system

Abstract

The system according to this embodiment aims to understand the health status of dementia patients and encourage appropriate actions. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a promotion unit, and a remote control unit. The data collection unit collects the patient's health data. The analysis unit analyzes the data collected by the data collection unit to understand the patient's health status. The promotion unit prompts the patient to take action based on the results obtained by the analysis unit. The remote control unit performs remote operation in emergencies.

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, the method including 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 health condition of a dementia patient and prompt necessary actions.

[0005] The system according to an embodiment aims to grasp the health condition of a dementia patient and prompt appropriate actions.

Means for Solving the Problems

[0006] The system according to an embodiment includes a collection unit, an analysis unit, a promotion unit, and a remote operation unit. The collection unit collects health data of a patient. The analysis unit analyzes the data collected by the collection unit to grasp the health condition. The promotion unit prompts the patient to take actions based on the result obtained by the analysis unit. The remote operation unit performs remote operations in case of emergency. [Effects of the Invention]

[0007] The system according to this embodiment can understand the health status of dementia patients and encourage appropriate actions. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 service for dementia patients according to an embodiment of the present invention is a system aimed at understanding the patient's health condition and encouraging appropriate actions. This system uses sensors installed on beds, chairs, tables, etc., to automatically measure the patient's "sleep," "weight," "body temperature," "heart rate," "blood glucose level," etc., and transmits this information to the HELPO system to understand the patient's health condition. For example, if the patient is not getting enough sleep at night, the HELPO system records this information and provides necessary advice. Next, "human presence," "temperature," "gas," and "water" sensors installed in the room are used to understand the room conditions and the patient's behavior. This allows monitoring of the patient's location in the room, whether the room temperature is appropriate, and whether the gas and water are functioning properly. For example, if the room temperature is too high, the air conditioner can be automatically turned on. Furthermore, the AI ​​robot "HELPO-kun" prompts the patient to take actions such as turning the air conditioner on / off, eating, and bathing. For example, if the patient has not eaten, "HELPO-kun" will prompt them to "eat." Also, if the patient has not bathed, "HELPO-kun" will prompt them to "at least take a shower, it was hot today." The primary objective of this service is to minimize cognitive decline by prompting patients to take action. However, in emergencies, it can remotely control functions such as forcibly turning on the air conditioner, shutting off gas and water, and contacting emergency services or the police as needed. Even when dementia patients have difficulty taking action on their own, this service can maintain their health and improve their quality of life through timely prompting. Furthermore, by continuously monitoring the patient's health data, it is possible to detect abnormalities early and take appropriate action. For example, if a patient does not get enough sleep at night, the HELPO system records this information and provides advice to prevent it from affecting their activities the next day. Also, if the room temperature is too high, the air conditioner is automatically turned on to reduce the risk of heatstroke. Thus, this invention aims to improve the quality of life of dementia patients by understanding their health status and prompting appropriate actions. As a result, services for dementia patients can automatically understand the patient's health status and prompt appropriate actions.

[0029] The service system for dementia patients according to this embodiment comprises a data collection unit, an analysis unit, a promotion unit, and a remote control unit. The data collection unit collects the patient's health data. The data collection unit automatically measures the patient's "sleep," "weight," "body temperature," "heart rate," and "blood glucose level" using sensors installed on, for example, a bed, chair, or table. The data collection unit monitors the patient's sleep state using a sensor installed on the bed. The data collection unit can also measure the patient's weight using a sensor installed on a chair. Furthermore, the data collection unit can measure the patient's body temperature using a sensor installed on a table. For example, the data collection unit monitors the patient's sleep state in real time using a sensor installed on the bed and evaluates the quality of sleep. The sensor installed on the chair measures the patient's weight when sitting and records the weight fluctuations. The sensor installed on the table measures the patient's body temperature when they place their hand on it and records the body temperature fluctuations. The analysis unit analyzes the data collected by the data collection unit to understand the patient's health status. The analysis unit evaluates the patient's health status based on the collected data. The analysis unit uses, for example, an algorithm to evaluate the patient's health status based on the collected data. The analysis unit can also use an AI model to evaluate the patient's health status based on the collected data. For example, the analysis unit scores the health status using an algorithm to evaluate the patient's health status based on the collected data. The analysis unit predicts the health status using an AI model to evaluate the patient's health status based on the collected data. The promotion unit prompts the patient to take action based on the results obtained by the analysis unit. For example, the promotion unit prompts the patient to turn the air conditioner on or off, eat, or take a bath. For example, the promotion unit prompts the patient to turn the air conditioner on or off. The promotion unit can also prompt the patient to eat. Furthermore, the promotion unit can also prompt the patient to take a bath. For example, the promotion unit sends a voice message to the patient to prompt them to turn the air conditioner on or off. The promotion unit notifies the patient of meal times to encourage them to eat. The promotion unit notifies the patient of bath times to encourage them to take a bath. The remote control unit performs remote operation in emergencies.The remote control unit can, for example, forcibly turn on the air conditioner or shut off the gas and water in an emergency. The remote control unit can also, for example, forcibly turn on the air conditioner in an emergency. Furthermore, the remote control unit can contact emergency services or the police as needed. For example, the remote control unit uses the remote control system to forcibly turn on the air conditioner in an emergency. The remote control unit uses the remote control system to shut off the gas and water in an emergency. The remote control unit uses the remote control system to contact emergency services or the police as needed. As a result, the service system for dementia patients according to this embodiment can improve the quality of life for dementia patients by collecting and analyzing patient health data and encouraging appropriate behavior.

[0030] The data collection unit collects patient health data. For example, it uses sensors installed on beds, chairs, tables, etc., to automatically measure the patient's "sleep," "weight," "body temperature," "heart rate," and "blood glucose level." Specifically, sensors installed on beds monitor the patient's sleep state in real time and evaluate sleep quality. This allows for the collection of detailed data, such as how deeply the patient sleeps or how many times they wake up during sleep. Sensors installed on chairs measure the patient's weight when they sit and record weight fluctuations. This allows for early detection of sudden weight changes and enables appropriate action. Sensors installed on tables measure the patient's body temperature when they place their hands on them and record temperature fluctuations. This allows for early detection of abnormalities such as fever or hypothermia. Furthermore, the data collection unit can also monitor the patient's heart rate and blood glucose level in real time using heart rate sensors and blood glucose sensors. This allows for early detection of abnormal heart rates or sudden changes in blood glucose levels and enables appropriate action. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and promotion units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the data collection unit to understand the patient's health status. For example, the analysis unit uses algorithms to evaluate the patient's health status based on the collected data. Specifically, it analyzes sleep data to evaluate sleep quality and patterns. For example, if the duration of deep sleep is short or the patient wakes up frequently, it can be determined that the quality of sleep is poor. It also analyzes weight data to evaluate weight fluctuations. If there is a rapid increase or decrease in weight, it can be determined that there may be a problem with the patient's health. Furthermore, it analyzes body temperature data to evaluate body temperature fluctuations. If there is a fever or hypothermia, it can be determined that there may be an infection or other health problem. Based on this data, the analysis unit can score the patient's health status and perform a comprehensive health assessment. In addition, the analysis unit can also use AI models to evaluate the patient's health status based on the collected data. The AI ​​model can predict health status based on past data and statistical information. For example, the AI ​​model can analyze sleep data, weight data, and body temperature data to predict future health risks. As a result, the analysis unit can quickly and accurately analyze the collected data and understand the patient's health status in real time. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling early warnings. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The promotion unit prompts the patient to take action based on the results obtained by the analysis unit. Specifically, it prompts the patient to take actions such as turning the air conditioner on or off, eating, or taking a bath. For example, if the analysis unit analyzes the patient's body temperature data and the temperature is high, the promotion unit will send a voice message to prompt the patient to turn on the air conditioner. Also, if the analysis unit analyzes the patient's weight data and the patient's weight is decreasing, the promotion unit will notify the patient of mealtime to encourage them to eat. Furthermore, if the analysis unit analyzes the patient's sleep data and the quality of sleep is decreasing, the promotion unit will notify the patient of bathtime to encourage them to take a bath. The promotion unit can use voice messages and notifications to prompt these actions. For example, it can use voice messages to prompt the patient to turn the air conditioner on or off. It can also use notifications to notify the patient of mealtimes or bathtimes. In addition, the promotion unit can record the patient's behavioral history and evaluate the effectiveness of the actions. For example, it can record changes in body temperature after prompting the patient to turn the air conditioner on or off, or changes in weight after prompting them to eat, and evaluate the effectiveness of the actions. In this way, the promotion unit can prompt the patient to take appropriate actions and improve their health condition. Furthermore, the promotion unit can analyze behavioral patterns based on the patient's behavioral history and propose more effective actions. This allows the promotion unit to continuously improve the patient's health and enhance their quality of life.

[0033] The remote control unit performs remote operations in emergencies. Specifically, it can forcibly turn on air conditioners and shut off gas and water in emergencies. For example, if a patient develops a high fever, the remote control unit can forcibly turn on the air conditioner to lower the room temperature and regulate the patient's body temperature. Also, if a gas leak or water leak is detected, the remote control unit can shut off the gas and water to prevent accidents. Furthermore, the remote control unit can contact emergency services and the police as needed. For example, if a patient falls, the remote control unit can use data from sensors to make an emergency contact and respond quickly. The remote control unit uses a remote control system to perform these operations. The remote control system can be operated via the internet, allowing for quick responses even from remote locations. For example, it can use a smartphone or tablet to forcibly turn on air conditioners or shut off gas and water. In addition, the remote control system is equipped with security measures to prevent unauthorized access. This allows the remote control unit to respond quickly and safely in emergencies. Furthermore, the remote control unit can coordinate with the patient's family and medical staff to share emergency response information. For example, it can notify the patient's family and medical staff of the emergency response situation, supporting a rapid response. This allows the remote control unit to ensure patient safety and minimize risks in emergencies.

[0034] The data collection unit can automatically measure the patient's "sleep," "weight," "body temperature," "heart rate," and "blood glucose level" using sensors installed on beds, chairs, tables, etc. For example, the data collection unit can monitor the patient's sleep state using a sensor installed on the bed. The data collection unit can also measure the patient's weight using a sensor installed on a chair. The data collection unit can also measure the patient's body temperature using a sensor installed on a table. For example, the data collection unit can monitor the patient's sleep state in real time using a sensor installed on the bed and evaluate the quality of sleep. The data collection unit can measure the patient's weight when they are sitting using a sensor installed on a chair and record the weight fluctuations. The data collection unit can measure the patient's body temperature when they place their hand on a table using a sensor installed on a table and record the body temperature fluctuations. In this way, the data collection unit can accurately understand the patient's health state by automatically measuring their health data. 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 sleep data acquired using sensors installed on the bed into a generating AI, which can then perform a sleep quality evaluation.

[0035] The data collection unit can understand the conditions in the room and the patient's behavior using "motion," "temperature," "gas," and "water" sensors installed in the room. For example, the data collection unit can determine the patient's location using the "motion" sensor installed in the room. The data collection unit can also monitor the room temperature using the "temperature" sensor installed in the room. The data collection unit can also detect gas leaks using the "gas" sensor installed in the room. The data collection unit can also understand water usage using the "water" sensor installed in the room. For example, the data collection unit can determine the patient's location in real time using the "motion" sensor installed in the room and monitor the patient's behavior. The data collection unit can monitor the room temperature using the "temperature" sensor installed in the room and maintain an appropriate temperature. The data collection unit can detect gas leaks using the "gas" sensor installed in the room and notify of abnormalities. The data collection unit can understand water usage using the "water" sensor installed in the room and notify of abnormalities. As a result, the data collection unit can understand the conditions in the room and the patient's behavior, enabling appropriate responses. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input location data acquired using a "human presence" sensor installed in the room into a generating AI, and have the generating AI perform monitoring of the patient's behavior.

[0036] The facilitator can prompt the patient to take actions such as turning the air conditioner on or off, eating, or taking a bath. For example, the facilitator can prompt the patient to turn the air conditioner on or off. The facilitator can also prompt the patient to eat. The facilitator can also prompt the patient to take a bath. For example, the facilitator can send a voice message to the patient to prompt them to turn the air conditioner on or off. The facilitator can notify the patient of meal times to prompt them to eat. The facilitator can notify the patient of bath times to encourage them to take a bath. In this way, the facilitator can improve the patient's quality of life by prompting them to take appropriate actions. Some or all of the above processing in the facilitator may be performed using AI, for example, or not using AI. For example, the facilitator can input a voice message prompting the patient to turn the air conditioner on or off into a generating AI, and have the generating AI perform the generation of the voice message.

[0037] The remote control unit can forcibly turn on the air conditioner, shut off gas and water in an emergency, and contact emergency services or the police as needed. For example, the remote control unit can forcibly turn on the air conditioner in an emergency. The remote control unit can also shut off gas and water in an emergency. The remote control unit can also contact emergency services or the police as needed. For example, the remote control unit uses a remote control system to forcibly turn on the air conditioner in an emergency. The remote control unit uses a remote control system to shut off gas and water in an emergency. The remote control unit uses a remote control system to contact emergency services or the police as needed. This allows the remote control unit to ensure patient safety by taking appropriate action in an emergency. Some or all of the above-described processes in the remote control unit may be performed using AI, for example, or without AI. For example, the remote control unit can input instructions to generate an AI to forcibly turn on the air conditioner in an emergency, and have the AI ​​generate the instructions.

[0038] The data collection unit can estimate the patient's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the patient is relaxed, the data collection unit will collect data frequently to obtain detailed health data. 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 sleeping, the data collection unit can refrain from collecting data and resume collection after the patient wakes up. For example, if the patient is relaxed, the data collection unit will collect data frequently to obtain detailed health data. If the patient is stressed, the data collection unit will reduce the frequency of data collection to alleviate the patient's burden. If the patient is sleeping, the data collection unit will refrain from collecting data and resume collection after the patient wakes up. In this way, the data collection unit can reduce the burden on the patient by adjusting the timing of data collection according to the patient's emotions. 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 emotional data into a generating AI and have the generating AI adjust the timing of data collection.

[0039] The data collection unit can analyze a patient's past health data and select the optimal sensor placement. For example, if the data collection unit determines from past data that data for a specific body part is important, it can concentrate sensors in that body part. For example, if the data collection unit determines from past data that abnormalities are likely to occur during a specific time period, it can activate sensors during that time period. For example, if the data collection unit determines from past data that abnormalities are likely to occur under specific environmental conditions, it can place sensors under those conditions. For example, if the data collection unit determines from past data that data for a specific body part is important, it can concentrate sensors in that body part. For example, if the data collection unit determines from past data that abnormalities are likely to occur during a specific time period, it can activate sensors during that time period. For example, if the data collection unit determines from past data that abnormalities are likely to occur under specific environmental conditions, it can place sensors under those conditions. In this way, the data collection unit can improve the accuracy of data collection by selecting the optimal sensor placement based on past data. 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 past health data into a generating AI, which can then select the optimal sensor placement.

[0040] The data collection unit can filter data based on the patient's current activity level during data collection. For example, if the patient is exercising, the data collection unit will prioritize collecting data related to exercise. For example, if the patient is resting, the data collection unit can prioritize collecting data related to rest. For example, if the patient is eating, the data collection unit can prioritize collecting data related to meals. In this way, the data collection unit can collect highly relevant data by filtering the data according to the patient's activity level. 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 activity data into a generating AI and have the generating AI perform data filtering.

[0041] 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 feeling anxious, the data collection unit will prioritize collecting data such as heart rate and blood pressure. For example, if the patient is relaxed, the data collection unit may prioritize collecting sleep data and body temperature data. For example, if the patient is agitated, the data collection unit may prioritize collecting blood glucose levels and weight data. For example, if the patient is feeling anxious, the data collection unit will prioritize collecting data such as heart rate and blood pressure. If the patient is relaxed, the data collection unit will prioritize collecting sleep data and body temperature data. If the patient is agitated, the data collection unit will prioritize collecting blood glucose levels and weight data. In this way, the data collection unit can prioritize the collection of important data by determining the priority of data according to the patient's emotions. 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 emotion data into a generating AI and have the generating AI perform the determination of data priority.

[0042] The data collection unit can prioritize the collection of highly relevant data based on the patient's living environment information during data collection. For example, if the patient is in a hot and humid environment, the data collection unit will prioritize the collection of body temperature and sweat volume data. For example, if the patient is in a cold environment, the data collection unit can also prioritize the collection of body temperature and blood pressure data. For example, if the patient is in a noisy environment, the data collection unit can also prioritize the collection of heart rate and stress level data. For example, if the patient is in a hot and humid environment, the data collection unit will prioritize the collection of body temperature and sweat volume data. If the patient is in a cold environment, the data collection unit will prioritize the collection of body temperature and blood pressure data. If the patient is in a noisy environment, the data collection unit will prioritize the collection of heart rate and stress level data. This allows the data collection unit to prioritize the collection of data according to the patient's living environment, enabling a more accurate understanding of their health status. Some or all of the above-described 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 environment information into a generating AI, allowing the AI ​​to prioritize 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, if the patient is experiencing stress on social media, the data collection unit may prioritize collecting heart rate and blood pressure data. For example, if the patient is relaxing on social media, the data collection unit may prioritize collecting sleep data and body temperature data. For example, if the patient is excited on social media, the data collection unit may prioritize collecting blood glucose and weight data. For example, if the patient is experiencing stress on social media, the data collection unit may prioritize collecting heart rate and blood pressure data. For example, if the patient is relaxing on social media, the data collection unit may prioritize collecting sleep data and body temperature data. For example, if the patient is excited on social media, the data collection unit may prioritize collecting blood glucose and weight data. This allows the data collection unit to more accurately understand the patient's emotions and behavior by collecting data based on 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 the relevant data.

[0044] The analysis unit can estimate the patient's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the patient is relaxed, the analysis unit provides detailed analysis results. For example, if the patient is stressed, the analysis unit can provide concise and to-the-point analysis results. For example, if the patient is agitated, the analysis unit can provide analysis results with visually stimulating effects. For example, if the patient is relaxed, the analysis unit provides detailed analysis results. If the patient is stressed, the analysis unit provides concise and to-the-point analysis results. If the patient is agitated, the analysis unit provides analysis results with visually stimulating effects. In this way, the analysis unit can provide analysis results that are easy for the patient to understand by adjusting the presentation of the analysis according to the patient's emotions. 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 the patient's emotion data into a generating AI and have the generating AI adjust the presentation of the analysis.

[0045] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during the analysis. For example, the analysis unit can perform a detailed analysis on important health data. For example, the analysis unit can perform a simplified analysis on less important health data. The analysis unit can also determine the priority of the analysis according to the importance of the health data. For example, the analysis unit can perform a detailed analysis on important health data. For example, the analysis unit can perform a simplified analysis on less important health data. The analysis unit can determine the priority of the analysis according to the importance of the health data. In this way, the analysis unit can perform a detailed analysis on important data by adjusting the level of detail of the analysis according to the importance of the health 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 the importance of the health data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0046] The analysis unit can apply different analysis algorithms depending on the category of health data during analysis. For example, the analysis unit can apply a heart rate variability analysis algorithm to heart rate data. For example, the analysis unit can also apply a blood glucose variability analysis algorithm to blood glucose data. For example, the analysis unit can also apply a body temperature variability analysis algorithm to body temperature data. For example, the analysis unit can apply a heart rate variability analysis algorithm to heart rate data. For example, the analysis unit can apply a blood glucose variability analysis algorithm to blood glucose data. For example, the analysis unit can apply a body temperature variability analysis algorithm to body temperature data. In this way, the analysis unit can improve the accuracy of the analysis by applying an appropriate analysis algorithm according to the category of health 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 the category of health data into a generating AI and have the generating AI execute the application of an appropriate analysis algorithm.

[0047] The analysis unit can estimate the patient's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the patient is relaxed, the analysis unit can perform a detailed analysis. For example, if the patient is stressed, the analysis unit can perform a concise analysis. For example, if the patient is agitated, the analysis unit can perform a visually stimulating analysis. For example, if the patient is relaxed, the analysis unit can perform a detailed analysis. If the patient is stressed, the analysis unit can perform a concise analysis. If the patient is agitated, the analysis unit can perform a visually stimulating analysis. In this way, the analysis unit can provide the patient with an appropriate analysis result by adjusting the length of the analysis according to the patient's emotions. 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 the patient's emotion data into a generating AI and have the generating AI adjust the length of the analysis.

[0048] The analysis unit can determine the priority of analysis based on the timing of health data collection during analysis. For example, the analysis unit may prioritize the analysis of recently collected data. The analysis unit may also analyze current data while referring to past data. The analysis unit may also determine the order of analysis based on the collection timing. For example, the analysis unit may prioritize the analysis of recently collected data. The analysis unit may analyze current data while referring to past data. The analysis unit may determine the order of analysis based on the collection timing. This allows the analysis unit to prioritize the analysis of the latest data by determining the priority of analysis based on the timing of health data collection. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may input the timing of health data collection into a generating AI and have the generating AI determine the priority of analysis.

[0049] The analysis unit can adjust the order of analysis based on the relevance of the health data during analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. The analysis unit may also postpone the analysis of less relevant data. The analysis unit can also determine the order of analysis based on the relevance of the data. For example, the analysis unit may prioritize the analysis of highly relevant data. The analysis unit may postpone the analysis of less relevant data. The analysis unit may determine the order of analysis based on the relevance of the data. In this way, the analysis unit can prioritize the analysis of highly relevant data by adjusting the order of analysis based on the relevance of the health data. 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 the relevance of the health data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0050] The facilitator can estimate the patient's emotions and adjust the method of behavioral guidance based on the estimated emotions. For example, if the patient is relaxed, the facilitator may encourage behavior in a gentle tone. If the patient is stressed, the facilitator may also provide concise and to-the-point instructions. If the patient is agitated, the facilitator may also provide instructions with visually stimulating effects. For example, if the patient is relaxed, the facilitator may encourage behavior in a gentle tone. If the patient is stressed, the facilitator may provide concise and to-the-point instructions. If the patient is agitated, the facilitator may provide instructions with visually stimulating effects. In this way, the facilitator can encourage appropriate behavior for the patient by adjusting the method of behavioral guidance according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the facilitator may be performed using AI, for example, or without AI. For example, the promotion unit can input patient emotional data into a generating AI and have the generating AI adjust the methods for promoting behavior.

[0051] The promotion unit can analyze the patient's past behavioral history to select the optimal promotion method when promoting behavior. For example, the promotion unit may reuse promotion methods that were effective in the past. The promotion unit may also avoid promotion methods that were ineffective in the past. The promotion unit may also propose new promotion methods based on the past behavioral history. For example, the promotion unit may reuse promotion methods that were effective in the past. The promotion unit may avoid promotion methods that were ineffective in the past. The promotion unit may propose new promotion methods based on the past behavioral history. In this way, the promotion unit can effectively promote behavior by selecting the optimal promotion method based on the past behavioral history. Some or all of the above processing in the promotion unit may be performed using AI, for example, or without AI. For example, the promotion unit may input the patient's past behavioral history into a generating AI and have the generating AI select the optimal promotion method.

[0052] The promotion unit can customize the means of promotion based on the patient's current living situation when promoting behavior. For example, if the patient is tired, the promotion unit may encourage rest. For example, if the patient is active, the promotion unit may encourage exercise. For example, if the patient has not eaten, the promotion unit may encourage eating. For example, if the patient is tired, the promotion unit may encourage rest. If the patient is active, the promotion unit may encourage exercise. If the patient has not eaten, the promotion unit may encourage eating. This allows the promotion unit to promote behavior more appropriately by customizing the means of promotion according to the patient's living situation. Some or all of the above processing in the promotion unit may be performed using AI, for example, or without AI. For example, the promotion unit may input patient living situation data into a generating AI and have the generating AI perform the customization of the means of promotion.

[0053] The facilitator can estimate the patient's emotions and determine the priority of behavioral facilitators based on the estimated emotions. For example, if the patient is feeling anxious, the facilitator may prioritize encouraging relaxing behaviors. If the patient is relaxed, the facilitator may also prioritize encouraging active behaviors. If the patient is agitated, the facilitator may also prioritize encouraging calming behaviors. For example, if the patient is feeling anxious, the facilitator may prioritize encouraging relaxing behaviors. If the patient is relaxed, the facilitator may prioritize encouraging active behaviors. If the patient is agitated, the facilitator may prioritize encouraging calming behaviors. This allows the facilitator to perform more effective behavioral facilitators by determining the priority of behavioral facilitators according to the patient's emotions. Some or all of the above processing in the facilitator may be performed using AI, for example, or without AI. For example, the facilitator can input patient emotion data into a generating AI and have the generating AI determine the priority of behavioral facilitators.

[0054] The promotion unit can select the optimal promotion method based on the patient's geographical location information when promoting behavior. For example, if the patient is at home, the promotion unit can encourage actions that can be done at home. For example, if the patient is out, the promotion unit can encourage actions that can be done at their destination. For example, if the patient is in a specific location, the promotion unit can encourage actions appropriate to that location. For example, if the patient is at home, the promotion unit can encourage actions that can be done at home. If the patient is out, the promotion unit can encourage actions that can be done at their destination. For example, if the patient is in a specific location, the promotion unit can encourage actions appropriate to that location. This allows the promotion unit to promote more appropriate behavior by selecting the optimal promotion method based on the patient's geographical location information. Some or all of the above processing in the promotion unit may be performed using AI, for example, or without AI. For example, the promotion unit can input the patient's geographical location information into a generating AI and have the generating AI select the optimal promotion method.

[0055] The Facilitation Unit can analyze the patient's social media activity and propose means of promotion when promoting behavior. For example, if the patient is relaxed on social media, the Facilitation Unit will encourage relaxing behavior. For example, if the patient is active on social media, the Facilitation Unit can also encourage active behavior. For example, if the patient is excited on social media, the Facilitation Unit can also encourage calming behavior. For example, if the patient is relaxed on social media, the Facilitation Unit will encourage relaxing behavior. If the patient is active on social media, the Facilitation Unit will encourage active behavior. If the patient is excited on social media, the Facilitation Unit will encourage calming behavior. In this way, the Facilitation Unit can propose means of promotion based on social media activity, enabling appropriate behavior promotion for the patient. Some or all of the above processing in the Facilitation Unit may be performed using AI, for example, or not using AI. For example, the Facilitation Unit can input the patient's social media activity data into a generating AI and have the generating AI propose means of promotion.

[0056] The remote control unit can estimate the patient's emotions and adjust its remote control method based on the estimated emotions. For example, if the patient is relaxed, the remote control unit will perform the remote control in a calm tone. If the patient is stressed, the remote control unit can also give concise and to-the-point instructions. If the patient is agitated, the remote control unit can also give instructions with visually stimulating effects. For example, if the patient is relaxed, the remote control unit will perform the remote control in a calm tone. If the patient is stressed, the remote control unit will give concise and to-the-point instructions. If the patient is agitated, the remote control unit will give instructions with visually stimulating effects. This allows the remote control unit to perform more appropriate remote control by adjusting its method according to 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-described processes in the remote control unit may be performed using AI, for example, or without AI. For example, the remote control unit can input patient emotion data into a generating AI and have the generating AI adjust the remote control method.

[0057] The remote control unit can analyze the patient's past emergency history and select the optimal operating method during remote operation. For example, the remote control unit may reuse operating methods that were effective in the past. The remote control unit may also avoid operating methods that were ineffective in the past. The remote control unit may also propose new operating methods based on the past emergency history. For example, the remote control unit may reuse operating methods that were effective in the past. The remote control unit may avoid operating methods that were ineffective in the past. The remote control unit may propose new operating methods based on the past emergency history. This enables effective remote operation by allowing the remote control unit to select the optimal operating method based on the past emergency history. Some or all of the above processing in the remote control unit may be performed using AI, for example, or without AI. For example, the remote control unit may input the patient's past emergency history into a generating AI and have the generating AI select the optimal operating method.

[0058] The remote control unit can estimate the patient's emotions and determine the priority of remote control actions based on the estimated emotions. For example, if the patient is feeling anxious, the remote control unit will prioritize actions to promote relaxation. If the patient is relaxed, the remote control unit may also prioritize active actions. If the patient is agitated, the remote control unit may also prioritize actions to calm down. For example, if the patient is feeling anxious, the remote control unit will prioritize actions to promote relaxation. If the patient is relaxed, the remote control unit will prioritize active actions. If the patient is agitated, the remote control unit will prioritize actions to calm down. This allows the remote control unit to perform more appropriate remote control actions by determining the priority of actions according to 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-described processes in the remote control unit may be performed using AI, for example, or without AI. For example, the remote control unit can input patient emotion data into a generating AI and have the generating AI determine the priority of remote operations.

[0059] The remote control unit can select the optimal operation method based on the patient's geographical location information during remote operation. For example, if the patient is at home, the remote control unit will prioritize operations that can be performed at home. For example, if the patient is out, the remote control unit can also prioritize operations that can be performed at their destination. For example, if the patient is in a specific location, the remote control unit can also prioritize operations appropriate for that location. For example, if the patient is at home, the remote control unit will prioritize operations that can be performed at home. If the patient is out, the remote control unit will prioritize operations that can be performed at their destination. For example, if the patient is in a specific location, the remote control unit will prioritize operations appropriate for that location. This allows the remote control unit to perform more appropriate remote operation by selecting the optimal operation method based on the patient's geographical location information. Some or all of the above processing in the remote control unit may be performed using AI, for example, or without AI. For example, the remote control unit can input the patient's geographical location information into a generating AI and have the generating AI select the optimal operation method.

[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 data collection unit can collect not only patient health data but also patient lifestyle data. For example, it can record patient diet, exercise levels, and medication adherence. This allows for a more comprehensive understanding of the patient's health status. The analysis unit can analyze the collected lifestyle data and evaluate its correlation with health status. For example, it can analyze the relationship between diet and blood glucose fluctuations, and between exercise levels and heart rate. The promotion unit can make specific lifestyle improvement suggestions to patients based on the analysis results. For example, it can suggest improvements to diet, recommend exercise, and adjust medication timing. This allows for more effective management of the patient's health status.

[0062] The data collection unit can analyze a patient's past health data and select the optimal sensor placement when collecting patient health data. For example, if past data indicates that data from a specific body part is important, sensors can be concentrated in that area. Furthermore, if abnormalities are more likely to occur during a specific time period, sensors can be activated during that time. Additionally, if abnormalities are more likely to occur under specific environmental conditions, sensors can be positioned under those conditions. This allows for improved data collection accuracy by selecting the optimal sensor placement based on past data.

[0063] The data collection unit can filter data based on the patient's current activity level during data collection. For example, if the patient is exercising, data related to exercise can be prioritized. Conversely, if the patient is resting, data related to rest can be prioritized. Similarly, if the patient is eating, data related to eating can be prioritized. This allows for the collection of highly relevant data by filtering it according to the patient's activity level.

[0064] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data. For example, it can perform a detailed analysis on important health data. Conversely, it can perform a simplified analysis on less important health data. It can also determine the priority of the analysis according to the importance of the health data. This allows for detailed analysis of important data by adjusting the level of detail according to the importance of the health data.

[0065] The behavioral promotion unit can analyze the patient's past behavioral history to select the optimal promotion method during behavioral promotion. For example, it can reuse promotion methods that were effective in the past. Conversely, it can avoid promotion methods that were ineffective in the past. It can also propose new promotion methods based on past behavioral history. As a result, by selecting the optimal promotion method based on past behavioral history, effective behavioral promotion becomes possible.

[0066] The remote control unit can select the optimal operation method based on the patient's geographical location during remote operation. For example, if the patient is at home, operations that can be performed at home will be prioritized. Conversely, if the patient is away from home, operations that can be performed at their current location will be prioritized. Furthermore, if the patient is in a specific location, operations appropriate for that location can be prioritized. This allows for more appropriate remote operation by selecting the optimal operation method based on the patient's geographical location.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The data collection unit collects the patient's health data. The data collection unit automatically measures the patient's "sleep," "weight," "body temperature," "heart rate," and "blood glucose level" using sensors installed on beds, chairs, tables, etc. For example, a sensor installed on the bed is used to monitor the patient's sleep state in real time and evaluate the quality of sleep. A sensor installed on the chair measures the patient's weight when they are sitting and records weight fluctuations. A sensor installed on the table measures the patient's body temperature when they place their hand on it and records temperature fluctuations. Step 2: The analysis unit analyzes the data collected by the collection unit to understand the patient's health status. Based on the collected data, the analysis unit uses algorithms and AI models to evaluate the patient's health status. For example, it may score the health status or predict it based on the collected data. Step 3: The Facilitation Unit prompts the patient to take action based on the results obtained by the Analysis Unit. The Facilitation Unit sends voice messages or notifications to the patient to prompt actions such as turning the air conditioner on / off, eating, or taking a bath. Step 4: The remote control unit performs remote operations in emergencies. The remote control unit uses a remote control system to forcibly turn on the air conditioner and shut off gas and water in emergencies. It can also contact emergency services and the police as needed.

[0069] (Example of form 2) The service for dementia patients according to an embodiment of the present invention is a system aimed at understanding the patient's health condition and encouraging appropriate actions. This system uses sensors installed on beds, chairs, tables, etc., to automatically measure the patient's "sleep," "weight," "body temperature," "heart rate," "blood glucose level," etc., and transmits this information to the HELPO system to understand the patient's health condition. For example, if the patient is not getting enough sleep at night, the HELPO system records this information and provides necessary advice. Next, "human presence," "temperature," "gas," and "water" sensors installed in the room are used to understand the room conditions and the patient's behavior. This allows monitoring of the patient's location in the room, whether the room temperature is appropriate, and whether the gas and water are functioning properly. For example, if the room temperature is too high, the air conditioner can be automatically turned on. Furthermore, the AI ​​robot "HELPO-kun" prompts the patient to take actions such as turning the air conditioner on / off, eating, and bathing. For example, if the patient has not eaten, "HELPO-kun" will prompt them to "eat." Also, if the patient has not bathed, "HELPO-kun" will prompt them to "at least take a shower, it was hot today." The primary objective of this service is to minimize cognitive decline by prompting patients to take action. However, in emergencies, it can remotely control functions such as forcibly turning on the air conditioner, shutting off gas and water, and contacting emergency services or the police as needed. Even when dementia patients have difficulty taking action on their own, this service can maintain their health and improve their quality of life through timely prompting. Furthermore, by continuously monitoring the patient's health data, it is possible to detect abnormalities early and take appropriate action. For example, if a patient does not get enough sleep at night, the HELPO system records this information and provides advice to prevent it from affecting their activities the next day. Also, if the room temperature is too high, the air conditioner is automatically turned on to reduce the risk of heatstroke. Thus, this invention aims to improve the quality of life of dementia patients by understanding their health status and prompting appropriate actions. As a result, services for dementia patients can automatically understand the patient's health status and prompt appropriate actions.

[0070] The service system for dementia patients according to this embodiment comprises a data collection unit, an analysis unit, a promotion unit, and a remote control unit. The data collection unit collects the patient's health data. The data collection unit automatically measures the patient's "sleep," "weight," "body temperature," "heart rate," and "blood glucose level" using sensors installed on, for example, a bed, chair, or table. The data collection unit monitors the patient's sleep state using a sensor installed on the bed. The data collection unit can also measure the patient's weight using a sensor installed on a chair. Furthermore, the data collection unit can measure the patient's body temperature using a sensor installed on a table. For example, the data collection unit monitors the patient's sleep state in real time using a sensor installed on the bed and evaluates the quality of sleep. The sensor installed on the chair measures the patient's weight when sitting and records the weight fluctuations. The sensor installed on the table measures the patient's body temperature when they place their hand on it and records the body temperature fluctuations. The analysis unit analyzes the data collected by the data collection unit to understand the patient's health status. The analysis unit evaluates the patient's health status based on the collected data. The analysis unit uses, for example, an algorithm to evaluate the patient's health status based on the collected data. The analysis unit can also use an AI model to evaluate the patient's health status based on the collected data. For example, the analysis unit scores the health status using an algorithm to evaluate the patient's health status based on the collected data. The analysis unit predicts the health status using an AI model to evaluate the patient's health status based on the collected data. The promotion unit prompts the patient to take action based on the results obtained by the analysis unit. For example, the promotion unit prompts the patient to turn the air conditioner on or off, eat, or take a bath. For example, the promotion unit prompts the patient to turn the air conditioner on or off. The promotion unit can also prompt the patient to eat. Furthermore, the promotion unit can also prompt the patient to take a bath. For example, the promotion unit sends a voice message to the patient to prompt them to turn the air conditioner on or off. The promotion unit notifies the patient of meal times to encourage them to eat. The promotion unit notifies the patient of bath times to encourage them to take a bath. The remote control unit performs remote operation in emergencies.The remote control unit can, for example, forcibly turn on the air conditioner or shut off the gas and water in an emergency. The remote control unit can also, for example, forcibly turn on the air conditioner in an emergency. Furthermore, the remote control unit can contact emergency services or the police as needed. For example, the remote control unit uses the remote control system to forcibly turn on the air conditioner in an emergency. The remote control unit uses the remote control system to shut off the gas and water in an emergency. The remote control unit uses the remote control system to contact emergency services or the police as needed. As a result, the service system for dementia patients according to this embodiment can improve the quality of life for dementia patients by collecting and analyzing patient health data and encouraging appropriate behavior.

[0071] The data collection unit collects patient health data. For example, it uses sensors installed on beds, chairs, tables, etc., to automatically measure the patient's "sleep," "weight," "body temperature," "heart rate," and "blood glucose level." Specifically, sensors installed on beds monitor the patient's sleep state in real time and evaluate sleep quality. This allows for the collection of detailed data, such as how deeply the patient sleeps or how many times they wake up during sleep. Sensors installed on chairs measure the patient's weight when they sit and record weight fluctuations. This allows for early detection of sudden weight changes and enables appropriate action. Sensors installed on tables measure the patient's body temperature when they place their hands on them and record temperature fluctuations. This allows for early detection of abnormalities such as fever or hypothermia. Furthermore, the data collection unit can also monitor the patient's heart rate and blood glucose level in real time using heart rate sensors and blood glucose sensors. This allows for early detection of abnormal heart rates or sudden changes in blood glucose levels and enables appropriate action. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and promotion units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0072] The analysis unit analyzes the data collected by the data collection unit to understand the patient's health status. For example, the analysis unit uses algorithms to evaluate the patient's health status based on the collected data. Specifically, it analyzes sleep data to evaluate sleep quality and patterns. For example, if the duration of deep sleep is short or the patient wakes up frequently, it can be determined that the quality of sleep is poor. It also analyzes weight data to evaluate weight fluctuations. If there is a rapid increase or decrease in weight, it can be determined that there may be a problem with the patient's health. Furthermore, it analyzes body temperature data to evaluate body temperature fluctuations. If there is a fever or hypothermia, it can be determined that there may be an infection or other health problem. Based on this data, the analysis unit can score the patient's health status and perform a comprehensive health assessment. In addition, the analysis unit can also use AI models to evaluate the patient's health status based on the collected data. The AI ​​model can predict health status based on past data and statistical information. For example, the AI ​​model can analyze sleep data, weight data, and body temperature data to predict future health risks. As a result, the analysis unit can quickly and accurately analyze the collected data and understand the patient's health status in real time. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling early warnings. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0073] The promotion unit prompts the patient to take action based on the results obtained by the analysis unit. Specifically, it prompts the patient to take actions such as turning the air conditioner on or off, eating, or taking a bath. For example, if the analysis unit analyzes the patient's body temperature data and the temperature is high, the promotion unit will send a voice message to prompt the patient to turn on the air conditioner. Also, if the analysis unit analyzes the patient's weight data and the patient's weight is decreasing, the promotion unit will notify the patient of mealtime to encourage them to eat. Furthermore, if the analysis unit analyzes the patient's sleep data and the quality of sleep is decreasing, the promotion unit will notify the patient of bathtime to encourage them to take a bath. The promotion unit can use voice messages and notifications to prompt these actions. For example, it can use voice messages to prompt the patient to turn the air conditioner on or off. It can also use notifications to notify the patient of mealtimes or bathtimes. In addition, the promotion unit can record the patient's behavioral history and evaluate the effectiveness of the actions. For example, it can record changes in body temperature after prompting the patient to turn the air conditioner on or off, or changes in weight after prompting them to eat, and evaluate the effectiveness of the actions. In this way, the promotion unit can prompt the patient to take appropriate actions and improve their health condition. Furthermore, the promotion unit can analyze behavioral patterns based on the patient's behavioral history and propose more effective actions. This allows the promotion unit to continuously improve the patient's health and enhance their quality of life.

[0074] The remote control unit performs remote operations in emergencies. Specifically, it can forcibly turn on air conditioners and shut off gas and water in emergencies. For example, if a patient develops a high fever, the remote control unit can forcibly turn on the air conditioner to lower the room temperature and regulate the patient's body temperature. Also, if a gas leak or water leak is detected, the remote control unit can shut off the gas and water to prevent accidents. Furthermore, the remote control unit can contact emergency services and the police as needed. For example, if a patient falls, the remote control unit can use data from sensors to make an emergency contact and respond quickly. The remote control unit uses a remote control system to perform these operations. The remote control system can be operated via the internet, allowing for quick responses even from remote locations. For example, it can use a smartphone or tablet to forcibly turn on air conditioners or shut off gas and water. In addition, the remote control system is equipped with security measures to prevent unauthorized access. This allows the remote control unit to respond quickly and safely in emergencies. Furthermore, the remote control unit can coordinate with the patient's family and medical staff to share emergency response information. For example, it can notify the patient's family and medical staff of the emergency response situation, supporting a rapid response. This allows the remote control unit to ensure patient safety and minimize risks in emergencies.

[0075] The data collection unit can automatically measure the patient's "sleep," "weight," "body temperature," "heart rate," and "blood glucose level" using sensors installed on beds, chairs, tables, etc. For example, the data collection unit can monitor the patient's sleep state using a sensor installed on the bed. The data collection unit can also measure the patient's weight using a sensor installed on a chair. The data collection unit can also measure the patient's body temperature using a sensor installed on a table. For example, the data collection unit can monitor the patient's sleep state in real time using a sensor installed on the bed and evaluate the quality of sleep. The data collection unit can measure the patient's weight when they are sitting using a sensor installed on a chair and record the weight fluctuations. The data collection unit can measure the patient's body temperature when they place their hand on a table using a sensor installed on a table and record the body temperature fluctuations. In this way, the data collection unit can accurately understand the patient's health state by automatically measuring their health data. 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 sleep data acquired using sensors installed on the bed into a generating AI, which can then perform a sleep quality evaluation.

[0076] The data collection unit can understand the conditions in the room and the patient's behavior using "motion," "temperature," "gas," and "water" sensors installed in the room. For example, the data collection unit can determine the patient's location using the "motion" sensor installed in the room. The data collection unit can also monitor the room temperature using the "temperature" sensor installed in the room. The data collection unit can also detect gas leaks using the "gas" sensor installed in the room. The data collection unit can also understand water usage using the "water" sensor installed in the room. For example, the data collection unit can determine the patient's location in real time using the "motion" sensor installed in the room and monitor the patient's behavior. The data collection unit can monitor the room temperature using the "temperature" sensor installed in the room and maintain an appropriate temperature. The data collection unit can detect gas leaks using the "gas" sensor installed in the room and notify of abnormalities. The data collection unit can understand water usage using the "water" sensor installed in the room and notify of abnormalities. As a result, the data collection unit can understand the conditions in the room and the patient's behavior, enabling appropriate responses. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input location data acquired using a "human presence" sensor installed in the room into a generating AI, and have the generating AI perform monitoring of the patient's behavior.

[0077] The facilitator can prompt the patient to take actions such as turning the air conditioner on or off, eating, or taking a bath. For example, the facilitator can prompt the patient to turn the air conditioner on or off. The facilitator can also prompt the patient to eat. The facilitator can also prompt the patient to take a bath. For example, the facilitator can send a voice message to the patient to prompt them to turn the air conditioner on or off. The facilitator can notify the patient of meal times to prompt them to eat. The facilitator can notify the patient of bath times to encourage them to take a bath. In this way, the facilitator can improve the patient's quality of life by prompting them to take appropriate actions. Some or all of the above processing in the facilitator may be performed using AI, for example, or not using AI. For example, the facilitator can input a voice message prompting the patient to turn the air conditioner on or off into a generating AI, and have the generating AI perform the generation of the voice message.

[0078] The remote control unit can forcibly turn on the air conditioner, shut off gas and water in an emergency, and contact emergency services or the police as needed. For example, the remote control unit can forcibly turn on the air conditioner in an emergency. The remote control unit can also shut off gas and water in an emergency. The remote control unit can also contact emergency services or the police as needed. For example, the remote control unit uses a remote control system to forcibly turn on the air conditioner in an emergency. The remote control unit uses a remote control system to shut off gas and water in an emergency. The remote control unit uses a remote control system to contact emergency services or the police as needed. This allows the remote control unit to ensure patient safety by taking appropriate action in an emergency. Some or all of the above-described processes in the remote control unit may be performed using AI, for example, or without AI. For example, the remote control unit can input instructions to generate an AI to forcibly turn on the air conditioner in an emergency, and have the AI ​​generate the instructions.

[0079] The data collection unit can estimate the patient's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the patient is relaxed, the data collection unit will collect data frequently to obtain detailed health data. 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 sleeping, the data collection unit can refrain from collecting data and resume collection after the patient wakes up. For example, if the patient is relaxed, the data collection unit will collect data frequently to obtain detailed health data. If the patient is stressed, the data collection unit will reduce the frequency of data collection to alleviate the patient's burden. If the patient is sleeping, the data collection unit will refrain from collecting data and resume collection after the patient wakes up. In this way, the data collection unit can reduce the burden on the patient by adjusting the timing of data collection according to the patient's emotions. 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 emotional data into a generating AI and have the generating AI adjust the timing of data collection.

[0080] The data collection unit can analyze a patient's past health data and select the optimal sensor placement. For example, if the data collection unit determines from past data that data for a specific body part is important, it can concentrate sensors in that body part. For example, if the data collection unit determines from past data that abnormalities are likely to occur during a specific time period, it can activate sensors during that time period. For example, if the data collection unit determines from past data that abnormalities are likely to occur under specific environmental conditions, it can place sensors under those conditions. For example, if the data collection unit determines from past data that data for a specific body part is important, it can concentrate sensors in that body part. For example, if the data collection unit determines from past data that abnormalities are likely to occur during a specific time period, it can activate sensors during that time period. For example, if the data collection unit determines from past data that abnormalities are likely to occur under specific environmental conditions, it can place sensors under those conditions. In this way, the data collection unit can improve the accuracy of data collection by selecting the optimal sensor placement based on past data. 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 past health data into a generating AI, which can then select the optimal sensor placement.

[0081] The data collection unit can filter data based on the patient's current activity level during data collection. For example, if the patient is exercising, the data collection unit will prioritize collecting data related to exercise. For example, if the patient is resting, the data collection unit can prioritize collecting data related to rest. For example, if the patient is eating, the data collection unit can prioritize collecting data related to meals. In this way, the data collection unit can collect highly relevant data by filtering the data according to the patient's activity level. 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 activity data into a generating AI and have the generating AI perform data filtering.

[0082] 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 feeling anxious, the data collection unit will prioritize collecting data such as heart rate and blood pressure. For example, if the patient is relaxed, the data collection unit may prioritize collecting sleep data and body temperature data. For example, if the patient is agitated, the data collection unit may prioritize collecting blood glucose levels and weight data. For example, if the patient is feeling anxious, the data collection unit will prioritize collecting data such as heart rate and blood pressure. If the patient is relaxed, the data collection unit will prioritize collecting sleep data and body temperature data. If the patient is agitated, the data collection unit will prioritize collecting blood glucose levels and weight data. In this way, the data collection unit can prioritize the collection of important data by determining the priority of data according to the patient's emotions. 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 emotion data into a generating AI and have the generating AI perform the determination of data priority.

[0083] The data collection unit can prioritize the collection of highly relevant data based on the patient's living environment information during data collection. For example, if the patient is in a hot and humid environment, the data collection unit will prioritize the collection of body temperature and sweat volume data. For example, if the patient is in a cold environment, the data collection unit can also prioritize the collection of body temperature and blood pressure data. For example, if the patient is in a noisy environment, the data collection unit can also prioritize the collection of heart rate and stress level data. For example, if the patient is in a hot and humid environment, the data collection unit will prioritize the collection of body temperature and sweat volume data. If the patient is in a cold environment, the data collection unit will prioritize the collection of body temperature and blood pressure data. If the patient is in a noisy environment, the data collection unit will prioritize the collection of heart rate and stress level data. This allows the data collection unit to prioritize the collection of data according to the patient's living environment, enabling a more accurate understanding of their health status. Some or all of the above-described 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 environment information into a generating AI, allowing the AI ​​to prioritize the collection of highly relevant data.

[0084] The data collection unit can analyze the patient's social media activity and collect relevant data during data collection. For example, if the patient is experiencing stress on social media, the data collection unit may prioritize collecting heart rate and blood pressure data. For example, if the patient is relaxing on social media, the data collection unit may prioritize collecting sleep data and body temperature data. For example, if the patient is excited on social media, the data collection unit may prioritize collecting blood glucose and weight data. For example, if the patient is experiencing stress on social media, the data collection unit may prioritize collecting heart rate and blood pressure data. For example, if the patient is relaxing on social media, the data collection unit may prioritize collecting sleep data and body temperature data. For example, if the patient is excited on social media, the data collection unit may prioritize collecting blood glucose and weight data. This allows the data collection unit to more accurately understand the patient's emotions and behavior by collecting data based on 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 the relevant data.

[0085] The analysis unit can estimate the patient's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the patient is relaxed, the analysis unit provides detailed analysis results. For example, if the patient is stressed, the analysis unit can provide concise and to-the-point analysis results. For example, if the patient is agitated, the analysis unit can provide analysis results with visually stimulating effects. For example, if the patient is relaxed, the analysis unit provides detailed analysis results. If the patient is stressed, the analysis unit provides concise and to-the-point analysis results. If the patient is agitated, the analysis unit provides analysis results with visually stimulating effects. In this way, the analysis unit can provide analysis results that are easy for the patient to understand by adjusting the presentation of the analysis according to the patient's emotions. 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 the patient's emotion data into a generating AI and have the generating AI adjust the presentation of the analysis.

[0086] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during the analysis. For example, the analysis unit can perform a detailed analysis on important health data. For example, the analysis unit can perform a simplified analysis on less important health data. The analysis unit can also determine the priority of the analysis according to the importance of the health data. For example, the analysis unit can perform a detailed analysis on important health data. For example, the analysis unit can perform a simplified analysis on less important health data. The analysis unit can determine the priority of the analysis according to the importance of the health data. In this way, the analysis unit can perform a detailed analysis on important data by adjusting the level of detail of the analysis according to the importance of the health 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 the importance of the health data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0087] The analysis unit can apply different analysis algorithms depending on the category of health data during analysis. For example, the analysis unit can apply a heart rate variability analysis algorithm to heart rate data. For example, the analysis unit can also apply a blood glucose variability analysis algorithm to blood glucose data. For example, the analysis unit can also apply a body temperature variability analysis algorithm to body temperature data. For example, the analysis unit can apply a heart rate variability analysis algorithm to heart rate data. For example, the analysis unit can apply a blood glucose variability analysis algorithm to blood glucose data. For example, the analysis unit can apply a body temperature variability analysis algorithm to body temperature data. In this way, the analysis unit can improve the accuracy of the analysis by applying an appropriate analysis algorithm according to the category of health 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 the category of health data into a generating AI and have the generating AI execute the application of an appropriate analysis algorithm.

[0088] The analysis unit can estimate the patient's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the patient is relaxed, the analysis unit can perform a detailed analysis. For example, if the patient is stressed, the analysis unit can perform a concise analysis. For example, if the patient is agitated, the analysis unit can perform a visually stimulating analysis. For example, if the patient is relaxed, the analysis unit can perform a detailed analysis. If the patient is stressed, the analysis unit can perform a concise analysis. If the patient is agitated, the analysis unit can perform a visually stimulating analysis. In this way, the analysis unit can provide the patient with an appropriate analysis result by adjusting the length of the analysis according to the patient's emotions. 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 the patient's emotion data into a generating AI and have the generating AI adjust the length of the analysis.

[0089] The analysis unit can determine the priority of analysis based on the timing of health data collection during analysis. For example, the analysis unit may prioritize the analysis of recently collected data. The analysis unit may also analyze current data while referring to past data. The analysis unit may also determine the order of analysis based on the collection timing. For example, the analysis unit may prioritize the analysis of recently collected data. The analysis unit may analyze current data while referring to past data. The analysis unit may determine the order of analysis based on the collection timing. This allows the analysis unit to prioritize the analysis of the latest data by determining the priority of analysis based on the timing of health data collection. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may input the timing of health data collection into a generating AI and have the generating AI determine the priority of analysis.

[0090] The analysis unit can adjust the order of analysis based on the relevance of the health data during analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. The analysis unit may also postpone the analysis of less relevant data. The analysis unit can also determine the order of analysis based on the relevance of the data. For example, the analysis unit may prioritize the analysis of highly relevant data. The analysis unit may postpone the analysis of less relevant data. The analysis unit may determine the order of analysis based on the relevance of the data. In this way, the analysis unit can prioritize the analysis of highly relevant data by adjusting the order of analysis based on the relevance of the health data. 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 the relevance of the health data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0091] The facilitator can estimate the patient's emotions and adjust the method of behavioral guidance based on the estimated emotions. For example, if the patient is relaxed, the facilitator may encourage behavior in a gentle tone. If the patient is stressed, the facilitator may also provide concise and to-the-point instructions. If the patient is agitated, the facilitator may also provide instructions with visually stimulating effects. For example, if the patient is relaxed, the facilitator may encourage behavior in a gentle tone. If the patient is stressed, the facilitator may provide concise and to-the-point instructions. If the patient is agitated, the facilitator may provide instructions with visually stimulating effects. In this way, the facilitator can encourage appropriate behavior for the patient by adjusting the method of behavioral guidance according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the facilitator may be performed using AI, for example, or without AI. For example, the promotion unit can input patient emotional data into a generating AI and have the generating AI adjust the methods for promoting behavior.

[0092] The promotion unit can analyze the patient's past behavioral history to select the optimal promotion method when promoting behavior. For example, the promotion unit may reuse promotion methods that were effective in the past. The promotion unit may also avoid promotion methods that were ineffective in the past. The promotion unit may also propose new promotion methods based on the past behavioral history. For example, the promotion unit may reuse promotion methods that were effective in the past. The promotion unit may avoid promotion methods that were ineffective in the past. The promotion unit may propose new promotion methods based on the past behavioral history. In this way, the promotion unit can effectively promote behavior by selecting the optimal promotion method based on the past behavioral history. Some or all of the above processing in the promotion unit may be performed using AI, for example, or without AI. For example, the promotion unit may input the patient's past behavioral history into a generating AI and have the generating AI select the optimal promotion method.

[0093] The promotion unit can customize the means of promotion based on the patient's current living situation when promoting behavior. For example, if the patient is tired, the promotion unit may encourage rest. For example, if the patient is active, the promotion unit may encourage exercise. For example, if the patient has not eaten, the promotion unit may encourage eating. For example, if the patient is tired, the promotion unit may encourage rest. If the patient is active, the promotion unit may encourage exercise. If the patient has not eaten, the promotion unit may encourage eating. This allows the promotion unit to promote behavior more appropriately by customizing the means of promotion according to the patient's living situation. Some or all of the above processing in the promotion unit may be performed using AI, for example, or without AI. For example, the promotion unit may input patient living situation data into a generating AI and have the generating AI perform the customization of the means of promotion.

[0094] The facilitator can estimate the patient's emotions and determine the priority of behavioral facilitators based on the estimated emotions. For example, if the patient is feeling anxious, the facilitator may prioritize encouraging relaxing behaviors. If the patient is relaxed, the facilitator may also prioritize encouraging active behaviors. If the patient is agitated, the facilitator may also prioritize encouraging calming behaviors. For example, if the patient is feeling anxious, the facilitator may prioritize encouraging relaxing behaviors. If the patient is relaxed, the facilitator may prioritize encouraging active behaviors. If the patient is agitated, the facilitator may prioritize encouraging calming behaviors. This allows the facilitator to perform more effective behavioral facilitators by determining the priority of behavioral facilitators according to the patient's emotions. Some or all of the above processing in the facilitator may be performed using AI, for example, or without AI. For example, the facilitator can input patient emotion data into a generating AI and have the generating AI determine the priority of behavioral facilitators.

[0095] The promotion unit can select the optimal promotion method based on the patient's geographical location information when promoting behavior. For example, if the patient is at home, the promotion unit can encourage actions that can be done at home. For example, if the patient is out, the promotion unit can encourage actions that can be done at their destination. For example, if the patient is in a specific location, the promotion unit can encourage actions appropriate to that location. For example, if the patient is at home, the promotion unit can encourage actions that can be done at home. If the patient is out, the promotion unit can encourage actions that can be done at their destination. For example, if the patient is in a specific location, the promotion unit can encourage actions appropriate to that location. This allows the promotion unit to promote more appropriate behavior by selecting the optimal promotion method based on the patient's geographical location information. Some or all of the above processing in the promotion unit may be performed using AI, for example, or without AI. For example, the promotion unit can input the patient's geographical location information into a generating AI and have the generating AI select the optimal promotion method.

[0096] The Facilitation Unit can analyze the patient's social media activity and propose means of promotion when promoting behavior. For example, if the patient is relaxed on social media, the Facilitation Unit will encourage relaxing behavior. For example, if the patient is active on social media, the Facilitation Unit can also encourage active behavior. For example, if the patient is excited on social media, the Facilitation Unit can also encourage calming behavior. For example, if the patient is relaxed on social media, the Facilitation Unit will encourage relaxing behavior. If the patient is active on social media, the Facilitation Unit will encourage active behavior. If the patient is excited on social media, the Facilitation Unit will encourage calming behavior. In this way, the Facilitation Unit can propose means of promotion based on social media activity, enabling appropriate behavior promotion for the patient. Some or all of the above processing in the Facilitation Unit may be performed using AI, for example, or not using AI. For example, the Facilitation Unit can input the patient's social media activity data into a generating AI and have the generating AI propose means of promotion.

[0097] The remote control unit can estimate the patient's emotions and adjust its remote control method based on the estimated emotions. For example, if the patient is relaxed, the remote control unit will perform the remote control in a calm tone. If the patient is stressed, the remote control unit can also give concise and to-the-point instructions. If the patient is agitated, the remote control unit can also give instructions with visually stimulating effects. For example, if the patient is relaxed, the remote control unit will perform the remote control in a calm tone. If the patient is stressed, the remote control unit will give concise and to-the-point instructions. If the patient is agitated, the remote control unit will give instructions with visually stimulating effects. This allows the remote control unit to perform more appropriate remote control by adjusting its method according to 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-described processes in the remote control unit may be performed using AI, for example, or without AI. For example, the remote control unit can input patient emotion data into a generating AI and have the generating AI adjust the remote control method.

[0098] The remote control unit can analyze the patient's past emergency history and select the optimal operating method during remote operation. For example, the remote control unit may reuse operating methods that were effective in the past. The remote control unit may also avoid operating methods that were ineffective in the past. The remote control unit may also propose new operating methods based on the past emergency history. For example, the remote control unit may reuse operating methods that were effective in the past. The remote control unit may avoid operating methods that were ineffective in the past. The remote control unit may propose new operating methods based on the past emergency history. This enables effective remote operation by allowing the remote control unit to select the optimal operating method based on the past emergency history. Some or all of the above processing in the remote control unit may be performed using AI, for example, or without AI. For example, the remote control unit may input the patient's past emergency history into a generating AI and have the generating AI select the optimal operating method.

[0099] The remote control unit can estimate the patient's emotions and determine the priority of remote control actions based on the estimated emotions. For example, if the patient is feeling anxious, the remote control unit will prioritize actions to promote relaxation. If the patient is relaxed, the remote control unit may also prioritize active actions. If the patient is agitated, the remote control unit may also prioritize actions to calm down. For example, if the patient is feeling anxious, the remote control unit will prioritize actions to promote relaxation. If the patient is relaxed, the remote control unit will prioritize active actions. If the patient is agitated, the remote control unit will prioritize actions to calm down. This allows the remote control unit to perform more appropriate remote control actions by determining the priority of actions according to 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-described processes in the remote control unit may be performed using AI, for example, or without AI. For example, the remote control unit can input patient emotion data into a generating AI and have the generating AI determine the priority of remote operations.

[0100] The remote control unit can select the optimal operation method based on the patient's geographical location information during remote operation. For example, if the patient is at home, the remote control unit will prioritize operations that can be performed at home. For example, if the patient is out, the remote control unit can also prioritize operations that can be performed at their destination. For example, if the patient is in a specific location, the remote control unit can also prioritize operations appropriate for that location. For example, if the patient is at home, the remote control unit will prioritize operations that can be performed at home. If the patient is out, the remote control unit will prioritize operations that can be performed at their destination. For example, if the patient is in a specific location, the remote control unit will prioritize operations appropriate for that location. This allows the remote control unit to perform more appropriate remote operation by selecting the optimal operation method based on the patient's geographical location information. Some or all of the above processing in the remote control unit may be performed using AI, for example, or without AI. For example, the remote control unit can input the patient's geographical location information into a generating AI and have the generating AI select the optimal operation method.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] The data collection unit can collect not only patient health data but also patient lifestyle data. For example, it can record patient diet, exercise levels, and medication adherence. This allows for a more comprehensive understanding of the patient's health status. The analysis unit can analyze the collected lifestyle data and evaluate its correlation with health status. For example, it can analyze the relationship between diet and blood glucose fluctuations, and between exercise levels and heart rate. The promotion unit can make specific lifestyle improvement suggestions to patients based on the analysis results. For example, it can suggest improvements to diet, recommend exercise, and adjust medication timing. This allows for more effective management of the patient's health status.

[0103] The data collection unit can estimate the patient's emotions when collecting health data and adjust the timing of data collection based on those emotions. For example, if the patient is relaxed, data can be collected more frequently to obtain detailed health data. Conversely, if the patient is stressed, the frequency of data collection can be reduced to lessen the patient's burden. Furthermore, data collection can be withheld while the patient is sleeping and resumed after they wake up. This allows for the collection of necessary data while reducing the patient's burden by adjusting the timing of data collection according to their emotions.

[0104] The data collection unit can analyze a patient's past health data and select the optimal sensor placement when collecting patient health data. For example, if past data indicates that data from a specific body part is important, sensors can be concentrated in that area. Furthermore, if abnormalities are more likely to occur during a specific time period, sensors can be activated during that time. Additionally, if abnormalities are more likely to occur under specific environmental conditions, sensors can be positioned under those conditions. This allows for improved data collection accuracy by selecting the optimal sensor placement based on past data.

[0105] The facilitator can estimate the patient's emotions and adjust the method of behavioral guidance based on those emotions. For example, if the patient is relaxed, it can encourage behavior in a calm tone. Conversely, if the patient is stressed, it can provide concise and to-the-point instructions. Furthermore, if the patient is agitated, it can provide instructions with visually stimulating effects. By adjusting the behavioral guidance method according to the patient's emotions, it can encourage appropriate behavior for the patient.

[0106] The remote control unit can estimate the patient's emotions and adjust the remote control method based on those estimates. For example, if the patient is relaxed, the remote control can be performed in a calm tone. Conversely, if the patient is stressed, concise and to-the-point instructions can be given. Furthermore, if the patient is agitated, instructions can be given with visually stimulating effects. This allows for more appropriate remote control by adjusting the method according to the patient's emotions.

[0107] The data collection unit can filter data based on the patient's current activity level during data collection. For example, if the patient is exercising, data related to exercise can be prioritized. Conversely, if the patient is resting, data related to rest can be prioritized. Similarly, if the patient is eating, data related to eating can be prioritized. This allows for the collection of highly relevant data by filtering it according to the patient's activity level.

[0108] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data. For example, it can perform a detailed analysis on important health data. Conversely, it can perform a simplified analysis on less important health data. It can also determine the priority of the analysis according to the importance of the health data. This allows for detailed analysis of important data by adjusting the level of detail according to the importance of the health data.

[0109] The behavioral promotion unit can analyze the patient's past behavioral history to select the optimal promotion method during behavioral promotion. For example, it can reuse promotion methods that were effective in the past. Conversely, it can avoid promotion methods that were ineffective in the past. It can also propose new promotion methods based on past behavioral history. As a result, by selecting the optimal promotion method based on past behavioral history, effective behavioral promotion becomes possible.

[0110] The facilitator can estimate the patient's emotions and determine the priority of behavioral facilitators based on those emotions. For example, if the patient is feeling anxious, it can prioritize encouraging relaxing behaviors. Conversely, if the patient is relaxed, it can prioritize encouraging active behaviors. It can also prioritize encouraging calming behaviors if the patient is agitated. This allows for more effective behavioral facilitator development by prioritizing behavioral facilitators according to the patient's emotions.

[0111] The remote control unit can select the optimal operation method based on the patient's geographical location during remote operation. For example, if the patient is at home, operations that can be performed at home will be prioritized. Conversely, if the patient is away from home, operations that can be performed at their current location will be prioritized. Furthermore, if the patient is in a specific location, operations appropriate for that location can be prioritized. This allows for more appropriate remote operation by selecting the optimal operation method based on the patient's geographical location.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The data collection unit collects the patient's health data. The data collection unit automatically measures the patient's "sleep," "weight," "body temperature," "heart rate," and "blood glucose level" using sensors installed on beds, chairs, tables, etc. For example, a sensor installed on the bed is used to monitor the patient's sleep state in real time and evaluate the quality of sleep. A sensor installed on the chair measures the patient's weight when they are sitting and records weight fluctuations. A sensor installed on the table measures the patient's body temperature when they place their hand on it and records temperature fluctuations. Step 2: The analysis unit analyzes the data collected by the collection unit to understand the patient's health status. Based on the collected data, the analysis unit uses algorithms and AI models to evaluate the patient's health status. For example, it may score the health status or predict it based on the collected data. Step 3: The Facilitation Unit prompts the patient to take action based on the results obtained by the Analysis Unit. The Facilitation Unit sends voice messages or notifications to the patient to prompt actions such as turning the air conditioner on / off, eating, or taking a bath. Step 4: The remote control unit performs remote operations in emergencies. The remote control unit uses a remote control system to forcibly turn on the air conditioner and shut off gas and water in emergencies. It can also contact emergency services and the police as needed.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] Each of the multiple elements described above, including the collection unit, analysis unit, promotion unit, and remote control unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects the patient's health data using the sensors of the smart device 14 and transmits it to the data processing unit 12. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12 to understand the patient's health status. The promotion unit prompts the patient to take action based on the analysis results using the specific processing unit 290 of the data processing unit 12. The remote control unit performs remote operation in emergencies 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.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] Each of the multiple elements described above, including the collection unit, analysis unit, promotion unit, and remote control 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 health data using the sensors of the smart glasses 214 and transmits it to the data processing unit 12. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12 to understand the patient's health status. The promotion unit prompts the patient to take action based on the analysis results using the specific processing unit 290 of the data processing unit 12. The remote control unit performs remote operation in emergencies 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.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] Each of the multiple elements described above, including the collection unit, analysis unit, promotion unit, and remote control unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects patient health data using the sensors of the headset terminal 314 and transmits it to the data processing unit 12. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12 to understand the patient's health status. The promotion unit prompts the patient to take action based on the analysis results using the specific processing unit 290 of the data processing unit 12. The remote control unit performs remote operation in emergencies 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.

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

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

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

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

[0161] In 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.

[0162] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

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

[0164] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0165] The data processing system 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.

[0166] Each of the multiple elements described above, including the collection unit, analysis unit, promotion unit, and remote control unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects the patient's health data using the sensors of the robot 414 and transmits it to the data processing unit 12. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12 to understand the patient's health status. The promotion unit prompts the patient to take action based on the analysis results using the specific processing unit 290 of the data processing unit 12. The remote control unit performs remote operation in emergencies 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] (Note 1) A data collection unit that collects patient health data, An analysis unit analyzes the data collected by the aforementioned collection unit to understand the health status, Based on the results obtained by the analysis unit, the promotion unit prompts the patient to take action, It includes a remote control unit for remote operation in emergencies. A system characterized by the following features. (Note 2) The aforementioned collection unit is Sensors installed on beds, chairs, tables, etc., automatically measure the patient's "sleep," "weight," "body temperature," "heart rate," and "blood glucose level." The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Using motion, temperature, gas, and water sensors installed in the room, the system monitors the room's conditions and the patient's behavior. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned promotion unit is Encourage patients to take actions such as turning the air conditioner on / off, eating, and taking a bath. The system described in Appendix 1, characterized by the features described herein. (Note 5) The remote control unit is In an emergency, the air conditioner will be forcibly turned on, gas and water will be shut off, and emergency services and the police will be contacted as needed. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We estimate the patient's emotions and adjust the timing of data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the patient's past health data to select the optimal sensor placement. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting data, filtering is performed based on the patient's current activity level. The system described in Appendix 1, characterized by the features described herein. (Note 9) 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 10) The aforementioned collection unit is During data collection, priority is given to collecting highly relevant data based on information about the patient's living environment. The system described in Appendix 1, characterized by the features described herein. (Note 11) 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 12) The aforementioned analysis unit, The system estimates the patient's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the health data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of health data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the patient's emotions and adjusts the length of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the health data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the health data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned promotion unit is The system estimates the patient's emotions and adjusts behavioral support methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned promotion unit is When promoting behavior, the patient's past behavioral history is analyzed to select the most appropriate promotion method. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned promotion unit is When promoting behavior, customize the means of promotion based on the patient's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned promotion unit is The system estimates the patient's emotions and determines the priority of behavioral support based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned promotion unit is When promoting behavior, the optimal promotion method is selected based on the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned promotion unit is When promoting behavior, analyze patients' social media activity and propose methods for promotion. The system described in Appendix 1, characterized by the features described herein. (Note 24) The remote control unit is The system estimates the patient's emotions and adjusts the remote control method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The remote control unit is During remote operation, the system analyzes the patient's past emergency history to select the optimal operating method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The remote control unit is The system estimates the patient's emotions and determines the priority of remote interventions based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The remote control unit is During remote operation, the optimal operating method is selected based on the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects patient health data, An analysis unit analyzes the data collected by the aforementioned collection unit to understand the health status, Based on the results obtained by the analysis unit, the promotion unit prompts the patient to take action, It includes a remote control unit for remote operation in emergencies. A system characterized by the following features.

2. The aforementioned collection unit is Sensors installed on beds, chairs, tables, etc., automatically measure the patient's sleep, weight, body temperature, heart rate, blood glucose levels, and other parameters. The system according to feature 1.

3. The aforementioned collection unit is Using motion, temperature, gas, and water sensors installed in the room, the room's conditions and the patient's behavior are monitored. The system according to feature 1.

4. The aforementioned promotion unit is Encourage patients to take actions such as turning the air conditioner on / off, eating, and taking a bath. The system according to feature 1.

5. The remote control unit is In an emergency, the air conditioner will be forcibly turned on, gas and water will be shut off, and emergency services and the police will be contacted as needed. The system according to feature 1.

6. The aforementioned collection unit is We estimate the patient's emotions and adjust the timing 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 to select the optimal sensor placement. The system according to feature 1.

8. The aforementioned collection unit is When collecting data, filtering is performed based on the patient's current activity level. The system according to feature 1.

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