system

Abstract

The system according to this embodiment aims to allow elderly people to operate it intuitively, monitor their daily lives, and share information with family members and medical institutions. [Solution] The system according to the embodiment comprises an operation unit, a monitoring unit, a sharing unit, a reminder unit, and a notification unit. The operation unit uses voice recognition technology to enable elderly people to operate it intuitively. The monitoring unit monitors the elderly person's life using IoT sensors. The sharing unit shares the information obtained by the monitoring unit with family members and medical institutions. The reminder unit has a medication reminder function. The notification unit automatically sends a notification in case of emergency.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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, systems that can be intuitively operated by the elderly, or systems that monitor life and share information with family members and medical institutions, are not sufficiently provided, and there is room for improvement.

[0005] The system according to the embodiment aims to be intuitively operable by the elderly, monitor life, and share information with family members and medical institutions.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an operation unit, a monitoring unit, a sharing unit, a reminder unit, and a notification unit. The operation unit uses voice recognition technology to enable elderly people to operate it intuitively. The monitoring unit monitors the elderly person's life using IoT sensors. The sharing unit shares the information obtained by the monitoring unit with family members and medical institutions. The reminder unit has a medication reminder function. The notification unit automatically sends notifications in case of emergencies. [Effects of the Invention]

[0007] The system according to this embodiment can be operated intuitively by elderly people, allows them to monitor their daily lives, and enables them to share information with family members and medical institutions. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The elderly support system according to an embodiment of the present invention is a system that supports the lives of elderly people using an AI agent. This elderly support system uses voice recognition technology to enable intuitive operation by elderly people. Next, it monitors the lives of elderly people using IoT sensors to understand their health status and living conditions. Furthermore, it shares information with family and medical institutions and provides necessary support. It also has a medication reminder function to ensure thorough medication management. Finally, it has a function to automatically notify in case of emergency. For example, if an elderly person says, "Tell me today's medication," the AI ​​agent will provide the day's medication information by voice. In this way, using voice recognition technology can reduce the difficulty elderly people have when operating digital devices. Next, it monitors the lives of elderly people using IoT sensors. For example, the sensor detects the movements of elderly people and notifies family and medical institutions if there is an abnormality. This allows for real-time understanding of the elderly person's health status and living conditions. Furthermore, it shares information with family and medical institutions. For example, by regularly reporting the elderly person's health status and living conditions to family and medical institutions, necessary support can be provided. This allows elderly people to live with peace of mind without being isolated. It also has a medication reminder function to ensure thorough medication management. For example, an AI agent can notify elderly individuals of medication times to prevent them from forgetting to take their medication. This helps prevent inadequate medication management. Finally, the system includes a function to automatically notify in emergencies. For example, if an elderly person falls, a sensor detects the abnormality and automatically notifies family members or medical institutions. This allows for a faster response in emergencies. In this way, using an AI agent ensures the continuity of health management for the elderly, alleviates loneliness and isolation, and speeds up emergency response. This aims to create a society where elderly people can age with peace of mind and in their own way. Thus, the elderly support system ensures the continuity of health management for the elderly, alleviates loneliness and isolation, and speeds up emergency response.

[0029] The elderly support system according to this embodiment comprises an operation unit, a monitoring unit, a sharing unit, a reminder unit, and a notification unit. The operation unit uses voice recognition technology to enable intuitive operation by the elderly. For example, when the elderly person says, "Tell me today's medication," the operation unit will provide the day's medication information by voice. The operation unit analyzes voice commands and generates appropriate responses. For example, when the elderly person says, "Turn on the light," the operation unit can turn on the light. The monitoring unit monitors the elderly person's life using IoT sensors. For example, the monitoring unit detects the elderly person's movements using sensors and notifies family members or medical institutions if there is an abnormality. For example, the monitoring unit monitors the room temperature using a temperature sensor and notifies if there is an abnormal temperature change. For example, the monitoring unit monitors the elderly person's heart rate using a heart rate sensor and notifies if there is an abnormality. The sharing unit shares the information obtained by the monitoring unit with family members or medical institutions. For example, the sharing unit periodically reports the elderly person's health status and living situation to family members or medical institutions. For example, the sharing unit has a function to notify abnormalities in real time. The sharing unit generates periodic reports and sends them to family members or medical institutions. The reminder unit has a medication reminder function. The reminder unit notifies elderly individuals of medication times to prevent them from forgetting to take their medication. The reminder unit provides voice notifications and alarm settings. The reminder unit has a function to optimize the timing of medication. The notification unit automatically makes notifications in emergencies. For example, if an elderly person falls, the sensor detects the abnormality and automatically notifies family members or medical institutions. The notification unit has a function to notify when it detects an abnormal heart rate. The notification unit has a function to customize the content of emergency notifications. As a result, the elderly support system according to this embodiment enables intuitive operation, lifestyle monitoring, information sharing, medication reminders, and emergency notifications to support the lives of elderly people.

[0030] The control unit uses voice recognition technology to enable intuitive operation by the elderly. Specifically, the control unit is equipped with a high-precision voice recognition engine that can accurately recognize the speech of the elderly. For example, if an elderly person says, "Tell me today's medication," the control unit analyzes the voice command, retrieves the day's medication information from the database, and communicates it verbally. The voice recognition engine has a noise-canceling function that removes ambient noise, allowing for clear recognition of the elderly person's voice. The control unit also uses natural language processing technology to generate responses to voice commands; for example, if an elderly person says, "Turn on the light," it sends a signal to turn on the light. Based on the analysis results of voice commands, the control unit can control home appliances and smart devices. This allows the elderly to intuitively operate the system using only their voice, without having to perform complex operations. Furthermore, to improve the accuracy of voice recognition, the control unit can learn the characteristics of each elderly person's voice and create a customized voice profile. This allows the control unit to provide voice recognition optimized for each elderly person, improving the ease of operation.

[0031] The monitoring unit monitors the lives of elderly people using IoT sensors. Specifically, the monitoring unit places multiple sensors in the elderly person's living space and collects data in real time. For example, motion sensors detect the elderly person's movements and notify family members or medical institutions if there is an abnormality. Motion sensors can detect abnormalities such as falls or prolonged periods of inactivity. Temperature sensors monitor the room temperature and notify if there is an abnormal temperature change. For example, temperature sensors can issue warnings if the room temperature is too high or too low. Heart rate sensors monitor the elderly person's heart rate and notify if there is an abnormality. Heart rate sensors can issue warnings if the heart rate rises or falls rapidly. The monitoring unit centrally manages the data collected from these sensors and immediately notifies if an abnormality is detected. Furthermore, the monitoring unit can analyze the collected data to understand the elderly person's lifestyle patterns and health status. As a result, the monitoring unit can comprehensively support the lives of elderly people and respond quickly if an abnormality occurs.

[0032] The sharing unit shares information obtained by the monitoring unit with families and medical institutions. Specifically, the sharing unit receives data transmitted from the monitoring unit and periodically reports it to families and medical institutions. For example, it periodically generates reports on the health and living conditions of elderly individuals and sends them to families and medical institutions. The sharing unit has a function to notify abnormalities in real time, and immediately notifies families and medical institutions if an abnormality is detected. Notifications are made via email, SMS, or a dedicated application. To ensure data security, the sharing unit protects data using encryption technology. This allows the sharing unit to quickly and accurately share necessary information while protecting the privacy of elderly individuals. Furthermore, the sharing unit can receive feedback from families and medical institutions and use it to improve the system. This allows the sharing unit to smoothly share information to support the lives of elderly individuals and improve the reliability and security of the entire system.

[0033] The reminder unit includes a medication reminder function. Specifically, the reminder unit notifies elderly individuals of medication time to prevent them from forgetting to take their medication. The reminder unit uses voice notifications and alarm settings to inform elderly individuals of the timing of their medication. For example, the reminder unit can announce "It's time for your medication" with a voice message and sound an alarm to draw the elderly individual's attention. The reminder unit has a function to optimize medication timing, allowing it to set the optimal timing according to the elderly individual's lifestyle and health condition. The reminder unit can record medication history and report it to family members or medical institutions. In this way, the reminder unit can support elderly individuals in taking their medication appropriately and assist in their health management. Furthermore, the reminder unit has a function to manage the type and amount of medication, preventing elderly individuals from accidentally taking too much medication. In this way, the reminder unit comprehensively supports the medication management of elderly individuals and minimizes health risks.

[0034] The notification unit automatically sends notifications in emergencies. Specifically, it automatically notifies family members and medical institutions if an elderly person falls or if an abnormal heart rate is detected. The notification unit receives abnormal data transmitted from the monitoring unit and immediately sends a notification. The notification content includes detailed information such as the type of abnormality, the location of the occurrence, and the elderly person's condition. The notification unit has a function to customize the notification content, allowing it to be adjusted according to the requests of family members and medical institutions. For example, it can be set to notify only specific medical institutions in the event of a specific abnormality. To ensure the reliability of notifications, the notification unit uses multiple communication methods. For example, if an internet connection is unavailable, it will send a notification using a mobile phone line. This ensures that the notification unit reliably sends notifications in emergencies and ensures the safety of elderly people. Furthermore, the notification unit can record the notification history and provide it to family members and medical institutions. This allows the notification unit to respond quickly and accurately in emergencies and comprehensively support the safety of elderly people.

[0035] The control unit can provide medication information for the day via voice when an elderly person says, "Tell me what medication I need today." For example, when an elderly person says, "Tell me what medication I need today," the AI ​​agent provides the medication information for the day via voice. The control unit can analyze the elderly person's voice command using, for example, speech recognition technology and obtain medication information. When providing medication information via voice, the control unit can provide detailed information such as the name of the medication, the time of administration, and the dosage. This allows the elderly person to obtain medication information via voice. Some or all of the above processing in the control unit may be performed using, for example, AI, or without AI. For example, the control unit can input the elderly person's voice command into a generating AI, and have the generating AI perform the acquisition of medication information and the generation of voice responses.

[0036] The monitoring unit can detect the movements of elderly people using sensors and notify family members or medical institutions if there is an abnormality. For example, the monitoring unit can use sensors to detect the movements of elderly people and notify family members or medical institutions if there is an abnormality. For example, the monitoring unit can use motion sensors to monitor the movements of elderly people in real time. For example, the monitoring unit has a function to notify when there is abnormal movement or no movement. For example, the monitoring unit can set specific criteria for abnormalities and detection methods. This allows for the detection of abnormalities in elderly people and rapid notification. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input data acquired from sensors into a generating AI and have the generating AI perform abnormality detection and notification generation.

[0037] The shared unit can periodically report the health status and living conditions of elderly individuals to their families and medical institutions. For example, the shared unit periodically reports the health status and living conditions of elderly individuals to their families and medical institutions. For example, the shared unit generates periodic reports and sends them to families and medical institutions. For example, the shared unit has a function to notify abnormalities in real time. For example, the shared unit can set the specific frequency and method of reporting. This allows for the provision of necessary support by periodically reporting the health status and living conditions of elderly individuals. Some or all of the above-described processes in the shared unit may be performed using AI, for example, or not using AI. For example, the shared unit can input data on the health status and living conditions of elderly individuals into a generating AI and have the generating AI perform the generation and transmission of reports.

[0038] The reminder unit can notify elderly individuals of their medication time to prevent them from forgetting to take their medication. For example, the reminder unit can notify elderly individuals of their medication time to prevent them from forgetting. The reminder unit can, for example, provide voice notifications or set alarms. The reminder unit can, for example, have a function to optimize the timing of medication. The reminder unit can, for example, set specific notification methods and timings. This ensures thorough medication management by notifying elderly individuals to avoid forgetting to take their medication. Some or all of the above-described processes in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input the elderly person's medication schedule into a generating AI and have the generating AI generate and send notifications.

[0039] The notification unit can automatically notify family members or medical institutions when an elderly person falls, based on the detection of an anomaly by a sensor. For example, if an elderly person falls, the notification unit will detect the anomaly by a sensor and automatically notify family members or medical institutions. The notification unit can detect falls using, for example, an acceleration sensor or a gyroscope sensor. The notification unit can, for example, set specific methods and conditions for detecting falls. The notification unit has a function to customize the content of emergency notifications. This allows for a quicker response to emergencies by promptly notifying when an elderly person falls. Some or all of the above-described processes in the notification unit may be performed using, for example, AI, or not using AI. For example, the notification unit can input data acquired from sensors into a generating AI, and have the generating AI perform fall detection and notification generation.

[0040] The control unit can analyze the elderly person's past operation history and propose the optimal operation method. For example, the control unit may prioritize suggesting operation methods that the elderly person has frequently used in the past. For example, the control unit may predict and suggest operation methods to be used during specific time periods based on the elderly person's operation history. For example, the control unit may analyze the elderly person's operation history and propose the most efficient operation method. In this way, the optimal operation method can be proposed by analyzing the elderly person's past operation history. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the elderly person's operation history data into a generating AI and have the generating AI execute the proposal of the optimal operation method.

[0041] The control unit can adjust its response to the elderly person's voice tone and speed. For example, if the elderly person speaks slowly, the control unit will respond slowly. If the elderly person speaks quickly, the control unit will respond quickly. If the elderly person speaks in a low tone, the control unit will respond calmly. By adjusting the response according to the elderly person's voice tone and speed, more appropriate operation becomes possible. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the elderly person's voice data into a generating AI and have the generating AI perform the response adjustment.

[0042] The control unit can optimize the timing of operations based on the elderly person's daily rhythm. For example, if the elderly person is active in the morning, the control unit will prioritize morning operations. For example, if the elderly person is relaxed at night, the control unit will simplify evening operations. For example, the control unit will adjust the timing of operations to match the elderly person's daily rhythm. By optimizing the timing of operations based on the elderly person's daily rhythm, more appropriate operations become possible. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the elderly person's daily rhythm data into a generating AI and have the generating AI perform the optimization of the timing of operations.

[0043] The control unit can customize the operation content based on the elderly person's hobbies and interests. For example, if the elderly person likes music, the control unit will prioritize music-related operations. For example, if the elderly person likes reading, the control unit will prioritize reading-related operations. The control unit customizes the operation content based on the elderly person's hobbies and interests. This allows for more appropriate operation by customizing the operation content based on the elderly person's hobbies and interests. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input data on the elderly person's hobbies and interests into a generating AI and have the generating AI perform the customization of the operation content.

[0044] The monitoring unit can analyze the past health data of elderly individuals to improve the accuracy of anomaly detection. For example, the monitoring unit can identify patterns of anomalies based on the elderly individual's past health data. For example, the monitoring unit can analyze the elderly individual's health data to perform early detection of anomalies. For example, the monitoring unit can predict anomalies based on the elderly individual's health data. In this way, the accuracy of anomaly detection can be improved by analyzing the elderly individual's past health data. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the elderly individual's health data into a generating AI and have the generating AI perform the task of improving the accuracy of anomaly detection.

[0045] The monitoring unit can change the focus of monitoring according to the elderly person's lifestyle. For example, if the elderly person is active at night, the monitoring unit will strengthen nighttime monitoring. For example, if the elderly person is active during the day, the monitoring unit will strengthen daytime monitoring. For example, the monitoring unit will adjust the focus of monitoring according to the elderly person's lifestyle. This allows for more appropriate monitoring by changing the focus of monitoring according to the elderly person's lifestyle. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input elderly person lifestyle pattern data into a generating AI and have the generating AI perform the adjustment of the monitoring focus.

[0046] The monitoring unit can optimize the monitoring range based on the elderly person's living environment. For example, if the elderly person lives alone, the monitoring unit will monitor all rooms. If the elderly person lives with family, the monitoring unit will monitor only specific rooms. The monitoring unit adjusts the monitoring range according to the elderly person's living environment. This allows for more appropriate monitoring by optimizing the monitoring range based on the elderly person's living environment. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the elderly person's living environment data into a generating AI and have the generating AI perform the optimization of the monitoring range.

[0047] The monitoring unit can select monitoring targets based on the elderly person's activity history. For example, the monitoring unit may prioritize monitoring rooms frequently used by the elderly person. For example, the monitoring unit may identify and monitor locations where abnormalities are likely to occur based on the elderly person's activity history. For example, the monitoring unit selects monitoring targets based on the elderly person's activity history. This allows for more appropriate monitoring by selecting monitoring targets based on the elderly person's activity history. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the elderly person's activity history data into a generating AI and have the generating AI perform the selection of monitoring targets.

[0048] The sharing unit can analyze the past health data of elderly individuals and improve the accuracy of the information it shares. For example, the sharing unit shares accurate information based on the past health data of elderly individuals. For example, the sharing unit analyzes the health data of elderly individuals to detect abnormalities early. For example, the sharing unit predicts abnormalities based on the health data of elderly individuals. In this way, by analyzing the past health data of elderly individuals, the accuracy of the information shared can be improved. Some or all of the above-described processes in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input the health data of elderly individuals into a generating AI and have the generating AI perform the task of improving the accuracy of the information.

[0049] The sharing unit can customize the method of information sharing according to the needs of the elderly person's family and medical institutions. For example, if the family needs detailed information, the sharing unit will share detailed information. For example, if the medical institution needs simplified information, the sharing unit will share simplified information. The sharing unit customizes the method of information sharing according to the needs of the family and medical institutions. This makes it possible to share information more appropriately by customizing the method of information sharing according to the needs of the elderly person's family and medical institutions. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input family and medical institution needs data into a generating AI and have the generating AI perform the customization of the information sharing method.

[0050] The sharing unit can optimize the timing of information sharing based on the elderly person's daily rhythm. For example, if the elderly person is active in the morning, the sharing unit will prioritize information sharing in the morning. For example, if the elderly person is relaxed at night, the sharing unit will simplify information sharing at night. For example, the sharing unit will adjust the timing of information sharing to match the elderly person's daily rhythm. By optimizing the timing of information sharing based on the elderly person's daily rhythm, more appropriate information sharing becomes possible. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input the elderly person's daily rhythm data into a generating AI and have the generating AI perform the optimization of the timing of information sharing.

[0051] The sharing unit can select the content of the information to be shared based on the health status of the elderly person. For example, if the elderly person is healthy, the sharing unit will share simple information. For example, if the elderly person is unwell, the sharing unit will share detailed information. The sharing unit selects the content of the information to be shared according to the health status of the elderly person. This makes it possible to share information more appropriately by selecting the content of the information to be shared based on the health status of the elderly person. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without using AI. For example, the sharing unit can input the health status data of the elderly person into a generating AI and have the generating AI perform the selection of the information content.

[0052] The reminder unit can analyze the elderly person's past medication history to improve the accuracy of reminders. For example, the reminder unit provides accurate reminders based on the elderly person's past medication history. For example, the reminder unit analyzes the elderly person's medication history to prevent them from forgetting to take their medication. For example, the reminder unit optimizes the timing of medication based on the elderly person's medication history. In this way, the accuracy of reminders can be improved by analyzing the elderly person's past medication history. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input the elderly person's medication history data into a generating AI and have the generating AI perform the task of improving the accuracy of reminders.

[0053] The reminder unit can customize the reminder method according to the elderly person's lifestyle. For example, if the elderly person takes medication in the morning, the reminder unit will prioritize morning reminders. For example, if the elderly person takes medication at night, the reminder unit will prioritize nighttime reminders. The reminder unit adjusts the reminder method according to the elderly person's lifestyle. By customizing the reminder method according to the elderly person's lifestyle, more appropriate reminders become possible. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input the elderly person's lifestyle pattern data into a generating AI and have the generating AI perform the customization of the reminder method.

[0054] The reminder unit can optimize the timing of reminders based on the elderly person's daily rhythm. For example, if the elderly person is active in the morning, the reminder unit will prioritize morning reminders. For example, if the elderly person is relaxed at night, the reminder unit will simplify evening reminders. For example, the reminder unit will adjust the timing of reminders to match the elderly person's daily rhythm. By optimizing the timing of reminders based on the elderly person's daily rhythm, more appropriate reminders become possible. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input the elderly person's daily rhythm data into a generating AI and have the generating AI perform the optimization of reminder timing.

[0055] The reminder unit can select the content of the reminder based on the health status of the elderly person. For example, if the elderly person is healthy, the reminder unit will send a simple reminder. For example, if the elderly person is unwell, the reminder unit will send a detailed reminder. The reminder unit selects the content of the reminder according to the health status of the elderly person. This makes it possible to send more appropriate reminders by selecting the content of the reminder based on the health status of the elderly person. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without using AI. For example, the reminder unit can input the elderly person's health status data into a generating AI and have the generating AI perform the selection of the reminder content.

[0056] The notification unit can analyze the past health data of elderly individuals to improve the accuracy of notifications. For example, the notification unit can make accurate notifications based on the past health data of elderly individuals. For example, the notification unit can analyze the health data of elderly individuals to detect abnormalities early. For example, the notification unit can predict abnormalities based on the health data of elderly individuals. In this way, the accuracy of notifications can be improved by analyzing the past health data of elderly individuals. Some or all of the above processes in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the health data of elderly individuals into a generating AI and have the generating AI perform the task of improving the accuracy of notifications.

[0057] The notification unit can customize the notification method according to the elderly person's lifestyle. For example, if the elderly person is active at night, the notification unit will strengthen nighttime notifications. For example, if the elderly person is active during the day, the notification unit will strengthen daytime notifications. For example, the notification unit will adjust the notification method according to the elderly person's lifestyle. By customizing the notification method according to the elderly person's lifestyle, more appropriate notifications can be made. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input elderly person lifestyle pattern data into a generating AI and have the generating AI perform the customization of the notification method.

[0058] The notification unit can optimize the scope of notifications based on the elderly person's living environment. For example, if the elderly person lives alone, the notification unit will notify all rooms. If the elderly person lives with family, the notification unit will notify only specific rooms. The notification unit adjusts the scope of notifications according to the elderly person's living environment. By optimizing the scope of notifications based on the elderly person's living environment, more appropriate notifications become possible. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input data on the elderly person's living environment into a generating AI and have the generating AI perform the optimization of the scope of notifications.

[0059] The notification unit can select the content of the notification based on the health status of the elderly person. For example, if the elderly person is healthy, the notification unit will send a simple notification. For example, if the elderly person is unwell, the notification unit will send a detailed notification. The notification unit selects the content of the notification according to the health status of the elderly person. This makes it possible to send more appropriate notifications by selecting the content of the notification based on the health status of the elderly person. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the health status data of the elderly person into a generating AI and have the generating AI perform the selection of the notification content.

[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 control unit can analyze the elderly person's past operating history and suggest the optimal operating method. For example, it can prioritize suggesting operating methods that the elderly person has frequently used in the past. It can also predict and suggest operating methods used during specific time periods based on the elderly person's operating history. Furthermore, it can analyze the elderly person's operating history and suggest the most efficient operating method. In this way, by analyzing the elderly person's past operating history, the optimal operating method can be suggested.

[0062] The monitoring unit can analyze past health data of elderly individuals to improve the accuracy of anomaly detection. For example, it can identify patterns of anomalies based on the elderly's past health data. It can also analyze the elderly's health data to perform early detection of anomalies. Furthermore, it can predict anomalies based on the elderly's health data. In this way, analyzing the elderly's past health data can improve the accuracy of anomaly detection.

[0063] The shared section allows for customization of information sharing methods according to the needs of elderly individuals' families and healthcare institutions. For example, if a family requires detailed information, that information can be shared. Similarly, if a healthcare institution requires simplified information, that information can be shared. Furthermore, the information sharing method can be customized according to the needs of families and healthcare institutions. This allows for more appropriate information sharing by customizing the information sharing method according to the needs of elderly individuals' families and healthcare institutions.

[0064] The reminder function can analyze the past medication history of elderly individuals to improve the accuracy of reminders. For example, it can provide accurate reminders based on their past medication history. It can also prevent missed doses by analyzing the patient's medication history. Furthermore, it can optimize the timing of medication based on the patient's medication history. In short, by analyzing the patient's past medication history, the accuracy of reminders can be improved.

[0065] The notification system can optimize the scope of notifications based on the elderly person's living environment. For example, if the elderly person lives alone, notifications can be sent to all rooms. If the elderly person lives with family, notifications can be sent to only specific rooms. Furthermore, the scope of notifications can be adjusted according to the elderly person's living environment. This allows for more appropriate notifications by optimizing the scope of notifications based on the elderly person's living environment.

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

[0067] Step 1: The control unit will use voice recognition technology to enable intuitive operation by the elderly. For example, if an elderly person says, "Tell me today's medication," the unit will provide the day's medication information by voice. Also, if an elderly person says, "Turn on the light," the unit will turn on the light. The control unit analyzes voice commands and generates appropriate responses. Step 2: The monitoring unit monitors the elderly person's life using IoT sensors. For example, the sensors detect the elderly person's movements and notify family members or medical institutions if there is an abnormality. It also monitors the room temperature using a temperature sensor and notifies if there is an abnormal temperature change. Furthermore, it monitors the elderly person's heart rate using a heart rate sensor and notifies if there is an abnormality. Step 3: The sharing unit shares the information obtained by the monitoring unit with family members and medical institutions. For example, it regularly reports the health status and living conditions of elderly individuals to family members and medical institutions. The sharing unit has a function to notify abnormalities in real time, generates periodic reports, and sends them to family members and medical institutions. Step 4: The reminder unit includes a medication reminder function. For example, it notifies elderly individuals of their medication time to prevent them from forgetting to take their medication. The reminder unit also includes functions to optimize medication timing, such as voice notifications and alarm settings. Step 5: The notification unit automatically sends an alert in case of an emergency. For example, if an elderly person falls, the sensor detects the abnormality and automatically notifies family members or medical institutions. The notification unit also has a function to send an alert when it detects an abnormal heart rate and has a function to customize the content of emergency alerts.

[0068] (Example of form 2) The elderly support system according to an embodiment of the present invention is a system that supports the lives of elderly people using an AI agent. This elderly support system uses voice recognition technology to enable intuitive operation by elderly people. Next, it monitors the lives of elderly people using IoT sensors to understand their health status and living conditions. Furthermore, it shares information with family and medical institutions and provides necessary support. It also has a medication reminder function to ensure thorough medication management. Finally, it has a function to automatically notify in case of emergency. For example, if an elderly person says, "Tell me today's medication," the AI ​​agent will provide the day's medication information by voice. In this way, using voice recognition technology can reduce the difficulty elderly people have when operating digital devices. Next, it monitors the lives of elderly people using IoT sensors. For example, the sensor detects the movements of elderly people and notifies family and medical institutions if there is an abnormality. This allows for real-time understanding of the elderly person's health status and living conditions. Furthermore, it shares information with family and medical institutions. For example, by regularly reporting the elderly person's health status and living conditions to family and medical institutions, necessary support can be provided. This allows elderly people to live with peace of mind without being isolated. It also has a medication reminder function to ensure thorough medication management. For example, an AI agent can notify elderly individuals of medication times to prevent them from forgetting to take their medication. This helps prevent inadequate medication management. Finally, the system includes a function to automatically notify in emergencies. For example, if an elderly person falls, a sensor detects the abnormality and automatically notifies family members or medical institutions. This allows for a faster response in emergencies. In this way, using an AI agent ensures the continuity of health management for the elderly, alleviates loneliness and isolation, and speeds up emergency response. This aims to create a society where elderly people can age with peace of mind and in their own way. Thus, the elderly support system ensures the continuity of health management for the elderly, alleviates loneliness and isolation, and speeds up emergency response.

[0069] The elderly support system according to this embodiment comprises an operation unit, a monitoring unit, a sharing unit, a reminder unit, and a notification unit. The operation unit uses voice recognition technology to enable intuitive operation by the elderly. For example, when the elderly person says, "Tell me today's medication," the operation unit will provide the day's medication information by voice. The operation unit analyzes voice commands and generates appropriate responses. For example, when the elderly person says, "Turn on the light," the operation unit can turn on the light. The monitoring unit monitors the elderly person's life using IoT sensors. For example, the monitoring unit detects the elderly person's movements using sensors and notifies family members or medical institutions if there is an abnormality. For example, the monitoring unit monitors the room temperature using a temperature sensor and notifies if there is an abnormal temperature change. For example, the monitoring unit monitors the elderly person's heart rate using a heart rate sensor and notifies if there is an abnormality. The sharing unit shares the information obtained by the monitoring unit with family members or medical institutions. For example, the sharing unit periodically reports the elderly person's health status and living situation to family members or medical institutions. For example, the sharing unit has a function to notify abnormalities in real time. The sharing unit generates periodic reports and sends them to family members or medical institutions. The reminder unit has a medication reminder function. The reminder unit notifies elderly individuals of medication times to prevent them from forgetting to take their medication. The reminder unit provides voice notifications and alarm settings. The reminder unit has a function to optimize the timing of medication. The notification unit automatically makes notifications in emergencies. For example, if an elderly person falls, the sensor detects the abnormality and automatically notifies family members or medical institutions. The notification unit has a function to notify when it detects an abnormal heart rate. The notification unit has a function to customize the content of emergency notifications. As a result, the elderly support system according to this embodiment enables intuitive operation, lifestyle monitoring, information sharing, medication reminders, and emergency notifications to support the lives of elderly people.

[0070] The control unit uses voice recognition technology to enable intuitive operation by the elderly. Specifically, the control unit is equipped with a high-precision voice recognition engine that can accurately recognize the speech of the elderly. For example, if an elderly person says, "Tell me today's medication," the control unit analyzes the voice command, retrieves the day's medication information from the database, and communicates it verbally. The voice recognition engine has a noise-canceling function that removes ambient noise, allowing for clear recognition of the elderly person's voice. The control unit also uses natural language processing technology to generate responses to voice commands; for example, if an elderly person says, "Turn on the light," it sends a signal to turn on the light. Based on the analysis results of voice commands, the control unit can control home appliances and smart devices. This allows the elderly to intuitively operate the system using only their voice, without having to perform complex operations. Furthermore, to improve the accuracy of voice recognition, the control unit can learn the characteristics of each elderly person's voice and create a customized voice profile. This allows the control unit to provide voice recognition optimized for each elderly person, improving the ease of operation.

[0071] The monitoring unit monitors the lives of elderly people using IoT sensors. Specifically, the monitoring unit places multiple sensors in the elderly person's living space and collects data in real time. For example, motion sensors detect the elderly person's movements and notify family members or medical institutions if there is an abnormality. Motion sensors can detect abnormalities such as falls or prolonged periods of inactivity. Temperature sensors monitor the room temperature and notify if there is an abnormal temperature change. For example, temperature sensors can issue warnings if the room temperature is too high or too low. Heart rate sensors monitor the elderly person's heart rate and notify if there is an abnormality. Heart rate sensors can issue warnings if the heart rate rises or falls rapidly. The monitoring unit centrally manages the data collected from these sensors and immediately notifies if an abnormality is detected. Furthermore, the monitoring unit can analyze the collected data to understand the elderly person's lifestyle patterns and health status. As a result, the monitoring unit can comprehensively support the lives of elderly people and respond quickly if an abnormality occurs.

[0072] The sharing unit shares information obtained by the monitoring unit with families and medical institutions. Specifically, the sharing unit receives data transmitted from the monitoring unit and periodically reports it to families and medical institutions. For example, it periodically generates reports on the health and living conditions of elderly individuals and sends them to families and medical institutions. The sharing unit has a function to notify abnormalities in real time, and immediately notifies families and medical institutions if an abnormality is detected. Notifications are made via email, SMS, or a dedicated application. To ensure data security, the sharing unit protects data using encryption technology. This allows the sharing unit to quickly and accurately share necessary information while protecting the privacy of elderly individuals. Furthermore, the sharing unit can receive feedback from families and medical institutions and use it to improve the system. This allows the sharing unit to smoothly share information to support the lives of elderly individuals and improve the reliability and security of the entire system.

[0073] The reminder unit includes a medication reminder function. Specifically, the reminder unit notifies elderly individuals of medication time to prevent them from forgetting to take their medication. The reminder unit uses voice notifications and alarm settings to inform elderly individuals of the timing of their medication. For example, the reminder unit can announce "It's time for your medication" with a voice message and sound an alarm to draw the elderly individual's attention. The reminder unit has a function to optimize medication timing, allowing it to set the optimal timing according to the elderly individual's lifestyle and health condition. The reminder unit can record medication history and report it to family members or medical institutions. In this way, the reminder unit can support elderly individuals in taking their medication appropriately and assist in their health management. Furthermore, the reminder unit has a function to manage the type and amount of medication, preventing elderly individuals from accidentally taking too much medication. In this way, the reminder unit comprehensively supports the medication management of elderly individuals and minimizes health risks.

[0074] The notification unit automatically sends notifications in emergencies. Specifically, it automatically notifies family members and medical institutions if an elderly person falls or if an abnormal heart rate is detected. The notification unit receives abnormal data transmitted from the monitoring unit and immediately sends a notification. The notification content includes detailed information such as the type of abnormality, the location of the occurrence, and the elderly person's condition. The notification unit has a function to customize the notification content, allowing it to be adjusted according to the requests of family members and medical institutions. For example, it can be set to notify only specific medical institutions in the event of a specific abnormality. To ensure the reliability of notifications, the notification unit uses multiple communication methods. For example, if an internet connection is unavailable, it will send a notification using a mobile phone line. This ensures that the notification unit reliably sends notifications in emergencies and ensures the safety of elderly people. Furthermore, the notification unit can record the notification history and provide it to family members and medical institutions. This allows the notification unit to respond quickly and accurately in emergencies and comprehensively support the safety of elderly people.

[0075] The control unit can provide medication information for the day via voice when an elderly person says, "Tell me what medication I need today." For example, when an elderly person says, "Tell me what medication I need today," the AI ​​agent provides the medication information for the day via voice. The control unit can analyze the elderly person's voice command using, for example, speech recognition technology and obtain medication information. When providing medication information via voice, the control unit can provide detailed information such as the name of the medication, the time of administration, and the dosage. This allows the elderly person to obtain medication information via voice. Some or all of the above processing in the control unit may be performed using, for example, AI, or without AI. For example, the control unit can input the elderly person's voice command into a generating AI, and have the generating AI perform the acquisition of medication information and the generation of voice responses.

[0076] The monitoring unit can detect the movements of elderly people using sensors and notify family members or medical institutions if there is an abnormality. For example, the monitoring unit can use sensors to detect the movements of elderly people and notify family members or medical institutions if there is an abnormality. For example, the monitoring unit can use motion sensors to monitor the movements of elderly people in real time. For example, the monitoring unit has a function to notify when there is abnormal movement or no movement. For example, the monitoring unit can set specific criteria for abnormalities and detection methods. This allows for the detection of abnormalities in elderly people and rapid notification. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input data acquired from sensors into a generating AI and have the generating AI perform abnormality detection and notification generation.

[0077] The shared unit can periodically report the health status and living conditions of elderly individuals to their families and medical institutions. For example, the shared unit periodically reports the health status and living conditions of elderly individuals to their families and medical institutions. For example, the shared unit generates periodic reports and sends them to families and medical institutions. For example, the shared unit has a function to notify abnormalities in real time. For example, the shared unit can set the specific frequency and method of reporting. This allows for the provision of necessary support by periodically reporting the health status and living conditions of elderly individuals. Some or all of the above-described processes in the shared unit may be performed using AI, for example, or not using AI. For example, the shared unit can input data on the health status and living conditions of elderly individuals into a generating AI and have the generating AI perform the generation and transmission of reports.

[0078] The reminder unit can notify elderly individuals of their medication time to prevent them from forgetting to take their medication. For example, the reminder unit can notify elderly individuals of their medication time to prevent them from forgetting. The reminder unit can, for example, provide voice notifications or set alarms. The reminder unit can, for example, have a function to optimize the timing of medication. The reminder unit can, for example, set specific notification methods and timings. This ensures thorough medication management by notifying elderly individuals to avoid forgetting to take their medication. Some or all of the above-described processes in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input the elderly person's medication schedule into a generating AI and have the generating AI generate and send notifications.

[0079] The notification unit can automatically notify family members or medical institutions when an elderly person falls, based on the detection of an anomaly by a sensor. For example, if an elderly person falls, the notification unit will detect the anomaly by a sensor and automatically notify family members or medical institutions. The notification unit can detect falls using, for example, an acceleration sensor or a gyroscope sensor. The notification unit can, for example, set specific methods and conditions for detecting falls. The notification unit has a function to customize the content of emergency notifications. This allows for a quicker response to emergencies by promptly notifying when an elderly person falls. Some or all of the above-described processes in the notification unit may be performed using, for example, AI, or not using AI. For example, the notification unit can input data acquired from sensors into a generating AI, and have the generating AI perform fall detection and notification generation.

[0080] The control unit can estimate the emotions of elderly users and adjust the timing and content of operations based on the estimated emotions. For example, if an elderly user is feeling anxious, the control unit will delay the timing of operations and provide guidance in a calm voice. For example, if an elderly user is relaxed, the control unit will speed up the timing of operations and provide guidance in a cheerful voice. For example, if an elderly user is in a hurry, the control unit will simplify the content of operations and provide guidance quickly. This allows for more appropriate operation by adjusting the timing and content of operations according to the emotions of the elderly user. 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 control unit may be performed using AI, for example, or without AI. For example, the control unit can input the elderly user's voice data into a generative AI and have the generative AI perform emotion estimation and operation adjustment.

[0081] The control unit can analyze the elderly person's past operation history and propose the optimal operation method. For example, the control unit may prioritize suggesting operation methods that the elderly person has frequently used in the past. For example, the control unit may predict and suggest operation methods to be used during specific time periods based on the elderly person's operation history. For example, the control unit may analyze the elderly person's operation history and propose the most efficient operation method. In this way, the optimal operation method can be proposed by analyzing the elderly person's past operation history. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the elderly person's operation history data into a generating AI and have the generating AI execute the proposal of the optimal operation method.

[0082] The control unit can adjust its response to the elderly person's voice tone and speed. For example, if the elderly person speaks slowly, the control unit will respond slowly. If the elderly person speaks quickly, the control unit will respond quickly. If the elderly person speaks in a low tone, the control unit will respond calmly. By adjusting the response according to the elderly person's voice tone and speed, more appropriate operation becomes possible. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the elderly person's voice data into a generating AI and have the generating AI perform the response adjustment.

[0083] The control unit can estimate the emotions of elderly users and determine the priority of operations based on the estimated emotions. For example, if an elderly user is feeling anxious, the control unit will prioritize guiding them through important operations. For example, if an elderly user is relaxed, the control unit will flexibly change the order of operations. For example, if an elderly user is in a hurry, the control unit will guide them through the most important operations first. This allows for more appropriate operation by determining the priority of operations according to the elderly user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the control unit may be performed using AI, for example, or not using AI. For example, the control unit can input the elderly user's voice data into a generative AI and have the generative AI perform emotion estimation and operation priority determination.

[0084] The control unit can optimize the timing of operations based on the elderly person's daily rhythm. For example, if the elderly person is active in the morning, the control unit will prioritize morning operations. For example, if the elderly person is relaxed at night, the control unit will simplify evening operations. For example, the control unit will adjust the timing of operations to match the elderly person's daily rhythm. By optimizing the timing of operations based on the elderly person's daily rhythm, more appropriate operations become possible. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the elderly person's daily rhythm data into a generating AI and have the generating AI perform the optimization of the timing of operations.

[0085] The control unit can customize the operation content based on the elderly person's hobbies and interests. For example, if the elderly person likes music, the control unit will prioritize music-related operations. For example, if the elderly person likes reading, the control unit will prioritize reading-related operations. The control unit customizes the operation content based on the elderly person's hobbies and interests. This allows for more appropriate operation by customizing the operation content based on the elderly person's hobbies and interests. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input data on the elderly person's hobbies and interests into a generating AI and have the generating AI perform the customization of the operation content.

[0086] The monitoring unit can estimate the emotions of elderly individuals and adjust the frequency and content of monitoring based on the estimated emotions. For example, if an elderly individual is feeling anxious, the monitoring unit increases the frequency of monitoring and provides more detailed information. For example, if an elderly individual is relaxed, the monitoring unit decreases the frequency of monitoring and provides simpler information. For example, if an elderly individual is in a hurry, the monitoring unit monitors only essential information. This allows for more appropriate monitoring by adjusting the frequency and content of monitoring according to the emotions of the elderly individual. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the elderly individual's emotion data into the generative AI and have the generative AI adjust the frequency and content of monitoring.

[0087] The monitoring unit can analyze the past health data of elderly individuals to improve the accuracy of anomaly detection. For example, the monitoring unit can identify patterns of anomalies based on the elderly individual's past health data. For example, the monitoring unit can analyze the elderly individual's health data to perform early detection of anomalies. For example, the monitoring unit can predict anomalies based on the elderly individual's health data. In this way, the accuracy of anomaly detection can be improved by analyzing the elderly individual's past health data. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the elderly individual's health data into a generating AI and have the generating AI perform the task of improving the accuracy of anomaly detection.

[0088] The monitoring unit can change the focus of monitoring according to the elderly person's lifestyle. For example, if the elderly person is active at night, the monitoring unit will strengthen nighttime monitoring. For example, if the elderly person is active during the day, the monitoring unit will strengthen daytime monitoring. For example, the monitoring unit will adjust the focus of monitoring according to the elderly person's lifestyle. This allows for more appropriate monitoring by changing the focus of monitoring according to the elderly person's lifestyle. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input elderly person lifestyle pattern data into a generating AI and have the generating AI perform the adjustment of the monitoring focus.

[0089] The monitoring unit can estimate the emotions of elderly individuals and adjust the notification method of monitoring results based on the estimated emotions. For example, if an elderly individual is feeling anxious, the monitoring unit can provide detailed notifications to offer reassurance. For example, if an elderly individual is relaxed, the monitoring unit can provide simple notifications to reduce stress. For example, if an elderly individual is in a hurry, the monitoring unit can notify only essential information. This allows for more appropriate notifications by adjusting the notification method of monitoring results according to the emotions of the elderly individual. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI or not using AI. For example, the monitoring unit can input the elderly individual's emotion data into the generative AI and have the generative AI adjust the notification method.

[0090] The monitoring unit can optimize the monitoring range based on the elderly person's living environment. For example, if the elderly person lives alone, the monitoring unit will monitor all rooms. If the elderly person lives with family, the monitoring unit will monitor only specific rooms. The monitoring unit adjusts the monitoring range according to the elderly person's living environment. This allows for more appropriate monitoring by optimizing the monitoring range based on the elderly person's living environment. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the elderly person's living environment data into a generating AI and have the generating AI perform the optimization of the monitoring range.

[0091] The monitoring unit can select monitoring targets based on the elderly person's activity history. For example, the monitoring unit may prioritize monitoring rooms frequently used by the elderly person. For example, the monitoring unit may identify and monitor locations where abnormalities are likely to occur based on the elderly person's activity history. For example, the monitoring unit selects monitoring targets based on the elderly person's activity history. This allows for more appropriate monitoring by selecting monitoring targets based on the elderly person's activity history. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the elderly person's activity history data into a generating AI and have the generating AI perform the selection of monitoring targets.

[0092] The sharing unit can estimate the emotions of elderly individuals and adjust the timing and content of information sharing based on the estimated emotions. For example, if an elderly individual is feeling anxious, the sharing unit will quickly share detailed information. If an elderly individual is relaxed, the sharing unit will share simple information. If an elderly individual is in a hurry, the sharing unit will share only essential information. This allows for more appropriate information sharing by adjusting the timing and content of information sharing according to the emotions of the elderly individual. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or 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 sharing unit may be performed using AI, or not using AI. For example, the sharing unit can input the elderly individual's emotion data into the generative AI and have the generative AI adjust the timing and content of information sharing.

[0093] The sharing unit can analyze the past health data of elderly individuals and improve the accuracy of the information it shares. For example, the sharing unit shares accurate information based on the past health data of elderly individuals. For example, the sharing unit analyzes the health data of elderly individuals to detect abnormalities early. For example, the sharing unit predicts abnormalities based on the health data of elderly individuals. In this way, by analyzing the past health data of elderly individuals, the accuracy of the information shared can be improved. Some or all of the above-described processes in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input the health data of elderly individuals into a generating AI and have the generating AI perform the task of improving the accuracy of the information.

[0094] The sharing unit can customize the method of information sharing according to the needs of the elderly person's family and medical institutions. For example, if the family needs detailed information, the sharing unit will share detailed information. For example, if the medical institution needs simplified information, the sharing unit will share simplified information. The sharing unit customizes the method of information sharing according to the needs of the family and medical institutions. This makes it possible to share information more appropriately by customizing the method of information sharing according to the needs of the elderly person's family and medical institutions. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input family and medical institution needs data into a generating AI and have the generating AI perform the customization of the information sharing method.

[0095] The sharing unit can estimate the emotions of elderly individuals and determine the priority of information sharing based on the estimated emotions. For example, if an elderly individual is feeling anxious, the sharing unit will prioritize sharing important information. For example, if an elderly individual is relaxed, the sharing unit will flexibly change the order of information sharing. For example, if an elderly individual is in a hurry, the sharing unit will share the most important information first. This allows for more appropriate information sharing by determining the priority of information sharing according to the emotions of elderly individuals. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sharing unit may be performed using AI, for example, or not using AI. For example, the sharing unit can input the emotional data of elderly individuals into a generative AI and have the generative AI determine the priority of information sharing.

[0096] The sharing unit can optimize the timing of information sharing based on the elderly person's daily rhythm. For example, if the elderly person is active in the morning, the sharing unit will prioritize information sharing in the morning. For example, if the elderly person is relaxed at night, the sharing unit will simplify information sharing at night. For example, the sharing unit will adjust the timing of information sharing to match the elderly person's daily rhythm. By optimizing the timing of information sharing based on the elderly person's daily rhythm, more appropriate information sharing becomes possible. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input the elderly person's daily rhythm data into a generating AI and have the generating AI perform the optimization of the timing of information sharing.

[0097] The sharing unit can select the content of the information to be shared based on the health status of the elderly person. For example, if the elderly person is healthy, the sharing unit will share simple information. For example, if the elderly person is unwell, the sharing unit will share detailed information. The sharing unit selects the content of the information to be shared according to the health status of the elderly person. This makes it possible to share information more appropriately by selecting the content of the information to be shared based on the health status of the elderly person. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without using AI. For example, the sharing unit can input the health status data of the elderly person into a generating AI and have the generating AI perform the selection of the information content.

[0098] The reminder unit can estimate the emotions of elderly individuals and adjust the timing and content of reminders based on the estimated emotions. For example, if an elderly individual is feeling anxious, the reminder unit may speed up the timing of the reminder and provide more detailed information. For example, if an elderly individual is relaxed, the reminder unit may delay the timing of the reminder and provide simpler information. For example, if an elderly individual is in a hurry, the reminder unit may only provide important reminders. This allows for more appropriate reminders by adjusting the timing and content of reminders according to the emotions of the elderly individual. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the reminder unit may be performed using AI, or not using AI. For example, the reminder unit can input the elderly individual's emotion data into the generative AI and have the generative AI adjust the timing and content of the reminders.

[0099] The reminder unit can analyze the elderly person's past medication history to improve the accuracy of reminders. For example, the reminder unit provides accurate reminders based on the elderly person's past medication history. For example, the reminder unit analyzes the elderly person's medication history to prevent them from forgetting to take their medication. For example, the reminder unit optimizes the timing of medication based on the elderly person's medication history. In this way, the accuracy of reminders can be improved by analyzing the elderly person's past medication history. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input the elderly person's medication history data into a generating AI and have the generating AI perform the task of improving the accuracy of reminders.

[0100] The reminder unit can customize the reminder method according to the elderly person's lifestyle. For example, if the elderly person takes medication in the morning, the reminder unit will prioritize morning reminders. For example, if the elderly person takes medication at night, the reminder unit will prioritize nighttime reminders. The reminder unit adjusts the reminder method according to the elderly person's lifestyle. By customizing the reminder method according to the elderly person's lifestyle, more appropriate reminders become possible. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input the elderly person's lifestyle pattern data into a generating AI and have the generating AI perform the customization of the reminder method.

[0101] The reminder unit can estimate the emotions of elderly individuals and determine the priority of reminders based on those estimated emotions. For example, if an elderly individual is feeling anxious, the reminder unit will prioritize important reminders. For example, if an elderly individual is relaxed, the reminder unit can flexibly change the order of reminders. For example, if an elderly individual is in a hurry, the reminder unit will notify the most important reminder first. This allows for more appropriate reminders by determining the priority of reminders according to the elderly individual's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reminder unit may be performed using AI or not. For example, the reminder unit can input the elderly individual's emotion data into a generative AI and have the generative AI determine the priority of reminders.

[0102] The reminder unit can optimize the timing of reminders based on the elderly person's daily rhythm. For example, if the elderly person is active in the morning, the reminder unit will prioritize morning reminders. For example, if the elderly person is relaxed at night, the reminder unit will simplify evening reminders. For example, the reminder unit will adjust the timing of reminders to match the elderly person's daily rhythm. By optimizing the timing of reminders based on the elderly person's daily rhythm, more appropriate reminders become possible. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input the elderly person's daily rhythm data into a generating AI and have the generating AI perform the optimization of reminder timing.

[0103] The reminder unit can select the content of the reminder based on the health status of the elderly person. For example, if the elderly person is healthy, the reminder unit will send a simple reminder. For example, if the elderly person is unwell, the reminder unit will send a detailed reminder. The reminder unit selects the content of the reminder according to the health status of the elderly person. This makes it possible to send more appropriate reminders by selecting the content of the reminder based on the health status of the elderly person. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without using AI. For example, the reminder unit can input the elderly person's health status data into a generating AI and have the generating AI perform the selection of the reminder content.

[0104] The notification unit can estimate the emotions of elderly individuals and adjust the timing and content of the notification based on the estimated emotions. For example, if an elderly person is feeling anxious, the notification unit may speed up the notification and provide detailed information. For example, if an elderly person is relaxed, the notification unit may delay the notification and provide brief information. For example, if an elderly person is in a hurry, the notification unit may only provide important information. This allows for more appropriate notifications by adjusting the timing and content of the notification according to the emotions of the elderly person. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the elderly person's emotion data into a generative AI and have the generative AI adjust the timing and content of the notification.

[0105] The notification unit can analyze the past health data of elderly individuals to improve the accuracy of notifications. For example, the notification unit can make accurate notifications based on the past health data of elderly individuals. For example, the notification unit can analyze the health data of elderly individuals to detect abnormalities early. For example, the notification unit can predict abnormalities based on the health data of elderly individuals. In this way, the accuracy of notifications can be improved by analyzing the past health data of elderly individuals. Some or all of the above processes in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the health data of elderly individuals into a generating AI and have the generating AI perform the task of improving the accuracy of notifications.

[0106] The notification unit can customize the notification method according to the elderly person's lifestyle. For example, if the elderly person is active at night, the notification unit will strengthen nighttime notifications. For example, if the elderly person is active during the day, the notification unit will strengthen daytime notifications. For example, the notification unit will adjust the notification method according to the elderly person's lifestyle. By customizing the notification method according to the elderly person's lifestyle, more appropriate notifications can be made. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input elderly person lifestyle pattern data into a generating AI and have the generating AI perform the customization of the notification method.

[0107] The notification unit can estimate the emotions of elderly individuals and determine the priority of notifications based on those estimated emotions. For example, if an elderly individual is feeling anxious, the notification unit will prioritize important notifications. For example, if an elderly individual is relaxed, the notification unit can flexibly change the order of notifications. For example, if an elderly individual is in a hurry, the notification unit will notify the most important notifications first. This allows for more appropriate notifications by prioritizing notifications according to the emotions of elderly individuals. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input elderly individuals' emotion data into a generative AI and have the generative AI determine the priority of notifications.

[0108] The notification unit can optimize the scope of notifications based on the elderly person's living environment. For example, if the elderly person lives alone, the notification unit will notify all rooms. If the elderly person lives with family, the notification unit will notify only specific rooms. The notification unit adjusts the scope of notifications according to the elderly person's living environment. By optimizing the scope of notifications based on the elderly person's living environment, more appropriate notifications become possible. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input data on the elderly person's living environment into a generating AI and have the generating AI perform the optimization of the scope of notifications.

[0109] The notification unit can select the content of the notification based on the health status of the elderly person. For example, if the elderly person is healthy, the notification unit will send a simple notification. For example, if the elderly person is unwell, the notification unit will send a detailed notification. The notification unit selects the content of the notification according to the health status of the elderly person. This makes it possible to send more appropriate notifications by selecting the content of the notification based on the health status of the elderly person. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the health status data of the elderly person into a generating AI and have the generating AI perform the selection of the notification content.

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

[0111] The control unit can estimate the user's tone of voice and emotions, and adjust the timing and content of operations based on the estimated emotions. For example, if an elderly person is feeling anxious, the timing of operations can be delayed and guidance can be given in a calm voice. Conversely, if an elderly person is relaxed, the timing of operations can be sped up and guidance can be given in a cheerful voice. Furthermore, if an elderly person is in a hurry, the content of operations can be simplified and guidance can be given quickly. In this way, by adjusting the timing and content of operations according to the emotions of the elderly person, more appropriate operation becomes possible.

[0112] The monitoring unit can estimate the emotions of elderly individuals and adjust the frequency and content of monitoring based on these estimates. For example, if an elderly person is feeling anxious, the monitoring frequency can be increased and more detailed information can be provided. Conversely, if an elderly person is relaxed, the monitoring frequency can be decreased and simpler information can be provided. Furthermore, if an elderly person is in a hurry, only essential information can be monitored. This allows for more appropriate monitoring by adjusting the frequency and content of monitoring according to the emotions of the elderly person.

[0113] The sharing function can estimate the emotions of elderly individuals and adjust the timing and content of information sharing based on those estimates. For example, if an elderly person is feeling anxious, detailed information can be shared quickly. If the elderly person is relaxed, simpler information can be shared. Furthermore, if the elderly person is in a hurry, only essential information can be shared. This allows for more appropriate information sharing by adjusting the timing and content of information sharing according to the elderly person's emotions.

[0114] The reminder function can estimate the emotions of elderly individuals and adjust the timing and content of reminders based on those estimates. For example, if an elderly person is feeling anxious, the reminder can be sent earlier and with more detailed information. Conversely, if an elderly person is relaxed, the reminder can be sent later and with simpler information. Furthermore, if an elderly person is in a hurry, only important reminders can be sent. This allows for more appropriate reminders by adjusting the timing and content according to the elderly person's emotions.

[0115] The reporting system can estimate the emotions of elderly individuals and adjust the timing and content of the report based on those estimates. For example, if an elderly person is feeling anxious, the report can be made earlier and more detailed. Conversely, if an elderly person is relaxed, the report can be delayed and a simpler report can be provided. Furthermore, if an elderly person is in a hurry, only the most important information can be sent. This allows for more appropriate reporting by adjusting the timing and content of the report according to the elderly person's emotions.

[0116] The control unit can analyze the elderly person's past operating history and suggest the optimal operating method. For example, it can prioritize suggesting operating methods that the elderly person has frequently used in the past. It can also predict and suggest operating methods used during specific time periods based on the elderly person's operating history. Furthermore, it can analyze the elderly person's operating history and suggest the most efficient operating method. In this way, by analyzing the elderly person's past operating history, the optimal operating method can be suggested.

[0117] The monitoring unit can analyze past health data of elderly individuals to improve the accuracy of anomaly detection. For example, it can identify patterns of anomalies based on the elderly's past health data. It can also analyze the elderly's health data to perform early detection of anomalies. Furthermore, it can predict anomalies based on the elderly's health data. In this way, analyzing the elderly's past health data can improve the accuracy of anomaly detection.

[0118] The shared section allows for customization of information sharing methods according to the needs of elderly individuals' families and healthcare institutions. For example, if a family requires detailed information, that information can be shared. Similarly, if a healthcare institution requires simplified information, that information can be shared. Furthermore, the information sharing method can be customized according to the needs of families and healthcare institutions. This allows for more appropriate information sharing by customizing the information sharing method according to the needs of elderly individuals' families and healthcare institutions.

[0119] The reminder function can analyze the past medication history of elderly individuals to improve the accuracy of reminders. For example, it can provide accurate reminders based on their past medication history. It can also prevent missed doses by analyzing the patient's medication history. Furthermore, it can optimize the timing of medication based on the patient's medication history. In short, by analyzing the patient's past medication history, the accuracy of reminders can be improved.

[0120] The notification system can optimize the scope of notifications based on the elderly person's living environment. For example, if the elderly person lives alone, notifications can be sent to all rooms. If the elderly person lives with family, notifications can be sent to only specific rooms. Furthermore, the scope of notifications can be adjusted according to the elderly person's living environment. This allows for more appropriate notifications by optimizing the scope of notifications based on the elderly person's living environment.

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

[0122] Step 1: The control unit will use voice recognition technology to enable intuitive operation by the elderly. For example, if an elderly person says, "Tell me today's medication," the unit will provide the day's medication information by voice. Also, if an elderly person says, "Turn on the light," the unit will turn on the light. The control unit analyzes voice commands and generates appropriate responses. Step 2: The monitoring unit monitors the elderly person's life using IoT sensors. For example, the sensors detect the elderly person's movements and notify family members or medical institutions if there is an abnormality. It also monitors the room temperature using a temperature sensor and notifies if there is an abnormal temperature change. Furthermore, it monitors the elderly person's heart rate using a heart rate sensor and notifies if there is an abnormality. Step 3: The sharing unit shares the information obtained by the monitoring unit with family members and medical institutions. For example, it regularly reports the health status and living conditions of elderly individuals to family members and medical institutions. The sharing unit has a function to notify abnormalities in real time, generates periodic reports, and sends them to family members and medical institutions. Step 4: The reminder unit includes a medication reminder function. For example, it notifies elderly individuals of their medication time to prevent them from forgetting to take their medication. The reminder unit also includes functions to optimize medication timing, such as voice notifications and alarm settings. Step 5: The notification unit automatically sends an alert in case of an emergency. For example, if an elderly person falls, the sensor detects the abnormality and automatically notifies family members or medical institutions. The notification unit also has a function to send an alert when it detects an abnormal heart rate and has a function to customize the content of emergency alerts.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the operation unit, monitoring unit, sharing unit, reminder unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the operation unit is implemented by the control unit 46A of the smart device 14, enabling intuitive operation by the elderly using voice recognition technology. The monitoring unit monitors the elderly person's life using the camera 42 and sensors of the smart device 14 and notifies if there is an abnormality. The sharing unit is implemented by the specific processing unit 290 of the data processing unit 12, sharing information obtained by the monitoring unit with family and medical institutions. The reminder unit is implemented by the control unit 46A of the smart device 14 and includes a medication reminder function. The notification unit is implemented by the specific processing unit 290 of the data processing unit 12, automatically notifying in emergencies. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

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

[0128] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the operation unit, monitoring unit, sharing unit, reminder unit, and notification unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the operation unit is implemented by the control unit 46A of the smart glasses 214, enabling intuitive operation by the elderly using voice recognition technology. The monitoring unit monitors the elderly person's life using the camera 42 and sensors of the smart glasses 214 and notifies if there is an abnormality. The sharing unit is implemented by the specific processing unit 290 of the data processing unit 12, sharing information obtained by the monitoring unit with family and medical institutions. The reminder unit is implemented by the control unit 46A of the smart glasses 214 and includes a medication reminder function. The notification unit is implemented by the specific processing unit 290 of the data processing unit 12, automatically notifying in emergencies. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

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

[0144] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the operation unit, monitoring unit, sharing unit, reminder unit, and notification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the operation unit is implemented by the control unit 46A of the headset terminal 314, enabling intuitive operation by the elderly using voice recognition technology. The monitoring unit monitors the elderly person's life using the camera 42 and sensors of the headset terminal 314 and notifies if there is an abnormality. The sharing unit is implemented by the specific processing unit 290 of the data processing unit 12, sharing information obtained by the monitoring unit with family and medical institutions. The reminder unit is implemented by the control unit 46A of the headset terminal 314 and includes a medication reminder function. The notification unit is implemented by the specific processing unit 290 of the data processing unit 12, automatically notifying in emergencies. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

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

[0160] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0166] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0167] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0168] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the operation unit, monitoring unit, sharing unit, reminder unit, and notification unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the operation unit is implemented by the control unit 46A of the robot 414 and uses voice recognition technology to enable intuitive operation by the elderly. The monitoring unit monitors the elderly person's life using the robot 414's camera 42 and sensors and notifies if there is an abnormality. The sharing unit is implemented by the specific processing unit 290 of the data processing unit 12 and shares the information obtained by the monitoring unit with family and medical institutions. The reminder unit is implemented by the control unit 46A of the robot 414 and has a medication reminder function. The notification unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically notifies in case of emergency. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0176] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0178] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0179] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0180] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0184] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0185] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0186] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0187] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0188] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0189] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0193] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0194] (Note 1) A control unit that uses voice recognition technology to allow elderly people to operate it intuitively, A monitoring unit that uses IoT sensors to monitor the lives of elderly people, The information obtained by the monitoring unit is shared with family members and medical institutions, A reminder unit equipped with a medication reminder function, It includes a notification unit that automatically sends an alert in case of an emergency. A system characterized by the following features. (Note 2) The aforementioned operating unit is When an elderly person asks, "Tell me what medication I need today," the system will provide the day's medication information via voice. The system described in Appendix 1, characterized by the features described herein. (Note 3) The monitoring unit, The sensor detects the movements of elderly people and notifies family members or medical institutions if any abnormalities are detected. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned shared portion is, Regularly report the health status and living conditions of elderly individuals to their families and medical institutions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reminder unit, To ensure that elderly people do not forget to take their medication, we will notify them of the time to take their medication. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reporting unit, If an elderly person falls, a sensor will detect the abnormality and automatically notify family members or medical institutions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned operating unit is The system estimates the emotions of elderly individuals and adjusts the timing and content of operations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned operating unit is We analyze the past operation history of elderly users and propose the optimal operating method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned operating unit is The system adjusts the response time to the tone and speed of the elderly person's voice. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned operating unit is The system estimates the emotions of elderly individuals and determines the priority of operations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned operating unit is Optimize the timing of operations based on the elderly person's daily routine. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned operating unit is Customize the operation based on the hobbies and interests of elderly people. The system described in Appendix 1, characterized by the features described herein. (Note 13) The monitoring unit, The system estimates the emotions of older adults and adjusts the frequency and content of monitoring based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The monitoring unit, Analyzing past health data of elderly individuals to improve the accuracy of anomaly detection. The system described in Appendix 1, characterized by the features described herein. (Note 15) The monitoring unit, The focus of monitoring should be changed according to the lifestyle patterns of elderly individuals. The system described in Appendix 1, characterized by the features described herein. (Note 16) The monitoring unit, The system estimates the emotions of older adults and adjusts the notification method of monitoring results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The monitoring unit, Optimize the scope of monitoring based on the living environment of the elderly. The system described in Appendix 1, characterized by the features described herein. (Note 18) The monitoring unit, Based on the activity history of elderly individuals, the subjects to be monitored will be selected. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned shared portion is, The system estimates the emotions of elderly individuals and adjusts the timing and content of information sharing based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned shared portion is, Analyzing past health data of elderly individuals to improve the accuracy of shared information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned shared portion is, Customize the method of information sharing according to the needs of the elderly person's family and healthcare institution. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned shared portion is, The system estimates the emotions of elderly individuals and prioritizes information sharing based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned shared portion is, Optimizing the timing of information sharing based on the daily routines of elderly people. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned shared portion is, The content of information to be shared will be selected based on the health status of the elderly. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned reminder unit, The system estimates the emotions of elderly individuals and adjusts the timing and content of reminders based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned reminder unit, Analyzing the past medication history of elderly individuals improves the accuracy of reminders. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned reminder unit, Customize reminder methods according to the lifestyle patterns of elderly individuals. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned reminder unit, The system estimates the emotions of elderly individuals and prioritizes reminders based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned reminder unit, Optimize reminder timing based on the elderly person's daily routine. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned reminder unit, The content of the reminder will be selected based on the health condition of the elderly person. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned reporting unit, The system estimates the emotions of elderly individuals and adjusts the timing and content of notifications based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned reporting unit, Analyzing past health data of elderly individuals improves the accuracy of emergency alerts. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned reporting unit, Customize the notification method according to the lifestyle patterns of elderly people. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned reporting unit, The system estimates the emotions of elderly individuals and prioritizes reporting based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned reporting unit, Optimize the scope of reporting based on the living environment of elderly people. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned reporting unit, The content of the report will be selected based on the health condition of the elderly person. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A control unit that uses voice recognition technology to allow elderly people to operate it intuitively, A monitoring unit that uses IoT sensors to monitor the lives of elderly people, The information obtained by the monitoring unit is shared with family members and medical institutions, A reminder unit equipped with a medication reminder function, It includes a notification unit that automatically sends an alert in case of an emergency. A system characterized by the following features.

2. The monitoring unit, The sensor detects the movements of elderly people and notifies family members or medical institutions if any abnormalities are detected. The system according to feature 1.

3. The aforementioned shared portion is, Regularly report the health status and living conditions of elderly individuals to their families and medical institutions. The system according to feature 1.

4. The aforementioned reminder unit, To ensure that elderly people do not forget to take their medication, we will notify them of the time to take their medication. The system according to feature 1.

5. The aforementioned reporting unit, If an elderly person falls, a sensor will detect the abnormality and automatically notify family members or medical institutions. The system according to feature 1.

6. The aforementioned operating unit is The system estimates the emotions of elderly individuals and adjusts the timing and content of operations based on those estimated emotions. The system according to feature 1.

7. The aforementioned operating unit is We analyze the past operation history of elderly users and propose the optimal operating method. The system according to feature 1.

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