system

Abstract

The system according to this embodiment aims to monitor the behavior of elderly people and pets in real time and to respond quickly if an abnormality is detected. [Solution] The system according to the embodiment comprises a monitoring unit, an analysis unit, a notification unit, a provision unit, and a care decision unit. The monitoring unit monitors the behavior of the elderly person or pet. The analysis unit analyzes the video monitored by the monitoring unit. The notification unit sends a notification when an abnormality is detected by the analysis unit. The provision unit provides video in real time to the user notified by the notification unit. The care decision unit analyzes the pet's behavior using the analysis unit and determines the necessary care.

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 conventional technology, it is difficult to grasp the living alone situation of the elderly and the health status of pets in real time, and there is a problem that it takes time in a situation where prompt response is required.

[0005] The system according to the embodiment aims to monitor the behaviors of the elderly and pets in real time and respond promptly when an abnormality is detected.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a monitoring unit, an analysis unit, a notification unit, a provision unit, and a care decision unit. The monitoring unit monitors the behavior of elderly people and pets. The analysis unit analyzes the video monitored by the monitoring unit. The notification unit sends a notification when an abnormality is detected by the analysis unit. The provision unit provides video in real time to users notified by the notification unit. The care decision unit analyzes the pet's behavior using the analysis unit and determines the necessary care. [Effects of the Invention]

[0007] The system according to this embodiment can monitor the behavior of elderly people and pets in real time and respond quickly if an abnormality is detected. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The monitoring system according to an embodiment of the present invention is a system that continuously monitors the behavior and situation of elderly people and pets by combining an online camera and generative AI analysis technology. This monitoring system reduces the risk that elderly people living alone may fall or become ill and not be noticed immediately, and allows for real-time monitoring of the health status and needs (feeding, litter box, etc.) of pets. It also solves the problem of insufficient means of real-time information sharing for visually understanding the situation. This system monitors the behavior of elderly people and pets using an online camera, and generative AI analyzes the video. If an abnormality is detected, a notification is sent to the user via a messaging app, and real-time video is provided as needed. This allows for a detailed visual understanding of the situation and enables a quick response. Furthermore, in the case of pets, the system can analyze their behavior to determine necessary care and issue instructions via voice or messaging app. This system enhances the safety of elderly people and the health management of pets, realizing a safe living environment. Family members and related parties can take appropriate action by receiving quick and accurate information. Real-time video sharing makes it easier to understand the situation and improves the quality of care and pet care. This improves the quality of life (QOL) and provides peace of mind to family members and pets. For example, an online camera monitors the behavior of elderly people or pets, and a generating AI analyzes the footage to detect anomalies. When an anomaly is detected, a notification is sent to the user via a messaging app, and real-time video is provided as needed. This allows the user to visually grasp the detailed situation and respond quickly. Furthermore, in the case of pets, the generating AI analyzes their behavior and determines the necessary care, such as when the pet wants food or needs to go to the bathroom. The generating AI can be controlled by voice or through a messaging app, allowing the user to quickly care for their pet. This system enhances the safety of the elderly and the health management of pets, creating a safer living environment. Family members and caregivers can receive quick and accurate information, enabling them to take appropriate action. Real-time video sharing makes it easier to understand the situation and improves the quality of care and pet care.This improves the quality of life (QOL) and provides peace of mind for families and pets. The monitoring system allows for continuous monitoring of the behavior of elderly individuals and pets, enabling a rapid response if any abnormalities are detected.

[0029] The monitoring system according to the embodiment comprises a monitoring unit, an analysis unit, a notification unit, a provision unit, and a care decision unit. The monitoring unit monitors the behavior of elderly people and pets. The monitoring unit monitors the behavior of elderly people and pets using, for example, an online camera. The monitoring unit can, for example, acquire camera footage in real time and monitor behavior. The monitoring unit can also, for example, monitor a wide area using multiple cameras. The analysis unit analyzes the footage monitored by the monitoring unit using a generative AI. The analysis unit, for example, uses a generative AI to analyze the footage and analyze the behavior of elderly people and pets. The analysis unit can, for example, use an algorithm for the generative AI to detect anomalies to analyze the footage. The analysis unit can also, for example, use a generative AI to learn behavior patterns and detect abnormal behavior early. The notification unit sends a notification to the user via a messaging application when an anomaly is detected. The notification unit can, for example, customize the notification content according to the type and urgency of the anomaly. Furthermore, the notification unit can, for example, quickly send notifications using multiple notification means. The provision unit provides real-time video to users notified by the notification unit. The provision unit can, for example, provide real-time video to users, allowing them to visually grasp the detailed situation. The provision unit can, for example, automatically adjust the video quality and frame rate to provide optimal video. The provision unit can also, for example, add audio information to the video to grasp the detailed situation. The care decision unit uses a generation AI to analyze the pet's behavior with the analysis unit and determine the necessary care. The care decision unit, for example, uses a generation AI to analyze the pet's behavior and determine the necessary care. The care decision unit can, for example, use a generation AI to learn the pet's behavior patterns and propose appropriate care methods. The care decision unit can also, for example, use a generation AI to analyze the pet's behavior and issue instructions via voice or messaging apps. As a result, the monitoring system according to the embodiment can continuously monitor the behavior of elderly people and pets and respond quickly when an abnormality is detected.

[0030] The monitoring unit monitors the behavior of elderly individuals and pets. For example, the monitoring unit uses online cameras to monitor the behavior of elderly individuals and pets. Specifically, the monitoring unit installs multiple high-resolution cameras, positioned to cover a wide area. The cameras are equipped with infrared and low-light capabilities to capture clear images day and night. This allows for accurate monitoring of elderly individuals and pets even at night. Furthermore, the cameras have pan-tilt-zoom (PTZ) functionality, allowing them to focus on specific areas or objects. This enables wide-area monitoring while also allowing for detailed monitoring as needed. The monitoring unit acquires camera footage in real time and transmits it to a central monitoring system. The footage is transmitted via a secure network and encrypted to prevent data leakage and unauthorized access. This ensures the security of the monitoring data. The monitoring unit centrally manages the video data and can also play back past footage as needed. This allows for detailed review of specific time periods or events.

[0031] The analysis unit uses generative AI to analyze video footage monitored by the monitoring unit. For example, the analysis unit uses generative AI to analyze video footage and analyze the behavior of elderly people and pets. Specifically, the generative AI uses deep learning technology to analyze video data and learn the behavioral patterns of elderly people and pets. The generative AI automatically recognizes people and pets in the video and tracks their movements. This allows it to detect specific behaviors and abnormal movements. For example, if an elderly person falls or a pet exhibits abnormal behavior, the generative AI can immediately detect this and flag it as an anomaly. The analysis unit can analyze video footage using algorithms designed for generative AI anomaly detection. This enables early detection of abnormal behavior and allows for a rapid response. Furthermore, the analysis unit can enable the generative AI to learn behavioral patterns and detect abnormal behavior early. The generative AI learns normal behavioral patterns based on past data and detects behaviors that differ from these as anomalies. This allows the analysis unit to achieve highly accurate anomaly detection and improve the overall reliability of the system.

[0032] The notification unit sends notifications to the user via the messaging app when an anomaly is detected. Specifically, the notification unit can customize the content of notifications according to the type and urgency of the anomaly. For example, if an elderly person falls or a pet exhibits unusual behavior, the notification unit will send a message to the user containing detailed information. The notification will include the location, time, and nature of the anomaly. This allows the user to quickly understand the situation and take appropriate action. The notification unit can also send notifications quickly using multiple notification methods. For example, it can send notifications not only via the messaging app but also via SMS, email, and voice calls. This ensures that the user receives the notification reliably. Furthermore, the notification unit can customize the notification priority and method according to the user's settings. This enables flexible notifications tailored to the user's needs.

[0033] The service provider delivers video in real time to users notified by the notification unit. For example, the service provider can provide users with real-time video, allowing them to visually grasp the detailed situation. Specifically, the service provider delivers high-resolution video to users in streaming format, and users can view the video through devices such as smartphones, tablets, and personal computers. The service provider can automatically adjust the video quality and frame rate to provide optimal video. This ensures that the best possible video quality is always maintained depending on the network conditions and device performance. The service provider can also add audio information to the video to grasp the detailed situation. For example, a microphone can be installed in the surveillance camera to acquire audio from the site in real time and provide it along with the video. This allows users to understand the situation not only through visual information but also through audio information. Furthermore, the service provider also provides a function for users to play back past video. This allows users to check the situation before and after an anomaly occurred in detail and obtain information to take appropriate action.

[0034] The care decision unit uses a generative AI to analyze the pet's behavior with the analysis unit and determine the necessary care. Specifically, the generative AI learns the pet's behavior patterns and detects abnormal behavior and changes in health. For example, if a pet exhibits behaviors such as having a smaller appetite than usual, being sluggish, or making unusual noises, the generative AI detects this as an abnormality and suggests appropriate care methods. The care decision unit can have the generative AI learn the pet's behavior patterns and suggest appropriate care methods. For example, if a pet is stressed, the generative AI suggests ways to help it relax. Also, if a pet shows signs of illness, the generative AI recommends a veterinary examination. Furthermore, the care decision unit can have the generative AI analyze the pet's behavior and issue instructions via voice or messaging apps. For example, if a pet exhibits abnormal behavior, the generative AI notifies the owner of specific actions to take via voice or message. This allows the owner to take quick and appropriate action. The care assessment unit can maintain the pet's health and well-being by continuously monitoring the pet's health status and determining the necessary care.

[0035] The analysis unit can analyze the behavior of elderly people and pets using generative AI and detect abnormalities. For example, the analysis unit can use generative AI to analyze the behavior of elderly people and detect abnormalities such as falls or poor health. The analysis unit can also use generative AI to analyze the behavior of pets and detect abnormal behavior or changes in their health. Furthermore, the analysis unit can use generative AI to learn behavioral patterns and detect abnormal behavior early. As a result, the accuracy of behavioral analysis is improved by using generative AI. Generative AI is implemented using technologies such as deep learning and reinforcement learning. For example, the generative AI analyzes behavior using a model that takes behavioral data of elderly people and pets as input and outputs abnormalities. For example, the generative AI uses an algorithm that extracts features from behavioral data and detects abnormal behavior. As a result, the analysis unit can use generative AI to analyze the behavior of elderly people and pets and detect abnormalities.

[0036] The notification unit can send notifications to the user via a messaging app when an anomaly is detected. For example, the notification unit sends a notification to the user via a messaging app when an anomaly is detected. The notification unit can customize the notification content according to the type and urgency of the anomaly. Furthermore, the notification unit can send notifications quickly using multiple notification methods. This allows for quick notification to the user when an anomaly is detected. This enables the notification unit to send notifications to the user via a messaging app when an anomaly is detected.

[0037] The service provider can provide users with real-time video. For example, the service provider can provide users with real-time video, allowing them to visually grasp the detailed situation. For example, the service provider can automatically adjust the video quality and frame rate to provide optimal video. The service provider can also add audio information to the video to grasp the detailed situation. This allows users to check the video in real time and respond quickly. Real-time has criteria such as the acceptable latency range and update frequency. For example, the service provider can provide real-time video with low latency. The service provider can also provide real-time video with a high update frequency. The service provider can also ensure real-timeness by providing video within an acceptable latency range. This allows the service provider to provide users with real-time video.

[0038] The care decision unit can analyze a pet's behavior using generative AI and determine the necessary care. For example, the care decision unit can use generative AI to analyze the pet's behavior and determine the necessary care. For example, the care decision unit can use generative AI to learn the pet's behavior patterns and suggest appropriate care methods. The care decision unit can also use generative AI to analyze the pet's behavior and issue instructions via voice or messaging apps. This improves the accuracy of pet behavior analysis and care decisions by using generative AI. Generative AI is implemented using technologies such as deep learning and reinforcement learning. For example, the generative AI analyzes behavior using a model that takes pet behavior data as input and outputs the necessary care. For example, the generative AI uses an algorithm that extracts features from behavior data and suggests appropriate care methods. This allows the care decision unit to analyze a pet's behavior using generative AI and determine the necessary care.

[0039] The care decision unit can analyze a pet's behavior using generative AI and issue instructions via voice or messaging apps. For example, the care decision unit uses generative AI to analyze the pet's behavior and issue instructions via voice or messaging apps. The care decision unit can, for example, use generative AI to learn the pet's behavior patterns and issue appropriate instructions. Furthermore, the care decision unit can use generative AI to analyze the pet's behavior and determine the necessary care. This allows for appropriate instructions to be given according to the pet's behavior. Generative AI is implemented using technologies such as deep learning and reinforcement learning. For example, the generative AI analyzes the behavior using a model that takes pet behavior data as input and outputs the necessary care. The generative AI uses algorithms that extract features from behavioral data and propose appropriate care methods. This allows the care decision unit to analyze the pet's behavior using generative AI and issue instructions via voice or messaging apps.

[0040] The monitoring unit can learn the behavioral patterns of elderly people and pets, enabling early detection of abnormal behavior. For example, the monitoring unit can learn the normal behavioral patterns of elderly people and detect abnormal movements or prolonged periods of stillness. For example, the monitoring unit can learn the eating and toileting patterns of pets and detect abnormal behavior early. In addition, the monitoring unit can learn the sleep patterns of elderly people and pets and detect abnormal sleep durations or movements. In this way, by learning behavioral patterns, abnormal behavior can be detected early. Behavioral patterns are learned based on criteria such as the repetition of daily actions or the frequency of specific actions. In this way, the monitoring unit can learn the behavioral patterns of elderly people and pets and enable early detection of abnormal behavior.

[0041] The monitoring unit can automatically adjust the resolution and frame rate of the acquired video according to the situation. For example, if an anomaly is detected, the monitoring unit will increase the video resolution to grasp the situation in detail. For example, the monitoring unit can also monitor at a low resolution under normal circumstances to save data communication. Furthermore, the monitoring unit can lower the frame rate at night or in dark places to acquire clear images even in low light. This allows for detailed situational awareness and data communication savings by adjusting the video resolution and frame rate according to the situation. The resolution and frame rate are adjusted based on criteria such as the resolution range and frame rate range. This allows the monitoring unit to automatically adjust the resolution and frame rate of the acquired video according to the situation.

[0042] The monitoring unit can add audio information to the acquired video and also perform audio analysis. For example, the monitoring unit can analyze the voices of elderly people to detect abnormal vocalizations or cries for help. For example, the monitoring unit can analyze the sounds of pets to detect abnormal sounds or signs of stress. The monitoring unit can also analyze ambient sounds to detect abnormal sounds (e.g., noises, the sound of breaking glass). By adding audio information, it is possible to detect abnormal vocalizations and sounds. The audio information is analyzed based on criteria such as the type of sound and speech recognition technology. As a result, the monitoring unit can add audio information to the acquired video and also perform audio analysis.

[0043] The monitoring unit can add environmental information such as temperature and humidity to the acquired video footage to gain a comprehensive understanding of the situation. For example, the monitoring unit can monitor the indoor temperature and detect abnormal temperature changes. For example, the monitoring unit can also monitor the indoor humidity and detect abnormal humidity changes. Furthermore, the monitoring unit can combine environmental information with behavioral patterns to make a comprehensive judgment about abnormal situations. This allows for a comprehensive judgment of abnormal situations by adding environmental information. Environmental information is acquired based on criteria such as temperature, humidity, and illuminance. This enables the monitoring unit to add environmental information such as temperature and humidity to the acquired video footage to gain a comprehensive understanding of the situation.

[0044] The analysis unit can accumulate behavioral history data of elderly individuals and pets and analyze changes in long-term behavioral patterns. For example, the analysis unit can analyze the long-term behavioral patterns of elderly individuals and detect changes in their health status. It can also analyze the long-term behavioral patterns of pets and detect changes in their health status and stress levels. Furthermore, the analysis unit can detect early signs of abnormal behavior based on behavioral history. This allows for the analysis of long-term behavioral patterns and the detection of changes in health status by accumulating behavioral history data. Behavioral history data is accumulated based on criteria such as data retention period and analysis algorithms. This enables the analysis unit to accumulate behavioral history data of elderly individuals and pets and analyze changes in their long-term behavioral patterns.

[0045] The analysis unit can simultaneously analyze multiple abnormality patterns when detecting an anomaly. For example, the analysis unit can detect an abnormal heart rate at the same time as an elderly person falls. For example, the analysis unit can also detect abnormal barking at the same time as abnormal behavior in a pet. Furthermore, the analysis unit can detect abnormal temperature changes in a room at the same time as abnormal behavior in an elderly person. This allows for more accurate anomaly detection by simultaneously analyzing multiple abnormality patterns. The abnormality patterns are analyzed based on criteria such as the frequency or duration of specific behaviors. This enables the analysis unit to simultaneously analyze multiple abnormality patterns when detecting an anomaly.

[0046] The analysis unit can perform not only video analysis but also audio analysis and environmental data analysis. For example, the analysis unit can perform audio analysis simultaneously with video analysis to detect abnormal vocalizations. For example, the analysis unit can also perform environmental data analysis simultaneously with video analysis to detect abnormal temperature changes. Furthermore, the analysis unit can combine audio analysis and environmental data analysis simultaneously with video analysis to comprehensively detect anomalies. This improves the accuracy of anomaly detection by performing audio analysis and environmental data analysis. Audio analysis and environmental data analysis are performed based on criteria such as speech recognition technology and the type of environmental data. This allows the analysis unit to perform not only video analysis but also audio analysis and environmental data analysis.

[0047] The analysis unit can also consider other sensor information when detecting anomalies. For example, the analysis unit can detect an abnormal heart rate simultaneously with a fall in an elderly person. For example, the analysis unit can also detect an abnormal respiratory rate simultaneously with abnormal behavior in a pet. Furthermore, the analysis unit can detect abnormal heart rate and respiratory rate simultaneously with abnormal behavior in an elderly person. This improves the accuracy of anomaly detection by considering other sensor information. Other sensor information is considered based on criteria such as heart rate, respiratory rate, and blood pressure. This allows the analysis unit to consider other sensor information when detecting anomalies.

[0048] The notification unit can use multiple notification methods simultaneously when it detects an anomaly. For example, when an anomaly is detected, the notification unit can send a notification simultaneously via email and phone. For example, when an anomaly is detected, the notification unit can also send a notification simultaneously via messaging apps and SMS. Furthermore, when an anomaly is detected, the notification unit can combine multiple notification methods to send a notification quickly. This allows for quick and reliable notification of anomalies by using multiple notification methods. Multiple notification methods are used based on criteria such as email, phone, and SMS. This allows the notification unit to use multiple notification methods simultaneously when it detects an anomaly.

[0049] The notification unit can automatically summarize and send notification content when it detects an anomaly. For example, when an anomaly is detected, the notification unit can summarize the detailed situation and send a notification. For example, when an anomaly is detected, the notification unit can also summarize important information and send a notification. Furthermore, when an anomaly is detected, the notification unit can send a notification that includes a concise summary. This allows important information to be conveyed quickly by summarizing the notification content. The method for automatically summarizing notification content is based on criteria such as a method for extracting important information and a summarization algorithm. This enables the notification unit to automatically summarize and send notification content when it detects an anomaly.

[0050] The notification unit can distribute notifications to multiple users when it detects an anomaly. For example, if an anomaly is detected, the notification unit can send a notification to all family members. The notification unit can also send notifications to multiple stakeholders when an anomaly is detected. Furthermore, when an anomaly is detected, the notification unit can distribute notifications to quickly share information. This allows for rapid information sharing by distributing notifications to multiple users. The method for distributing notifications to multiple users is based on criteria such as the user's role or the importance of the notification. This allows the notification unit to distribute notifications to multiple users when it detects an anomaly.

[0051] The notification unit can attach historical data related to the notification content when it detects an anomaly. For example, when an anomaly is detected, the notification unit can send a notification with past behavioral history attached. For example, when an anomaly is detected, the notification unit can also send a notification with past health status data attached. Furthermore, when an anomaly is detected, the notification unit can send a notification with past environmental data attached. This allows the notification unit to provide background information on the anomaly by attaching historical data. The historical data is attached based on criteria such as past anomaly history and related behavioral data. This allows the notification unit to attach historical data related to the notification content when it detects an anomaly.

[0052] The service provider can automatically adjust the video quality when providing real-time video. For example, the service provider can automatically adjust the video quality according to the network bandwidth. It can also automatically adjust the video quality according to the performance of the user's device. Furthermore, the service provider can enhance the quality of important parts of the video depending on its content. This automatic adjustment of video quality enables optimal video delivery based on network and device conditions. Video quality is adjusted based on criteria such as resolution and frame rate. This allows the service provider to automatically adjust the video quality when providing real-time video.

[0053] The service provider can add audio information to real-time video. For example, it can provide the voice of an elderly person in real time and detect abnormal vocalizations. It can also provide the sound of a pet barking in real time and detect abnormal sounds. Furthermore, it can provide ambient sounds in real time and detect abnormal sounds. By adding audio information to the video, a more detailed understanding of the situation becomes possible. The audio information is added based on criteria such as the type of sound and speech recognition technology. This allows the service provider to add audio information to real-time video.

[0054] The information provider can display text information related to the video when providing real-time video. For example, the information provider can display text information about the behavior of elderly people in real time. For example, the information provider can also display text information about the behavior of pets in real time. In addition, the information provider can display text information about the environment in real time. This makes it possible to provide more detailed information by displaying text information related to the video. The text information is displayed based on criteria such as relevant descriptions and summaries of important information. This allows the information provider to display text information related to the video when providing real-time video.

[0055] The service provider can add a function to enlarge a portion of the video when providing real-time video. For example, the service provider can enlarge the face or hands of an elderly person to understand the situation in detail. For example, the service provider can enlarge the behavior of a pet to understand the situation in detail. The service provider can also enlarge the area where an anomaly has been detected to understand the situation in detail. This makes it possible to understand the situation in detail by enlarging a portion of the video. The method for enlarging a portion of the video is based on criteria such as the range of enlargement and the timing of enlargement. This allows the service provider to add a function to enlarge a portion of the video when providing real-time video.

[0056] The care decision unit can refer to past behavioral history when analyzing a pet's behavior. For example, it can refer to the pet's past feeding patterns to determine the appropriate timing for feeding. It can also refer to the pet's past toileting patterns to determine the appropriate timing for toileting. Furthermore, it can refer to the pet's past play patterns to determine the appropriate timing for playtime. This allows the care decision unit to determine the appropriate timing for care by referring to past behavioral history. Past behavioral history is referenced based on criteria such as data retention period and reference algorithm. This allows the care decision unit to refer to past behavioral history when analyzing a pet's behavior.

[0057] The care decision unit can suggest multiple care methods when analyzing a pet's behavior. For example, if the pet wants food, the care decision unit can suggest multiple food options. For example, if the pet needs to go to the toilet, the care decision unit can suggest multiple toilet options. Also, if the pet wants to play, the care decision unit can suggest multiple play options. By suggesting multiple care methods, more appropriate care becomes possible. The multiple care methods are suggested based on criteria such as the type of care and the timing of the suggestion. This allows the care decision unit to suggest multiple care methods when analyzing a pet's behavior.

[0058] The care decision unit can also consider environmental data when analyzing a pet's behavior. For example, the care decision unit can consider the room temperature and suggest an appropriate care method. For example, the care decision unit can also consider the room humidity and suggest an appropriate care method. Furthermore, the care decision unit can combine environmental data and behavioral patterns to suggest the optimal care method. This allows for the suggestion of a more appropriate care method by considering environmental data. Environmental data is considered based on criteria such as temperature, humidity, and illuminance. This allows the care decision unit to also consider environmental data when analyzing a pet's behavior.

[0059] The care decision unit can compare a pet's behavior with that of other pets when analyzing its behavior. For example, it can detect abnormal behavior early by comparing it with other pets' behavior data. It can also suggest appropriate care methods by comparing it with other pets' behavior data. Furthermore, it can determine the optimal care method by comparing it with other pets' behavior data. This allows for the early detection of abnormal behavior by comparing it with other pets' behavior data. The behavior data of other pets is compared based on criteria such as data type and comparison algorithms. This enables the care decision unit to compare a pet's behavior with that of other pets when analyzing its behavior.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The monitoring unit can automatically adjust the resolution and frame rate of the acquired video according to the situation. For example, if an anomaly is detected, the video resolution can be increased to grasp the situation in detail. Conversely, it can monitor at a low resolution during normal times to conserve data. Furthermore, at night or in dark places, the frame rate can be lowered to acquire clear images even in low light. This allows for detailed situational awareness and data conservation by adjusting the video resolution and frame rate according to the situation. The resolution and frame rate are adjusted based on criteria such as the resolution range and frame rate range. This enables the monitoring unit to automatically adjust the resolution and frame rate of the acquired video according to the situation.

[0062] The analysis unit can accumulate behavioral history data of elderly individuals and pets and analyze long-term changes in their behavioral patterns. For example, it can analyze the long-term behavioral patterns of elderly individuals to detect changes in their health status. It can also analyze the long-term behavioral patterns of pets to detect changes in their health status and stress levels. Furthermore, it can detect early signs of abnormal behavior based on behavioral history. In this way, by accumulating behavioral history data, it is possible to analyze long-term changes in behavioral patterns and detect changes in health status. Behavioral history data is accumulated based on criteria such as data retention period and analysis algorithms. This allows the analysis unit to accumulate behavioral history data of elderly individuals and pets and analyze long-term changes in their behavioral patterns.

[0063] The notification unit can automatically summarize and send notification content when it detects an anomaly. For example, if an anomaly is detected, it can send a notification summarizing the detailed situation. It can also send a notification summarizing important information when an anomaly is detected. Furthermore, it can send a notification that includes a concise summary when an anomaly is detected. This allows important information to be conveyed quickly by summarizing the notification content. The method for automatically summarizing notification content is based on criteria such as the method for extracting important information and the summarization algorithm. This enables the notification unit to automatically summarize and send notification content when it detects an anomaly.

[0064] The service provider can display text information related to the video when providing real-time video. For example, it can display text information about the behavior of elderly people in real time. It can also display text information about the behavior of pets in real time. Furthermore, it can display text information about the environment in real time. This allows for the provision of more detailed information by displaying text information related to the video. The text information is displayed based on criteria such as relevant descriptions and summaries of important information. This enables the service provider to display text information related to the video when providing real-time video.

[0065] The care decision unit can suggest multiple care methods when analyzing a pet's behavior. For example, if a pet wants food, it can suggest several food options. Similarly, if a pet needs to use the toilet, it can suggest several toilet options. Furthermore, if a pet wants to play, it can suggest several play options. By suggesting multiple care methods, more appropriate care becomes possible. These multiple care methods are suggested based on criteria such as the type of care and the timing of the suggestion. This allows the care decision unit to suggest multiple care methods when analyzing a pet's behavior.

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

[0067] Step 1: The monitoring unit monitors the behavior of elderly people and pets. For example, it can use online cameras to capture the behavior of elderly people and pets in real time, and multiple cameras can be used to monitor a wide area. Step 2: The analysis unit uses a generation AI to analyze the video monitored by the monitoring unit. For example, the generation AI analyzes the video, analyzes the behavior of elderly people or pets, and uses algorithms to detect abnormalities. The generation AI can also learn behavioral patterns and detect abnormal behavior at an early stage. Step 3: The notification unit sends a notification to the user via a messaging app when an anomaly is detected. For example, the notification content can be customized according to the type and urgency of the anomaly, and notifications can be sent quickly using multiple notification methods. Step 4: The providing unit provides real-time video to users notified by the notification unit. For example, it can provide real-time video to users, allowing them to visually grasp the detailed situation. It can also automatically adjust the video quality and frame rate to provide optimal video, and add audio information to the video to grasp the detailed situation. Step 5: The care decision unit uses generative AI to analyze the pet's behavior with the analysis unit and determine the necessary care. For example, the generative AI analyzes the pet's behavior, learns its behavioral patterns, and suggests appropriate care methods. Furthermore, the generative AI can analyze the pet's behavior and issue instructions via voice or messaging apps.

[0068] (Example of form 2) The monitoring system according to an embodiment of the present invention is a system that continuously monitors the behavior and situation of elderly people and pets by combining an online camera and generative AI analysis technology. This monitoring system reduces the risk that elderly people living alone may fall or become ill and not be noticed immediately, and allows for real-time monitoring of the health status and needs (feeding, litter box, etc.) of pets. It also solves the problem of insufficient means of real-time information sharing for visually understanding the situation. This system monitors the behavior of elderly people and pets using an online camera, and generative AI analyzes the video. If an abnormality is detected, a notification is sent to the user via a messaging app, and real-time video is provided as needed. This allows for a detailed visual understanding of the situation and enables a quick response. Furthermore, in the case of pets, the system can analyze their behavior to determine necessary care and issue instructions via voice or messaging app. This system enhances the safety of elderly people and the health management of pets, realizing a safe living environment. Family members and related parties can take appropriate action by receiving quick and accurate information. Real-time video sharing makes it easier to understand the situation and improves the quality of care and pet care. This improves the quality of life (QOL) and provides peace of mind to family members and pets. For example, an online camera monitors the behavior of elderly people or pets, and a generating AI analyzes the footage to detect anomalies. When an anomaly is detected, a notification is sent to the user via a messaging app, and real-time video is provided as needed. This allows the user to visually grasp the detailed situation and respond quickly. Furthermore, in the case of pets, the generating AI analyzes their behavior and determines the necessary care, such as when the pet wants food or needs to go to the bathroom. The generating AI can be controlled by voice or through a messaging app, allowing the user to quickly care for their pet. This system enhances the safety of the elderly and the health management of pets, creating a safer living environment. Family members and caregivers can receive quick and accurate information, enabling them to take appropriate action. Real-time video sharing makes it easier to understand the situation and improves the quality of care and pet care.This improves the quality of life (QOL) and provides peace of mind for families and pets. The monitoring system allows for continuous monitoring of the behavior of elderly individuals and pets, enabling a rapid response if any abnormalities are detected.

[0069] The monitoring system according to the embodiment comprises a monitoring unit, an analysis unit, a notification unit, a provision unit, and a care decision unit. The monitoring unit monitors the behavior of elderly people and pets. The monitoring unit monitors the behavior of elderly people and pets using, for example, an online camera. The monitoring unit can, for example, acquire camera footage in real time and monitor behavior. The monitoring unit can also, for example, monitor a wide area using multiple cameras. The analysis unit analyzes the footage monitored by the monitoring unit using a generative AI. The analysis unit, for example, uses a generative AI to analyze the footage and analyze the behavior of elderly people and pets. The analysis unit can, for example, use an algorithm for the generative AI to detect anomalies to analyze the footage. The analysis unit can also, for example, use a generative AI to learn behavior patterns and detect abnormal behavior early. The notification unit sends a notification to the user via a messaging application when an anomaly is detected. The notification unit can, for example, customize the notification content according to the type and urgency of the anomaly. Furthermore, the notification unit can, for example, quickly send notifications using multiple notification means. The provision unit provides real-time video to users notified by the notification unit. The provision unit can, for example, provide real-time video to users, allowing them to visually grasp the detailed situation. The provision unit can, for example, automatically adjust the video quality and frame rate to provide optimal video. The provision unit can also, for example, add audio information to the video to grasp the detailed situation. The care decision unit uses a generation AI to analyze the pet's behavior with the analysis unit and determine the necessary care. The care decision unit, for example, uses a generation AI to analyze the pet's behavior and determine the necessary care. The care decision unit can, for example, use a generation AI to learn the pet's behavior patterns and propose appropriate care methods. The care decision unit can also, for example, use a generation AI to analyze the pet's behavior and issue instructions via voice or messaging apps. As a result, the monitoring system according to the embodiment can continuously monitor the behavior of elderly people and pets and respond quickly when an abnormality is detected.

[0070] The monitoring unit monitors the behavior of elderly individuals and pets. For example, the monitoring unit uses online cameras to monitor the behavior of elderly individuals and pets. Specifically, the monitoring unit installs multiple high-resolution cameras, positioned to cover a wide area. The cameras are equipped with infrared and low-light capabilities to capture clear images day and night. This allows for accurate monitoring of elderly individuals and pets even at night. Furthermore, the cameras have pan-tilt-zoom (PTZ) functionality, allowing them to focus on specific areas or objects. This enables wide-area monitoring while also allowing for detailed monitoring as needed. The monitoring unit acquires camera footage in real time and transmits it to a central monitoring system. The footage is transmitted via a secure network and encrypted to prevent data leakage and unauthorized access. This ensures the security of the monitoring data. The monitoring unit centrally manages the video data and can also play back past footage as needed. This allows for detailed review of specific time periods or events.

[0071] The analysis unit uses generative AI to analyze video footage monitored by the monitoring unit. For example, the analysis unit uses generative AI to analyze video footage and analyze the behavior of elderly people and pets. Specifically, the generative AI uses deep learning technology to analyze video data and learn the behavioral patterns of elderly people and pets. The generative AI automatically recognizes people and pets in the video and tracks their movements. This allows it to detect specific behaviors and abnormal movements. For example, if an elderly person falls or a pet exhibits abnormal behavior, the generative AI can immediately detect this and flag it as an anomaly. The analysis unit can analyze video footage using algorithms designed for generative AI anomaly detection. This enables early detection of abnormal behavior and allows for a rapid response. Furthermore, the analysis unit can enable the generative AI to learn behavioral patterns and detect abnormal behavior early. The generative AI learns normal behavioral patterns based on past data and detects behaviors that differ from these as anomalies. This allows the analysis unit to achieve highly accurate anomaly detection and improve the overall reliability of the system.

[0072] The notification unit sends notifications to the user via the messaging app when an anomaly is detected. Specifically, the notification unit can customize the content of notifications according to the type and urgency of the anomaly. For example, if an elderly person falls or a pet exhibits unusual behavior, the notification unit will send a message to the user containing detailed information. The notification will include the location, time, and nature of the anomaly. This allows the user to quickly understand the situation and take appropriate action. The notification unit can also send notifications quickly using multiple notification methods. For example, it can send notifications not only via the messaging app but also via SMS, email, and voice calls. This ensures that the user receives the notification reliably. Furthermore, the notification unit can customize the notification priority and method according to the user's settings. This enables flexible notifications tailored to the user's needs.

[0073] The service provider delivers video in real time to users notified by the notification unit. For example, the service provider can provide users with real-time video, allowing them to visually grasp the detailed situation. Specifically, the service provider delivers high-resolution video to users in streaming format, and users can view the video through devices such as smartphones, tablets, and personal computers. The service provider can automatically adjust the video quality and frame rate to provide optimal video. This ensures that the best possible video quality is always maintained depending on the network conditions and device performance. The service provider can also add audio information to the video to grasp the detailed situation. For example, a microphone can be installed in the surveillance camera to acquire audio from the site in real time and provide it along with the video. This allows users to understand the situation not only through visual information but also through audio information. Furthermore, the service provider also provides a function for users to play back past video. This allows users to check the situation before and after an anomaly occurred in detail and obtain information to take appropriate action.

[0074] The care decision unit uses a generative AI to analyze the pet's behavior with the analysis unit and determine the necessary care. Specifically, the generative AI learns the pet's behavior patterns and detects abnormal behavior and changes in health. For example, if a pet exhibits behaviors such as having a smaller appetite than usual, being sluggish, or making unusual noises, the generative AI detects this as an abnormality and suggests appropriate care methods. The care decision unit can have the generative AI learn the pet's behavior patterns and suggest appropriate care methods. For example, if a pet is stressed, the generative AI suggests ways to help it relax. Also, if a pet shows signs of illness, the generative AI recommends a veterinary examination. Furthermore, the care decision unit can have the generative AI analyze the pet's behavior and issue instructions via voice or messaging apps. For example, if a pet exhibits abnormal behavior, the generative AI notifies the owner of specific actions to take via voice or message. This allows the owner to take quick and appropriate action. The care assessment unit can maintain the pet's health and well-being by continuously monitoring the pet's health status and determining the necessary care.

[0075] The analysis unit can analyze the behavior of elderly people and pets using generative AI and detect abnormalities. For example, the analysis unit can use generative AI to analyze the behavior of elderly people and detect abnormalities such as falls or poor health. The analysis unit can also use generative AI to analyze the behavior of pets and detect abnormal behavior or changes in their health. Furthermore, the analysis unit can use generative AI to learn behavioral patterns and detect abnormal behavior early. As a result, the accuracy of behavioral analysis is improved by using generative AI. Generative AI is implemented using technologies such as deep learning and reinforcement learning. For example, the generative AI analyzes behavior using a model that takes behavioral data of elderly people and pets as input and outputs abnormalities. For example, the generative AI uses an algorithm that extracts features from behavioral data and detects abnormal behavior. As a result, the analysis unit can use generative AI to analyze the behavior of elderly people and pets and detect abnormalities.

[0076] The notification unit can send notifications to the user via a messaging app when an anomaly is detected. For example, the notification unit sends a notification to the user via a messaging app when an anomaly is detected. The notification unit can customize the notification content according to the type and urgency of the anomaly. Furthermore, the notification unit can send notifications quickly using multiple notification methods. This allows for quick notification to the user when an anomaly is detected. This enables the notification unit to send notifications to the user via a messaging app when an anomaly is detected.

[0077] The service provider can provide users with real-time video. For example, the service provider can provide users with real-time video, allowing them to visually grasp the detailed situation. For example, the service provider can automatically adjust the video quality and frame rate to provide optimal video. The service provider can also add audio information to the video to grasp the detailed situation. This allows users to check the video in real time and respond quickly. Real-time has criteria such as the acceptable latency range and update frequency. For example, the service provider can provide real-time video with low latency. The service provider can also provide real-time video with a high update frequency. The service provider can also ensure real-timeness by providing video within an acceptable latency range. This allows the service provider to provide users with real-time video.

[0078] The care decision unit can analyze a pet's behavior using generative AI and determine the necessary care. For example, the care decision unit can use generative AI to analyze the pet's behavior and determine the necessary care. For example, the care decision unit can use generative AI to learn the pet's behavior patterns and suggest appropriate care methods. The care decision unit can also use generative AI to analyze the pet's behavior and issue instructions via voice or messaging apps. This improves the accuracy of pet behavior analysis and care decisions by using generative AI. Generative AI is implemented using technologies such as deep learning and reinforcement learning. For example, the generative AI analyzes behavior using a model that takes pet behavior data as input and outputs the necessary care. For example, the generative AI uses an algorithm that extracts features from behavior data and suggests appropriate care methods. This allows the care decision unit to analyze a pet's behavior using generative AI and determine the necessary care.

[0079] The care decision unit can analyze a pet's behavior using generative AI and issue instructions via voice or messaging apps. For example, the care decision unit uses generative AI to analyze the pet's behavior and issue instructions via voice or messaging apps. The care decision unit can, for example, use generative AI to learn the pet's behavior patterns and issue appropriate instructions. Furthermore, the care decision unit can use generative AI to analyze the pet's behavior and determine the necessary care. This allows for appropriate instructions to be given according to the pet's behavior. Generative AI is implemented using technologies such as deep learning and reinforcement learning. For example, the generative AI analyzes the behavior using a model that takes pet behavior data as input and outputs the necessary care. The generative AI uses algorithms that extract features from behavioral data and propose appropriate care methods. This allows the care decision unit to analyze the pet's behavior using generative AI and issue instructions via voice or messaging apps.

[0080] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated emotions. For example, if the user is stressed, the monitoring unit can increase the monitoring frequency to detect anomalies early. For example, if the user is relaxed, the monitoring unit can lower the monitoring frequency to respect privacy. Furthermore, if the user is anxious, the monitoring unit can set the monitoring frequency to a moderate level to provide a sense of security. In this way, by adjusting the monitoring frequency according to the user's emotions, anomalies can be detected early while respecting privacy. The user's emotions are estimated using methods such as facial recognition, voice analysis, and survey results. The monitoring frequency is adjusted based on criteria such as the monitoring interval and the length of the monitoring time. In this way, the monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated emotions.

[0081] The monitoring unit can learn the behavioral patterns of elderly people and pets, enabling early detection of abnormal behavior. For example, the monitoring unit can learn the normal behavioral patterns of elderly people and detect abnormal movements or prolonged periods of stillness. For example, the monitoring unit can learn the eating and toileting patterns of pets and detect abnormal behavior early. In addition, the monitoring unit can learn the sleep patterns of elderly people and pets and detect abnormal sleep durations or movements. In this way, by learning behavioral patterns, abnormal behavior can be detected early. Behavioral patterns are learned based on criteria such as the repetition of daily actions or the frequency of specific actions. In this way, the monitoring unit can learn the behavioral patterns of elderly people and pets and enable early detection of abnormal behavior.

[0082] The monitoring unit can automatically adjust the resolution and frame rate of the acquired video according to the situation. For example, if an anomaly is detected, the monitoring unit will increase the video resolution to grasp the situation in detail. For example, the monitoring unit can also monitor at a low resolution under normal circumstances to save data communication. Furthermore, the monitoring unit can lower the frame rate at night or in dark places to acquire clear images even in low light. This allows for detailed situational awareness and data communication savings by adjusting the video resolution and frame rate according to the situation. The resolution and frame rate are adjusted based on criteria such as the resolution range and frame rate range. This allows the monitoring unit to automatically adjust the resolution and frame rate of the acquired video according to the situation.

[0083] The monitoring unit can estimate the user's emotions and determine the priority of what to monitor based on those emotions. For example, if the user is feeling anxious, the monitoring unit will prioritize monitoring elderly people. If the user is relaxed, the monitoring unit may also prioritize monitoring pets. Furthermore, if the user is feeling stressed, the monitoring unit can balance monitoring both. This allows for more appropriate monitoring by determining the priority of what to monitor according to the user's emotions. The user's emotions are estimated using methods such as facial recognition, voice analysis, and survey results. The priority of what to monitor is determined based on criteria such as the severity of the anomaly or the user's level of interest. This allows the monitoring unit to estimate the user's emotions and determine the priority of what to monitor based on those emotions.

[0084] The monitoring unit can add audio information to the acquired video and also perform audio analysis. For example, the monitoring unit can analyze the voices of elderly people to detect abnormal vocalizations or cries for help. For example, the monitoring unit can analyze the sounds of pets to detect abnormal sounds or signs of stress. The monitoring unit can also analyze ambient sounds to detect abnormal sounds (e.g., noises, the sound of breaking glass). By adding audio information, it is possible to detect abnormal vocalizations and sounds. The audio information is analyzed based on criteria such as the type of sound and speech recognition technology. As a result, the monitoring unit can add audio information to the acquired video and also perform audio analysis.

[0085] The monitoring unit can add environmental information such as temperature and humidity to the acquired video footage to gain a comprehensive understanding of the situation. For example, the monitoring unit can monitor the indoor temperature and detect abnormal temperature changes. For example, the monitoring unit can also monitor the indoor humidity and detect abnormal humidity changes. Furthermore, the monitoring unit can combine environmental information with behavioral patterns to make a comprehensive judgment about abnormal situations. This allows for a comprehensive judgment of abnormal situations by adding environmental information. Environmental information is acquired based on criteria such as temperature, humidity, and illuminance. This enables the monitoring unit to add environmental information such as temperature and humidity to the acquired video footage to gain a comprehensive understanding of the situation.

[0086] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, 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 of the analysis results according to the user's emotions. The user's emotions are estimated using methods such as facial recognition, voice analysis, and survey results. The display method of the analysis results is adjusted based on criteria such as display format and the priority of display content. This allows the analysis unit to estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions.

[0087] The analysis unit can accumulate behavioral history data of elderly individuals and pets and analyze changes in long-term behavioral patterns. For example, the analysis unit can analyze the long-term behavioral patterns of elderly individuals and detect changes in their health status. It can also analyze the long-term behavioral patterns of pets and detect changes in their health status and stress levels. Furthermore, the analysis unit can detect early signs of abnormal behavior based on behavioral history. This allows for the analysis of long-term behavioral patterns and the detection of changes in health status by accumulating behavioral history data. Behavioral history data is accumulated based on criteria such as data retention period and analysis algorithms. This enables the analysis unit to accumulate behavioral history data of elderly individuals and pets and analyze changes in their long-term behavioral patterns.

[0088] The analysis unit can simultaneously analyze multiple abnormality patterns when detecting an anomaly. For example, the analysis unit can detect an abnormal heart rate at the same time as an elderly person falls. For example, the analysis unit can also detect abnormal barking at the same time as abnormal behavior in a pet. Furthermore, the analysis unit can detect abnormal temperature changes in a room at the same time as abnormal behavior in an elderly person. This allows for more accurate anomaly detection by simultaneously analyzing multiple abnormality patterns. The abnormality patterns are analyzed based on criteria such as the frequency or duration of specific behaviors. This enables the analysis unit to simultaneously analyze multiple abnormality patterns when detecting an anomaly.

[0089] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit will prioritize analysis of elderly people. For example, if the user is relaxed, the analysis unit may prioritize analysis of pets. Furthermore, if the user is feeling stressed, the analysis unit can balance both types of analysis. This allows for more appropriate analysis by determining the priority of analysis according to the user's emotions. The user's emotions are estimated using methods such as facial recognition, voice analysis, and survey results. The priority of analysis is determined based on criteria such as the severity of the abnormality or the user's level of interest. This allows the analysis unit to estimate the user's emotions and determine the priority of analysis based on the estimated emotions.

[0090] The analysis unit can perform not only video analysis but also audio analysis and environmental data analysis. For example, the analysis unit can perform audio analysis simultaneously with video analysis to detect abnormal vocalizations. For example, the analysis unit can also perform environmental data analysis simultaneously with video analysis to detect abnormal temperature changes. Furthermore, the analysis unit can combine audio analysis and environmental data analysis simultaneously with video analysis to comprehensively detect anomalies. This improves the accuracy of anomaly detection by performing audio analysis and environmental data analysis. Audio analysis and environmental data analysis are performed based on criteria such as speech recognition technology and the type of environmental data. This allows the analysis unit to perform not only video analysis but also audio analysis and environmental data analysis.

[0091] The analysis unit can also consider other sensor information when detecting anomalies. For example, the analysis unit can detect an abnormal heart rate simultaneously with a fall in an elderly person. For example, the analysis unit can also detect an abnormal respiratory rate simultaneously with abnormal behavior in a pet. Furthermore, the analysis unit can detect abnormal heart rate and respiratory rate simultaneously with abnormal behavior in an elderly person. This improves the accuracy of anomaly detection by considering other sensor information. Other sensor information is considered based on criteria such as heart rate, respiratory rate, and blood pressure. This allows the analysis unit to consider other sensor information when detecting anomalies.

[0092] The notification unit can estimate the user's emotions and adjust the urgency of notifications based on those emotions. For example, if the user is stressed, the notification unit will prioritize sending high-urgency notifications. For example, if the user is relaxed, the notification unit can also send low-urgency notifications. Furthermore, if the user is in a hurry, the notification unit can quickly send high-urgency notifications. This allows for more appropriate notifications by adjusting the urgency of notifications according to the user's emotions. The user's emotions are estimated using methods such as facial recognition, voice analysis, and survey results. The urgency of notifications is adjusted based on criteria such as the severity of the anomaly or the user's level of interest. This allows the notification unit to estimate the user's emotions and adjust the urgency of notifications based on those emotions.

[0093] The notification unit can use multiple notification methods simultaneously when it detects an anomaly. For example, when an anomaly is detected, the notification unit can send a notification simultaneously via email and phone. For example, when an anomaly is detected, the notification unit can also send a notification simultaneously via messaging apps and SMS. Furthermore, when an anomaly is detected, the notification unit can combine multiple notification methods to send a notification quickly. This allows for quick and reliable notification of anomalies by using multiple notification methods. Multiple notification methods are used based on criteria such as email, phone, and SMS. This allows the notification unit to use multiple notification methods simultaneously when it detects an anomaly.

[0094] The notification unit can automatically summarize and send notification content when it detects an anomaly. For example, when an anomaly is detected, the notification unit can summarize the detailed situation and send a notification. For example, when an anomaly is detected, the notification unit can also summarize important information and send a notification. Furthermore, when an anomaly is detected, the notification unit can send a notification that includes a concise summary. This allows important information to be conveyed quickly by summarizing the notification content. The method for automatically summarizing notification content is based on criteria such as a method for extracting important information and a summarization algorithm. This enables the notification unit to automatically summarize and send notification content when it detects an anomaly.

[0095] The notification unit can estimate the user's emotions and customize the content of notifications based on those emotions. For example, if the user is feeling stressed, the notification unit can send a notification containing detailed information. If the user is relaxed, the notification unit can also send a notification containing concise information. Furthermore, if the user is in a hurry, the notification unit can send a notification that gets straight to the point. This allows for more appropriate information to be provided by customizing notification content according to the user's emotions. The user's emotions are estimated using methods such as facial recognition, voice analysis, and survey results. The content of notifications is customized based on criteria such as the user's level of interest or the severity of the anomaly. This allows the notification unit to estimate the user's emotions and customize the content of notifications based on those emotions.

[0096] The notification unit can distribute notifications to multiple users when it detects an anomaly. For example, if an anomaly is detected, the notification unit can send a notification to all family members. The notification unit can also send notifications to multiple stakeholders when an anomaly is detected. Furthermore, when an anomaly is detected, the notification unit can distribute notifications to quickly share information. This allows for rapid information sharing by distributing notifications to multiple users. The method for distributing notifications to multiple users is based on criteria such as the user's role or the importance of the notification. This allows the notification unit to distribute notifications to multiple users when it detects an anomaly.

[0097] The notification unit can attach historical data related to the notification content when it detects an anomaly. For example, when an anomaly is detected, the notification unit can send a notification with past behavioral history attached. For example, when an anomaly is detected, the notification unit can also send a notification with past health status data attached. Furthermore, when an anomaly is detected, the notification unit can send a notification with past environmental data attached. This allows the notification unit to provide background information on the anomaly by attaching historical data. The historical data is attached based on criteria such as past anomaly history and related behavioral data. This allows the notification unit to attach historical data related to the notification content when it detects an anomaly.

[0098] The service provider can estimate the user's emotions and adjust the video delivery method based on those estimated emotions. For example, if the user is nervous, the service provider can provide a simple, highly visible video. If the user is relaxed, the service provider can also provide a video containing detailed information. Furthermore, if the user is in a hurry, the service provider can provide a video that gets straight to the point. By adjusting the video delivery method according to the user's emotions, more appropriate information can be provided. The user's emotions are estimated using methods such as facial recognition, voice analysis, and survey results. The video delivery method is adjusted based on criteria such as video quality and delivery timing. This allows the service provider to estimate the user's emotions and adjust the video delivery method based on those estimated emotions.

[0099] The service provider can automatically adjust the video quality when providing real-time video. For example, the service provider can automatically adjust the video quality according to the network bandwidth. It can also automatically adjust the video quality according to the performance of the user's device. Furthermore, the service provider can enhance the quality of important parts of the video depending on its content. This automatic adjustment of video quality enables optimal video delivery based on network and device conditions. Video quality is adjusted based on criteria such as resolution and frame rate. This allows the service provider to automatically adjust the video quality when providing real-time video.

[0100] The service provider can add audio information to real-time video. For example, it can provide the voice of an elderly person in real time and detect abnormal vocalizations. It can also provide the sound of a pet barking in real time and detect abnormal sounds. Furthermore, it can provide ambient sounds in real time and detect abnormal sounds. By adding audio information to the video, a more detailed understanding of the situation becomes possible. The audio information is added based on criteria such as the type of sound and speech recognition technology. This allows the service provider to add audio information to real-time video.

[0101] The service provider can estimate the user's emotions and determine the order in which videos are presented based on those estimated emotions. For example, if the user is feeling anxious, the service provider may prioritize providing videos of elderly people. If the user is relaxed, the service provider may also prioritize providing videos of pets. Furthermore, if the user is feeling stressed, the service provider may provide a balanced mix of both types of videos. This allows for more appropriate information delivery by determining the order in which videos are presented according to the user's emotions. The user's emotions are estimated using methods such as facial recognition, voice analysis, and survey results. The order in which videos are presented is determined based on criteria such as the severity of the abnormality or the user's level of interest. This allows the service provider to estimate the user's emotions and determine the order in which videos are presented based on those estimated emotions.

[0102] The information provider can display text information related to the video when providing real-time video. For example, the information provider can display text information about the behavior of elderly people in real time. For example, the information provider can also display text information about the behavior of pets in real time. In addition, the information provider can display text information about the environment in real time. This makes it possible to provide more detailed information by displaying text information related to the video. The text information is displayed based on criteria such as relevant descriptions and summaries of important information. This allows the information provider to display text information related to the video when providing real-time video.

[0103] The service provider can add a function to enlarge a portion of the video when providing real-time video. For example, the service provider can enlarge the face or hands of an elderly person to understand the situation in detail. For example, the service provider can enlarge the behavior of a pet to understand the situation in detail. The service provider can also enlarge the area where an anomaly has been detected to understand the situation in detail. This makes it possible to understand the situation in detail by enlarging a portion of the video. The method for enlarging a portion of the video is based on criteria such as the range of enlargement and the timing of enlargement. This allows the service provider to add a function to enlarge a portion of the video when providing real-time video.

[0104] The care decision unit can estimate the user's emotions and determine care priorities based on those estimated emotions. For example, if the user is feeling anxious, the care decision unit will prioritize the care of the elderly. If the user is relaxed, the care decision unit may also prioritize the care of the pet. Furthermore, if the user is feeling stressed, the care decision unit can balance both types of care. This allows for more appropriate care by determining care priorities according to the user's emotions. The user's emotions are estimated using methods such as facial recognition, voice analysis, and survey results. Care priorities are determined based on criteria such as the pet's health condition and the severity of its behavior. This allows the care decision unit to estimate the user's emotions and determine care priorities based on those estimated emotions.

[0105] The care decision unit can refer to past behavioral history when analyzing a pet's behavior. For example, it can refer to the pet's past feeding patterns to determine the appropriate timing for feeding. It can also refer to the pet's past toileting patterns to determine the appropriate timing for toileting. Furthermore, it can refer to the pet's past play patterns to determine the appropriate timing for playtime. This allows the care decision unit to determine the appropriate timing for care by referring to past behavioral history. Past behavioral history is referenced based on criteria such as data retention period and reference algorithm. This allows the care decision unit to refer to past behavioral history when analyzing a pet's behavior.

[0106] The care decision unit can suggest multiple care methods when analyzing a pet's behavior. For example, if the pet wants food, the care decision unit can suggest multiple food options. For example, if the pet needs to go to the toilet, the care decision unit can suggest multiple toilet options. Also, if the pet wants to play, the care decision unit can suggest multiple play options. By suggesting multiple care methods, more appropriate care becomes possible. The multiple care methods are suggested based on criteria such as the type of care and the timing of the suggestion. This allows the care decision unit to suggest multiple care methods when analyzing a pet's behavior.

[0107] The care decision unit can estimate the user's emotions and customize the care content based on those emotions. For example, if the user is feeling anxious, the care decision unit can provide detailed care content. For example, if the user is relaxed, the care decision unit can provide concise care content. Furthermore, if the user is in a hurry, the care decision unit can provide concise care content. This allows for more appropriate care by customizing the care content according to the user's emotions. The user's emotions are estimated using methods such as facial recognition, voice analysis, and survey results. The care content is customized based on criteria such as the pet's health condition and the user's requests. This allows the care decision unit to estimate the user's emotions and customize the care content based on those emotions.

[0108] The care decision unit can also consider environmental data when analyzing a pet's behavior. For example, the care decision unit can consider the room temperature and suggest an appropriate care method. For example, the care decision unit can also consider the room humidity and suggest an appropriate care method. Furthermore, the care decision unit can combine environmental data and behavioral patterns to suggest the optimal care method. This allows for the suggestion of a more appropriate care method by considering environmental data. Environmental data is considered based on criteria such as temperature, humidity, and illuminance. This allows the care decision unit to also consider environmental data when analyzing a pet's behavior.

[0109] The care decision unit can compare a pet's behavior with that of other pets when analyzing its behavior. For example, it can detect abnormal behavior early by comparing it with other pets' behavior data. It can also suggest appropriate care methods by comparing it with other pets' behavior data. Furthermore, it can determine the optimal care method by comparing it with other pets' behavior data. This allows for the early detection of abnormal behavior by comparing it with other pets' behavior data. The behavior data of other pets is compared based on criteria such as data type and comparison algorithms. This enables the care decision unit to compare a pet's behavior with that of other pets when analyzing its behavior.

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

[0111] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on those estimates. For example, if the user is stressed, the monitoring frequency can be increased to detect anomalies early. Conversely, if the user is relaxed, the monitoring frequency can be decreased to respect their privacy. Furthermore, if the user is anxious, the monitoring frequency can be set to a moderate level to provide a sense of security. This allows for early detection of anomalies while respecting privacy by adjusting the monitoring frequency according to the user's emotions. The user's emotions are estimated using methods such as facial recognition, voice analysis, and survey results. The monitoring frequency is adjusted based on criteria such as the monitoring interval and the length of the monitoring time. This allows the monitoring unit to estimate the user's emotions and adjust the monitoring frequency based on those estimates.

[0112] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the essentials can be provided. By adjusting the display method of the analysis results according to the user's emotions, it becomes possible to provide more appropriate information. The user's emotions are estimated using facial recognition, voice analysis, survey results, etc. The display method of the analysis results is adjusted based on criteria such as display format and the priority of displayed content. This allows the analysis unit to estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions.

[0113] The notification unit can estimate the user's emotions and adjust the urgency of notifications based on those emotions. For example, if the user is stressed, it can prioritize sending high-urgency notifications. Conversely, if the user is relaxed, it can send low-urgency notifications. Furthermore, if the user is in a hurry, it can quickly send high-urgency notifications. This allows for more appropriate notifications by adjusting the urgency of notifications according to the user's emotions. The user's emotions are estimated using methods such as facial recognition, voice analysis, and survey results. The urgency of notifications is adjusted based on criteria such as the severity of the anomaly and the user's level of interest. This allows the notification unit to estimate the user's emotions and adjust the urgency of notifications based on those emotions.

[0114] The service provider can estimate the user's emotions and adjust the video delivery method based on those estimates. For example, if the user is nervous, a simple and highly visible video can be provided. If the user is relaxed, a video containing detailed information can be provided. Furthermore, if the user is in a hurry, a video that gets straight to the point can be provided. By adjusting the video delivery method according to the user's emotions, more appropriate information can be provided. The user's emotions are estimated using methods such as facial recognition, voice analysis, and survey results. The video delivery method is adjusted based on criteria such as video quality and delivery timing. This allows the service provider to estimate the user's emotions and adjust the video delivery method based on those estimates.

[0115] The care decision unit can estimate the user's emotions and determine care priorities based on those estimated emotions. For example, if the user is feeling anxious, it can prioritize the care of the elderly. If the user is relaxed, it can prioritize the care of the pet. Furthermore, if the user is stressed, it can balance both types of care. This allows for more appropriate care by determining care priorities according to the user's emotions. The user's emotions are estimated using methods such as facial recognition, voice analysis, and survey results. Care priorities are determined based on criteria such as the pet's health condition and the severity of its behavior. This allows the care decision unit to estimate the user's emotions and determine care priorities based on those estimated emotions.

[0116] The monitoring unit can automatically adjust the resolution and frame rate of the acquired video according to the situation. For example, if an anomaly is detected, the video resolution can be increased to grasp the situation in detail. Conversely, it can monitor at a low resolution during normal times to conserve data. Furthermore, at night or in dark places, the frame rate can be lowered to acquire clear images even in low light. This allows for detailed situational awareness and data conservation by adjusting the video resolution and frame rate according to the situation. The resolution and frame rate are adjusted based on criteria such as the resolution range and frame rate range. This enables the monitoring unit to automatically adjust the resolution and frame rate of the acquired video according to the situation.

[0117] The analysis unit can accumulate behavioral history data of elderly individuals and pets and analyze long-term changes in their behavioral patterns. For example, it can analyze the long-term behavioral patterns of elderly individuals to detect changes in their health status. It can also analyze the long-term behavioral patterns of pets to detect changes in their health status and stress levels. Furthermore, it can detect early signs of abnormal behavior based on behavioral history. In this way, by accumulating behavioral history data, it is possible to analyze long-term changes in behavioral patterns and detect changes in health status. Behavioral history data is accumulated based on criteria such as data retention period and analysis algorithms. This allows the analysis unit to accumulate behavioral history data of elderly individuals and pets and analyze long-term changes in their behavioral patterns.

[0118] The notification unit can automatically summarize and send notification content when it detects an anomaly. For example, if an anomaly is detected, it can send a notification summarizing the detailed situation. It can also send a notification summarizing important information when an anomaly is detected. Furthermore, it can send a notification that includes a concise summary when an anomaly is detected. This allows important information to be conveyed quickly by summarizing the notification content. The method for automatically summarizing notification content is based on criteria such as the method for extracting important information and the summarization algorithm. This enables the notification unit to automatically summarize and send notification content when it detects an anomaly.

[0119] The service provider can display text information related to the video when providing real-time video. For example, it can display text information about the behavior of elderly people in real time. It can also display text information about the behavior of pets in real time. Furthermore, it can display text information about the environment in real time. This allows for the provision of more detailed information by displaying text information related to the video. The text information is displayed based on criteria such as relevant descriptions and summaries of important information. This enables the service provider to display text information related to the video when providing real-time video.

[0120] The care decision unit can suggest multiple care methods when analyzing a pet's behavior. For example, if a pet wants food, it can suggest several food options. Similarly, if a pet needs to use the toilet, it can suggest several toilet options. Furthermore, if a pet wants to play, it can suggest several play options. By suggesting multiple care methods, more appropriate care becomes possible. These multiple care methods are suggested based on criteria such as the type of care and the timing of the suggestion. This allows the care decision unit to suggest multiple care methods when analyzing a pet's behavior.

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

[0122] Step 1: The monitoring unit monitors the behavior of elderly people and pets. For example, it can use online cameras to capture the behavior of elderly people and pets in real time, and multiple cameras can be used to monitor a wide area. Step 2: The analysis unit uses a generation AI to analyze the video monitored by the monitoring unit. For example, the generation AI analyzes the video, analyzes the behavior of elderly people or pets, and uses algorithms to detect abnormalities. The generation AI can also learn behavioral patterns and detect abnormal behavior at an early stage. Step 3: The notification unit sends a notification to the user via a messaging app when an anomaly is detected. For example, the notification content can be customized according to the type and urgency of the anomaly, and notifications can be sent quickly using multiple notification methods. Step 4: The providing unit provides real-time video to users notified by the notification unit. For example, it can provide real-time video to users, allowing them to visually grasp the detailed situation. It can also automatically adjust the video quality and frame rate to provide optimal video, and add audio information to the video to grasp the detailed situation. Step 5: The care decision unit uses generative AI to analyze the pet's behavior with the analysis unit and determine the necessary care. For example, the generative AI analyzes the pet's behavior, learns its behavioral patterns, and suggests appropriate care methods. Furthermore, the generative AI can analyze the pet's behavior and issue instructions via voice or messaging apps.

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

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

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

[0126] Each of the multiple elements described above, including the monitoring unit, analysis unit, notification unit, provision unit, and care decision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the monitoring unit monitors the behavior of the elderly person or pet using the camera 42 of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the video using generating AI. The notification unit is implemented in the control unit 46A of the smart device 14 and sends a notification to the user via a messaging app when an abnormality is detected. The provision unit is implemented in the control unit 46A of the smart device 14 and provides the user with video in real time. The care decision unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the pet's behavior using generating AI to determine the necessary care. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0142] Each of the multiple elements described above, including the monitoring unit, analysis unit, notification unit, provision unit, and care decision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the monitoring unit monitors the behavior of the elderly person or pet using the camera 42 of the smart glasses 214. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and analyzes the video using generating AI. The notification unit is implemented in the control unit 46A of the smart glasses 214 and sends a notification to the user via a messaging app when an abnormality is detected. The provision unit is implemented in the control unit 46A of the smart glasses 214 and provides the user with video in real time. The care decision unit is implemented in the identification processing unit 290 of the data processing unit 12 and analyzes the pet's behavior using generating AI to determine the necessary care. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0158] Each of the multiple elements described above, including the monitoring unit, analysis unit, notification unit, provision unit, and care decision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the monitoring unit monitors the behavior of the elderly person or pet using the camera 42 of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the video using generating AI. The notification unit is implemented in the control unit 46A of the headset terminal 314 and sends a notification to the user via a messaging application when an abnormality is detected. The provision unit is implemented in the control unit 46A of the headset terminal 314 and provides the user with video in real time. The care decision unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the pet's behavior using generating AI to determine the necessary care. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0175] Each of the multiple elements described above, including the monitoring unit, analysis unit, notification unit, provision unit, and care decision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the monitoring unit monitors the behavior of the elderly person or pet using the camera 42 of the robot 414. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the video using generating AI. The notification unit is implemented in the control unit 46A of the robot 414 and sends a notification to the user via a messaging application when an abnormality is detected. The provision unit is implemented in the control unit 46A of the robot 414 and provides the user with video in real time. The care decision unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the behavior of the pet using generating AI and determines the necessary care. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0194] (Note 1) A monitoring department that monitors the behavior of the elderly and pets, An analysis unit analyzes the video footage monitored by the aforementioned monitoring unit, A notification unit that sends a notification when an abnormality is detected by the analysis unit, A provisioning unit that provides video in real time to users notified by the aforementioned notification unit, The system includes a care determination unit that analyzes the pet's behavior using the aforementioned analysis unit and determines the necessary care. A system characterized by the following features. (Note 2) The aforementioned analysis unit, The generated AI analyzes the behavior of the elderly and pets to detect abnormalities. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned notification unit, If an anomaly is detected, a notification will be sent to the user via the messaging app. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Providing users with real-time video. The system described in Appendix 1, characterized by the features described herein. (Note 5) The care determination unit is, The generated AI analyzes the pet's behavior and determines the necessary care. The system described in Appendix 1, characterized by the features described herein. (Note 6) The care determination unit is, The system uses AI to analyze pet behavior and issue commands via voice or messaging apps. The system described in Appendix 5, characterized by the features described herein. (Note 7) The aforementioned monitoring unit, It estimates the user's emotions and adjusts the monitoring frequency based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned monitoring unit, It learns the behavioral patterns of the elderly and pets, enabling early detection of abnormal behavior. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned monitoring unit, The system automatically adjusts the resolution and frame rate of the acquired video according to the situation. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned monitoring unit, It estimates the user's emotions and determines the priority of targets to monitor based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned monitoring unit, Audio information is added to the acquired video, and audio analysis is also performed. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned monitoring unit, Environmental information such as temperature and humidity is added to the acquired video footage to gain a comprehensive understanding of the situation. 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 how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, We collect behavioral history data of the elderly and pets and analyze changes in their long-term behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, When detecting anomalies, multiple anomaly patterns are analyzed simultaneously. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, In addition to video analysis, we also perform audio analysis and environmental data analysis. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, When detecting anomalies, other sensor information should also be considered. 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 urgency of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned notification unit, When an anomaly is detected, use multiple notification methods simultaneously. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned notification unit, When an anomaly is detected, the notification content is automatically summarized and sent. 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 customizes the content of notifications based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned notification unit, When an anomaly is detected, the notification is distributed to multiple users. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned notification unit, When an anomaly is detected, attach past data related to the notification content. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the way the video is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing real-time video, the video quality is automatically adjusted. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing real-time video, add audio information to the video. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, The system estimates the user's emotions and determines the order in which videos are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing real-time video, display text information related to the video. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing real-time video, add a function to enlarge a portion of the video. The system described in Appendix 1, characterized by the features described herein. (Note 31) The care determination unit is, It estimates the user's emotions and determines the priority of care based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The care determination unit is, When analyzing a pet's behavior, refer to its past behavioral history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The care determination unit is, When analyzing pet behavior, we suggest multiple care methods. The system described in Appendix 1, characterized by the features described herein. (Note 34) The care determination unit is, It estimates the user's emotions and customizes the care content based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The care determination unit is, When analyzing pet behavior, environmental data should also be considered. The system described in Appendix 1, characterized by the features described herein. (Note 36) The care determination unit is, When analyzing a pet's behavior, compare it with data from other pets. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

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

Claims

1. A monitoring department that monitors the behavior of the elderly and pets, An analysis unit analyzes the video footage monitored by the aforementioned monitoring unit, A notification unit that sends a notification when an abnormality is detected by the analysis unit, A provisioning unit that provides video in real time to users notified by the aforementioned notification unit, The system includes a care determination unit that analyzes the pet's behavior using the aforementioned analysis unit and determines the necessary care. A system characterized by the following features.

2. The aforementioned analysis unit, The generated AI analyzes the behavior of the elderly and pets to detect abnormalities. The system according to feature 1.

3. The aforementioned notification unit, If an anomaly is detected, a notification will be sent to the user via the messaging app. The system according to feature 1.

4. The aforementioned supply unit is, Providing users with real-time video. The system according to feature 1.

5. The care determination unit is, The generated AI analyzes the pet's behavior and determines the necessary care. The system according to feature 1.

6. The care determination unit is, The system uses AI to analyze pet behavior and issues commands via voice or messaging apps. The system according to claim 5, characterized in that it is the same as described in claim 5.

7. The aforementioned monitoring unit, It estimates the user's emotions and adjusts the monitoring frequency based on the estimated emotions. The system according to feature 1.

8. The aforementioned monitoring unit, It learns the behavioral patterns of the elderly and pets, enabling early detection of abnormal behavior. The system according to feature 1.

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