system

Abstract

The system according to this embodiment aims to analyze location information and health data of children and the elderly, and to detect and notify of any abnormalities. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a notification unit. The collection unit collects location information and health data. The analysis unit analyzes the data collected by the collection unit. The notification unit detects abnormalities based on the data analyzed by the analysis unit and provides notifications.

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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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 was a problem that it was difficult to efficiently monitor the safety of children and the elderly remotely.

[0005] The system according to the embodiment aims to analyze the location information and physical condition data of children and the elderly, detect abnormalities, and give notifications.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a notification unit. The collection unit collects location information and physical condition data. The analysis unit analyzes the data collected by the collection unit. The notification unit detects an abnormality based on the data analyzed by the analysis unit and gives a notification.

Effects of the Invention

[0007] The system according to this embodiment can analyze location information and health data of children and the elderly, and detect and notify of abnormalities. [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 monitoring system according to an embodiment of the present invention is a system that combines a generative AI and a smart device. This monitoring system aims to remotely check the safety of children and the health of the elderly. Specifically, it is a service provided to the following two targets: 1. A service for dual-income households with children. This service is intended to ensure the safety of children when they are going to school or extracurricular activities. Using location information from a smartphone, it determines whether the child's route is appropriate and whether they are going to an extracurricular activity, and sends a notification to the guardian if an abnormality is detected. By using generative AI, it is possible to make accurate judgments even for complex patterns. For example, it can learn complex schedules such as tutoring on Mondays and sports every other Tuesday, and determine whether a notification is necessary. It can also understand whether the child is on a vehicle based on the speed of movement in the location information and notify the guardian. 2. A service for households with elderly relatives living alone. This service is intended to remotely check whether there are any abnormalities in the health of the elderly. By combining the impact detection function, accelerometer, and charging function of a smartwatch or smartphone with generative AI, it can detect abnormal values ​​in health or impacts and send notifications to family members. The generating AI references pre-existing medical conditions and normal data, enabling accurate judgments even in complex patterns. For example, it detects abnormal values ​​measured by the smartwatch's health management function, or detects an impact when a smartphone is dropped, or detects abnormalities such as overcharging, and sends notifications. Furthermore, after a smartphone is dropped, it uses an accelerometer to measure whether it can be picked up, and if it cannot be picked up for an extended period, it determines there is a risk of injury from a fall and sends a notification. In this way, by utilizing the generating AI and smart devices, it is possible to remotely monitor the safety of children and the health of the elderly, providing peace of mind to families. Thus, the monitoring system allows for remote confirmation of the safety of children and the health of the elderly.

[0029] The monitoring system according to this embodiment comprises a data collection unit, an analysis unit, and a notification unit. The data collection unit collects location information and health data. The data collection unit collects location information and health data from, for example, a smartphone or smartwatch. The data collection unit can acquire location information using GPS data or Wi-Fi location information. The data collection unit can also acquire health data such as heart rate, body temperature, and blood pressure. For example, the data collection unit acquires location information using the GPS function of a smartphone. The data collection unit can also measure heart rate and body temperature using the sensors of a smartwatch. Furthermore, the data collection unit can also acquire indoor location information using Wi-Fi location information. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the collected data using generative AI and learns complex patterns. For example, the analysis unit analyzes the data using a deep learning model. The analysis unit can also analyze the data using a generative adversarial network (GAN). Furthermore, the analysis unit can learn an algorithm for detecting anomalies based on the collected data. The notification unit detects anomalies based on data analyzed by the analysis unit and issues notifications. For example, the notification unit sends notifications to parents or family members. When an anomaly is detected, the notification unit can send notifications to parents or family members via email or SMS. The notification unit can also send notifications through a smartphone app. Furthermore, the notification unit can adjust the content and method of notifications according to the type and severity of the anomaly. For example, if the anomaly is of high severity, the notification unit will provide detailed notifications using multiple notification methods. As a result, the monitoring system according to this embodiment allows for remote monitoring of the safety of children and the health of the elderly.

[0030] The data collection unit collects location information and health data. For example, it collects location information and health data from smartphones and smartwatches. Specifically, it can accurately determine the user's current location using the smartphone's GPS function. GPS data is updated in real time, recording the user's movement routes and places of stay in detail. Furthermore, by utilizing Wi-Fi location information, indoor location information can also be obtained, which is particularly effective when detailed location identification within buildings is required. In addition, the data collection unit uses smartwatch sensors to acquire health data such as heart rate, body temperature, and blood pressure. This data is important for monitoring the user's health status in real time. For example, if a sudden change in heart rate or an abnormal rise in body temperature is detected, an abnormality can be detected early. The data collection unit centrally manages this data and sends it to a cloud server, making it accessible to the analysis and notification units. The frequency and accuracy of data collection can be adjusted according to the user's needs and circumstances, allowing for flexible responses under specific conditions. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the data collection unit. The analysis unit uses generative AI to analyze the collected data and learn complex patterns. Specifically, it uses deep learning models to analyze time-series data of location information and health data to understand user behavior patterns and fluctuations in health status. For example, if a user deviates from their normal range of activity or if abnormal fluctuations are observed in health data, the analysis unit detects this as an anomaly. Furthermore, by using generative adversarial networks (GANs), data generation and identification are performed, enabling more accurate anomaly detection. GANs are powerful tools for distinguishing between normal and abnormal data, improving the accuracy of anomaly detection. In addition, the analysis unit can learn algorithms for detecting anomalies based on the collected data. This allows the analysis unit to monitor changes in user behavior and health status in real time and detect anomalies early. The analysis unit can also utilize historical data and statistical information to perform long-term risk assessments and trend analysis. For example, based on historical health data, it can predict fluctuations in health risks during specific time periods or seasons and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The notification unit detects anomalies based on data analyzed by the analysis unit and sends notifications. Specifically, it sends notifications to parents and family members. When an anomaly is detected, the notification unit can send notifications to parents and family members via email or SMS. For example, if a user's location data deviates from their normal range of activity, or if an abnormality is found in their health data, the notification unit immediately sends a notification to parents and family members. The notification unit can also send notifications through a smartphone app. Notifications via the app can quickly and reliably alert users to anomalies using push notifications, alert sounds, and vibrations. Furthermore, the notification unit can adjust the content and method of notifications according to the type and severity of the anomaly. For example, if the anomaly is of high severity, it will send detailed notifications using multiple notification methods. Specifically, it will use voice calls and in-app notifications in addition to email and SMS to ensure that important information is reliably conveyed. The notification unit can also collect user feedback and continuously improve the accuracy and effectiveness of notifications. For example, based on feedback from parents and family members who receive notifications, it will review the timing and content of notifications and explore more effective notification methods. This allows the notification unit to provide users with quick and reliable instructions, minimizing the risk of disaster.

[0033] The data collection unit can collect location information and health data from smartphones and smartwatches. For example, the data collection unit can acquire location information using the GPS function of a smartphone. The data collection unit can also measure heart rate and body temperature using the sensors of a smartwatch. The data collection unit can also acquire indoor location information using Wi-Fi location information. For example, the data collection unit can track a child's route to school using the GPS function of a smartphone. The data collection unit can also monitor the heart rate and body temperature of elderly people using the sensors of a smartwatch. Furthermore, the data collection unit can acquire indoor location information using Wi-Fi location information to check whether a child is attending an extracurricular activity. In this way, by collecting data from smartphones and smartwatches, the situation of children and the elderly can be accurately understood. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input GPS data from a smartphone into a generating AI and have the generating AI perform the acquisition of location information.

[0034] The analysis unit can analyze collected data using generative AI and learn complex patterns. For example, the analysis unit can analyze data using a deep learning model. The analysis unit can also analyze data using a generative adversarial network (GAN). Based on the collected data, the analysis unit can learn algorithms for detecting anomalies. For example, the analysis unit can use a deep learning model to detect anomalies in children's school routes. Furthermore, the analysis unit can use a generative adversarial network (GAN) to detect anomalies in the health data of elderly people. In addition, the analysis unit can learn algorithms for detecting anomalies based on the collected data and analyze complex patterns. This allows for the learning of complex patterns and more accurate analysis by using generative AI. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input collected data into a generative AI and have the generative AI perform the data analysis.

[0035] The notification unit can detect anomalies based on analysis results and send notifications to parents or family members. For example, if an anomaly is detected, the notification unit can send notifications to parents or family members via email or SMS. The notification unit can also send notifications through a smartphone app. The notification unit can adjust the content and method of notifications depending on the type and severity of the anomaly. For example, if the anomaly is of high severity, the notification unit will provide detailed notifications using multiple notification methods. The notification unit can also customize the content of notifications depending on the type of anomaly. For example, for location anomalies, the notification unit will provide notifications that include a map display. This allows for a quick response by detecting and notifying of anomalies. Some or all of the above-described processes in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input analysis results into a generating AI and have the generating AI perform anomaly detection and notification.

[0036] The analysis unit can determine the usage status of a vehicle from the movement speed of the location information. For example, the analysis unit can set a threshold for movement speed and determine the usage status of the vehicle. The analysis unit can also analyze movement patterns and determine the usage status of a vehicle. For example, the analysis unit can determine that a vehicle is being used if the movement speed exceeds a certain threshold. The analysis unit can also determine that a vehicle is being used if the movement pattern matches a specific pattern. This makes it possible to analyze location information more accurately by determining the usage status of a vehicle from the movement speed. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the movement speed data of the location information into a generating AI and have the generating AI perform the determination of the usage status of the vehicle.

[0037] The notification unit can detect falls and impacts using the smartphone's impact detection function and accelerometer, and notify of abnormalities. For example, the notification unit can detect falls and impacts using the smartphone's accelerometer. The notification unit can also detect abnormalities using the impact detection function. For example, the notification unit can detect falls and impacts when the smartphone's accelerometer exceeds a certain threshold. In addition, the notification unit can send a notification to a guardian or family member when the impact detection function detects an abnormality. This allows for a quick response by detecting and notifying of falls and impacts. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input smartphone accelerometer data into a generating AI and have the generating AI perform fall and impact detection.

[0038] The data collection unit can analyze the user's past behavior patterns during data collection and select the optimal collection timing. For example, if the user has traveled during a specific time period in the past, the collection unit can concentrate data collection during that time period. If the user has been more active on a specific day of the week in the past, the collection unit can also intensify data collection on that day. The collection unit can predict the optimal timing for data collection based on the user's past behavior patterns and collect data efficiently. This enables efficient data collection by analyzing past behavior patterns. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's past behavior data into a generating AI and have the generating AI select the optimal collection timing.

[0039] The data collection unit can filter data based on the user's current activity level during collection. For example, if the user is exercising, the data collection unit can prioritize collecting data such as heart rate and steps. If the user is resting, the data collection unit can prioritize collecting data such as body temperature and blood pressure. If the user is moving, the data collection unit can prioritize collecting data such as location information and speed. This allows for the priority collection of important data by filtering data based on the user's current activity level. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user activity data into a generating AI and have the generating AI perform data filtering.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is at school, the data collection unit can prioritize the collection of location information and travel routes. If the user is at a hospital, the data collection unit can prioritize the collection of health data and heart rate. If the user is at home, the data collection unit can prioritize the collection of activity levels and sleep data. This allows for the priority collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information data into a generating AI and have the generating AI perform the priority collection of highly relevant data.

[0041] The data collection unit can analyze the user's social media activity and collect relevant data during the collection process. For example, if the user posts about exercise on social media, the data collection unit can prioritize collecting exercise data. If the user posts about health on social media, the data collection unit can also prioritize collecting health data. If the user posts about travel on social media, the data collection unit can prioritize collecting location information and travel routes. This allows for the priority collection of relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect the relevant data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected data during the analysis. For example, the analysis unit can perform a detailed analysis on high-importance data and a simplified analysis on low-importance data. The analysis unit can also determine the priority of the analysis according to the importance of the collected data. For high-importance data, the analysis unit can apply multiple analysis algorithms to perform a detailed analysis. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the collected data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a movement path analysis algorithm to location data. The analysis unit can also apply a health status analysis algorithm to physical condition data. The analysis unit can apply a fall detection algorithm to impact data. By applying different analysis algorithms depending on the data category, more accurate analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0044] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit may prioritize the analysis of the most recent data and postpone the analysis of older data. The analysis unit can also prioritize the analysis of data collected during a specific time period. The analysis unit can dynamically adjust the analysis priority based on the collection timing. This enables efficient analysis by determining the analysis priority based on the collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI determine the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data and postpone the analysis of less relevant data. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the data. The analysis unit can perform detailed analysis on highly relevant data and simplified analysis on less relevant data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI adjust the order of analysis.

[0046] The notification unit can adjust the level of detail of notifications based on the severity of the anomaly. For example, the notification unit can provide detailed notifications for high-severity anomalies and simplified notifications for low-severity anomalies. The notification unit can also determine the priority of notifications according to the severity of the anomaly. For high-severity anomalies, the notification unit can provide detailed notifications using multiple notification methods. This enables efficient notification by adjusting the level of detail of notifications based on the severity of the anomaly. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the severity of the anomaly into a generating AI and have the generating AI perform the adjustment of the level of detail of the notifications.

[0047] The notification unit can apply different notification methods depending on the category of the anomaly when it issues a notification. For example, for anomalies related to location information, the notification unit can provide a notification that includes a map display. For anomalies related to health data, the notification unit can also provide a notification that includes details of the health status. For anomalies related to impact data, the notification unit can provide a notification indicating the possibility of a fall. By applying different notification methods depending on the category of the anomaly, more appropriate notifications can be made. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the category of the anomaly into a generating AI and have the generating AI execute the application of different notification methods.

[0048] The notification unit can adjust the order of notifications based on when the anomaly occurred. For example, the notification unit can prioritize notifying of the most recent anomaly and postpone older anomalies. The notification unit can also prioritize notifying of anomalies that occurred during a specific time period. The notification unit can dynamically adjust the order of notifications based on when the anomalies occurred. This allows for prioritizing notifications of the most recent anomaly by adjusting the order of notifications based on when the anomaly occurred. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the time of anomaly occurrence into a generating AI and have the generating AI perform the adjustment of the notification order.

[0049] The notification unit can customize the content of notifications based on the relevance of the anomalies. For example, the notification unit can provide detailed notifications for highly relevant anomalies and simplified notifications for less relevant anomalies. The notification unit can also dynamically customize the content of notifications according to the relevance of the anomalies. For highly relevant anomalies, the notification unit can provide detailed notifications using multiple notification methods. This allows for more appropriate notifications by customizing the content of notifications based on the relevance of the anomalies. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the relevance of the anomalies into a generating AI and have the generating AI customize the content of the notifications.

[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 monitoring system can also be equipped with an environmental sensor unit. The environmental sensor unit collects environmental data around the user (e.g., temperature, humidity, illuminance, etc.) and transmits it to the analysis unit. The analysis unit analyzes the collected environmental data and can evaluate the user's comfort level and health risks. For example, the environmental sensor unit can warn of the risk of heatstroke if the room temperature is too high. It can also notify the user of health risks due to dryness if the humidity is too low. This makes it possible to monitor the user's health more comprehensively by utilizing environmental data.

[0052] The monitoring system can also be equipped with a behavior prediction unit. This unit learns the user's behavioral patterns based on collected data and predicts future actions. For example, it can learn and predict a user's pattern of going for a walk at a fixed time every morning. It can also learn and predict a user's tendency to perform specific activities on specific days of the week. This allows the behavior prediction unit to send notifications and alerts at the appropriate time based on the predicted behavior. This enables a better understanding of the user's behavior in advance, leading to more effective monitoring.

[0053] The monitoring system can also be equipped with an energy management unit. The energy management unit monitors and optimizes the overall energy consumption of the system. For example, it adjusts the operation of the data collection and analysis units to reduce battery consumption. Furthermore, the energy management unit can optimize energy consumption according to the system's operating status. This allows the energy management unit to support the continuous operation of the system and improve user convenience.

[0054] The monitoring system can also be equipped with a health advice unit. Based on collected health data, the health advice unit provides users with health-related advice. For example, it can analyze the user's heart rate and body temperature data to provide appropriate exercise and dietary advice. Furthermore, it can provide advice to improve sleep quality based on the user's sleep data. In this way, the health advice unit can comprehensively support the user's health.

[0055] The monitoring system can also be equipped with an emergency response unit. This unit responds quickly if the user experiences an emergency. For example, if the user falls, the emergency response unit automatically sends a notification to emergency contacts. It can also contact medical institutions if the user complains of feeling unwell. This ensures the user's safety and enables a swift response.

[0056] The monitoring system can also be equipped with a reminder function. This reminder function manages the user's schedule and tasks, sending reminders at appropriate times. For example, it can notify the user when it's time to take their medication. It can also remind the user to avoid forgetting important appointments. In this way, the reminder function supports the user's daily life and helps them remember and complete important tasks.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The data collection unit collects location information and health data. For example, the data collection unit collects location information and health data from smartphones and smartwatches. The data collection unit can acquire location information using GPS data or Wi-Fi location information, and acquire health data such as heart rate, body temperature, and blood pressure. For example, it can acquire location information using the GPS function of a smartphone and measure heart rate and body temperature using the sensors of a smartwatch. It can also acquire indoor location information using Wi-Fi location information. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses generative AI to analyze the collected data and learn complex patterns. For example, it uses deep learning models or generative adversarial networks (GANs) to analyze the data and learn algorithms for detecting anomalies. Step 3: The notification unit detects anomalies based on the data analyzed by the analysis unit and sends notifications. The notification unit can send notifications to parents and family members via email or SMS, and can also send notifications through a smartphone app. Furthermore, it adjusts the content and method of the notification according to the type and severity of the anomaly, and if the severity is high, it uses multiple notification methods to provide detailed notifications.

[0059] (Example of form 2) The monitoring system according to an embodiment of the present invention is a system that combines a generative AI and a smart device. This monitoring system aims to remotely check the safety of children and the health of the elderly. Specifically, it is a service provided to the following two targets: 1. A service for dual-income households with children. This service is intended to ensure the safety of children when they are going to school or extracurricular activities. Using location information from a smartphone, it determines whether the child's route is appropriate and whether they are going to an extracurricular activity, and sends a notification to the guardian if an abnormality is detected. By using generative AI, it is possible to make accurate judgments even for complex patterns. For example, it can learn complex schedules such as tutoring on Mondays and sports every other Tuesday, and determine whether a notification is necessary. It can also understand whether the child is on a vehicle based on the speed of movement in the location information and notify the guardian. 2. A service for households with elderly relatives living alone. This service is intended to remotely check whether there are any abnormalities in the health of the elderly. By combining the impact detection function, accelerometer, and charging function of a smartwatch or smartphone with generative AI, it can detect abnormal values ​​in health or impacts and send notifications to family members. The generating AI references pre-existing medical conditions and normal data, enabling accurate judgments even in complex patterns. For example, it detects abnormal values ​​measured by the smartwatch's health management function, or detects an impact when a smartphone is dropped, or detects abnormalities such as overcharging, and sends notifications. Furthermore, after a smartphone is dropped, it uses an accelerometer to measure whether it can be picked up, and if it cannot be picked up for an extended period, it determines there is a risk of injury from a fall and sends a notification. In this way, by utilizing the generating AI and smart devices, it is possible to remotely monitor the safety of children and the health of the elderly, providing peace of mind to families. Thus, the monitoring system allows for remote confirmation of the safety of children and the health of the elderly.

[0060] The monitoring system according to this embodiment comprises a data collection unit, an analysis unit, and a notification unit. The data collection unit collects location information and health data. The data collection unit collects location information and health data from, for example, a smartphone or smartwatch. The data collection unit can acquire location information using GPS data or Wi-Fi location information. The data collection unit can also acquire health data such as heart rate, body temperature, and blood pressure. For example, the data collection unit acquires location information using the GPS function of a smartphone. The data collection unit can also measure heart rate and body temperature using the sensors of a smartwatch. Furthermore, the data collection unit can also acquire indoor location information using Wi-Fi location information. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the collected data using generative AI and learns complex patterns. For example, the analysis unit analyzes the data using a deep learning model. The analysis unit can also analyze the data using a generative adversarial network (GAN). Furthermore, the analysis unit can learn an algorithm for detecting anomalies based on the collected data. The notification unit detects anomalies based on data analyzed by the analysis unit and issues notifications. For example, the notification unit sends notifications to parents or family members. When an anomaly is detected, the notification unit can send notifications to parents or family members via email or SMS. The notification unit can also send notifications through a smartphone app. Furthermore, the notification unit can adjust the content and method of notifications according to the type and severity of the anomaly. For example, if the anomaly is of high severity, the notification unit will provide detailed notifications using multiple notification methods. As a result, the monitoring system according to this embodiment allows for remote monitoring of the safety of children and the health of the elderly.

[0061] The data collection unit collects location information and health data. For example, it collects location information and health data from smartphones and smartwatches. Specifically, it can accurately determine the user's current location using the smartphone's GPS function. GPS data is updated in real time, recording the user's movement routes and places of stay in detail. Furthermore, by utilizing Wi-Fi location information, indoor location information can also be obtained, which is particularly effective when detailed location identification within buildings is required. In addition, the data collection unit uses smartwatch sensors to acquire health data such as heart rate, body temperature, and blood pressure. This data is important for monitoring the user's health status in real time. For example, if a sudden change in heart rate or an abnormal rise in body temperature is detected, an abnormality can be detected early. The data collection unit centrally manages this data and sends it to a cloud server, making it accessible to the analysis and notification units. The frequency and accuracy of data collection can be adjusted according to the user's needs and circumstances, allowing for flexible responses under specific conditions. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0062] The analysis unit analyzes the data collected by the data collection unit. The analysis unit uses generative AI to analyze the collected data and learn complex patterns. Specifically, it uses deep learning models to analyze time-series data of location information and health data to understand user behavior patterns and fluctuations in health status. For example, if a user deviates from their normal range of activity or if abnormal fluctuations are observed in health data, the analysis unit detects this as an anomaly. Furthermore, by using generative adversarial networks (GANs), data generation and identification are performed, enabling more accurate anomaly detection. GANs are powerful tools for distinguishing between normal and abnormal data, improving the accuracy of anomaly detection. In addition, the analysis unit can learn algorithms for detecting anomalies based on the collected data. This allows the analysis unit to monitor changes in user behavior and health status in real time and detect anomalies early. The analysis unit can also utilize historical data and statistical information to perform long-term risk assessments and trend analysis. For example, based on historical health data, it can predict fluctuations in health risks during specific time periods or seasons and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0063] The notification unit detects anomalies based on data analyzed by the analysis unit and sends notifications. Specifically, it sends notifications to parents and family members. When an anomaly is detected, the notification unit can send notifications to parents and family members via email or SMS. For example, if a user's location data deviates from their normal range of activity, or if an abnormality is found in their health data, the notification unit immediately sends a notification to parents and family members. The notification unit can also send notifications through a smartphone app. Notifications via the app can quickly and reliably alert users to anomalies using push notifications, alert sounds, and vibrations. Furthermore, the notification unit can adjust the content and method of notifications according to the type and severity of the anomaly. For example, if the anomaly is of high severity, it will send detailed notifications using multiple notification methods. Specifically, it will use voice calls and in-app notifications in addition to email and SMS to ensure that important information is reliably conveyed. The notification unit can also collect user feedback and continuously improve the accuracy and effectiveness of notifications. For example, based on feedback from parents and family members who receive notifications, it will review the timing and content of notifications and explore more effective notification methods. This allows the notification unit to provide users with quick and reliable instructions, minimizing the risk of disaster.

[0064] The data collection unit can collect location information and health data from smartphones and smartwatches. For example, the data collection unit can acquire location information using the GPS function of a smartphone. The data collection unit can also measure heart rate and body temperature using the sensors of a smartwatch. The data collection unit can also acquire indoor location information using Wi-Fi location information. For example, the data collection unit can track a child's route to school using the GPS function of a smartphone. The data collection unit can also monitor the heart rate and body temperature of elderly people using the sensors of a smartwatch. Furthermore, the data collection unit can acquire indoor location information using Wi-Fi location information to check whether a child is attending an extracurricular activity. In this way, by collecting data from smartphones and smartwatches, the situation of children and the elderly can be accurately understood. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input GPS data from a smartphone into a generating AI and have the generating AI perform the acquisition of location information.

[0065] The analysis unit can analyze collected data using generative AI and learn complex patterns. For example, the analysis unit can analyze data using a deep learning model. The analysis unit can also analyze data using a generative adversarial network (GAN). Based on the collected data, the analysis unit can learn algorithms for detecting anomalies. For example, the analysis unit can use a deep learning model to detect anomalies in children's school routes. Furthermore, the analysis unit can use a generative adversarial network (GAN) to detect anomalies in the health data of elderly people. In addition, the analysis unit can learn algorithms for detecting anomalies based on the collected data and analyze complex patterns. This allows for the learning of complex patterns and more accurate analysis by using generative AI. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input collected data into a generative AI and have the generative AI perform the data analysis.

[0066] The notification unit can detect anomalies based on analysis results and send notifications to parents or family members. For example, if an anomaly is detected, the notification unit can send notifications to parents or family members via email or SMS. The notification unit can also send notifications through a smartphone app. The notification unit can adjust the content and method of notifications depending on the type and severity of the anomaly. For example, if the anomaly is of high severity, the notification unit will provide detailed notifications using multiple notification methods. The notification unit can also customize the content of notifications depending on the type of anomaly. For example, for location anomalies, the notification unit will provide notifications that include a map display. This allows for a quick response by detecting and notifying of anomalies. Some or all of the above-described processes in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input analysis results into a generating AI and have the generating AI perform anomaly detection and notification.

[0067] The analysis unit can determine the usage status of a vehicle from the movement speed of the location information. For example, the analysis unit can set a threshold for movement speed and determine the usage status of the vehicle. The analysis unit can also analyze movement patterns and determine the usage status of a vehicle. For example, the analysis unit can determine that a vehicle is being used if the movement speed exceeds a certain threshold. The analysis unit can also determine that a vehicle is being used if the movement pattern matches a specific pattern. This makes it possible to analyze location information more accurately by determining the usage status of a vehicle from the movement speed. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the movement speed data of the location information into a generating AI and have the generating AI perform the determination of the usage status of the vehicle.

[0068] The notification unit can detect falls and impacts using the smartphone's impact detection function and accelerometer, and notify of abnormalities. For example, the notification unit can detect falls and impacts using the smartphone's accelerometer. The notification unit can also detect abnormalities using the impact detection function. For example, the notification unit can detect falls and impacts when the smartphone's accelerometer exceeds a certain threshold. In addition, the notification unit can send a notification to a guardian or family member when the impact detection function detects an abnormality. This allows for a quick response by detecting and notifying of falls and impacts. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input smartphone accelerometer data into a generating AI and have the generating AI perform fall and impact detection.

[0069] The data collection unit can estimate the user's emotions and adjust the frequency of collecting location and health data based on the estimated emotions. For example, if the user is stressed, the data collection unit can increase the collection frequency to collect more detailed data. If the user is relaxed, the data collection unit can also decrease the collection frequency to conserve battery power. If the user is in a hurry, the data collection unit can adjust the collection frequency appropriately to collect only the important data. This allows for more appropriate data collection by adjusting the collection frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input the user's emotion data into the generative AI and have the generative AI adjust the collection frequency.

[0070] The data collection unit can analyze the user's past behavior patterns during data collection and select the optimal collection timing. For example, if the user has traveled during a specific time period in the past, the collection unit can concentrate data collection during that time period. If the user has been more active on a specific day of the week in the past, the collection unit can also intensify data collection on that day. The collection unit can predict the optimal timing for data collection based on the user's past behavior patterns and collect data efficiently. This enables efficient data collection by analyzing past behavior patterns. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's past behavior data into a generating AI and have the generating AI select the optimal collection timing.

[0071] The data collection unit can filter data based on the user's current activity level during collection. For example, if the user is exercising, the data collection unit can prioritize collecting data such as heart rate and steps. If the user is resting, the data collection unit can prioritize collecting data such as body temperature and blood pressure. If the user is moving, the data collection unit can prioritize collecting data such as location information and speed. This allows for the priority collection of important data by filtering data based on the user's current activity level. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user activity data into a generating AI and have the generating AI perform data filtering.

[0072] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit may prioritize collecting data such as heart rate and blood pressure. If the user is relaxed, the data collection unit may also prioritize collecting data such as body temperature and respiratory rate. If the user is in a hurry, the data collection unit may prioritize collecting data such as location information and movement speed. This allows for the collection of more important data by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the data priority.

[0073] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is at school, the data collection unit can prioritize the collection of location information and travel routes. If the user is at a hospital, the data collection unit can prioritize the collection of health data and heart rate. If the user is at home, the data collection unit can prioritize the collection of activity levels and sleep data. This allows for the priority collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information data into a generating AI and have the generating AI perform the priority collection of highly relevant data.

[0074] The data collection unit can analyze the user's social media activity and collect relevant data during the collection process. For example, if the user posts about exercise on social media, the data collection unit can prioritize collecting exercise data. If the user posts about health on social media, the data collection unit can also prioritize collecting health data. If the user posts about travel on social media, the data collection unit can prioritize collecting location information and travel routes. This allows for the priority collection of relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect the relevant data.

[0075] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is stressed, the analysis unit will prioritize stress-related data in its analysis. If the user is relaxed, the analysis unit can also prioritize relaxation-related data in its analysis. If the user is in a hurry, the analysis unit can prioritize movement speed and location information in its analysis. This allows for more accurate analysis by adjusting the analysis algorithm according to the user'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 processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the analysis algorithm.

[0076] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected data during the analysis. For example, the analysis unit can perform a detailed analysis on high-importance data and a simplified analysis on low-importance data. The analysis unit can also determine the priority of the analysis according to the importance of the collected data. For high-importance data, the analysis unit can apply multiple analysis algorithms to perform a detailed analysis. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the collected data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0077] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a movement path analysis algorithm to location data. The analysis unit can also apply a health status analysis algorithm to physical condition data. The analysis unit can apply a fall detection algorithm to impact data. By applying different analysis algorithms depending on the data category, more accurate analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0078] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. This allows for more appropriate information to be provided by adjusting the display method according to the user'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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the display method.

[0079] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit may prioritize the analysis of the most recent data and postpone the analysis of older data. The analysis unit can also prioritize the analysis of data collected during a specific time period. The analysis unit can dynamically adjust the analysis priority based on the collection timing. This enables efficient analysis by determining the analysis priority based on the collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI determine the analysis priority.

[0080] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data and postpone the analysis of less relevant data. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the data. The analysis unit can perform detailed analysis on highly relevant data and simplified analysis on less relevant data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI adjust the order of analysis.

[0081] The notification unit can estimate the user's emotions and adjust the way notifications are expressed based on those emotions. For example, if the user is tense, the notification unit can use calm language for notifications. If the user is relaxed, the notification unit can use cheerful language for notifications. If the user is in a hurry, the notification unit can use quick and concise language for notifications. By adjusting the way notifications are expressed according to the user's emotions, more appropriate notifications can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI, or not using AI. For example, the notification unit can input user emotion data into a generative AI and have the generative AI adjust the way notifications are expressed.

[0082] The notification unit can adjust the level of detail of notifications based on the severity of the anomaly. For example, the notification unit can provide detailed notifications for high-severity anomalies and simplified notifications for low-severity anomalies. The notification unit can also determine the priority of notifications according to the severity of the anomaly. For high-severity anomalies, the notification unit can provide detailed notifications using multiple notification methods. This enables efficient notification by adjusting the level of detail of notifications based on the severity of the anomaly. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the severity of the anomaly into a generating AI and have the generating AI perform the adjustment of the level of detail of the notifications.

[0083] The notification unit can apply different notification methods depending on the category of the anomaly when it issues a notification. For example, for anomalies related to location information, the notification unit can provide a notification that includes a map display. For anomalies related to health data, the notification unit can also provide a notification that includes details of the health status. For anomalies related to impact data, the notification unit can provide a notification indicating the possibility of a fall. By applying different notification methods depending on the category of the anomaly, more appropriate notifications can be made. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the category of the anomaly into a generating AI and have the generating AI execute the application of different notification methods.

[0084] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated emotions. For example, if the user is stressed, the notification unit will prioritize important notifications. If the user is relaxed, the notification unit can also send less important notifications. If the user is in a hurry, the notification unit can quickly send important notifications. This allows for prioritizing more important notifications by determining the priority of notifications according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not using AI. For example, the notification unit can input user emotion data into a generative AI and have the generative AI determine the priority of notifications.

[0085] The notification unit can adjust the order of notifications based on when the anomaly occurred. For example, the notification unit can prioritize notifying of the most recent anomaly and postpone older anomalies. The notification unit can also prioritize notifying of anomalies that occurred during a specific time period. The notification unit can dynamically adjust the order of notifications based on when the anomalies occurred. This allows for prioritizing notifications of the most recent anomaly by adjusting the order of notifications based on when the anomaly occurred. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the time of anomaly occurrence into a generating AI and have the generating AI perform the adjustment of the notification order.

[0086] The notification unit can customize the content of notifications based on the relevance of the anomalies. For example, the notification unit can provide detailed notifications for highly relevant anomalies and simplified notifications for less relevant anomalies. The notification unit can also dynamically customize the content of notifications according to the relevance of the anomalies. For highly relevant anomalies, the notification unit can provide detailed notifications using multiple notification methods. This allows for more appropriate notifications by customizing the content of notifications based on the relevance of the anomalies. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the relevance of the anomalies into a generating AI and have the generating AI customize the content of the notifications.

[0087] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0088] The monitoring system can also be equipped with a voice recognition unit. The voice recognition unit collects the user's voice and transmits it to an analysis unit. The analysis unit analyzes the collected voice data and can estimate the user's emotions and health condition. For example, the voice recognition unit can analyze the tone and speed of the user's voice to detect signs of stress or fatigue. The voice recognition unit can also detect specific keywords uttered by the user (e.g., "help" or "it hurts") and determine if it is an emergency. This allows for a more comprehensive understanding of the user's condition and enables appropriate responses by utilizing voice data.

[0089] The monitoring system can also be equipped with an environmental sensor unit. The environmental sensor unit collects environmental data around the user (e.g., temperature, humidity, illuminance, etc.) and transmits it to the analysis unit. The analysis unit analyzes the collected environmental data and can evaluate the user's comfort level and health risks. For example, the environmental sensor unit can warn of the risk of heatstroke if the room temperature is too high. It can also notify the user of health risks due to dryness if the humidity is too low. This makes it possible to monitor the user's health more comprehensively by utilizing environmental data.

[0090] The monitoring system can also be equipped with a behavior prediction unit. This unit learns the user's behavioral patterns based on collected data and predicts future actions. For example, it can learn and predict a user's pattern of going for a walk at a fixed time every morning. It can also learn and predict a user's tendency to perform specific activities on specific days of the week. This allows the behavior prediction unit to send notifications and alerts at the appropriate time based on the predicted behavior. This enables a better understanding of the user's behavior in advance, leading to more effective monitoring.

[0091] The monitoring system can also be equipped with an emotional feedback unit. This unit estimates the user's emotions and provides feedback based on those estimates. For example, if the user is feeling stressed, it can offer relaxation advice or music. It can also send encouraging messages if the user is feeling sad. In this way, the emotional feedback unit can provide appropriate support tailored to the user's emotions, thereby supporting the user's mental well-being.

[0092] The monitoring system can also be equipped with an energy management unit. The energy management unit monitors and optimizes the overall energy consumption of the system. For example, it adjusts the operation of the data collection and analysis units to reduce battery consumption. Furthermore, the energy management unit can optimize energy consumption according to the system's operating status. This allows the energy management unit to support the continuous operation of the system and improve user convenience.

[0093] The monitoring system can also be equipped with a communication unit. This unit facilitates communication between the user and family and friends. For example, if the user is feeling lonely, the unit can send notifications prompting them to contact family and friends. It can also send notifications to family and friends when the user is approaching a specific event (such as a birthday or anniversary). In this way, the communication unit can strengthen the user's social connections and support their mental well-being.

[0094] The monitoring system can also be equipped with a health advice unit. Based on collected health data, the health advice unit provides users with health-related advice. For example, it can analyze the user's heart rate and body temperature data to provide appropriate exercise and dietary advice. Furthermore, it can provide advice to improve sleep quality based on the user's sleep data. In this way, the health advice unit can comprehensively support the user's health.

[0095] The monitoring system can also be equipped with an emergency response unit. This unit responds quickly if the user experiences an emergency. For example, if the user falls, the emergency response unit automatically sends a notification to emergency contacts. It can also contact medical institutions if the user complains of feeling unwell. This ensures the user's safety and enables a swift response.

[0096] The monitoring system can also be equipped with a reminder function. This reminder function manages the user's schedule and tasks, sending reminders at appropriate times. For example, it can notify the user when it's time to take their medication. It can also remind the user to avoid forgetting important appointments. In this way, the reminder function supports the user's daily life and helps them remember and complete important tasks.

[0097] The monitoring system can also be equipped with an emotion logging unit. This unit periodically records the user's emotions and tracks long-term emotional changes. For example, it can provide an interface for the user to input their daily emotions and display emotional changes in a graph. Furthermore, it can analyze the user's emotional data to detect early signs of stress or depression. This allows the emotion logging unit to provide long-term support for the user's mental health.

[0098] The following briefly describes the processing flow for example form 2.

[0099] Step 1: The data collection unit collects location information and health data. For example, the data collection unit collects location information and health data from smartphones and smartwatches. The data collection unit can acquire location information using GPS data or Wi-Fi location information, and acquire health data such as heart rate, body temperature, and blood pressure. For example, it can acquire location information using the GPS function of a smartphone and measure heart rate and body temperature using the sensors of a smartwatch. It can also acquire indoor location information using Wi-Fi location information. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses generative AI to analyze the collected data and learn complex patterns. For example, it uses deep learning models or generative adversarial networks (GANs) to analyze the data and learn algorithms for detecting anomalies. Step 3: The notification unit detects anomalies based on the data analyzed by the analysis unit and sends notifications. The notification unit can send notifications to parents and family members via email or SMS, and can also send notifications through a smartphone app. Furthermore, it adjusts the content and method of the notification according to the type and severity of the anomaly, and if the severity is high, it uses multiple notification methods to provide detailed notifications.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] Each of the multiple elements described above, including the collection unit, analysis unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit acquires location information and health data using the GPS function and sensors of the smart device 14. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data using generated AI and detects abnormalities. The notification unit is implemented by the control unit 46A of the smart device 14, which sends a notification to a guardian or family member when an abnormality is detected. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0104] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] Each of the multiple elements described above, including the collection unit, analysis unit, and notification unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit acquires location information and health data using the GPS function and sensors of the smart glasses 214. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data using generated AI and detects abnormalities. The notification unit is implemented by the control unit 46A of the smart glasses 214, which sends a notification to a guardian or family member when an abnormality is detected. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0120] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] Each of the multiple elements described above, including the collection unit, analysis unit, and notification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit acquires location information and health data using the GPS function and sensors of the headset terminal 314. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data using generated AI and detects abnormalities. The notification unit is implemented by the control unit 46A of the headset terminal 314, which sends a notification to a guardian or family member when an abnormality is detected. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0136] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements described above, including the collection unit, analysis unit, and notification unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit acquires location information and health data using the GPS function and sensors of the robot 414. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data using generated AI and detects abnormalities. The notification unit is implemented by the control unit 46A of the robot 414, which sends a notification to a guardian or family member when an abnormality is detected. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] (Note 1) A collection unit that collects location information and health data, An analysis unit analyzes the data collected by the aforementioned collection unit, A notification unit detects an anomaly based on the data analyzed by the aforementioned analysis unit and provides notification, Equipped with A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect location information and health data from smartphones and smartwatches. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed using generative AI, and complex patterns are learned. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned notification unit, Based on the analysis results, anomalies are detected and notifications are sent to parents and family members. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, The vehicle usage status is determined from the speed of movement based on location information. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned notification unit, Using the smartphone's impact detection function and accelerometer, it detects falls and impacts and notifies the user of any abnormalities. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the frequency of collecting location and health data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is During data collection, the system analyzes the user's past behavior patterns to select the optimal collection timing. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During data collection, the data is filtered based on the user's current activity status. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system analyzes the user's social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the collected data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned notification unit, It estimates the user's emotions and adjusts the way notifications are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned notification unit, When a notification is sent, adjust the level of detail in the notification based on the severity of the anomaly. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned notification unit, When a notification is sent, different notification methods will be applied depending on the category of the anomaly. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned notification unit, When sending notifications, adjust the order of notifications based on when the anomaly occurred. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned notification unit, When sending a notification, customize the content of the notification based on the relevance of the anomaly. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0172] 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 collection unit that collects location information and health data, An analysis unit analyzes the data collected by the aforementioned collection unit, A notification unit detects an anomaly based on the data analyzed by the aforementioned analysis unit and provides notification, Equipped with A system characterized by the following features.

2. The aforementioned collection unit is Collect location information and health data from smartphones and smartwatches. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed using generative AI, and complex patterns are learned. The system according to feature 1.

4. The aforementioned notification unit, Based on the analysis results, anomalies are detected and notifications are sent to parents and family members. The system according to feature 1.

5. The aforementioned analysis unit, The vehicle usage status is determined from the speed of movement based on location information. The system according to feature 1.

6. The aforementioned notification unit, Using the smartphone's impact detection function and accelerometer, it detects falls and impacts and notifies the user of any abnormalities. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the frequency of collecting location and health data based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is During data collection, the system analyzes the user's past behavior patterns to select the optimal collection timing. The system according to feature 1.

9. The aforementioned collection unit is During data collection, the data is filtered based on the user's current activity status. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

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