system

Abstract

The system according to this embodiment aims to automatically monitor the behavior of elderly people and reduce the burden on caregivers. [Solution] The system according to the embodiment comprises a monitoring unit, an image processing unit, a notification unit, and a replenishment unit. The monitoring unit installs small AI surveillance cameras in each main room. The image processing unit automatically performs AI image processing on the video collected by the monitoring unit. The notification unit sends a completion notification based on the results analyzed by the image processing unit. The replenishment unit replenishes food and daily necessities.

Description

Technical Field

[0006] , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, in the care of the elderly, there is a problem that it is difficult for caregivers to monitor 24 hours a day and the economic burden is large.

[0005] The system according to the embodiment aims to automatically monitor the behavior of the elderly and reduce the burden on caregivers.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a monitoring unit, an image processing unit, a notification unit, and a replenishment unit. The monitoring unit installs small AI surveillance cameras in each main room. The image processing unit automatically performs AI image processing on the video collected by the monitoring unit. The notification unit sends a completion notification based on the results analyzed by the image processing unit. The replenishment unit replenishes food and daily necessities. [Effects of the Invention]

[0007] The system according to this embodiment can automatically monitor the behavior of elderly people and reduce the burden on caregivers. [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, etc. 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 AI ​​system according to an embodiment of the present invention is a system for reducing the burden of caregiving in an aging society. This system is a mechanism that allows AI to take over the tasks of home helpers. First, small AI surveillance cameras are installed in each main room, and activity is managed through 24-hour remote monitoring. These surveillance cameras also allow for conversation (communication) through images. Next, the AI ​​automatically processes the video and sends completion notifications to the caregiver. Examples include notifications for meal completion, medication completion, toilet / bathing completion, and visitor confirmation. Furthermore, the caregiver can replenish food and daily necessities through online shopping and can also confirm that replenishment is complete. This mechanism reduces the burden on caregivers and supports the independence of those receiving care. As a result, the AI ​​system can efficiently provide care for the elderly.

[0029] The AI ​​system according to this embodiment comprises a monitoring unit, an image processing unit, a notification unit, and a replenishment unit. The monitoring unit installs small AI monitoring cameras in each main room. For example, the monitoring unit installs cameras in main rooms such as the living room, bedroom, and kitchen. The monitoring unit can perform behavioral management through 24-hour remote monitoring. For example, the monitoring unit can transmit video in real time via the internet and monitor from a remote location. The monitoring unit can also communicate through images. For example, the monitoring unit can use a microphone and speaker built into the camera to allow the caregiver and the person being cared for to converse. The image processing unit automatically performs AI image processing on the video collected by the monitoring unit. For example, the image processing unit can identify the face of the person being cared for using facial recognition technology. The image processing unit can also analyze the behavior of the person being cared for using motion detection technology. The image processing unit can also detect abnormal behavior such as falls or prolonged periods of inactivity using abnormal behavior detection technology. The notification unit sends completion notifications based on the results analyzed by the image processing unit. For example, the notification unit sends a meal completion notification. The notification unit can also send a medication completion notification. The notification unit can also send notifications when toilet use or bathing is complete. The notification unit can also send visitor confirmation notifications. The replenishment unit replenishes food and daily necessities. The replenishment unit orders food and daily necessities, for example, through online shopping. The replenishment unit can also confirm that replenishment is complete. The replenishment unit confirms, for example, that the ordered items have been delivered. This allows the AI ​​system according to the embodiment to efficiently provide care for the elderly.

[0030] The monitoring unit installs small AI surveillance cameras in each main room. For example, cameras are installed in key rooms such as the living room, bedroom, and kitchen. These cameras are equipped with wide-angle lenses and can cover the entire room. Furthermore, the cameras are high-resolution and can capture images with clear detail. The monitoring unit can perform behavioral management through 24-hour remote monitoring. For example, the monitoring unit can transmit video in real time via the internet and monitor remotely. This allows caregivers to check on the care recipient's situation from home or work. The monitoring unit also enables video-based conversation (communication). For example, the monitoring unit allows caregivers and care recipients to communicate using the microphone and speaker built into the camera. This enables a quick response in emergencies. Furthermore, the monitoring unit has night vision capabilities and can provide clear images even at night. This enables nighttime monitoring and ensures the safety of the care recipient. For privacy protection, the monitoring unit can remotely control the on / off status of the cameras as needed. This allows for necessary monitoring while respecting the care recipient's privacy.

[0031] The image processing unit automatically performs AI image processing on video collected by the monitoring unit. For example, the image processing unit can identify the face of the person being cared for using facial recognition technology. Facial recognition technology learns the facial features of the person being cared for and can identify faces in real time. This allows for confirmation of whether the person being cared for is in front of the camera. The image processing unit can also analyze the actions of the person being cared for using motion detection technology. Motion detection technology can track the movements of the person being cared for and detect specific actions. For example, it can detect actions such as the person being cared for standing up or walking. The image processing unit can also detect abnormal behavior such as falls or prolonged periods of inactivity using abnormal behavior detection technology. Abnormal behavior detection technology learns the movement patterns of the person being cared for and can detect actions that are different from normal. This allows for early detection of abnormal behavior such as falls or prolonged periods of inactivity and prompt response. Furthermore, the image processing unit can also analyze changes in the facial expressions and physical condition of the person being cared for. For example, it can infer emotional states from the facial expressions of the person being cared for and detect changes in physical condition. This allows for real-time monitoring of the person's health status and appropriate responses.

[0032] The notification unit sends completion notifications based on the results analyzed by the image processing unit. For example, the notification unit can send a meal completion notification. This notification confirms that the person receiving care has finished eating and notifies the caregiver. The notification unit can also send a medication completion notification. This notification confirms that the person receiving care has taken their medication and notifies the caregiver. The notification unit can also send a toilet / bath completion notification. This notification confirms that the person receiving care has finished using the toilet or bathing and notifies the caregiver. The notification unit can also send a visitor confirmation notification. This notification confirms that a visitor has arrived and notifies the caregiver. This allows the notification unit to grasp the person receiving care's actions in real time and provide caregivers with appropriate information. Furthermore, the notification unit can customize the content of notifications. For example, the content of notifications can be changed according to specific actions or time periods. This allows caregivers to efficiently receive the necessary information. The notification unit can send notifications using multiple communication methods. For example, notifications can be sent using smartphone apps, email, SMS, etc. This allows caregivers to receive notifications anytime, anywhere.

[0033] The replenishment unit replenishes food and daily necessities. For example, the replenishment unit orders food and daily necessities through online shopping. It can integrate with online shopping sites and automatically order the necessary items. The replenishment unit can also confirm the completion of replenishment. For example, the replenishment unit confirms that the ordered items have been delivered. It can track the delivery status in real time and confirm delivery completion. This ensures that the person receiving care always has the necessary items on hand. Furthermore, the replenishment unit can learn the person receiving care's consumption patterns and predict and order the necessary items. For example, if the person receiving care regularly consumes certain foods or medicines, it can automatically place orders based on that consumption pattern. This ensures that the person receiving care never runs out of necessary items and always has them on hand. The replenishment unit can also customize the order contents. For example, it can select appropriate items based on the person receiving care's preferences and allergy information. This allows for replenishment tailored to the person receiving care's needs. The replenishment unit can integrate with multiple online shopping sites and select the optimal price and delivery conditions. This allows those receiving care to quickly obtain necessary items while keeping costs down.

[0034] The monitoring unit can perform behavioral management through 24-hour remote monitoring. The monitoring unit can, for example, transmit video in real time via the internet and monitor from a remote location. The monitoring unit can also communicate through images. For example, the monitoring unit can use a microphone and speaker built into the camera to allow the caregiver and the person being cared for to communicate. This ensures the safety of the person being cared for through 24-hour remote monitoring. 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 video data acquired by the camera into a generating AI and have the generating AI perform analysis of the video data.

[0035] The image processing unit can send completion notifications for each action. For example, the image processing unit can send a notification when a meal is completed. It can also send a notification when medication has been taken. It can also send a notification when toileting or bathing has been completed. It can also send a notification when a visitor has been identified. By notifying caregivers of the completion of various actions, the burden on caregivers can be reduced. Some or all of the above-described processing in the image processing unit may be performed using AI, for example, or without AI. For example, the image processing unit can input video data acquired by a camera into a generating AI and have the generating AI perform analysis of the video data.

[0036] The notification unit can send notifications for meal completion, medication completion, toilet / bathing completion, and visitor confirmation. For example, the notification unit can send a meal completion notification. The notification unit can also send a medication completion notification. The notification unit can also send a toilet / bathing completion notification. The notification unit can also send a visitor confirmation notification. This can improve the efficiency of care by notifying caregivers of important information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the results analyzed by the image processing unit into a generation AI and have the generation AI generate the notification content.

[0037] The replenishment unit can replenish food and daily necessities through online shopping and confirm the completion of replenishment. For example, the replenishment unit orders food and daily necessities through online shopping. The replenishment unit can also confirm the completion of replenishment. For example, the replenishment unit can confirm that the ordered items have been delivered. This allows for efficient replenishment of food and daily necessities. Some or all of the above processes in the replenishment unit may be performed using AI, for example, or not using AI. For example, the replenishment unit can input online shopping order data into a generating AI and have the generating AI perform order optimization.

[0038] The monitoring unit can detect abnormal behavior in real time based on the collected video data and take immediate action. For example, if a user falls, the monitoring unit can immediately issue an alert and notify the caregiver. The monitoring unit can also detect if a user remains motionless for a long period of time and prompt the caregiver to check on the user. If a user takes unexpected action, the monitoring unit can detect it as abnormal behavior and report it to the caregiver. This ensures the safety of the person being cared for by detecting abnormal behavior in real time and taking immediate action. 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 video data acquired by a camera into a generating AI and have the generating AI perform abnormal behavior detection.

[0039] The monitoring unit can periodically evaluate the health status of the person being cared for using the collected video data and notify if any abnormalities are detected. For example, the monitoring unit can analyze the user's walking pattern and notify if any abnormalities are detected. The monitoring unit can also monitor the user's eating behavior and notify if the amount of food consumed decreases. The monitoring unit can also monitor the user's sleep pattern and notify if any abnormalities are detected. This allows for early intervention by periodically evaluating the health status and notifying if any abnormalities are detected. 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 video data acquired by a camera into a generating AI and have the generating AI perform a health status evaluation.

[0040] The monitoring unit can use the collected video data to learn the lifestyle patterns of the person being cared for and automatically generate an optimal monitoring schedule. For example, the monitoring unit can learn the user's wake-up and bedtime and adjust the monitoring schedule accordingly. The monitoring unit can also learn the user's meal times and enhance monitoring during meals. The monitoring unit can also learn the user's outing times and enhance monitoring during those times. This enables efficient monitoring by learning lifestyle patterns and automatically generating an optimal monitoring schedule. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input video data acquired by the camera into a generating AI and have the generating AI perform the learning of lifestyle patterns.

[0041] The monitoring unit can use the collected video data to suggest appropriate entertainment and relaxation based on the preferences and habits of the person being cared for. For example, the monitoring unit can suggest the user's favorite TV programs. The monitoring unit can also play music that the user likes. The monitoring unit can also suggest an environment where the user can relax. In this way, by suggesting appropriate entertainment and relaxation based on preferences and habits, the quality of life of the person being cared for can be improved. 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 video data acquired by a camera into a generating AI and have the generating AI execute suggestions for entertainment and relaxation.

[0042] The image processing unit can classify the movements and actions of the person being cared for in detail based on the video data being analyzed, and detect the completion of each action with high accuracy. For example, the image processing unit can classify eating behavior in detail and detect the completion of eating with high accuracy. The image processing unit can also classify medication taking behavior in detail and detect the completion of medication taking with high accuracy. The image processing unit can also classify toileting behavior in detail and detect the completion of toileting with high accuracy. By classifying movements and actions in detail and detecting the completion of each action with high accuracy, the efficiency of care can be improved. Some or all of the above processing in the image processing unit may be performed using AI, for example, or without using AI. For example, the image processing unit can input video data acquired by a camera into a generating AI and have the generating AI perform the classification of movements and actions.

[0043] The image processing unit can use the analyzed video data to detect changes or abnormalities in the care recipient's physical condition at an early stage and prompt appropriate action. For example, the image processing unit can detect changes in the user's complexion to detect poor health early. The image processing unit can also detect changes in the user's movements to detect abnormalities early. The image processing unit can also detect changes in the user's posture to detect poor health early. This allows for the early detection of changes or abnormalities in physical condition and prompts appropriate action, thereby maintaining the health of the care recipient. Some or all of the above-described processing in the image processing unit may be performed using AI, for example, or without AI. For example, the image processing unit can input video data acquired by a camera into a generating AI and have the generating AI perform the detection of changes or abnormalities in physical condition.

[0044] The image processing unit can use the analyzed video data to make suggestions for optimizing the living environment of the person receiving care. For example, the image processing unit can analyze the user's living environment and suggest the optimal furniture arrangement. The image processing unit can also analyze the user's living environment and suggest the optimal lighting settings. The image processing unit can also analyze the user's living environment and suggest the optimal temperature settings. By making suggestions to optimize the living environment, the quality of life for the person receiving care can be improved. Some or all of the above processing in the image processing unit may be performed using AI, for example, or without AI. For example, the image processing unit can input video data acquired by a camera into a generating AI and have the generating AI execute suggestions for optimizing the living environment.

[0045] The image processing unit can use the analyzed video data to perform risk assessments to ensure the safety of the person being cared for. For example, the image processing unit can analyze the user's movements and assess the risk of falls. The image processing unit can also analyze the user's living environment and assess the risk of fire. The image processing unit can also analyze the user's actions and assess the risk of accidents. By performing risk assessments to ensure safety, the safety of the person being cared for can be ensured. Some or all of the above processing in the image processing unit may be performed using AI, for example, or without AI. For example, the image processing unit can input video data acquired by a camera into a generating AI and have the generating AI perform the risk assessment.

[0046] The notification unit can include detailed reports of the care recipient's activity history and health status in the notifications it sends. For example, the notification unit can send notifications that include the user's meal history. The notification unit can also send notifications that include the user's medication history. The notification unit can also send notifications that include the user's toilet history. This allows caregivers to take more appropriate action by including detailed reports of activity history and health status. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input activity history and health status data into a generating AI and have the generating AI generate the report.

[0047] The notification unit can include advice and suggestions based on the behavior of the person being cared for in the notifications it sends. For example, the notification unit can send notifications that include nutritional advice based on the user's eating behavior. The notification unit can also send notifications that include medication advice based on the user's medication taking behavior. The notification unit can also send notifications that include health advice based on the user's toileting behavior. This allows caregivers to respond more appropriately by including behavior-based advice and suggestions. Some or all of the processing described above in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input behavioral data into a generating AI and have the generating AI generate advice and suggestions.

[0048] The notification unit can include personalized messages in the notifications it sends, based on the preferences and habits of the person being cared for. For example, the notification unit can send notifications that include meal suggestions based on the user's preferences. The notification unit can also send notifications that include exercise suggestions based on the user's habits. The notification unit can also send notifications that include entertainment suggestions based on the user's preferences. This allows caregivers to respond more appropriately by including personalized messages based on preferences and habits. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input user preference and habit data into a generating AI and have the generating AI generate personalized messages.

[0049] The notification unit can include emergency response instructions based on the living environment and circumstances of the person being cared for in the notifications it sends. For example, the notification unit can send a notification that includes instructions for fire response based on the user's living environment. The notification unit can also send a notification that includes instructions for fall response based on the user's circumstances. The notification unit can also send a notification that includes instructions for crime prevention response based on the user's living environment. This allows caregivers to take more appropriate action by including emergency response instructions based on the living environment and circumstances. 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 living environment and situation data into a generating AI and have the generating AI generate emergency response instructions.

[0050] The replenishment unit can learn the consumption patterns of the person being cared for based on the data it collects and automatically generate an optimal replenishment schedule. For example, the replenishment unit can learn the user's food consumption patterns and generate an optimal replenishment schedule for food ingredients. The replenishment unit can also learn the user's daily necessities consumption patterns and generate an optimal replenishment schedule for daily necessities. The replenishment unit can also learn the user's medication consumption patterns and generate an optimal replenishment schedule for medications. This enables efficient replenishment by learning consumption patterns and automatically generating an optimal replenishment schedule. Some or all of the above processes in the replenishment unit may be performed using AI, for example, or without AI. For example, the replenishment unit can input consumption data into a generating AI and have the generating AI generate the replenishment schedule.

[0051] The replenishment unit can use the collected data to suggest items that take into account the nutritional balance and health condition of the person being cared for. For example, the replenishment unit can analyze the user's nutritional balance and suggest necessary ingredients. The replenishment unit can also analyze the user's health condition and suggest appropriate supplements. The replenishment unit can also analyze the user's eating history and suggest a balanced meal menu. In this way, by suggesting items that take into account nutritional balance and health condition, the health of the person being cared for can be maintained. Some or all of the above processing in the replenishment unit may be performed using AI, for example, or not using AI. For example, the replenishment unit can input nutritional data and health condition data into a generating AI and have the generating AI perform item suggestions.

[0052] The replenishment unit can use the collected data to suggest personalized items based on the preferences and habits of the person being cared for. For example, the replenishment unit can suggest food items based on the user's preferences. The replenishment unit can also suggest daily necessities based on the user's habits. The replenishment unit can also suggest entertainment items based on the user's preferences. By suggesting personalized items based on preferences and habits, the quality of life of the person being cared for can be improved. Some or all of the above processing in the replenishment unit may be performed using AI, for example, or without AI. For example, the replenishment unit can input preference and habit data into a generating AI and have the generating AI perform the task of suggesting personalized items.

[0053] The replenishment unit can use the collected data to propose the optimal replenishment method based on the living environment and circumstances of the person receiving care. For example, the replenishment unit can analyze the user's living environment and propose the optimal food replenishment method. The replenishment unit can also analyze the user's living environment and propose the optimal daily necessities replenishment method. The replenishment unit can also analyze the user's living environment and propose the optimal medicine replenishment method. This enables efficient replenishment by proposing the optimal replenishment method based on the living environment and circumstances. Some or all of the above processing in the replenishment unit may be performed using AI, for example, or without AI. For example, the replenishment unit can input living environment and situation data into a generating AI and have the generating AI propose the optimal replenishment method.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The monitoring unit can detect abnormal behavior in real time based on the collected video data and take immediate action. For example, if a user falls, the monitoring unit can immediately issue an alert and notify the caregiver. The monitoring unit can also detect if a user remains motionless for a long period of time and prompt the caregiver to check on the user. If a user takes unexpected action, the monitoring unit can detect it as abnormal behavior and report it to the caregiver. This ensures the safety of the person being cared for by detecting abnormal behavior in real time and taking immediate action. 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 video data acquired by a camera into a generating AI and have the generating AI perform abnormal behavior detection.

[0056] The monitoring unit can periodically evaluate the health status of the person being cared for using the collected video data and notify if any abnormalities are detected. For example, the monitoring unit can analyze the user's walking pattern and notify if any abnormalities are detected. The monitoring unit can also monitor the user's eating behavior and notify if the amount of food consumed decreases. The monitoring unit can also monitor the user's sleep pattern and notify if any abnormalities are detected. This allows for early intervention by periodically evaluating the health status and notifying if any abnormalities are detected. 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 video data acquired by a camera into a generating AI and have the generating AI perform a health status evaluation.

[0057] The monitoring unit can use the collected video data to learn the lifestyle patterns of the person being cared for and automatically generate an optimal monitoring schedule. For example, the monitoring unit can learn the user's wake-up and bedtime and adjust the monitoring schedule accordingly. The monitoring unit can also learn the user's meal times and enhance monitoring during meals. The monitoring unit can also learn the user's outing times and enhance monitoring during those times. This enables efficient monitoring by learning lifestyle patterns and automatically generating an optimal monitoring schedule. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input video data acquired by the camera into a generating AI and have the generating AI perform the learning of lifestyle patterns.

[0058] The image processing unit can classify the movements and actions of the person being cared for in detail based on the video data being analyzed, and detect the completion of each action with high accuracy. For example, the image processing unit can classify eating behavior in detail and detect the completion of eating with high accuracy. The image processing unit can also classify medication taking behavior in detail and detect the completion of medication taking with high accuracy. The image processing unit can also classify toileting behavior in detail and detect the completion of toileting with high accuracy. By classifying movements and actions in detail and detecting the completion of each action with high accuracy, the efficiency of care can be improved. Some or all of the above processing in the image processing unit may be performed using AI, for example, or without using AI. For example, the image processing unit can input video data acquired by a camera into a generating AI and have the generating AI perform the classification of movements and actions.

[0059] The image processing unit can use the analyzed video data to detect changes or abnormalities in the care recipient's physical condition at an early stage and prompt appropriate action. For example, the image processing unit can detect changes in the user's complexion to detect poor health early. The image processing unit can also detect changes in the user's movements to detect abnormalities early. The image processing unit can also detect changes in the user's posture to detect poor health early. This allows for the early detection of changes or abnormalities in physical condition and prompts appropriate action, thereby maintaining the health of the care recipient. Some or all of the above-described processing in the image processing unit may be performed using AI, for example, or without AI. For example, the image processing unit can input video data acquired by a camera into a generating AI and have the generating AI perform the detection of changes or abnormalities in physical condition.

[0060] The image processing unit can use the analyzed video data to make suggestions for optimizing the living environment of the person receiving care. For example, the image processing unit can analyze the user's living environment and suggest the optimal furniture arrangement. The image processing unit can also analyze the user's living environment and suggest the optimal lighting settings. The image processing unit can also analyze the user's living environment and suggest the optimal temperature settings. By making suggestions to optimize the living environment, the quality of life for the person receiving care can be improved. Some or all of the above processing in the image processing unit may be performed using AI, for example, or without AI. For example, the image processing unit can input video data acquired by a camera into a generating AI and have the generating AI execute suggestions for optimizing the living environment.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The monitoring unit installs small AI surveillance cameras in each main room. For example, cameras can be installed in key rooms such as the living room, bedroom, and kitchen, allowing for 24-hour remote monitoring and activity management. The monitoring unit can transmit video in real time via the internet, enabling remote monitoring. In addition, the built-in microphone and speaker in the camera allow caregivers and those being cared for to communicate via video. Step 2: The image processing unit automatically performs AI image processing on the video collected by the monitoring unit. For example, it can identify the face of the person being cared for using facial recognition technology and analyze the person's actions using motion detection technology. It can also detect abnormal behaviors such as falls or prolonged periods of inactivity using abnormal behavior detection technology. Step 3: The notification unit sends a completion notification based on the results analyzed by the image processing unit. For example, it can send notifications for meal completion, medication completion, toilet / bathing completion, visitor confirmation, etc. Step 4: The replenishment department replenishes food and daily necessities. For example, they can order food and daily necessities through online shopping and confirm that the replenishment is complete. This also includes confirming that the ordered items have been delivered.

[0063] (Example of form 2) The AI ​​system according to an embodiment of the present invention is a system for reducing the burden of caregiving in an aging society. This system is a mechanism that allows AI to take over the tasks of home helpers. First, small AI surveillance cameras are installed in each main room, and activity is managed through 24-hour remote monitoring. These surveillance cameras also allow for conversation (communication) through images. Next, the AI ​​automatically processes the video and sends completion notifications to the caregiver. Examples include notifications for meal completion, medication completion, toilet / bathing completion, and visitor confirmation. Furthermore, the caregiver can replenish food and daily necessities through online shopping and can also confirm that replenishment is complete. This mechanism reduces the burden on caregivers and supports the independence of those receiving care. As a result, the AI ​​system can efficiently provide care for the elderly.

[0064] The AI ​​system according to this embodiment comprises a monitoring unit, an image processing unit, a notification unit, and a replenishment unit. The monitoring unit installs small AI monitoring cameras in each main room. For example, the monitoring unit installs cameras in main rooms such as the living room, bedroom, and kitchen. The monitoring unit can perform behavioral management through 24-hour remote monitoring. For example, the monitoring unit can transmit video in real time via the internet and monitor from a remote location. The monitoring unit can also communicate through images. For example, the monitoring unit can use a microphone and speaker built into the camera to allow the caregiver and the person being cared for to converse. The image processing unit automatically performs AI image processing on the video collected by the monitoring unit. For example, the image processing unit can identify the face of the person being cared for using facial recognition technology. The image processing unit can also analyze the behavior of the person being cared for using motion detection technology. The image processing unit can also detect abnormal behavior such as falls or prolonged periods of inactivity using abnormal behavior detection technology. The notification unit sends completion notifications based on the results analyzed by the image processing unit. For example, the notification unit sends a meal completion notification. The notification unit can also send a medication completion notification. The notification unit can also send notifications when toilet use or bathing is complete. The notification unit can also send visitor confirmation notifications. The replenishment unit replenishes food and daily necessities. The replenishment unit orders food and daily necessities, for example, through online shopping. The replenishment unit can also confirm that replenishment is complete. The replenishment unit confirms, for example, that the ordered items have been delivered. This allows the AI ​​system according to the embodiment to efficiently provide care for the elderly.

[0065] The monitoring unit installs small AI surveillance cameras in each main room. For example, cameras are installed in key rooms such as the living room, bedroom, and kitchen. These cameras are equipped with wide-angle lenses and can cover the entire room. Furthermore, the cameras are high-resolution and can capture images with clear detail. The monitoring unit can perform behavioral management through 24-hour remote monitoring. For example, the monitoring unit can transmit video in real time via the internet and monitor remotely. This allows caregivers to check on the care recipient's situation from home or work. The monitoring unit also enables video-based conversation (communication). For example, the monitoring unit allows caregivers and care recipients to communicate using the microphone and speaker built into the camera. This enables a quick response in emergencies. Furthermore, the monitoring unit has night vision capabilities and can provide clear images even at night. This enables nighttime monitoring and ensures the safety of the care recipient. For privacy protection, the monitoring unit can remotely control the on / off status of the cameras as needed. This allows for necessary monitoring while respecting the care recipient's privacy.

[0066] The image processing unit automatically performs AI image processing on video collected by the monitoring unit. For example, the image processing unit can identify the face of the person being cared for using facial recognition technology. Facial recognition technology learns the facial features of the person being cared for and can identify faces in real time. This allows for confirmation of whether the person being cared for is in front of the camera. The image processing unit can also analyze the actions of the person being cared for using motion detection technology. Motion detection technology can track the movements of the person being cared for and detect specific actions. For example, it can detect actions such as the person being cared for standing up or walking. The image processing unit can also detect abnormal behavior such as falls or prolonged periods of inactivity using abnormal behavior detection technology. Abnormal behavior detection technology learns the movement patterns of the person being cared for and can detect actions that are different from normal. This allows for early detection of abnormal behavior such as falls or prolonged periods of inactivity and prompt response. Furthermore, the image processing unit can also analyze changes in the facial expressions and physical condition of the person being cared for. For example, it can infer emotional states from the facial expressions of the person being cared for and detect changes in physical condition. This allows for real-time monitoring of the person's health status and appropriate responses.

[0067] The notification unit sends completion notifications based on the results analyzed by the image processing unit. For example, the notification unit can send a meal completion notification. This notification confirms that the person receiving care has finished eating and notifies the caregiver. The notification unit can also send a medication completion notification. This notification confirms that the person receiving care has taken their medication and notifies the caregiver. The notification unit can also send a toilet / bath completion notification. This notification confirms that the person receiving care has finished using the toilet or bathing and notifies the caregiver. The notification unit can also send a visitor confirmation notification. This notification confirms that a visitor has arrived and notifies the caregiver. This allows the notification unit to grasp the person receiving care's actions in real time and provide caregivers with appropriate information. Furthermore, the notification unit can customize the content of notifications. For example, the content of notifications can be changed according to specific actions or time periods. This allows caregivers to efficiently receive the necessary information. The notification unit can send notifications using multiple communication methods. For example, notifications can be sent using smartphone apps, email, SMS, etc. This allows caregivers to receive notifications anytime, anywhere.

[0068] The replenishment unit replenishes food and daily necessities. For example, the replenishment unit orders food and daily necessities through online shopping. It can integrate with online shopping sites and automatically order the necessary items. The replenishment unit can also confirm the completion of replenishment. For example, the replenishment unit confirms that the ordered items have been delivered. It can track the delivery status in real time and confirm delivery completion. This ensures that the person receiving care always has the necessary items on hand. Furthermore, the replenishment unit can learn the person receiving care's consumption patterns and predict and order the necessary items. For example, if the person receiving care regularly consumes certain foods or medicines, it can automatically place orders based on that consumption pattern. This ensures that the person receiving care never runs out of necessary items and always has them on hand. The replenishment unit can also customize the order contents. For example, it can select appropriate items based on the person receiving care's preferences and allergy information. This allows for replenishment tailored to the person receiving care's needs. The replenishment unit can integrate with multiple online shopping sites and select the optimal price and delivery conditions. This allows those receiving care to quickly obtain necessary items while keeping costs down.

[0069] The monitoring unit can perform behavioral management through 24-hour remote monitoring. The monitoring unit can, for example, transmit video in real time via the internet and monitor from a remote location. The monitoring unit can also communicate through images. For example, the monitoring unit can use a microphone and speaker built into the camera to allow the caregiver and the person being cared for to communicate. This ensures the safety of the person being cared for through 24-hour remote monitoring. 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 video data acquired by the camera into a generating AI and have the generating AI perform analysis of the video data.

[0070] The image processing unit can send completion notifications for each action. For example, the image processing unit can send a notification when a meal is completed. It can also send a notification when medication has been taken. It can also send a notification when toileting or bathing has been completed. It can also send a notification when a visitor has been identified. By notifying caregivers of the completion of various actions, the burden on caregivers can be reduced. Some or all of the above-described processing in the image processing unit may be performed using AI, for example, or without AI. For example, the image processing unit can input video data acquired by a camera into a generating AI and have the generating AI perform analysis of the video data.

[0071] The notification unit can send notifications for meal completion, medication completion, toilet / bathing completion, and visitor confirmation. For example, the notification unit can send a meal completion notification. The notification unit can also send a medication completion notification. The notification unit can also send a toilet / bathing completion notification. The notification unit can also send a visitor confirmation notification. This can improve the efficiency of care by notifying caregivers of important information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the results analyzed by the image processing unit into a generation AI and have the generation AI generate the notification content.

[0072] The replenishment unit can replenish food and daily necessities through online shopping and confirm the completion of replenishment. For example, the replenishment unit orders food and daily necessities through online shopping. The replenishment unit can also confirm the completion of replenishment. For example, the replenishment unit can confirm that the ordered items have been delivered. This allows for efficient replenishment of food and daily necessities. Some or all of the above processes in the replenishment unit may be performed using AI, for example, or not using AI. For example, the replenishment unit can input online shopping order data into a generating AI and have the generating AI perform order optimization.

[0073] The monitoring unit can estimate the user's emotions and automatically adjust the viewpoint and angle of the surveillance camera based on the estimated emotions. For example, if the user is feeling anxious, the monitoring unit can widen the camera's viewpoint to collect more information. If the user is relaxed, the monitoring unit can also narrow the camera's viewpoint to focus on a specific area. If the user is excited, the monitoring unit can frequently change the camera's angle to follow their movements. This allows for more appropriate monitoring by adjusting the camera's viewpoint and angle according to the user'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 monitoring unit may be performed using AI or not. For example, the monitoring unit can input video data acquired by the camera into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0074] The monitoring unit can detect abnormal behavior in real time based on the collected video data and take immediate action. For example, if a user falls, the monitoring unit can immediately issue an alert and notify the caregiver. The monitoring unit can also detect if a user remains motionless for a long period of time and prompt the caregiver to check on the user. If a user takes unexpected action, the monitoring unit can detect it as abnormal behavior and report it to the caregiver. This ensures the safety of the person being cared for by detecting abnormal behavior in real time and taking immediate action. 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 video data acquired by a camera into a generating AI and have the generating AI perform abnormal behavior detection.

[0075] The monitoring unit can periodically evaluate the health status of the person being cared for using the collected video data and notify if any abnormalities are detected. For example, the monitoring unit can analyze the user's walking pattern and notify if any abnormalities are detected. The monitoring unit can also monitor the user's eating behavior and notify if the amount of food consumed decreases. The monitoring unit can also monitor the user's sleep pattern and notify if any abnormalities are detected. This allows for early intervention by periodically evaluating the health status and notifying if any abnormalities are detected. 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 video data acquired by a camera into a generating AI and have the generating AI perform a health status evaluation.

[0076] The monitoring unit can estimate the user's emotions and adjust the frequency and timing of monitoring based on the estimated emotions. For example, if the user is feeling anxious, the monitoring unit can increase the frequency of monitoring. If the user is relaxed, the monitoring unit can also decrease the frequency of monitoring. If the user is excited, the monitoring unit can adjust the timing of monitoring to enhance real-time monitoring. This allows for more appropriate monitoring by adjusting the frequency and timing of monitoring according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI or not using AI. For example, the monitoring unit can input video data acquired by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0077] The monitoring unit can use the collected video data to learn the lifestyle patterns of the person being cared for and automatically generate an optimal monitoring schedule. For example, the monitoring unit can learn the user's wake-up and bedtime and adjust the monitoring schedule accordingly. The monitoring unit can also learn the user's meal times and enhance monitoring during meals. The monitoring unit can also learn the user's outing times and enhance monitoring during those times. This enables efficient monitoring by learning lifestyle patterns and automatically generating an optimal monitoring schedule. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input video data acquired by the camera into a generating AI and have the generating AI perform the learning of lifestyle patterns.

[0078] The monitoring unit can use the collected video data to suggest appropriate entertainment and relaxation based on the preferences and habits of the person being cared for. For example, the monitoring unit can suggest the user's favorite TV programs. The monitoring unit can also play music that the user likes. The monitoring unit can also suggest an environment where the user can relax. In this way, by suggesting appropriate entertainment and relaxation based on preferences and habits, the quality of life of the person being cared for can be improved. 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 video data acquired by a camera into a generating AI and have the generating AI execute suggestions for entertainment and relaxation.

[0079] The image processing unit can estimate the user's emotions and dynamically adjust the image processing algorithm based on the estimated emotions. For example, if the user is feeling anxious, the image processing unit can improve the accuracy of image processing. If the user is relaxed, the image processing unit can also adjust the accuracy of image processing. If the user is excited, the image processing unit can also increase the speed of image processing. This allows for more appropriate image processing by adjusting the image processing algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the image processing unit may be performed using AI, or not using AI. For example, the image processing unit can input video data acquired by a camera into a generative AI and have the generative AI adjust the image processing algorithm.

[0080] The image processing unit can classify the movements and actions of the person being cared for in detail based on the video data being analyzed, and detect the completion of each action with high accuracy. For example, the image processing unit can classify eating behavior in detail and detect the completion of eating with high accuracy. The image processing unit can also classify medication taking behavior in detail and detect the completion of medication taking with high accuracy. The image processing unit can also classify toileting behavior in detail and detect the completion of toileting with high accuracy. By classifying movements and actions in detail and detecting the completion of each action with high accuracy, the efficiency of care can be improved. Some or all of the above processing in the image processing unit may be performed using AI, for example, or without using AI. For example, the image processing unit can input video data acquired by a camera into a generating AI and have the generating AI perform the classification of movements and actions.

[0081] The image processing unit can use the analyzed video data to detect changes or abnormalities in the care recipient's physical condition at an early stage and prompt appropriate action. For example, the image processing unit can detect changes in the user's complexion to detect poor health early. The image processing unit can also detect changes in the user's movements to detect abnormalities early. The image processing unit can also detect changes in the user's posture to detect poor health early. This allows for the early detection of changes or abnormalities in physical condition and prompts appropriate action, thereby maintaining the health of the care recipient. Some or all of the above-described processing in the image processing unit may be performed using AI, for example, or without AI. For example, the image processing unit can input video data acquired by a camera into a generating AI and have the generating AI perform the detection of changes or abnormalities in physical condition.

[0082] The image processing unit can estimate the user's emotions and determine the priority of image processing based on the estimated emotions. For example, if the user is feeling anxious, the image processing unit will prioritize image processing of important actions. If the user is relaxed, the image processing unit can also perform normal image processing. If the user is excited, the image processing unit can also prioritize image processing of urgent actions. This allows for more appropriate image processing by determining the priority of image processing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the image processing unit may be performed using AI or not using AI. For example, the image processing unit can input video data acquired by a camera into a generative AI and have the generative AI determine the priority of image processing.

[0083] The image processing unit can use the analyzed video data to make suggestions for optimizing the living environment of the person receiving care. For example, the image processing unit can analyze the user's living environment and suggest the optimal furniture arrangement. The image processing unit can also analyze the user's living environment and suggest the optimal lighting settings. The image processing unit can also analyze the user's living environment and suggest the optimal temperature settings. By making suggestions to optimize the living environment, the quality of life for the person receiving care can be improved. Some or all of the above processing in the image processing unit may be performed using AI, for example, or without AI. For example, the image processing unit can input video data acquired by a camera into a generating AI and have the generating AI execute suggestions for optimizing the living environment.

[0084] The image processing unit can use the analyzed video data to perform risk assessments to ensure the safety of the person being cared for. For example, the image processing unit can analyze the user's movements and assess the risk of falls. The image processing unit can also analyze the user's living environment and assess the risk of fire. The image processing unit can also analyze the user's actions and assess the risk of accidents. By performing risk assessments to ensure safety, the safety of the person being cared for can be ensured. Some or all of the above processing in the image processing unit may be performed using AI, for example, or without AI. For example, the image processing unit can input video data acquired by a camera into a generating AI and have the generating AI perform the risk assessment.

[0085] The notification unit can estimate the user's emotions and adjust the content and format of the notification based on the estimated emotions. For example, if the user is feeling anxious, the notification unit can provide detailed notification content. If the user is relaxed, the notification unit can also provide concise notification content. If the user is excited, the notification unit can also provide urgent notification content. By adjusting the content and format of the notification according to the user's emotions, more appropriate notifications can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the emotion estimation results into the generative AI and have the generative AI adjust the notification content.

[0086] The notification unit can include detailed reports of the care recipient's activity history and health status in the notifications it sends. For example, the notification unit can send notifications that include the user's meal history. The notification unit can also send notifications that include the user's medication history. The notification unit can also send notifications that include the user's toilet history. This allows caregivers to take more appropriate action by including detailed reports of activity history and health status. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input activity history and health status data into a generating AI and have the generating AI generate the report.

[0087] The notification unit can include advice and suggestions based on the behavior of the person being cared for in the notifications it sends. For example, the notification unit can send notifications that include nutritional advice based on the user's eating behavior. The notification unit can also send notifications that include medication advice based on the user's medication taking behavior. The notification unit can also send notifications that include health advice based on the user's toileting behavior. This allows caregivers to respond more appropriately by including behavior-based advice and suggestions. Some or all of the processing described above in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input behavioral data into a generating AI and have the generating AI generate advice and suggestions.

[0088] The notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated emotions. For example, if the user is feeling anxious, the notification unit will send a notification immediately. If the user is relaxed, the notification unit can also send a notification at an appropriate time. If the user is excited, the notification unit can also send a notification at a time of high urgency. By adjusting the timing of notifications according to the user's emotions, notifications can be sent at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input the emotion estimation results into the generative AI and have the generative AI adjust the notification timing.

[0089] The notification unit can include personalized messages in the notifications it sends, based on the preferences and habits of the person being cared for. For example, the notification unit can send notifications that include meal suggestions based on the user's preferences. The notification unit can also send notifications that include exercise suggestions based on the user's habits. The notification unit can also send notifications that include entertainment suggestions based on the user's preferences. This allows caregivers to respond more appropriately by including personalized messages based on preferences and habits. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input user preference and habit data into a generating AI and have the generating AI generate personalized messages.

[0090] The notification unit can include emergency response instructions based on the living environment and circumstances of the person being cared for in the notifications it sends. For example, the notification unit can send a notification that includes instructions for fire response based on the user's living environment. The notification unit can also send a notification that includes instructions for fall response based on the user's circumstances. The notification unit can also send a notification that includes instructions for crime prevention response based on the user's living environment. This allows caregivers to take more appropriate action by including emergency response instructions based on the living environment and circumstances. 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 living environment and situation data into a generating AI and have the generating AI generate emergency response instructions.

[0091] The replenishment unit can estimate the user's emotions and determine the priority of items to replenish based on the estimated emotions. For example, if the user is feeling anxious, the replenishment unit will prioritize replenishing necessary items. If the user is relaxed, the replenishment unit can maintain the normal replenishment schedule. If the user is excited, the replenishment unit can also prioritize replenishing items of high urgency. This allows for more appropriate replenishment by determining the priority of items to replenish according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the replenishment unit may be performed using AI or not. For example, the replenishment unit can input the emotion estimation results into the generative AI and have the generative AI determine the priority of replenishment items.

[0092] The replenishment unit can learn the consumption patterns of the person being cared for based on the data it collects and automatically generate an optimal replenishment schedule. For example, the replenishment unit can learn the user's food consumption patterns and generate an optimal replenishment schedule for food ingredients. The replenishment unit can also learn the user's daily necessities consumption patterns and generate an optimal replenishment schedule for daily necessities. The replenishment unit can also learn the user's medication consumption patterns and generate an optimal replenishment schedule for medications. This enables efficient replenishment by learning consumption patterns and automatically generating an optimal replenishment schedule. Some or all of the above processes in the replenishment unit may be performed using AI, for example, or without AI. For example, the replenishment unit can input consumption data into a generating AI and have the generating AI generate the replenishment schedule.

[0093] The replenishment unit can use the collected data to suggest items that take into account the nutritional balance and health condition of the person being cared for. For example, the replenishment unit can analyze the user's nutritional balance and suggest necessary ingredients. The replenishment unit can also analyze the user's health condition and suggest appropriate supplements. The replenishment unit can also analyze the user's eating history and suggest a balanced meal menu. In this way, by suggesting items that take into account nutritional balance and health condition, the health of the person being cared for can be maintained. Some or all of the above processing in the replenishment unit may be performed using AI, for example, or not using AI. For example, the replenishment unit can input nutritional data and health condition data into a generating AI and have the generating AI perform item suggestions.

[0094] The replenishment unit can estimate the user's emotions and adjust the timing of replenishment based on the estimated emotions. For example, if the user is feeling anxious, the replenishment unit will replenish earlier. If the user is relaxed, the replenishment unit can maintain the normal replenishment timing. If the user is excited, the replenishment unit can quickly replenish high-priority items. This allows for more appropriate timing of replenishment by adjusting the timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the replenishment unit may be performed using AI or not using AI. For example, the replenishment unit can input the emotion estimation results into the generative AI and have the generative AI adjust the replenishment timing.

[0095] The replenishment unit can use the collected data to suggest personalized items based on the preferences and habits of the person being cared for. For example, the replenishment unit can suggest food items based on the user's preferences. The replenishment unit can also suggest daily necessities based on the user's habits. The replenishment unit can also suggest entertainment items based on the user's preferences. By suggesting personalized items based on preferences and habits, the quality of life of the person being cared for can be improved. Some or all of the above processing in the replenishment unit may be performed using AI, for example, or without AI. For example, the replenishment unit can input preference and habit data into a generating AI and have the generating AI perform the task of suggesting personalized items.

[0096] The replenishment unit can use the collected data to propose the optimal replenishment method based on the living environment and circumstances of the person receiving care. For example, the replenishment unit can analyze the user's living environment and propose the optimal food replenishment method. The replenishment unit can also analyze the user's living environment and propose the optimal daily necessities replenishment method. The replenishment unit can also analyze the user's living environment and propose the optimal medicine replenishment method. This enables efficient replenishment by proposing the optimal replenishment method based on the living environment and circumstances. Some or all of the above processing in the replenishment unit may be performed using AI, for example, or without AI. For example, the replenishment unit can input living environment and situation data into a generating AI and have the generating AI propose the optimal replenishment method.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The monitoring unit can estimate the user's emotions and automatically adjust the viewpoint and angle of the surveillance camera based on the estimated emotions. For example, if the user is feeling anxious, the monitoring unit can widen the camera's viewpoint to collect more information. If the user is relaxed, the monitoring unit can also narrow the camera's viewpoint to focus on a specific area. If the user is excited, the monitoring unit can frequently change the camera's angle to follow their movements. This allows for more appropriate monitoring by adjusting the camera's viewpoint and angle according to the user'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 monitoring unit may be performed using AI or not. For example, the monitoring unit can input video data acquired by the camera into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0099] The monitoring unit can detect abnormal behavior in real time based on the collected video data and take immediate action. For example, if a user falls, the monitoring unit can immediately issue an alert and notify the caregiver. The monitoring unit can also detect if a user remains motionless for a long period of time and prompt the caregiver to check on the user. If a user takes unexpected action, the monitoring unit can detect it as abnormal behavior and report it to the caregiver. This ensures the safety of the person being cared for by detecting abnormal behavior in real time and taking immediate action. 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 video data acquired by a camera into a generating AI and have the generating AI perform abnormal behavior detection.

[0100] The monitoring unit can periodically evaluate the health status of the person being cared for using the collected video data and notify if any abnormalities are detected. For example, the monitoring unit can analyze the user's walking pattern and notify if any abnormalities are detected. The monitoring unit can also monitor the user's eating behavior and notify if the amount of food consumed decreases. The monitoring unit can also monitor the user's sleep pattern and notify if any abnormalities are detected. This allows for early intervention by periodically evaluating the health status and notifying if any abnormalities are detected. 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 video data acquired by a camera into a generating AI and have the generating AI perform a health status evaluation.

[0101] The monitoring unit can estimate the user's emotions and adjust the frequency and timing of monitoring based on the estimated emotions. For example, if the user is feeling anxious, the monitoring unit can increase the frequency of monitoring. If the user is relaxed, the monitoring unit can also decrease the frequency of monitoring. If the user is excited, the monitoring unit can adjust the timing of monitoring to enhance real-time monitoring. This allows for more appropriate monitoring by adjusting the frequency and timing of monitoring according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI or not using AI. For example, the monitoring unit can input video data acquired by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0102] The monitoring unit can use the collected video data to learn the lifestyle patterns of the person being cared for and automatically generate an optimal monitoring schedule. For example, the monitoring unit can learn the user's wake-up and bedtime and adjust the monitoring schedule accordingly. The monitoring unit can also learn the user's meal times and enhance monitoring during meals. The monitoring unit can also learn the user's outing times and enhance monitoring during those times. This enables efficient monitoring by learning lifestyle patterns and automatically generating an optimal monitoring schedule. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input video data acquired by the camera into a generating AI and have the generating AI perform the learning of lifestyle patterns.

[0103] The image processing unit can estimate the user's emotions and dynamically adjust the image processing algorithm based on the estimated emotions. For example, if the user is feeling anxious, the image processing unit can improve the accuracy of image processing. If the user is relaxed, the image processing unit can also adjust the accuracy of image processing. If the user is excited, the image processing unit can also increase the speed of image processing. This allows for more appropriate image processing by adjusting the image processing algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the image processing unit may be performed using AI, or not using AI. For example, the image processing unit can input video data acquired by a camera into a generative AI and have the generative AI adjust the image processing algorithm.

[0104] The image processing unit can classify the movements and actions of the person being cared for in detail based on the video data being analyzed, and detect the completion of each action with high accuracy. For example, the image processing unit can classify eating behavior in detail and detect the completion of eating with high accuracy. The image processing unit can also classify medication taking behavior in detail and detect the completion of medication taking with high accuracy. The image processing unit can also classify toileting behavior in detail and detect the completion of toileting with high accuracy. By classifying movements and actions in detail and detecting the completion of each action with high accuracy, the efficiency of care can be improved. Some or all of the above processing in the image processing unit may be performed using AI, for example, or without using AI. For example, the image processing unit can input video data acquired by a camera into a generating AI and have the generating AI perform the classification of movements and actions.

[0105] The image processing unit can use the analyzed video data to detect changes or abnormalities in the care recipient's physical condition at an early stage and prompt appropriate action. For example, the image processing unit can detect changes in the user's complexion to detect poor health early. The image processing unit can also detect changes in the user's movements to detect abnormalities early. The image processing unit can also detect changes in the user's posture to detect poor health early. This allows for the early detection of changes or abnormalities in physical condition and prompts appropriate action, thereby maintaining the health of the care recipient. Some or all of the above-described processing in the image processing unit may be performed using AI, for example, or without AI. For example, the image processing unit can input video data acquired by a camera into a generating AI and have the generating AI perform the detection of changes or abnormalities in physical condition.

[0106] The image processing unit can estimate the user's emotions and determine the priority of image processing based on the estimated emotions. For example, if the user is feeling anxious, the image processing unit will prioritize image processing of important actions. If the user is relaxed, the image processing unit can also perform normal image processing. If the user is excited, the image processing unit can also prioritize image processing of urgent actions. This allows for more appropriate image processing by determining the priority of image processing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the image processing unit may be performed using AI or not using AI. For example, the image processing unit can input video data acquired by a camera into a generative AI and have the generative AI determine the priority of image processing.

[0107] The image processing unit can use the analyzed video data to make suggestions for optimizing the living environment of the person receiving care. For example, the image processing unit can analyze the user's living environment and suggest the optimal furniture arrangement. The image processing unit can also analyze the user's living environment and suggest the optimal lighting settings. The image processing unit can also analyze the user's living environment and suggest the optimal temperature settings. By making suggestions to optimize the living environment, the quality of life for the person receiving care can be improved. Some or all of the above processing in the image processing unit may be performed using AI, for example, or without AI. For example, the image processing unit can input video data acquired by a camera into a generating AI and have the generating AI execute suggestions for optimizing the living environment.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The monitoring unit installs small AI surveillance cameras in each main room. For example, cameras can be installed in key rooms such as the living room, bedroom, and kitchen, allowing for 24-hour remote monitoring and activity management. The monitoring unit can transmit video in real time via the internet, enabling remote monitoring. In addition, the built-in microphone and speaker in the camera allow caregivers and those being cared for to communicate via video. Step 2: The image processing unit automatically performs AI image processing on the video collected by the monitoring unit. For example, it can identify the face of the person being cared for using facial recognition technology and analyze the person's actions using motion detection technology. It can also detect abnormal behaviors such as falls or prolonged periods of inactivity using abnormal behavior detection technology. Step 3: The notification unit sends a completion notification based on the results analyzed by the image processing unit. For example, it can send notifications for meal completion, medication completion, toilet / bathing completion, visitor confirmation, etc. Step 4: The replenishment department replenishes food and daily necessities. For example, they can order food and daily necessities through online shopping and confirm that the replenishment is complete. This also includes confirming that the ordered items have been delivered.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] Each of the multiple elements described above, including the monitoring unit, image processing unit, notification unit, and replenishment unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the monitoring unit acquires video of the main room using the camera 42 of the smart device 14 and performs 24-hour remote monitoring by the control unit 46A. The image processing unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and performs AI image processing on the collected video. The notification unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and sends a completion notification based on the analysis results. The replenishment unit orders food and daily necessities through online shopping by the control unit 46A of the smart device 14 and confirms that the replenishment is complete. 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.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] Each of the multiple elements described above, including the monitoring unit, image processing unit, notification unit, and replenishment unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the monitoring unit acquires video of the main room using the camera 42 of the smart glasses 214 and performs 24-hour remote monitoring via the control unit 46A. The image processing unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and performs AI image processing on the collected video. The notification unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and sends a completion notification based on the analysis results. The replenishment unit orders food and daily necessities through online shopping via the control unit 46A of the smart glasses 214 and confirms that the replenishment is complete. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Each of the multiple elements described above, including the monitoring unit, image processing unit, notification unit, and replenishment unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the monitoring unit acquires video of the main room using the camera 42 of the headset terminal 314 and performs 24-hour remote monitoring by the control unit 46A. The image processing unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and performs AI image processing on the collected video. The notification unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and sends a completion notification based on the analysis results. The replenishment unit orders food and daily necessities through online shopping by the control unit 46A of the headset terminal 314 and confirms that the replenishment is complete. 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.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

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

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

[0157] In 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.

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

[0159] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0161] The data processing system 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.

[0162] Each of the multiple elements described above, including the monitoring unit, image processing unit, notification unit, and replenishment unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the monitoring unit acquires video of the main room using the camera 42 of the robot 414 and performs 24-hour remote monitoring by the control unit 46A. The image processing unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and performs AI image processing on the collected video. The notification unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and sends a completion notification based on the analysis results. The replenishment unit orders food and daily necessities through online shopping by the control unit 46A of the robot 414 and confirms that the replenishment is complete. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] (Note 1) The surveillance unit installs small AI surveillance cameras in each main room, An image processing unit that automatically performs AI image processing on the video collected by the aforementioned monitoring unit, A notification unit that sends a completion notification based on the results analyzed by the image processing unit, It includes a replenishment section for replenishing food and daily necessities. A system characterized by the following features. (Note 2) The aforementioned monitoring unit, Behavior is monitored remotely 24 hours a day. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned image processing unit, Send completion notifications for each action. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned notification unit, Send notifications for meal completion, medication completion, toilet / bathing completion, and visitor confirmation. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned replenishment unit is Replenish your food and daily necessities through online shopping and confirm that the replenishment is complete. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned monitoring unit, It estimates the user's emotions and automatically adjusts the viewpoint and angle of the surveillance camera based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned monitoring unit, Based on the collected video data, abnormal behavior is detected in real time, and immediate action is taken. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned monitoring unit, The collected video data will be used to periodically assess the health status of the person receiving care and to notify them if any abnormalities are detected. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned monitoring unit, It estimates the user's emotions and adjusts the frequency and timing of monitoring based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned monitoring unit, Using collected video data, the system learns the lifestyle patterns of the person receiving care and automatically generates an optimal monitoring schedule. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned monitoring unit, Using the collected video data, we propose appropriate entertainment and relaxation based on the preferences and habits of the person receiving care. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned image processing unit, It estimates the user's emotions and dynamically adjusts the image processing algorithm based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned image processing unit, Based on the video data being analyzed, the system classifies the movements and actions of the person receiving care in detail and detects the completion of each action with high accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned image processing unit, By using the analyzed video data, we can detect changes or abnormalities in the care recipient's physical condition early and prompt appropriate responses. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned image processing unit, It estimates the user's emotions and determines the priority of image processing based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned image processing unit, Using the analyzed video data, we will propose ways to optimize the living environment for those receiving care. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned image processing unit, Using the video data to be analyzed, a risk assessment is conducted to ensure the safety of those receiving care. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned notification unit, It estimates the user's emotions and adjusts the content and format of notifications based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned notification unit, The notifications sent will include detailed reports on the care recipient's activity history and health status. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned notification unit, Include advice and suggestions based on the care recipient's behavior in the notifications sent. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned notification unit, It estimates the user's emotions and adjusts the timing of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned notification unit, Include personalized messages in notifications that are based on the preferences and habits of the person being cared for. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned notification unit, The notifications sent will include emergency response instructions based on the living environment and circumstances of the person receiving care. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned replenishment unit is It estimates the user's emotions and determines the priority of items to replenish based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned replenishment unit is Based on the collected data, the system learns the consumption patterns of those receiving care and automatically generates an optimal replenishment schedule. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned replenishment unit is Using the collected data, we propose items that take into account the nutritional balance and health condition of the person receiving care. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned replenishment unit is It estimates the user's emotions and adjusts the timing of replenishment based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned replenishment unit is The collected data is used to suggest personalized items based on the preferences and habits of the person receiving care. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned replenishment unit is Using the collected data, we propose the optimal supplementation method based on the living environment and circumstances of the person receiving care. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The surveillance unit installs small AI surveillance cameras in each main room, An image processing unit that automatically performs AI image processing on the video collected by the aforementioned monitoring unit, A notification unit that sends a completion notification based on the results analyzed by the image processing unit, It includes a replenishment section for replenishing food and daily necessities. A system characterized by the following features.

2. The aforementioned monitoring unit, Behavior is monitored remotely 24 hours a day. The system according to feature 1.

3. The aforementioned image processing unit, Send a completion notification for each action. The system according to feature 1.

4. The aforementioned notification unit, Sends notifications for meal completion, medication completion, toilet / bathing completion, and visitor confirmation. The system according to feature 1.

5. The aforementioned replenishment unit is Replenish your food and daily necessities through online shopping and confirm that the replenishment is complete. The system according to feature 1.

6. The aforementioned monitoring unit, It estimates the user's emotions and automatically adjusts the viewpoint and angle of the surveillance camera based on the estimated user emotions. The system according to feature 1.

7. The aforementioned monitoring unit, Based on the collected video data, abnormal behavior is detected in real time, and immediate action is taken. The system according to feature 1.

8. The aforementioned monitoring unit, The collected video data will be used to periodically assess the health status of the person receiving care and to notify them if any abnormalities are detected. The system according to feature 1.

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