately. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input patient category data into a generating AI and have the generating AI execute the application of different analytical methods.

[0089] The analysis unit can estimate the patient's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the patient is stressed, the analysis unit can provide a simple and highly visible display method. For example, if the patient is relaxed, the analysis unit can also provide a display method that includes detailed information. For example, if the patient is anxious, the analysis unit can also provide a display method that provides reassurance. By adjusting the display method of the analysis results based on the patient's emotions, it becomes possible to provide a display that is easy for the patient to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input patient emotion data into the generative AI and have the generative AI adjust the display method of the analysis results.

[0090] The analysis department can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis department can prioritize the analysis of the latest data to quickly grasp changes in physical condition. The analysis department can also, for example, refer to past data and determine the priority of analysis based on the submission date. The analysis department can also, for example, postpone the analysis of older data and prioritize the analysis of the latest data. This allows for the rapid analysis of the latest data by determining the priority of analysis based on the data submission date. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input the data submission date into a generating AI and have the generating AI perform the determination of the analysis priority.

[0091] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data to quickly grasp changes in physical condition. The analysis unit can also prioritize the analysis of important data, for example, by postponing the analysis of less relevant data. The analysis unit can also optimize the order of analysis based on the relevance of the data. This allows for the prioritization of important data by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0092] The notification unit can estimate the patient's emotions and adjust the way notifications are presented based on the estimated emotions. For example, if the patient is stressed, the notification unit can provide a simple and easily visible notification. If the patient is relaxed, the notification unit can also provide a notification containing detailed information. If the patient is anxious, the notification unit can also provide a reassuring notification. By adjusting the way notifications are presented based on the patient's emotions, it becomes possible to provide notifications that are easy for the patient to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not using AI. For example, the notification unit can input patient emotion data into the generative AI and have the generative AI adjust the way notifications are presented.

[0093] The notification unit can adjust the level of detail of notifications based on the importance of the user's health condition. For example, if the user's health condition is deteriorating, the notification unit will provide a detailed notification. For example, if the user's health condition is stable, the notification unit can provide a concise notification. The notification unit can also adjust the level of detail of notifications according to the importance of the user's health condition. This allows important information to be prioritized and notified based on the importance of the user's health condition. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input health data into a generating AI and have the generating AI perform the adjustment of the level of detail of the notifications.

[0094] The notification unit can apply different notification algorithms depending on the health condition category when a notification is sent. For example, the notification unit can apply an appropriate notification algorithm to changes in body temperature. For example, the notification unit can also apply an appropriate notification algorithm to changes in blood pressure. For example, the notification unit can also apply an appropriate notification algorithm to changes in heart rate. By applying an appropriate notification algorithm according to the health condition category, more effective notifications become possible. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input health condition category data into a generating AI and have the generating AI execute the application of the notification algorithm.

[0095] The notification unit can estimate the patient's emotions and adjust the length of the notification based on the estimated emotions. For example, if the patient is stressed, the notification unit can provide a short, concise notification. If the patient is relaxed, the notification unit can also provide a detailed notification. If the patient is anxious, the notification unit can also provide a reassuring notification. By adjusting the length of the notification based on the patient's emotions, it is possible to provide notifications of an appropriate length for the patient. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not using AI. For example, the notification unit can input patient emotion data into the generative AI and have the generative AI adjust the length of the notification.

[0096] The notification unit can determine the priority of notifications based on the submission date of health information. For example, the notification unit may prioritize notifications based on the most recent health data. The notification unit may also refer to past health data and determine the priority of notifications based on the submission date. For example, the notification unit may postpone notifications for older health data and prioritize notifications for the most recent health data. This allows for the rapid notification of the latest health data by determining the priority of notifications based on the submission date of health information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit may input the submission date of health data into a generating AI and have the generating AI determine the priority of notifications.

[0097] The notification unit can adjust the order of notifications based on the relevance of health information. For example, the notification unit may prioritize notifying users of highly relevant health data. The notification unit may also, for example, postpone notifying users of less relevant health data and prioritize notifying them of important health data. The notification unit may also optimize the order of notifications based on the relevance of health data. This allows for prioritizing notifications of important health data by adjusting the order of notifications based on the relevance of health information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit may input the relevance of health data into a generating AI and have the generating AI perform the adjustment of the notification order.

[0098] The suggestion unit can estimate the patient's emotions and adjust the way the suggestions are presented based on the estimated emotions. For example, if the patient is stressed, the suggestion unit will present simple and easily understandable suggestions. If the patient is relaxed, the suggestion unit may also present suggestions that include more detailed information. If the patient is anxious, the suggestion unit may also present suggestions that provide reassurance. By adjusting the way suggestions are presented based on the patient's emotions, it becomes possible to create suggestions that are easy for the patient to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input patient emotion data into a generative AI and have the generative AI adjust the way suggestions are presented.

[0099] The proposal unit can adjust the level of detail in its proposals based on the importance of the user's health condition. For example, if the user's health condition is deteriorating, the proposal unit will provide detailed suggestions. If the user's health condition is stable, the proposal unit can also provide concise suggestions. The proposal unit can adjust the level of detail in its suggestions according to the importance of the user's health condition. This allows the proposal unit to prioritize important information by adjusting the level of detail based on the user's health condition. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input health data into a generating AI and have the generating AI adjust the level of detail in its suggestions.

[0100] The suggestion unit can apply different suggestion algorithms depending on the health condition category when making suggestions. For example, the suggestion unit can apply an appropriate suggestion algorithm to changes in body temperature. For example, the suggestion unit can also apply an appropriate suggestion algorithm to changes in blood pressure. For example, the suggestion unit can also apply an appropriate suggestion algorithm to changes in heart rate. This allows for more effective suggestions by applying an appropriate suggestion algorithm according to the health condition category. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input health condition category data into a generating AI and have the generating AI execute the application of the suggestion algorithm.

[0101] The suggestion unit can estimate the patient's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the patient is stressed, the suggestion unit can provide a short, concise suggestion. If the patient is relaxed, the suggestion unit can provide a more detailed suggestion. If the patient is anxious, the suggestion unit can provide a reassuring suggestion. By adjusting the length of the suggestion based on the patient's emotions, it becomes possible to provide a suggestion of an appropriate length for the patient. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input patient emotion data into a generative AI and have the generative AI adjust the length of the suggestion.

[0102] The proposal department can determine the priority of proposals based on the submission date of health data. For example, the proposal department may prioritize proposals based on the most recent health data. The proposal department may also determine the priority of proposals based on past health data and the submission date. For example, the proposal department may postpone older health data submissions and prioritize the most recent health data. This allows for the rapid proposal of the latest health data by determining the priority of proposals based on the submission date of health data. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department may input the submission date of health data into a generating AI and have the generating AI determine the priority of proposals.

[0103] The suggestion unit can adjust the order of suggestions based on the relevance of health conditions during the suggestion process. For example, the suggestion unit may prioritize suggesting health data with high relevance. The suggestion unit may also prioritize suggesting important health data, for example, by delaying suggesting less relevant health data. The suggestion unit can also optimize the order of suggestions based on the relevance of health data. This allows for the prioritization of important health data by adjusting the order of suggestions based on the relevance of health conditions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the relevance of health data into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0104] The processing unit can estimate the patient's emotions and determine task priorities based on the estimated emotions. For example, if the patient is stressed, the processing unit will prioritize and prioritize only important tasks. For example, if the patient is relaxed, the processing unit may also prioritize and prioritize detailed tasks. For example, if the patient is anxious, the processing unit may also prioritize and prioritize tasks that provide reassurance. This allows for prioritizing important tasks by determining task priorities based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, 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 processing unit may be performed using AI or not using AI. For example, the processing unit can input patient emotion data into a generative AI and have the generative AI determine task priorities.

[0105] The task organization unit can select the optimal task organization method by referring to past task history during the organization process. For example, the task organization unit can select the most effective task organization method based on past task history. For example, the task organization unit can concentrate tasks in specific time periods based on past task history. For example, the task organization unit can analyze past task history and optimize the timing of task organization. This allows the optimal task organization method to be selected by referring to past task history. Some or all of the above processes in the task organization unit may be performed using AI, for example, or without AI. For example, the task organization unit can input past task history into a generating AI and have the generating AI select the optimal task organization method.

[0106] The sorting unit can adjust the order of tasks based on their importance and urgency during the sorting process. For example, the sorting unit can prioritize sorting tasks with high importance. The sorting unit can also prioritize sorting tasks with high urgency. The sorting unit can also optimize the order of tasks based on their importance and urgency. This allows important tasks to be prioritized by adjusting the order of tasks based on their importance and urgency. Some or all of the above processes in the sorting unit may be performed using AI, for example, or without AI. For example, the sorting unit can input task importance and urgency data into a generating AI and have the generating AI perform the task order adjustment.

[0107] The processing unit can estimate the patient's emotions and adjust the way tasks are displayed based on the estimated emotions. For example, if the patient is stressed, the processing unit can provide a simple and highly visible display. For example, if the patient is relaxed, the processing unit can also provide a display that includes detailed information. For example, if the patient is anxious, the processing unit can also provide a display that provides reassurance. By adjusting the way tasks are displayed based on the patient's emotions, a display that is easy for the patient to understand can be made. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the processing unit may be performed using AI, for example, or not using AI. For example, the processing unit can input patient emotion data into a generative AI and have the generative AI adjust the way tasks are displayed.

[0108] The sorting unit can determine task priorities based on the submission date during sorting. For example, the sorting unit prioritizes the most recent tasks. The sorting unit can also refer to past tasks and determine task priorities based on the submission date. For example, the sorting unit can postpone older tasks and prioritize the most recent tasks. This allows for the rapid sorting of the most recent tasks by determining task priorities based on the submission date. Some or all of the above processes in the sorting unit may be performed using AI, for example, or without AI. For example, the sorting unit can input task submission date data into a generating AI and have the generating AI determine task priorities.

[0109] The sorting unit can adjust the order of tasks based on their relevance during sorting. For example, the sorting unit can prioritize sorting tasks that are highly relevant. The sorting unit can also prioritize sorting tasks that are less relevant and postpone sorting tasks that are less relevant. The sorting unit can also optimize the order of tasks based on their relevance. This allows for prioritizing important tasks by adjusting the order of tasks based on their relevance. Some or all of the above processes in the sorting unit may be performed using AI, for example, or without AI. For example, the sorting unit can input task relevance data into a generating AI and have the generating AI perform the task order adjustment.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The caregiver support AI agent can estimate the patient's emotions and adjust the content of notifications sent to caregivers based on those estimates. For example, if the patient is stressed, the notification can be concise and provide specific advice to the caregiver on how to reduce stress. If the patient is relaxed, the notification can contain detailed information and offer suggestions to the caregiver on how to maintain the patient's relaxed state. Furthermore, if the patient is anxious, the notification can provide reassurance and suggest specific measures to reduce anxiety. By adjusting notifications based on the patient's emotions, caregivers can respond more appropriately and quickly.

[0112] The AI ​​caregiver support agent can propose an optimal care plan based on the patient's past health data. For example, it can analyze patterns of changes in the patient's condition from past data and suggest focused care during periods when their condition is likely to worsen. It can also understand the patient's eating and exercise tendencies from past data and propose specific meal menus and exercise plans for maintaining health. Furthermore, it can propose a medication schedule tailored to the patient's lifestyle based on past data. In this way, it can provide the most suitable care plan for the patient by utilizing past health data.

[0113] The caregiver support AI agent can estimate a patient's emotions and adjust meal menus based on those estimates. For example, if a patient is stressed, it can suggest a menu using ingredients effective in reducing stress. If a patient is relaxed, it can suggest a menu using ingredients that enhance relaxation. Furthermore, if a patient is anxious, it can suggest a menu using ingredients that promote a sense of security. By adjusting meal menus based on the patient's emotions, it is possible to maintain a better state of health for the patient.

[0114] The caregiver support AI agent can optimize data collection methods based on the patient's living environment. For example, if the patient is at home, data can be collected using sensors within the home. If the patient is out, data can be collected using mobile devices or wearable devices. Furthermore, if the patient is in a hospital, data can be collected directly from medical equipment. This allows for the selection of the most appropriate data collection method according to the patient's living environment, resulting in more accurate data.

[0115] The caregiver support AI agent can estimate the patient's emotions and adjust the rest schedule based on those emotions. For example, if the patient is stressed, it can suggest rest methods that promote relaxation. If the patient is relaxed, it can suggest rest methods that help maintain that relaxed state. Furthermore, if the patient is anxious, it can suggest rest methods that provide a sense of security. By adjusting the rest schedule based on the patient's emotions, it is possible to better maintain the patient's health.

[0116] The caregiver support AI agent can suggest the most suitable care services based on the patient's geographical location. For example, if the patient is at home, it can suggest nearby home care services. If the patient is out, it can suggest nearby medical facilities and pharmacies. Furthermore, if the patient is traveling, it can suggest medical facilities and care services at their travel destination. This allows for the provision of optimal care services based on the patient's geographical location.

[0117] The caregiver support AI agent can estimate the patient's emotions and adjust task priorities based on those estimates. For example, if the patient is stressed, it can prioritize suggesting tasks that are effective in reducing stress. If the patient is relaxed, it can prioritize suggesting tasks that enhance relaxation. Furthermore, if the patient is anxious, it can prioritize suggesting tasks that provide a sense of security. By adjusting task priorities based on the patient's emotions, this can help maintain the patient's health in a better state.

[0118] The caregiver support AI agent can analyze a patient's social media activity and detect changes in their health early. For example, it can detect signs of stress or anxiety from the content a patient posts on social media. It can also understand changes in a patient's physical condition from changes in the frequency and content of their posts. Furthermore, it can detect changes in lifestyle habits from photos and videos that patients share on social media. This allows for early detection of changes in a patient's health through their social media activity, enabling appropriate responses.

[0119] The caregiver support AI agent can estimate the patient's emotions and adjust the timing of notifications based on those emotions. For example, if the patient is stressed, the frequency of notifications can be reduced, and only important notifications can be sent. If the patient is relaxed, detailed notifications can be sent, providing information to help the patient maintain a relaxed state. Furthermore, if the patient is anxious, reassuring notifications can be sent, and specific measures to reduce anxiety can be suggested. In this way, by adjusting the timing of notifications based on the patient's emotions, notifications can be sent at the optimal time for the patient.

[0120] The caregiver support AI agent can optimize the timing of data collection based on the patient's daily rhythm. For example, if the patient has a morning routine, data collection can be concentrated in the morning hours. Similarly, if the patient has a night owl routine, data collection can be concentrated in the evening hours. Furthermore, the frequency of data collection can be adjusted to match the patient's daily rhythm. By optimizing the timing of data collection based on the patient's daily rhythm, more accurate data can be collected.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The data collection unit collects the patient's medical and lifestyle data. For example, it collects data such as the patient's body temperature, blood pressure, heart rate, diet, and exercise level. The data collection unit can collect data such as body temperature and blood pressure recorded by the patient daily, as well as data recorded using smartphones or wearable devices. Step 2: The analysis unit analyzes the data collected by the data collection unit and predicts the risk of health deterioration. For example, it analyzes collected body temperature and blood pressure data to understand changes in health. By using AI to analyze the data, it is possible to predict the risk of health deterioration. Step 3: The notification unit notifies the user of necessary precautions and countermeasures based on the analysis results obtained by the analysis unit. For example, if the user's body temperature is elevated, it will send a notification such as, "Your body temperature is elevated. Please stay hydrated and rest." AI can be used to send notifications based on the analysis results. Step 4: The suggestion unit makes specific suggestions for meals, medication, and rest schedules based on the information received by the notification unit. For example, it suggests meal menus, medication timing, and rest periods tailored to the patient's physical condition and lifestyle. AI can be used to make these suggestions. Step 5: The organization unit automatically prioritizes tasks based on the information proposed by the proposal unit. For example, it lists the tasks that a caregiver needs to perform and prioritizes them according to their importance and urgency. AI can be used to prioritize tasks.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the collection unit, analysis unit, notification unit, proposal unit, and organization unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects the patient's medical and lifestyle data using the camera 42 and microphone 38B of the smart device 14. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12 to predict the risk of deterioration of health. The notification unit notifies the patient of necessary precautions and countermeasures based on the analysis results obtained by the specific processing unit 290 of the data processing unit 12. The proposal unit makes specific suggestions for meal, medication, and rest schedules using the control unit 46A of the smart device 14. The organization unit automatically prioritizes tasks using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the collection unit, analysis unit, notification unit, suggestion unit, and organization unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects the patient's medical and lifestyle data using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit analyzes the collected data using, for example, the specific processing unit 290 of the data processing unit 12 and predicts the risk of deterioration of health. The notification unit notifies the patient of necessary precautions and countermeasures based on the analysis results obtained by the specific processing unit 290 of the data processing unit 12. The suggestion unit makes specific suggestions for meal, medication, and rest schedules using, for example, the control unit 46A of the smart glasses 214. The organization unit automatically prioritizes tasks using, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the collection unit, analysis unit, notification unit, proposal unit, and organization unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the collection unit collects the patient's medical and lifestyle data using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12 to predict the risk of deterioration of health. The notification unit notifies the patient of necessary precautions and countermeasures based on the analysis results obtained by the specific processing unit 290 of the data processing unit 12. The proposal unit makes specific suggestions for meal, medication, and rest schedules using the control unit 46A of the headset terminal 314. The organization unit automatically prioritizes tasks using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the collection unit, analysis unit, notification unit, proposal unit, and organization unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects the patient's medical and lifestyle data using the camera 42 and microphone 238 of the robot 414. The analysis unit analyzes the collected data by, for example, the specific processing unit 290 of the data processing unit 12 and predicts the risk of deterioration of health. The notification unit notifies the patient of necessary precautions and countermeasures based on the analysis results obtained by the specific processing unit 290 of the data processing unit 12. The proposal unit makes specific suggestions for meal, medication, and rest schedules by, for example, the control unit 46A of the robot 414. The organization unit automatically prioritizes tasks by, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) The data collection department collects patients' medical and lifestyle data, An analysis unit analyzes the data collected by the aforementioned collection unit and predicts the risk of health deterioration, A notification unit that notifies necessary points to note and countermeasures based on the analysis results obtained by the aforementioned analysis unit, Based on the information notified by the aforementioned notification unit, the proposal unit makes specific suggestions regarding meal, medication, and rest schedules. The system includes a sorting unit that automatically sorts the task priorities based on the information proposed by the proposal unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect data such as the patient's body temperature, blood pressure, heart rate, diet, and exercise level. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is The collected data is analyzed to understand changes in physical condition. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned notification unit, If your body temperature is elevated, it will send a notification such as, "Your body temperature is elevated. Please stay hydrated and rest." The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We propose meal plans, medication timing, and rest periods tailored to the patient's physical condition and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned editing unit, List the tasks that caregivers need to perform and prioritize them according to their importance and urgency. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the patient's emotions and adjusts the frequency of data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the patient's past health data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed based on the patient's current living situation and environment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system estimates the patient's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data based on the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, analyze patients' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is The system estimates the patient's emotions and adjusts the data analysis algorithm based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, past data is referenced to predict current changes in physical condition. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During the analysis, different analytical methods are applied to each patient category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is The system estimates the patient's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned notification unit, The system estimates the patient's emotions and adjusts the way notifications are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned notification unit, When sending notifications, adjust the level of detail based on the importance of your health condition. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned notification unit, When sending notifications, different notification algorithms are applied depending on the health category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned notification unit, The system estimates the patient's emotions and adjusts the length of the notification based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned notification unit, When notifying, the priority of notifications will be determined based on when the health condition report was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned notification unit, When sending notifications, the order of notifications will be adjusted based on their relevance to your health condition. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, The system estimates the patient's emotions and adjusts the way the proposal is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the patient's health. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the health category. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, Estimate the patient's emotions and adjust the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When submitting proposals, prioritize them based on the timing of submission, considering your health condition. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on their relevance to the person's physical condition. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned editing unit, The system estimates the patient's emotions and prioritizes tasks based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned editing unit, When organizing tasks, refer to past task history to select the most suitable task organization method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned editing unit, When organizing, adjust the order of tasks based on their importance and urgency. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned editing unit, The system estimates the patient's emotions and adjusts how tasks are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned editing unit, When organizing, prioritize tasks based on their submission deadlines. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned editing unit, When organizing, adjust the order of tasks based on their relevance. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0195] 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. The data collection department collects patients' medical and lifestyle data, The data collected by the aforementioned collection unit is analyzed by an analysis unit that predicts the risk of health deterioration, A notification unit that notifies necessary points to note and countermeasures based on the analysis results obtained by the aforementioned analysis unit, Based on the information notified by the aforementioned notification unit, the proposal unit makes specific suggestions regarding meal, medication, and rest schedules. The system includes a sorting unit that automatically sorts the task priorities based on the information proposed by the proposal unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect data such as the patient's body temperature, blood pressure, heart rate, diet, and exercise level. The system according to feature 1.

3. The aforementioned analysis unit is The collected data is analyzed to understand changes in physical condition. The system according to feature 1.

4. The aforementioned proposal section is, We propose meal plans, medication timing, and rest periods tailored to the patient's physical condition and lifestyle. The system according to feature 1.

5. The aforementioned editing unit, List the tasks that caregivers need to perform and prioritize them according to their importance and urgency. The system according to feature 1.

6. The aforementioned collection unit is The system estimates the patient's emotions and adjusts the frequency of data collection based on the estimated emotions. The system according to feature 1.

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

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