system

Abstract

The system according to this embodiment aims to constantly monitor the health status and movements of the parent and to respond quickly when an abnormality occurs. [Solution] The system according to the embodiment comprises an operation confirmation unit, a health monitoring unit, and an abnormality detection unit. The operation confirmation unit confirms the parent's movements within the parent's home. The health monitoring unit monitors changes in the parent's heart rate or body temperature. The abnormality detection unit detects abnormalities and sends an alert.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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 prior art, there is a problem that it is difficult to constantly monitor the health status and movements of parents, and it is difficult to respond promptly when an abnormality occurs.

[0005] The system according to the embodiment aims to constantly monitor the health status and movements of parents and respond promptly when an abnormality occurs.

Means for Solving the Problems

[0006] The system according to the embodiment includes an operation confirmation unit, a health monitoring unit, and an abnormality detection unit. The operation confirmation unit confirms the movements of the parent within the parental home. The health monitoring unit monitors changes in the parent's heart rate or body temperature. The abnormality detection unit detects an abnormality and sends an alert.

Effects of the Invention

[0007] The system according to this embodiment can constantly monitor the health status and movements of the parent and respond quickly when an abnormality occurs. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The home monitoring system according to an embodiment of the present invention is a system that allows you to check on the condition of elderly parents at any time when they live far away. This home monitoring system can check the movements, heart rate, and changes in body temperature of the parents within their home, and if any problem occurs, the AI ​​will automatically detect it and send an alert. For example, the home monitoring system monitors how the parents are moving in real time through sensors and cameras installed in the parents' home. For example, it can check if the parents are moving from the living room to the kitchen or resting in the bedroom. This allows you to understand the parents' daily rhythm and any unusual movements. Next, the home monitoring system uses sensors that monitor the parents' heart rate and body temperature to monitor their health in real time. For example, if the heart rate suddenly increases or the body temperature becomes abnormally high, it can immediately detect the abnormality. This allows you to constantly understand the parents' health condition and respond early if there is an abnormality. Furthermore, the home monitoring system constantly monitors the parents' movements and health condition, and immediately sends an alert if an abnormality occurs. For example, if the parents fall or if abnormalities are observed in their heart rate or body temperature, the AI ​​will automatically detect it and send an alert. This allows you to stay informed about the situation of elderly parents living far away and be prepared for any unforeseen circumstances. This system is for everyone with elderly parents, and because it allows you to check on the situation of your parents living far away at any time, you can live with peace of mind. In addition, by regularly monitoring, you can keep track of your parents' health and respond early if there is any abnormality. This can improve the quality of life for your parents and increase the sense of security for your family. In short, this home monitoring system allows you to keep track of your parents' situation at all times and respond quickly if any abnormality occurs.

[0029] The home monitoring system according to the embodiment comprises an operation confirmation unit, a health monitoring unit, and an abnormality detection unit. The operation confirmation unit confirms the movements of the parents within their home. The operation confirmation unit monitors the parents' movements in real time, for example, through sensors and cameras installed within the parents' home. The operation confirmation unit can, for example, confirm that the parents are moving from the living room to the kitchen or resting in the bedroom. The operation confirmation unit can, for example, understand the parents' daily rhythm and any abnormal movements. The health monitoring unit monitors the parents' heart rate and changes in body temperature. The health monitoring unit monitors the parents' health status in real time, for example, using sensors that monitor the parents' heart rate and body temperature. The health monitoring unit can, for example, immediately detect abnormalities if the heart rate suddenly increases or the body temperature becomes abnormally high. The health monitoring unit can, for example, constantly monitor the parents' health status and respond early if there is an abnormality. The abnormality detection unit detects abnormalities and sends an alert. The abnormality detection unit constantly monitors the parents' movements and health status and immediately sends an alert if an abnormality occurs. The anomaly detection unit automatically detects abnormalities such as a parent falling or abnormalities in heart rate or body temperature, and sends an alert. The anomaly detection unit can constantly monitor the parent's condition and prepare for any unforeseen circumstances. As a result, the home monitoring system can constantly monitor the parent's condition and respond quickly if an abnormality occurs.

[0030] The motion monitoring unit checks the movements of the parents within their home. For example, the unit monitors the parents' movements in real time through sensors and cameras installed within the home. Specifically, it utilizes multiple cameras and motion sensors placed in key areas such as the living room, kitchen, bedroom, and hallway. These devices detect the parents' movements with high precision and transmit video and motion data to a central management system. The management system analyzes this data in real time to understand the parents' current location and actions. For example, if a parent moves from the living room to the kitchen, the camera tracks their movement, and the motion sensor detects the direction and speed of their movement. This allows the system to understand the parents' daily routines and any unusual movements. Furthermore, the motion monitoring unit learns the parents' movement patterns and can detect movements that deviate from normal behavior. For example, if a parent does not wake up at the usual time or stays in the same place for a long period, it may be considered an abnormality. This allows the motion monitoring unit to understand the parents' daily routines and respond quickly if an abnormality occurs. To protect the parents' privacy, the motion monitoring unit encrypts video data and controls access, providing only necessary information to relevant parties. Furthermore, the motion monitoring unit can record the parent's movements and analyze long-term changes in behavioral patterns by referring to past data. This allows the motion monitoring unit to ensure the parent's safety and enable early detection and response to abnormalities.

[0031] The health monitoring unit monitors changes in the parent's heart rate and body temperature. For example, it uses sensors to monitor the parent's heart rate and body temperature, providing real-time health monitoring. Specifically, it uses wearable devices worn by the parent and sensors installed on beds and chairs. These devices continuously measure vital signs such as the parent's heart rate, body temperature, blood pressure, and respiratory rate, and transmit the data to a central management system. The management system analyzes this data in real time to understand the parent's health status. For example, if the heart rate suddenly increases or the body temperature becomes abnormally high, an abnormality can be detected immediately. If an abnormality is detected, the health monitoring unit sends an alert and notifies relevant parties. This allows for constant monitoring of the parent's health status and early intervention if an abnormality occurs. Furthermore, the health monitoring unit can record the parent's health data over the long term and analyze changes in health status by comparing it with past data. For example, based on past data, it can predict fluctuations in health status during specific seasons or time periods and take preventative measures. Furthermore, the Health Monitoring Department regularly prepares reports on the parents' health status and provides them to medical institutions and families. This allows the Health Monitoring Department to comprehensively manage the parents' health and enable early detection and response to any abnormalities.

[0032] The anomaly detection unit detects abnormalities and sends an alert. For example, the anomaly detection unit constantly monitors the parent's movements and health status, and immediately sends an alert if an abnormality occurs. Specifically, the anomaly detection unit analyzes data transmitted from the motion confirmation unit and health monitoring unit in real time to detect signs of abnormality. For example, if a parent falls, the camera and motion sensor of the motion confirmation unit detect the movement and notify the anomaly detection unit. The anomaly detection unit uses AI to analyze this data, and if it determines that there is a high probability of a fall, it immediately sends an alert. Similarly, it analyzes heart rate and body temperature data transmitted from the health monitoring unit, and sends an alert if abnormal fluctuations are detected. When sending an alert, the anomaly detection unit provides detailed information about the parent's current situation and the nature of the abnormality, and quickly notifies relevant parties. For example, if a parent falls, it notifies the location and time of the fall and the parent's current condition to encourage a quick response. This allows the anomaly detection unit to constantly understand the parent's situation and prepare for any unforeseen circumstances. Furthermore, the anomaly detection unit can record past anomaly data and analyze the frequency and patterns of anomalies to take preventative measures. For example, if anomalies frequently occur during specific times or in specific locations, it can identify the cause and implement countermeasures. In addition, the anomaly detection unit can continuously improve its anomaly detection accuracy by utilizing AI learning capabilities. As a result, the anomaly detection unit can ensure the safety of parents and support quick and appropriate responses.

[0033] The motion verification unit can monitor the parents' movements in real time through sensors and cameras installed in the parents' home. For example, the motion verification unit can monitor the parents' movements in real time through sensors and cameras installed in the parents' home. For example, the motion verification unit can check if the parents move from the living room to the kitchen or rest in the bedroom. For example, the motion verification unit can understand the parents' daily rhythm and any abnormal movements. By monitoring the parents' movements in real time, it is possible to understand the parents' daily rhythm and any abnormal movements. Some or all of the above processing in the motion verification unit may be performed using AI, for example, or without AI. For example, in order to monitor the parents' movements in real time, the motion verification unit can input data from sensors and cameras into a generating AI, which can then analyze the movements.

[0034] The health monitoring unit can monitor the parent's health status in real time using sensors that monitor the parent's heart rate and body temperature. For example, the health monitoring unit can monitor the parent's health status in real time using sensors that monitor the parent's heart rate and body temperature. For example, the health monitoring unit can immediately detect abnormalities if the heart rate suddenly increases or the body temperature becomes abnormally high. For example, the health monitoring unit can constantly monitor the parent's health status and respond early if an abnormality is found. This allows for early response if an abnormality is found by monitoring the parent's health status in real time. Some or all of the above processing in the health monitoring unit may be performed using AI, for example, or without AI. For example, the health monitoring unit can input data on the parent's heart rate and body temperature into a generating AI, which can then analyze the health status.

[0035] The anomaly detection unit constantly monitors the parent's movements and health status and can immediately send an alert if an anomaly occurs. For example, the anomaly detection unit constantly monitors the parent's movements and health status and immediately sends an alert if an anomaly occurs. For example, if the parent falls or if abnormalities are detected in their heart rate or body temperature, the AI ​​will automatically detect and send an alert. The anomaly detection unit can constantly monitor the parent's situation and prepare for any unforeseen circumstances. This allows for a quick response by immediately sending an alert when an anomaly occurs. Some or all of the above processing in the anomaly detection unit may be performed using AI, or not using AI. For example, the anomaly detection unit can input data on the parent's movements and health status into a generating AI, which can then detect anomalies.

[0036] The motion confirmation unit can check when a parent is moving from the living room to the kitchen or resting in the bedroom. For example, the motion confirmation unit can check when a parent is moving from the living room to the kitchen. For example, the motion confirmation unit can check when a parent is resting in the bedroom. For example, the motion confirmation unit can detect abnormal movements early by understanding the parent's daily rhythm. This allows for early detection of abnormal movements by understanding the parent's daily rhythm. Some or all of the above processing in the motion confirmation unit may be performed using AI, for example, or without AI. For example, the motion confirmation unit can input data from sensors and cameras into a generating AI to monitor the parent's movements in real time, and the generating AI can analyze the movements.

[0037] The health monitoring unit can immediately detect abnormalities if the heart rate increases rapidly or the body temperature becomes abnormally high. For example, the health monitoring unit can detect an abnormality if the heart rate increases rapidly. For example, the health monitoring unit can detect an abnormality if the body temperature becomes abnormally high. By detecting abnormalities in heart rate and body temperature early, the health monitoring unit can respond quickly. This enables a quick response by detecting abnormalities in heart rate and body temperature early. Some or all of the above processing in the health monitoring unit may be performed using AI, for example, or without AI. For example, the health monitoring unit can input parent's heart rate and body temperature data into a generating AI, which can then detect abnormalities.

[0038] The operation verification unit can analyze the parent's past operation patterns and set criteria for detecting abnormal movements. For example, the operation verification unit can set criteria for detecting abnormal movements based on operation patterns that the parent frequently performed in the past. For example, the operation verification unit can analyze the parent's past operation patterns and predict the likelihood of abnormal movements occurring during a specific time period. For example, the operation verification unit can optimize an algorithm for detecting abnormal movements based on the parent's past operation patterns. This enables early detection of anomalies by detecting abnormal movements based on past operation patterns. Some or all of the above processing in the operation verification unit may be performed using AI, for example, or without AI. For example, the operation verification unit can input the parent's past operation data into a generating AI, which can then perform anomaly detection.

[0039] The operation verification unit can apply different monitoring modes depending on the parent's daily rhythm during operation verification. For example, if the parent is resting at night, the operation verification unit can apply a low-frequency monitoring mode to avoid disturbing their sleep. For example, if the parent is active during the day, the operation verification unit can apply a high-frequency monitoring mode to perform detailed operation verification. For example, the operation verification unit can apply a mode that monitors only during specific time periods depending on the parent's daily rhythm. This makes it possible to monitor without disturbing the parent's life by applying a monitoring mode that matches the parent's daily rhythm. Some or all of the above processing in the operation verification unit may be performed using AI, for example, or without using AI. For example, the operation verification unit can input the parent's daily rhythm data into a generating AI, which can then apply the monitoring mode.

[0040] The motion verification unit can prioritize monitoring highly relevant movements by considering the parent's geographical location information during motion verification. For example, if the parent stays in a particular room for an extended period, the motion verification unit can prioritize monitoring movements in that room. For example, if the parent is out, the motion verification unit can prioritize monitoring movements at their destination. For example, if the parent exhibits abnormal movements in a particular location, the motion verification unit can prioritize monitoring movements in that location. This allows for the prioritization of highly relevant movements by monitoring movements while considering the parent's geographical location information. Some or all of the above processing in the motion verification unit may be performed using AI, for example, or without AI. For example, the motion verification unit can input the parent's geographical location information into a generating AI, which can then prioritize monitoring highly relevant movements.

[0041] The operation verification unit can analyze the parent's social media activity and monitor related movements during operation verification. For example, if the parent is engaging in a specific activity on social media, the operation verification unit can monitor movements related to that activity. For example, if the parent is making an unusual post on social media, the operation verification unit can monitor movements related to that post. For example, if the parent is participating in a specific event on social media, the operation verification unit can monitor movements related to that event. This allows for efficient monitoring of related movements by analyzing the parent's social media activity. Some or all of the above processing in the operation verification unit may be performed using AI, for example, or without AI. For example, the operation verification unit can input the parent's social media activity data into a generating AI, which can then monitor related movements.

[0042] The health monitoring unit can set criteria for detecting anomalies by referring to the parent's past health data during health monitoring. For example, the health monitoring unit can set criteria for detecting anomalies based on the parent's past health data. For example, the health monitoring unit can analyze the parent's past health data and predict the likelihood of an anomaly occurring during a specific time period. For example, the health monitoring unit can optimize an anomaly detection algorithm based on the parent's past health data. This makes it possible to detect anomalies early by setting criteria for anomaly detection based on past health data. Some or all of the above processes in the health monitoring unit may be performed using AI, for example, or without AI. For example, the health monitoring unit can input the parent's past health data into a generating AI, which can then detect anomalies.

[0043] The health monitoring unit can apply different monitoring modes depending on the parent's lifestyle during health monitoring. For example, if the parent is resting at night, the health monitoring unit can apply a low-frequency monitoring mode to avoid disturbing their sleep. For example, if the parent is active during the day, the health monitoring unit can apply a high-frequency monitoring mode to perform detailed health monitoring. For example, the health monitoring unit can apply a mode that monitors only during specific time periods, depending on the parent's lifestyle. This allows monitoring without disturbing the parent's life by applying a monitoring mode that is appropriate to the parent's lifestyle. Some or all of the above processing in the health monitoring unit may be performed using AI, for example, or without AI. For example, the health monitoring unit can input parent lifestyle data into a generating AI, which can then apply the monitoring mode.

[0044] The health monitoring unit can prioritize monitoring highly relevant health data by considering the parent's geographical location information during health monitoring. For example, if the parent is staying in a specific location for an extended period, the health monitoring unit can prioritize monitoring health data at that location. For example, if the parent is out, the health monitoring unit can prioritize monitoring health data at their destination. For example, if the parent shows abnormal health data at a specific location, the health monitoring unit can prioritize monitoring health data at that location. This allows for the prioritization of highly relevant health data by monitoring health data while considering the parent's geographical location information. Some or all of the above processing in the health monitoring unit may be performed using AI, for example, or without AI. For example, the health monitoring unit can input the parent's geographical location information into a generating AI, which can then prioritize monitoring highly relevant health data.

[0045] The health monitoring unit can analyze parents' social media activity and monitor relevant health data during health monitoring. For example, if a parent is engaging in a specific activity on social media, the health monitoring unit can monitor health data related to that activity. For example, if a parent is making an unusual post on social media, the health monitoring unit can monitor health data related to that post. For example, if a parent is participating in a specific event on social media, the health monitoring unit can monitor health data related to that event. This allows for efficient monitoring of relevant health data by analyzing parents' social media activity. Some or all of the above processing in the health monitoring unit may be performed using AI, for example, or without AI. For example, the health monitoring unit can input parents' social media activity data into a generating AI, which can then monitor the relevant health data.

[0046] The anomaly detection unit can improve the accuracy of anomaly detection by referring to the parent's past anomaly data when an anomaly is detected. For example, the anomaly detection unit can improve the accuracy of anomaly detection based on the parent's past anomaly data. For example, the anomaly detection unit can analyze the parent's past anomaly data and predict the likelihood of an anomaly occurring during a specific time period. For example, the anomaly detection unit can optimize the algorithm for anomaly detection based on the parent's past anomaly data. This improves the accuracy of anomaly detection based on past anomaly data, enabling early detection of anomalies. Some or all of the above-described processes in the anomaly detection unit may be performed using AI, for example, or without AI. For example, the anomaly detection unit can input the parent's past anomaly data into a generating AI, which can then perform anomaly detection.

[0047] The anomaly detection unit can apply different anomaly detection modes depending on the parent's daily rhythm when an anomaly is detected. For example, if the parent is resting at night, the anomaly detection unit can apply a low-frequency anomaly detection mode to avoid disturbing their sleep. For example, if the parent is active during the day, the anomaly detection unit can apply a high-frequency anomaly detection mode to perform detailed anomaly detection. For example, the anomaly detection unit can apply a mode that performs anomaly detection only during specific time periods, depending on the parent's daily rhythm. This allows for anomaly detection without disturbing the parent's life by applying an anomaly detection mode that matches the parent's daily rhythm. Some or all of the above processing in the anomaly detection unit may be performed using AI, for example, or without AI. For example, the anomaly detection unit can input the parent's daily rhythm data into a generating AI, which can then apply the anomaly detection mode.

[0048] The anomaly detection unit can prioritize detecting highly relevant anomalies by considering the parent's geographical location information when an anomaly is detected. For example, if the parent is staying in a specific location for a long period of time, the anomaly detection unit will prioritize detecting anomalies at that location. For example, if the parent is out, the anomaly detection unit will prioritize detecting anomalies at their destination. For example, if the parent is exhibiting unusual behavior in a specific location, the anomaly detection unit will prioritize detecting anomalies at that location. By detecting anomalies while considering the parent's geographical location information, highly relevant anomalies can be prioritized. Some or all of the above processing in the anomaly detection unit may be performed using AI, for example, or without AI. For example, the anomaly detection unit can input the parent's geographical location information into a generating AI, which can then prioritize detecting highly relevant anomalies.

[0049] The anomaly detection unit can analyze the parent's social media activity and detect related anomalies when an anomaly is detected. For example, if the parent is engaging in a specific activity on social media, the anomaly detection unit can detect anomalies related to that activity. For example, if the parent is making an unusual post on social media, the anomaly detection unit can detect anomalies related to that post. For example, if the parent is participating in a specific event on social media, the anomaly detection unit can detect anomalies related to that event. In this way, related anomalies can be efficiently detected by analyzing the parent's social media activity. Some or all of the above processing in the anomaly detection unit may be performed using AI, for example, or without AI. For example, the anomaly detection unit can input the parent's social media activity data into a generating AI, which can then detect related anomalies.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The activity monitoring unit can prioritize monitoring specific actions based on the parent's hobbies and interests when checking their activities. For example, if the parent enjoys gardening, their movements in the garden can be prioritized. If the parent enjoys reading, their movements in the study can be monitored intensively. Furthermore, if the parent enjoys cooking, their movements in the kitchen can be monitored in detail. This allows for activity monitoring tailored to the parent's hobbies and interests, thereby improving the parent's quality of life.

[0052] The health monitoring unit can analyze a parent's diet and identify factors that influence their health when monitoring their health status. For example, if a parent frequently consumes high-calorie meals, the health monitoring unit can take this into account when monitoring their health. Similarly, if a parent is deficient in a particular nutrient, the unit can monitor their health while considering the impact of this deficiency. Furthermore, it can verify whether a parent adheres to specific dietary restrictions and reflect the effects of this in their health status. This enables health monitoring based on the parent's diet, leading to more accurate health management.

[0053] The anomaly detection unit can improve the accuracy of anomaly detection by referring to the parent's past medical history when detecting an anomaly in the parent. For example, if the parent has a history of heart disease, the unit can detect abnormal heart rate by taking that history into account. Also, if the parent has a history of fractures, the unit can identify factors that increase the risk of falls based on that history. Furthermore, if the parent has a history of a specific illness, the unit can prioritize monitoring for anomalies related to that illness. This enables anomaly detection based on the parent's medical history, allowing for faster and more appropriate responses.

[0054] The operation verification unit can apply different monitoring modes depending on the parent's activity level when checking the parent's behavior. For example, if the parent is normally very active, a high-frequency monitoring mode can be applied to perform detailed operation checks. Conversely, if the parent is relatively quiet, a low-frequency monitoring mode can be applied to perform only the minimum necessary checks. Furthermore, if the parent's activity level fluctuates, the monitoring mode can be automatically adjusted accordingly. This enables flexible operation checks that are tailored to the parent's activity level, allowing for monitoring without disrupting the parent's life.

[0055] The health monitoring unit can analyze parents' sleep patterns and identify factors that affect their health when monitoring their health status. For example, if a parent wakes up frequently at night, the unit can take this into account when monitoring their health. Similarly, if a parent sleeps for long periods, the unit can take this into account when monitoring their health. Furthermore, if the quality of a parent's sleep is poor, this can be reflected in their health status. This enables health monitoring based on parents' sleep patterns, leading to more accurate health management.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The operation verification unit checks the movements of the parents within their home. The operation verification unit monitors the parents' movements in real time, for example, through sensors and cameras installed within the parents' home. The operation verification unit can confirm whether the parents are moving from the living room to the kitchen or resting in their bedroom. It can also understand the parents' daily routine and any unusual movements. Step 2: The health monitoring unit monitors the parent's heart rate and body temperature changes. The health monitoring unit uses sensors to monitor the parent's heart rate and body temperature, for example, to monitor their health status in real time. If the heart rate suddenly increases or the body temperature becomes abnormally high, the abnormality can be detected immediately. The health status of the parent is constantly monitored, and any abnormalities can be addressed early. Step 3: The anomaly detection unit detects an anomaly and sends an alert. For example, the anomaly detection unit constantly monitors the parent's movements and health status, and immediately sends an alert if an anomaly occurs. If the parent falls or if abnormalities are detected in their heart rate or body temperature, the AI ​​automatically detects this and sends an alert. This allows the system to constantly monitor the parent's situation and prepare for any unforeseen circumstances.

[0058] (Example of form 2) The home monitoring system according to an embodiment of the present invention is a system that allows you to check on the condition of elderly parents at any time when they live far away. This home monitoring system can check the movements, heart rate, and changes in body temperature of the parents within their home, and if any problem occurs, the AI ​​will automatically detect it and send an alert. For example, the home monitoring system monitors how the parents are moving in real time through sensors and cameras installed in the parents' home. For example, it can check if the parents are moving from the living room to the kitchen or resting in the bedroom. This allows you to understand the parents' daily rhythm and any unusual movements. Next, the home monitoring system uses sensors that monitor the parents' heart rate and body temperature to monitor their health in real time. For example, if the heart rate suddenly increases or the body temperature becomes abnormally high, it can immediately detect the abnormality. This allows you to constantly understand the parents' health condition and respond early if there is an abnormality. Furthermore, the home monitoring system constantly monitors the parents' movements and health condition, and immediately sends an alert if an abnormality occurs. For example, if the parents fall or if abnormalities are observed in their heart rate or body temperature, the AI ​​will automatically detect it and send an alert. This allows you to stay informed about the situation of elderly parents living far away and be prepared for any unforeseen circumstances. This system is for everyone with elderly parents, and because it allows you to check on the situation of your parents living far away at any time, you can live with peace of mind. In addition, by regularly monitoring, you can keep track of your parents' health and respond early if there is any abnormality. This can improve the quality of life for your parents and increase the sense of security for your family. In short, this home monitoring system allows you to keep track of your parents' situation at all times and respond quickly if any abnormality occurs.

[0059] The home monitoring system according to the embodiment comprises an operation confirmation unit, a health monitoring unit, and an abnormality detection unit. The operation confirmation unit confirms the movements of the parents within their home. The operation confirmation unit monitors the parents' movements in real time, for example, through sensors and cameras installed within the parents' home. The operation confirmation unit can, for example, confirm that the parents are moving from the living room to the kitchen or resting in the bedroom. The operation confirmation unit can, for example, understand the parents' daily rhythm and any abnormal movements. The health monitoring unit monitors the parents' heart rate and changes in body temperature. The health monitoring unit monitors the parents' health status in real time, for example, using sensors that monitor the parents' heart rate and body temperature. The health monitoring unit can, for example, immediately detect abnormalities if the heart rate suddenly increases or the body temperature becomes abnormally high. The health monitoring unit can, for example, constantly monitor the parents' health status and respond early if there is an abnormality. The abnormality detection unit detects abnormalities and sends an alert. The abnormality detection unit constantly monitors the parents' movements and health status and immediately sends an alert if an abnormality occurs. The anomaly detection unit automatically detects abnormalities such as a parent falling or abnormalities in heart rate or body temperature, and sends an alert. The anomaly detection unit can constantly monitor the parent's condition and prepare for any unforeseen circumstances. As a result, the home monitoring system can constantly monitor the parent's condition and respond quickly if an abnormality occurs.

[0060] The motion monitoring unit checks the movements of the parents within their home. For example, the unit monitors the parents' movements in real time through sensors and cameras installed within the home. Specifically, it utilizes multiple cameras and motion sensors placed in key areas such as the living room, kitchen, bedroom, and hallway. These devices detect the parents' movements with high precision and transmit video and motion data to a central management system. The management system analyzes this data in real time to understand the parents' current location and actions. For example, if a parent moves from the living room to the kitchen, the camera tracks their movement, and the motion sensor detects the direction and speed of their movement. This allows the system to understand the parents' daily routines and any unusual movements. Furthermore, the motion monitoring unit learns the parents' movement patterns and can detect movements that deviate from normal behavior. For example, if a parent does not wake up at the usual time or stays in the same place for a long period, it may be considered an abnormality. This allows the motion monitoring unit to understand the parents' daily routines and respond quickly if an abnormality occurs. To protect the parents' privacy, the motion monitoring unit encrypts video data and controls access, providing only necessary information to relevant parties. Furthermore, the motion monitoring unit can record the parent's movements and analyze long-term changes in behavioral patterns by referring to past data. This allows the motion monitoring unit to ensure the parent's safety and enable early detection and response to abnormalities.

[0061] The health monitoring unit monitors changes in the parent's heart rate and body temperature. For example, it uses sensors to monitor the parent's heart rate and body temperature, providing real-time health monitoring. Specifically, it uses wearable devices worn by the parent and sensors installed on beds and chairs. These devices continuously measure vital signs such as the parent's heart rate, body temperature, blood pressure, and respiratory rate, and transmit the data to a central management system. The management system analyzes this data in real time to understand the parent's health status. For example, if the heart rate suddenly increases or the body temperature becomes abnormally high, an abnormality can be detected immediately. If an abnormality is detected, the health monitoring unit sends an alert and notifies relevant parties. This allows for constant monitoring of the parent's health status and early intervention if an abnormality occurs. Furthermore, the health monitoring unit can record the parent's health data over the long term and analyze changes in health status by comparing it with past data. For example, based on past data, it can predict fluctuations in health status during specific seasons or time periods and take preventative measures. Furthermore, the Health Monitoring Department regularly prepares reports on the parents' health status and provides them to medical institutions and families. This allows the Health Monitoring Department to comprehensively manage the parents' health and enable early detection and response to any abnormalities.

[0062] The anomaly detection unit detects abnormalities and sends an alert. For example, the anomaly detection unit constantly monitors the parent's movements and health status, and immediately sends an alert if an abnormality occurs. Specifically, the anomaly detection unit analyzes data transmitted from the motion confirmation unit and health monitoring unit in real time to detect signs of abnormality. For example, if a parent falls, the camera and motion sensor of the motion confirmation unit detect the movement and notify the anomaly detection unit. The anomaly detection unit uses AI to analyze this data, and if it determines that there is a high probability of a fall, it immediately sends an alert. Similarly, it analyzes heart rate and body temperature data transmitted from the health monitoring unit, and sends an alert if abnormal fluctuations are detected. When sending an alert, the anomaly detection unit provides detailed information about the parent's current situation and the nature of the abnormality, and quickly notifies relevant parties. For example, if a parent falls, it notifies the location and time of the fall and the parent's current condition to encourage a quick response. This allows the anomaly detection unit to constantly understand the parent's situation and prepare for any unforeseen circumstances. Furthermore, the anomaly detection unit can record past anomaly data and analyze the frequency and patterns of anomalies to take preventative measures. For example, if anomalies frequently occur during specific times or in specific locations, it can identify the cause and implement countermeasures. In addition, the anomaly detection unit can continuously improve its anomaly detection accuracy by utilizing AI learning capabilities. As a result, the anomaly detection unit can ensure the safety of parents and support quick and appropriate responses.

[0063] The motion verification unit can monitor the parents' movements in real time through sensors and cameras installed in the parents' home. For example, the motion verification unit can monitor the parents' movements in real time through sensors and cameras installed in the parents' home. For example, the motion verification unit can check if the parents move from the living room to the kitchen or rest in the bedroom. For example, the motion verification unit can understand the parents' daily rhythm and any abnormal movements. By monitoring the parents' movements in real time, it is possible to understand the parents' daily rhythm and any abnormal movements. Some or all of the above processing in the motion verification unit may be performed using AI, for example, or without AI. For example, in order to monitor the parents' movements in real time, the motion verification unit can input data from sensors and cameras into a generating AI, which can then analyze the movements.

[0064] The health monitoring unit can monitor the parent's health status in real time using sensors that monitor the parent's heart rate and body temperature. For example, the health monitoring unit can monitor the parent's health status in real time using sensors that monitor the parent's heart rate and body temperature. For example, the health monitoring unit can immediately detect abnormalities if the heart rate suddenly increases or the body temperature becomes abnormally high. For example, the health monitoring unit can constantly monitor the parent's health status and respond early if an abnormality is found. This allows for early response if an abnormality is found by monitoring the parent's health status in real time. Some or all of the above processing in the health monitoring unit may be performed using AI, for example, or without AI. For example, the health monitoring unit can input data on the parent's heart rate and body temperature into a generating AI, which can then analyze the health status.

[0065] The anomaly detection unit constantly monitors the parent's movements and health status and can immediately send an alert if an anomaly occurs. For example, the anomaly detection unit constantly monitors the parent's movements and health status and immediately sends an alert if an anomaly occurs. For example, if the parent falls or if abnormalities are detected in their heart rate or body temperature, the AI ​​will automatically detect and send an alert. The anomaly detection unit can constantly monitor the parent's situation and prepare for any unforeseen circumstances. This allows for a quick response by immediately sending an alert when an anomaly occurs. Some or all of the above processing in the anomaly detection unit may be performed using AI, or not using AI. For example, the anomaly detection unit can input data on the parent's movements and health status into a generating AI, which can then detect anomalies.

[0066] The motion confirmation unit can check when a parent is moving from the living room to the kitchen or resting in the bedroom. For example, the motion confirmation unit can check when a parent is moving from the living room to the kitchen. For example, the motion confirmation unit can check when a parent is resting in the bedroom. For example, the motion confirmation unit can detect abnormal movements early by understanding the parent's daily rhythm. This allows for early detection of abnormal movements by understanding the parent's daily rhythm. Some or all of the above processing in the motion confirmation unit may be performed using AI, for example, or without AI. For example, the motion confirmation unit can input data from sensors and cameras into a generating AI to monitor the parent's movements in real time, and the generating AI can analyze the movements.

[0067] The health monitoring unit can immediately detect abnormalities if the heart rate increases rapidly or the body temperature becomes abnormally high. For example, the health monitoring unit can detect an abnormality if the heart rate increases rapidly. For example, the health monitoring unit can detect an abnormality if the body temperature becomes abnormally high. By detecting abnormalities in heart rate and body temperature early, the health monitoring unit can respond quickly. This enables a quick response by detecting abnormalities in heart rate and body temperature early. Some or all of the above processing in the health monitoring unit may be performed using AI, for example, or without AI. For example, the health monitoring unit can input parent's heart rate and body temperature data into a generating AI, which can then detect abnormalities.

[0068] The behavioral monitoring unit can estimate the parent's emotions and adjust the frequency of behavioral monitoring based on the estimated emotions. For example, if the parent is stressed, the behavioral monitoring unit can reduce the frequency of behavioral monitoring to respect the parent's privacy. For example, if the parent is relaxed, the behavioral monitoring unit can increase the frequency of behavioral monitoring to perform more detailed monitoring. For example, if the parent is anxious, the behavioral monitoring unit can appropriately adjust the frequency of behavioral monitoring to provide a sense of security. In this way, by adjusting the frequency of behavioral monitoring according to the parent's emotions, a sense of security can be provided while respecting the parent's privacy. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the behavioral monitoring unit may be performed using AI, for example, or without AI. For example, the behavioral monitoring unit can input the parent's emotion data into the generative AI, which can then estimate the emotions.

[0069] The operation verification unit can analyze the parent's past operation patterns and set criteria for detecting abnormal movements. For example, the operation verification unit can set criteria for detecting abnormal movements based on operation patterns that the parent frequently performed in the past. For example, the operation verification unit can analyze the parent's past operation patterns and predict the likelihood of abnormal movements occurring during a specific time period. For example, the operation verification unit can optimize an algorithm for detecting abnormal movements based on the parent's past operation patterns. This enables early detection of anomalies by detecting abnormal movements based on past operation patterns. Some or all of the above processing in the operation verification unit may be performed using AI, for example, or without AI. For example, the operation verification unit can input the parent's past operation data into a generating AI, which can then perform anomaly detection.

[0070] The operation verification unit can apply different monitoring modes depending on the parent's daily rhythm during operation verification. For example, if the parent is resting at night, the operation verification unit can apply a low-frequency monitoring mode to avoid disturbing their sleep. For example, if the parent is active during the day, the operation verification unit can apply a high-frequency monitoring mode to perform detailed operation verification. For example, the operation verification unit can apply a mode that monitors only during specific time periods depending on the parent's daily rhythm. This makes it possible to monitor without disturbing the parent's life by applying a monitoring mode that matches the parent's daily rhythm. Some or all of the above processing in the operation verification unit may be performed using AI, for example, or without using AI. For example, the operation verification unit can input the parent's daily rhythm data into a generating AI, which can then apply the monitoring mode.

[0071] The behavioral verification unit can estimate the parent's emotions and determine the priority of behavioral verification based on the estimated parent's emotions. For example, if the parent is feeling anxious, the behavioral verification unit will set a high priority for behavioral verification and perform the verification quickly. For example, if the parent is relaxed, the behavioral verification unit will set a low priority for behavioral verification and perform the minimum necessary verification. For example, if the parent is feeling stressed, the behavioral verification unit will set a medium priority for behavioral verification and perform an appropriate verification. This allows for quick and appropriate verification by determining the priority of behavioral verification according to the parent's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the behavioral verification unit may be performed using AI, for example, or without AI. For example, the behavioral verification unit can input the parent's emotion data into the generative AI, which can then estimate the emotion.

[0072] The motion verification unit can prioritize monitoring highly relevant movements by considering the parent's geographical location information during motion verification. For example, if the parent stays in a particular room for an extended period, the motion verification unit can prioritize monitoring movements in that room. For example, if the parent is out, the motion verification unit can prioritize monitoring movements at their destination. For example, if the parent exhibits abnormal movements in a particular location, the motion verification unit can prioritize monitoring movements in that location. This allows for the prioritization of highly relevant movements by monitoring movements while considering the parent's geographical location information. Some or all of the above processing in the motion verification unit may be performed using AI, for example, or without AI. For example, the motion verification unit can input the parent's geographical location information into a generating AI, which can then prioritize monitoring highly relevant movements.

[0073] The operation verification unit can analyze the parent's social media activity and monitor related movements during operation verification. For example, if the parent is engaging in a specific activity on social media, the operation verification unit can monitor movements related to that activity. For example, if the parent is making an unusual post on social media, the operation verification unit can monitor movements related to that post. For example, if the parent is participating in a specific event on social media, the operation verification unit can monitor movements related to that event. This allows for efficient monitoring of related movements by analyzing the parent's social media activity. Some or all of the above processing in the operation verification unit may be performed using AI, for example, or without AI. For example, the operation verification unit can input the parent's social media activity data into a generating AI, which can then monitor related movements.

[0074] The health monitoring unit can estimate the parent's emotions and adjust the frequency of health monitoring based on the estimated emotions. For example, if the parent is stressed, the health monitoring unit can reduce the frequency of health monitoring to respect the parent's privacy. For example, if the parent is relaxed, the health monitoring unit can increase the frequency of health monitoring and perform more detailed monitoring. For example, if the parent is anxious, the health monitoring unit can appropriately adjust the frequency of health monitoring to provide a sense of security. In this way, by adjusting the frequency of health monitoring according to the parent's emotions, a sense of security can be provided while respecting the parent's privacy. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the health monitoring unit may be performed using AI, for example, or without AI. For example, the health monitoring unit can input parent emotion data into a generative AI, and the generative AI can perform emotion estimation.

[0075] The health monitoring unit can set criteria for detecting anomalies by referring to the parent's past health data during health monitoring. For example, the health monitoring unit can set criteria for detecting anomalies based on the parent's past health data. For example, the health monitoring unit can analyze the parent's past health data and predict the likelihood of an anomaly occurring during a specific time period. For example, the health monitoring unit can optimize an anomaly detection algorithm based on the parent's past health data. This makes it possible to detect anomalies early by setting criteria for anomaly detection based on past health data. Some or all of the above processes in the health monitoring unit may be performed using AI, for example, or without AI. For example, the health monitoring unit can input the parent's past health data into a generating AI, which can then detect anomalies.

[0076] The health monitoring unit can apply different monitoring modes depending on the parent's lifestyle during health monitoring. For example, if the parent is resting at night, the health monitoring unit can apply a low-frequency monitoring mode to avoid disturbing their sleep. For example, if the parent is active during the day, the health monitoring unit can apply a high-frequency monitoring mode to perform detailed health monitoring. For example, the health monitoring unit can apply a mode that monitors only during specific time periods, depending on the parent's lifestyle. This allows monitoring without disturbing the parent's life by applying a monitoring mode that is appropriate to the parent's lifestyle. Some or all of the above processing in the health monitoring unit may be performed using AI, for example, or without AI. For example, the health monitoring unit can input parent lifestyle data into a generating AI, which can then apply the monitoring mode.

[0077] The health monitoring unit can estimate the parent's emotions and determine the priority of health monitoring based on the estimated parent's emotions. For example, if the parent is feeling anxious, the health monitoring unit will set a high priority for health monitoring and perform a quick check. For example, if the parent is relaxed, the health monitoring unit will set a low priority for health monitoring and perform only the minimum necessary check. For example, if the parent is stressed, the health monitoring unit will set a medium priority for health monitoring and perform a moderate check. This allows for quick and appropriate checks by determining the priority of health monitoring according to the parent'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 health monitoring unit may be performed using AI, for example, or not using AI. For example, the health monitoring unit can input parent emotion data into a generative AI, which can then estimate the emotions.

[0078] The health monitoring unit can prioritize monitoring highly relevant health data by considering the parent's geographical location information during health monitoring. For example, if the parent is staying in a specific location for an extended period, the health monitoring unit can prioritize monitoring health data at that location. For example, if the parent is out, the health monitoring unit can prioritize monitoring health data at their destination. For example, if the parent shows abnormal health data at a specific location, the health monitoring unit can prioritize monitoring health data at that location. This allows for the prioritization of highly relevant health data by monitoring health data while considering the parent's geographical location information. Some or all of the above processing in the health monitoring unit may be performed using AI, for example, or without AI. For example, the health monitoring unit can input the parent's geographical location information into a generating AI, which can then prioritize monitoring highly relevant health data.

[0079] The health monitoring unit can analyze parents' social media activity and monitor relevant health data during health monitoring. For example, if a parent is engaging in a specific activity on social media, the health monitoring unit can monitor health data related to that activity. For example, if a parent is making an unusual post on social media, the health monitoring unit can monitor health data related to that post. For example, if a parent is participating in a specific event on social media, the health monitoring unit can monitor health data related to that event. This allows for efficient monitoring of relevant health data by analyzing parents' social media activity. Some or all of the above processing in the health monitoring unit may be performed using AI, for example, or without AI. For example, the health monitoring unit can input parents' social media activity data into a generating AI, which can then monitor the relevant health data.

[0080] The anomaly detection unit can estimate the parent's emotions and adjust the anomaly detection criteria based on the estimated parent's emotions. For example, if the parent is stressed, the anomaly detection unit can relax the anomaly detection criteria to prevent excessive alerts. For example, if the parent is relaxed, the anomaly detection unit can tighten the anomaly detection criteria to perform detailed monitoring. For example, if the parent is anxious, the anomaly detection unit can appropriately adjust the anomaly detection criteria to provide a sense of security. In this way, by adjusting the anomaly detection criteria according to the parent's emotions, it is possible to provide a sense of security while preventing excessive alerts. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the anomaly detection unit may be performed using AI, for example, or without AI. For example, the anomaly detection unit can input parent emotion data into a generative AI, which can then estimate the emotions.

[0081] The anomaly detection unit can improve the accuracy of anomaly detection by referring to the parent's past anomaly data when an anomaly is detected. For example, the anomaly detection unit can improve the accuracy of anomaly detection based on the parent's past anomaly data. For example, the anomaly detection unit can analyze the parent's past anomaly data and predict the likelihood of an anomaly occurring during a specific time period. For example, the anomaly detection unit can optimize the algorithm for anomaly detection based on the parent's past anomaly data. This improves the accuracy of anomaly detection based on past anomaly data, enabling early detection of anomalies. Some or all of the above-described processes in the anomaly detection unit may be performed using AI, for example, or without AI. For example, the anomaly detection unit can input the parent's past anomaly data into a generating AI, which can then perform anomaly detection.

[0082] The anomaly detection unit can apply different anomaly detection modes depending on the parent's daily rhythm when an anomaly is detected. For example, if the parent is resting at night, the anomaly detection unit can apply a low-frequency anomaly detection mode to avoid disturbing their sleep. For example, if the parent is active during the day, the anomaly detection unit can apply a high-frequency anomaly detection mode to perform detailed anomaly detection. For example, the anomaly detection unit can apply a mode that performs anomaly detection only during specific time periods, depending on the parent's daily rhythm. This allows for anomaly detection without disturbing the parent's life by applying an anomaly detection mode that matches the parent's daily rhythm. Some or all of the above processing in the anomaly detection unit may be performed using AI, for example, or without AI. For example, the anomaly detection unit can input the parent's daily rhythm data into a generating AI, which can then apply the anomaly detection mode.

[0083] The anomaly detection unit can estimate the parent's emotions and determine the priority of anomaly detection based on the estimated parent's emotions. For example, if the parent is feeling anxious, the anomaly detection unit will set the priority of anomaly detection to high and perform a quick check. For example, if the parent is relaxed, the anomaly detection unit will set the priority of anomaly detection to low and perform the minimum necessary check. For example, if the parent is feeling stressed, the anomaly detection unit will set the priority of anomaly detection to medium and perform a moderate check. This allows for quick and appropriate checks by determining the priority of anomaly detection according to the parent's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the anomaly detection unit may be performed using AI, for example, or without AI. For example, the anomaly detection unit can input the parent's emotion data into a generative AI, which can then estimate the emotion.

[0084] The anomaly detection unit can prioritize detecting highly relevant anomalies by considering the parent's geographical location information when an anomaly is detected. For example, if the parent is staying in a specific location for a long period of time, the anomaly detection unit will prioritize detecting anomalies at that location. For example, if the parent is out, the anomaly detection unit will prioritize detecting anomalies at their destination. For example, if the parent is exhibiting unusual behavior in a specific location, the anomaly detection unit will prioritize detecting anomalies at that location. By detecting anomalies while considering the parent's geographical location information, highly relevant anomalies can be prioritized. Some or all of the above processing in the anomaly detection unit may be performed using AI, for example, or without AI. For example, the anomaly detection unit can input the parent's geographical location information into a generating AI, which can then prioritize detecting highly relevant anomalies.

[0085] The anomaly detection unit can analyze the parent's social media activity and detect related anomalies when an anomaly is detected. For example, if the parent is engaging in a specific activity on social media, the anomaly detection unit can detect anomalies related to that activity. For example, if the parent is making an unusual post on social media, the anomaly detection unit can detect anomalies related to that post. For example, if the parent is participating in a specific event on social media, the anomaly detection unit can detect anomalies related to that event. In this way, related anomalies can be efficiently detected by analyzing the parent's social media activity. Some or all of the above processing in the anomaly detection unit may be performed using AI, for example, or without AI. For example, the anomaly detection unit can input the parent's social media activity data into a generating AI, which can then detect related anomalies.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The activity monitoring unit can prioritize monitoring specific actions based on the parent's hobbies and interests when checking their activities. For example, if the parent enjoys gardening, their movements in the garden can be prioritized. If the parent enjoys reading, their movements in the study can be monitored intensively. Furthermore, if the parent enjoys cooking, their movements in the kitchen can be monitored in detail. This allows for activity monitoring tailored to the parent's hobbies and interests, thereby improving the parent's quality of life.

[0088] The health monitoring unit can analyze a parent's diet and identify factors that influence their health when monitoring their health status. For example, if a parent frequently consumes high-calorie meals, the health monitoring unit can take this into account when monitoring their health. Similarly, if a parent is deficient in a particular nutrient, the unit can monitor their health while considering the impact of this deficiency. Furthermore, it can verify whether a parent adheres to specific dietary restrictions and reflect the effects of this in their health status. This enables health monitoring based on the parent's diet, leading to more accurate health management.

[0089] The anomaly detection unit can improve the accuracy of anomaly detection by referring to the parent's past medical history when detecting an anomaly in the parent. For example, if the parent has a history of heart disease, the unit can detect abnormal heart rate by taking that history into account. Also, if the parent has a history of fractures, the unit can identify factors that increase the risk of falls based on that history. Furthermore, if the parent has a history of a specific illness, the unit can prioritize monitoring for anomalies related to that illness. This enables anomaly detection based on the parent's medical history, allowing for faster and more appropriate responses.

[0090] The operation verification unit can apply different monitoring modes depending on the parent's activity level when checking the parent's behavior. For example, if the parent is normally very active, a high-frequency monitoring mode can be applied to perform detailed operation checks. Conversely, if the parent is relatively quiet, a low-frequency monitoring mode can be applied to perform only the minimum necessary checks. Furthermore, if the parent's activity level fluctuates, the monitoring mode can be automatically adjusted accordingly. This enables flexible operation checks that are tailored to the parent's activity level, allowing for monitoring without disrupting the parent's life.

[0091] The health monitoring unit can analyze parents' sleep patterns and identify factors that affect their health when monitoring their health status. For example, if a parent wakes up frequently at night, the unit can take this into account when monitoring their health. Similarly, if a parent sleeps for long periods, the unit can take this into account when monitoring their health. Furthermore, if the quality of a parent's sleep is poor, this can be reflected in their health status. This enables health monitoring based on parents' sleep patterns, leading to more accurate health management.

[0092] The behavioral monitoring unit can estimate the parent's emotions and adjust the timing of behavioral monitoring based on those emotions. For example, if the parent is stressed, the timing of behavioral monitoring can be delayed to respect the parent's privacy. Conversely, if the parent is relaxed, the timing of behavioral monitoring can be advanced to allow for more detailed monitoring. Furthermore, if the parent is anxious, the timing of behavioral monitoring can be appropriately adjusted to provide a sense of security. In this way, by adjusting the timing of behavioral monitoring according to the parent's emotions, a sense of security can be provided while respecting the parent's privacy.

[0093] The health monitoring unit can estimate the parent's emotions and adjust the health monitoring method based on the estimated emotions. For example, if the parent is stressed, the health monitoring method can be eased and the parent's privacy respected. Conversely, if the parent is relaxed, the health monitoring method can be strengthened and more detailed monitoring can be performed. Furthermore, if the parent is anxious, the health monitoring method can be appropriately adjusted to provide a sense of security. In this way, by adjusting the health monitoring method according to the parent's emotions, a sense of security can be provided while respecting the parent's privacy.

[0094] The anomaly detection unit can estimate the parent's emotions and adjust the frequency of anomaly detection based on those emotions. For example, if the parent is stressed, the frequency of anomaly detection is reduced to prevent excessive alerts. Conversely, if the parent is relaxed, the frequency of anomaly detection is increased to allow for more detailed monitoring. Furthermore, if the parent is anxious, the frequency of anomaly detection can be appropriately adjusted to provide a sense of security. In this way, by adjusting the frequency of anomaly detection according to the parent's emotions, it is possible to prevent excessive alerts while providing a sense of security.

[0095] The monitoring unit can estimate the parent's emotions and adjust the scope of monitoring based on those emotions. For example, if the parent is stressed, the monitoring scope can be narrowed to respect the parent's privacy. If the parent is relaxed, the monitoring scope can be widened to allow for more detailed monitoring. Furthermore, if the parent is anxious, the monitoring scope can be appropriately adjusted to provide a sense of security. In this way, by adjusting the monitoring scope according to the parent's emotions, it is possible to provide a sense of security while respecting the parent's privacy.

[0096] The health monitoring unit can estimate the parent's emotions and determine the priority of health monitoring based on those emotions. For example, if the parent is feeling anxious, the health monitoring priority will be set high and a quick check will be performed. If the parent is relaxed, the health monitoring priority will be set low and only the bare minimum check will be performed. Furthermore, if the parent is feeling stressed, the health monitoring priority will be set to a medium level and an appropriate check will be performed. In this way, by determining the priority of health monitoring according to the parent's emotions, quick and appropriate checks can be performed.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The operation verification unit checks the movements of the parents within their home. The operation verification unit monitors the parents' movements in real time, for example, through sensors and cameras installed within the parents' home. The operation verification unit can confirm whether the parents are moving from the living room to the kitchen or resting in their bedroom. It can also understand the parents' daily routine and any unusual movements. Step 2: The health monitoring unit monitors the parent's heart rate and body temperature changes. The health monitoring unit uses sensors to monitor the parent's heart rate and body temperature, for example, to monitor their health status in real time. If the heart rate suddenly increases or the body temperature becomes abnormally high, the abnormality can be detected immediately. The health status of the parent is constantly monitored, and any abnormalities can be addressed early. Step 3: The anomaly detection unit detects an anomaly and sends an alert. For example, the anomaly detection unit constantly monitors the parent's movements and health status, and immediately sends an alert if an anomaly occurs. If the parent falls or if abnormalities are detected in their heart rate or body temperature, the AI ​​automatically detects this and sends an alert. This allows the system to constantly monitor the parent's situation and prepare for any unforeseen circumstances.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] Each of the multiple elements described above, including the operation verification unit, health monitoring unit, and anomaly detection unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the operation verification unit uses the camera 42 and sensors of the smart device 14 to monitor the parent's movements in real time, and the control unit 46A understands the parent's daily rhythm and any abnormal movements. The health monitoring unit uses the sensors of the smart device 14 to monitor the parent's heart rate and body temperature, and the control unit 46A detects any abnormalities. The anomaly detection unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and constantly monitors the parent's movements and health status, sending an alert immediately if an abnormality occurs. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] Each of the multiple elements described above, including the operation confirmation unit, health monitoring unit, and anomaly detection unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the operation confirmation unit uses the camera 42 and sensors of the smart glasses 214 to monitor the parent's movements in real time, and the control unit 46A understands the parent's daily rhythm and any abnormal movements. The health monitoring unit uses the sensors of the smart glasses 214 to monitor the parent's heart rate and body temperature, and the control unit 46A detects any abnormalities. The anomaly detection unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and constantly monitors the parent's movements and health status, sending an alert immediately if an abnormality occurs. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the operation confirmation unit, health monitoring unit, and anomaly detection unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the operation confirmation unit uses the camera 42 and sensors of the headset terminal 314 to monitor the parent's movements in real time, and the control unit 46A grasps the parent's daily rhythm and any abnormal movements. The health monitoring unit uses the sensors of the headset terminal 314 to monitor the parent's heart rate and body temperature, and the control unit 46A detects any abnormalities. The anomaly detection unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and constantly monitors the parent's movements and health status, and immediately sends an alert if an abnormality occurs. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The 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.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS 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).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] Each of the multiple elements described above, including the operation verification unit, health monitoring unit, and anomaly detection unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the operation verification unit uses the camera 42 and sensors of the robot 414 to monitor the parent's movements in real time, and the control unit 46A understands the parent's daily rhythm and any abnormal movements. The health monitoring unit uses the sensors of the robot 414 to monitor the parent's heart rate and body temperature, and the control unit 46A detects any abnormalities. The anomaly detection unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, which constantly monitors the parent's movements and health status, and immediately sends an alert if an abnormality occurs. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] (Note 1) A function check unit that confirms movement within the parents' home, A health monitoring unit that monitors changes in the parent's heart rate or body temperature, It includes an anomaly detection unit that detects an anomaly and sends an alert. A system characterized by the following features. (Note 2) The aforementioned operation verification unit is The parents' movements are monitored in real time through sensors and cameras installed inside their home. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned health monitoring department, Using sensors that monitor the parent's heart rate and body temperature, their health status can be monitored in real time. The system described in Appendix 1, characterized by the features described herein. (Note 4) The abnormality detection unit, It constantly monitors the parent's movements and health status, and sends an immediate alert if any abnormality occurs. The system described in Appendix 1, characterized by the features described herein. (Note 5) The system according to Appendix 1, characterized in that the operation confirmation unit confirms whether the parent is moving from the living room to the kitchen or resting in the bedroom. (Note 6) The system described in Appendix 1 is characterized in that the health monitoring unit immediately detects an abnormality when the heart rate rises rapidly or the body temperature becomes abnormally high. (Note 7) The aforementioned operation verification unit is The system estimates the parent's emotions and adjusts the frequency of behavioral checks based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned operation verification unit is Analyze the parents' past behavioral patterns and set criteria for detecting abnormal movements. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned operation verification unit is During operational checks, different monitoring modes are applied depending on the parent's daily routine. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned operation verification unit is The system estimates the parent's emotions and determines the priority of behavioral checks based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned operation verification unit is During operation verification, the system prioritizes monitoring highly relevant movements, taking into account the parent's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned operation verification unit is During the functionality check, we analyze the parents' social media activity and monitor related movements. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned health monitoring department, The system estimates parental emotions and adjusts the frequency of health monitoring based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned health monitoring department, During health monitoring, the criteria for detecting abnormalities are set by referring to the parent's past health data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned health monitoring department, When monitoring health, different monitoring modes are applied depending on the parent's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned health monitoring department, The system estimates parental emotions and prioritizes health monitoring based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned health monitoring department, During health monitoring, the system prioritizes monitoring of highly relevant health data, taking into account the parents' geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned health monitoring department, During health monitoring, we analyze parents' social media activity and monitor relevant health data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The abnormality detection unit, The system estimates the parent's emotions and adjusts the criteria for detecting abnormalities based on the estimated parent's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The abnormality detection unit, When an anomaly is detected, the accuracy of the anomaly detection is improved by referring to the parent's past anomaly data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The abnormality detection unit, When an anomaly is detected, different anomaly detection modes are applied according to the parent's daily routine. The system described in Appendix 1, characterized by the features described herein. (Note 22) The abnormality detection unit, It estimates the parent's emotions and determines the priority of anomaly detection based on the estimated parent's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The abnormality detection unit, When an anomaly is detected, the system prioritizes detecting highly relevant anomalies by considering the parent's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The abnormality detection unit, When an anomaly is detected, the system analyzes the parent's social media activity to identify related anomalies. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A function check unit that confirms movement within the parents' home, A health monitoring unit that monitors changes in the parent's heart rate or body temperature, It includes an anomaly detection unit that detects an anomaly and sends an alert. A system characterized by the following features.

2. The aforementioned operation verification unit is The parents' movements are monitored in real time through sensors and cameras installed inside their home. The system according to feature 1.

3. The aforementioned health monitoring department, Using sensors that monitor the parent's heart rate and body temperature, their health status can be monitored in real time. The system according to feature 1.

4. The abnormality detection unit, It constantly monitors the parent's movements and health status, and sends an immediate alert if any abnormality occurs. The system according to feature 1.

5. The system according to claim 1, characterized in that the operation confirmation unit confirms whether the parent is moving from the living room to the kitchen or resting in the bedroom.

6. The system according to claim 1, characterized in that the health monitoring unit immediately detects an abnormality when the heart rate rises rapidly or the body temperature becomes abnormally high.

7. The aforementioned operation verification unit is The system estimates the parent's emotions and adjusts the frequency of behavioral checks based on the estimated emotions. The system according to feature 1.

8. The aforementioned operation verification unit is Analyze the parents' past behavioral patterns and set criteria for detecting abnormal movements. The system according to feature 1.

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