System

Abstract

An object of the system according to the embodiment is to monitor the behavior of a pet, detect a health problem, and take appropriate measures.SOLUTION: A system includes a Web camera, a behavior analysis part, a health detection part, a nutrition management part, and an emergency call part. The web camera monitors the behavior of the pet. The action analysis part analyzes video data acquired by the Web camera. The health detection unit detects a health problem based on the data analyzed by the behavior analysis unit. The nutrition management unit supports daily nutrition management based on the health problem detected by the health detection unit. The emergency call unit automatically transmits an emergency call in an emergency.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has the problem that it is difficult to detect health problems and take appropriate measures simply by monitoring a pet's behavior.

[0005] The system according to the embodiment aims to monitor the behavior of pets, detect health problems and take appropriate measures. [Means for solving the problem]

[0006] The system according to the embodiment includes a web camera, a behavior analysis unit, a health detection unit, a nutrition management unit, and an emergency call unit. The web camera monitors the behavior of a pet. The behavior analysis unit analyzes video data acquired by the web camera. The health detection unit detects health problems based on the data analyzed by the behavior analysis unit. The nutrition management unit supports daily nutrition management based on health problems detected by the health detection unit. The emergency call unit automatically makes an emergency call in the event of an emergency. [Effects of the Invention]

[0007] The system according to the embodiment can monitor the behavior of pets, detect health problems and take appropriate measures. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A pet health management system according to an embodiment of the present invention supports pet health management and emergency response by linking a webcam that monitors pet behavior with AI. This allows the pet health management system to analyze pet behavior, detect abnormal behavior and health problems, and support daily nutritional management. It can also automatically make an emergency call in the event of an emergency.

[0029] A pet health management system according to an embodiment includes a behavior analysis unit, a health detection unit, a nutrition management unit, and an emergency call unit. The behavior analysis unit analyzes video data acquired by a webcam that monitors the pet's behavior. For example, the behavior analysis unit learns the pet's normal behavioral patterns and detects abnormal behavior. The behavior analysis unit can also detect itchy or painful behaviors based on the pet's behavioral data. The health detection unit detects health problems based on the data analyzed by the behavior analysis unit. For example, if the pet frequently shows signs of itching, the health detection unit detects a skin problem. The health detection unit can also detect a loss of appetite or abnormal weight fluctuations as a health problem. The nutrition management unit supports daily nutritional management based on the health problems detected by the health detection unit. For example, the nutrition management unit monitors whether the pet is consuming an appropriate amount of food and provides advice to the owner as needed. The nutrition management unit can also analyze the pet's dietary data and suggest nutritional balance. The emergency call unit automatically sends an emergency call in the event of an emergency. For example, if a pet suddenly collapses or exhibits abnormal behavior, the emergency call unit automatically sends an emergency call to the owner or an emergency contact. This allows the pet health management system according to the embodiment to efficiently manage the health of pets and respond to emergencies. For example, even when the owner is out, the pet's health condition can be monitored in real time, and any abnormalities can be dealt with immediately. In addition, by supporting daily nutritional management, the pet's health can be maintained. Furthermore, by automatically sending an emergency call in the event of an emergency, a prompt response is possible.

[0030] The behavior analysis unit takes into account the individual personality and past behavioral history of each pet, enabling more accurate detection of abnormal behavior. The behavior analysis unit, for example, stores the individual personality and past behavioral history of each pet in a database and detects abnormal behavior based on that information. For example, if a particular pet is usually active but suddenly stops moving, this is detected as abnormal behavior. The behavior analysis unit also learns the pet's normal behavior patterns by collecting the pet's behavioral history over a long period of time and analyzing that data. This makes it possible to detect even subtle abnormal behavior. For example, if a pet does not behave in a specific manner during a specific time period, this is detected as an abnormality. The behavior analysis unit also analyzes behavior based on the pet's personality data. For example, because the criteria for abnormal behavior differ for introverted and extroverted pets, criteria for abnormal behavior are set according to each personality. This improves the accuracy of abnormal behavior detection by taking into account the individual personality and past behavioral history of each pet.

[0031] The behavior analysis unit can simultaneously analyze biometric data such as a pet's body temperature and heart rate to identify the cause of abnormal behavior. The behavior analysis unit collects biometric data using, for example, a body temperature sensor or heart rate monitor and integrates it with behavioral data for analysis. For example, if a pet moves around abnormally, it can check for rising body temperature and fluctuations in heart rate to detect the possibility of heatstroke. The behavior analysis unit also collects biometric data in real time and analyzes that data to correlate abnormal behavior with abnormalities in the body. For example, if a pet frequently shows signs of itching, it can check for rising skin temperature and detect the possibility of dermatitis. The behavior analysis unit also collects behavioral data and biometric data of a pet over a long period of time and analyzes that data to identify the cause of abnormal behavior. For example, if a pet shows a loss of appetite, it can check for a drop in heart rate and fluctuations in body temperature to detect digestive problems. This allows the analysis of biometric data to identify the cause of abnormal behavior.

[0032] The behavior analysis unit uses multiple cameras and sensors to perform multifaceted behavior analysis, allowing for a more detailed understanding of behavioral patterns. For example, the behavior analysis unit installs multiple cameras and captures images of a pet's behavior from different angles to analyze behavioral patterns in more detail. For example, if a pet is behaving abnormally in the corner of a room, the unit integrates and analyzes footage from multiple cameras. The behavior analysis unit also combines not only cameras but also motion sensors and audio sensors to analyze a pet's behavior in a multifaceted manner. For example, if a pet makes an abnormal noise, the unit associates the source of the sound with the pet's behavior for analysis. The behavior analysis unit also collects behavioral data of a pet using multiple sensors and integrates and analyzes the data. For example, if a pet is behaving abnormally in a specific location, the temperature and humidity data of that location are also included in the analysis. In this way, by using multiple cameras and sensors, a more detailed understanding of behavioral patterns can be obtained.

[0033] The behavior analysis unit can develop an AI model that learns the behavioral patterns of each type of pet to accommodate different types of pets. The behavior analysis unit collects behavioral data of different types of pets, such as dogs, cats, and birds, and learns the behavioral patterns of each type. For example, it compares a specific behavioral pattern of a dog with that of a cat to detect abnormal behavior in each type. The behavior analysis unit also develops an AI model that learns the behavioral patterns of each type of pet to accommodate different types of pets. For example, if a bird exhibits abnormal behavior of spreading its wings, it detects that behavior as abnormal. The behavior analysis unit also analyzes the behavioral patterns of each type of pet based on the behavioral data of different types of pets and detects abnormal behavior. For example, if a cat frequently sharpens its claws, it detects that behavior as abnormal. In this way, by developing an AI model that learns the behavioral patterns of each type of pet to accommodate different types of pets, the accuracy of detecting abnormal behavior is improved.

[0034] The health detection unit can detect health problems with higher accuracy by referring to the pet's past health checkup data and medical history. The health detection unit, for example, stores the pet's past health checkup data and medical history in a database and detects health problems based on that. For example, if a pet that previously suffered from a skin disease shows signs of itching again, it detects this as a recurrence of the skin disease. The health detection unit also collects the pet's past health checkup data over a long period of time and analyzes that data to detect health problems with high accuracy. For example, it detects abnormal weight fluctuations based on past weight fluctuation data. The health detection unit also detects health problems based on the pet's medical history data. For example, if a pet that previously had a digestive problem shows a loss of appetite, it detects this as a recurrence of the problem. In this way, by referring to the past health checkup data and medical history, the accuracy of health problem detection is improved.

[0035] The health detection unit can identify health problems caused by environmental factors by combining and analyzing pet behavioral data and environmental data. For example, the health detection unit integrates and analyzes behavioral data and environmental data. For example, if a pet coughs frequently, air quality data can be analyzed to detect possible allergies. The health detection unit also collects environmental data in real time and combines and analyzes that data with behavioral data to identify health problems caused by environmental factors. For example, if a pet becomes abnormally immobile in a high-temperature environment, it can detect possible heatstroke. The health detection unit also collects pet behavioral data and environmental data over a long period of time and analyzes that data to identify health problems caused by environmental factors. For example, if a pet exhibits skin problems in a high-humidity environment, the environmental factors can be identified. In this way, health problems caused by environmental factors can be identified by combining and analyzing behavioral data and environmental data.

[0036] The health detection unit comprehensively monitors the entire pet rearing environment and can detect health problems. The health detection unit, for example, builds a system that integrates and analyzes data such as diet, exercise, and sleep. For example, if a pet shows a loss of appetite, the amount of exercise and sleep time are also analyzed to identify the cause. The health detection unit also develops a system that monitors the entire pet rearing environment and detects health problems based on the data. For example, if a pet becomes abnormally motionless, the diet data and sleep data are analyzed to identify the cause. The health detection unit also collects data such as diet, exercise, and sleep in real time and integrates and analyzes the data to build a system that comprehensively detects health problems. For example, if a pet frequently shows signs of itching, the diet data and exercise data are analyzed to identify the cause. In this way, health problems can be comprehensively detected by monitoring the entire pet rearing environment.

[0037] The health detection unit can develop specialized AI models to address different health issues. The health detection unit develops specialized AI models to address different health issues, such as skin diseases, digestive system problems, and musculoskeletal problems. For example, an AI model is developed to detect signs of skin diseases. The health detection unit also develops multiple specialized AI models to address different health issues, with each model detecting a specific health issue. For example, an AI model is developed to detect digestive system problems. The health detection unit also develops AI models specialized for each health issue, with each model detecting a specific health issue with high accuracy. For example, an AI model is developed to detect musculoskeletal problems. In this way, by developing specialized AI models to address different health issues, the accuracy of health problem detection is improved.

[0038] The nutrition management unit can propose an optimal nutritional balance by taking into account individual health data such as the pet's age, weight, and activity level. For example, when analyzing a pet's dietary data, the nutrition management unit proposes an optimal nutritional balance based on individual health data such as age, weight, and activity level. For example, a high-protein diet is proposed for a growing pet. The nutrition management unit also analyzes dietary data based on the pet's individual health data and proposes an optimal nutritional balance. For example, a low-calorie diet is proposed for a pet that is gaining weight. The nutrition management unit also analyzes dietary data based on data such as age, weight, and activity level and proposes an optimal nutritional balance. For example, an easy-to-digest diet is proposed for an elderly pet. In this way, the optimal nutritional balance can be proposed by taking into account individual health data.

[0039] The nutritional management unit can learn a pet's dietary history and preferences and perform nutritional management according to the dietary preferences. For example, when analyzing a pet's dietary data, the nutritional management unit learns the dietary history and preferences and performs nutritional management according to the preferences. For example, for a pet that prefers a particular food, it will suggest a nutritional balance based on that food. The nutritional management unit also collects a pet's dietary history over a long period of time and performs nutritional management according to the preferences based on that data. For example, for a pet that prefers a particular ingredient, it will suggest a meal that includes that ingredient. The nutritional management unit also develops an AI model that learns the dietary history and preferences and performs nutritional management according to the pet's preferences. For example, for a pet that prefers a particular flavor, it will suggest a meal based on that flavor. In this way, by learning the dietary history and preferences, it is possible to perform nutritional management according to the preferences.

[0040] The nutrition management department can expand daily nutrition management into a system that links pet exercise data and sleep data to provide comprehensive health management. For example, the nutrition management department will build a system that integrates exercise data and sleep data when analyzing pet dietary data. For example, if a pet is not getting enough exercise, the amount of food will be adjusted based on that data. The nutrition management department will also collect pet exercise and sleep data in real time and develop a system that provides comprehensive health management based on that data. For example, if a pet is not getting enough sleep, the nutritional balance will be adjusted based on that data. The nutrition management department will also develop an AI model that integrates and analyzes dietary data, exercise data, and sleep data to build a system that provides comprehensive health management. For example, if a pet exercises a lot, the data will be used to suggest high-calorie meals. This will enable comprehensive health management by linking it with exercise and sleep data.

[0041] The nutrition management department can develop an AI model that provides nutritional management plans for each type of pet to accommodate different types of pets. The nutrition management department collects nutritional data for different types of pets, such as dogs, cats, and birds, and develops a model that provides nutritional management plans for each type. For example, it might suggest a high-protein diet for dogs and a high-fat diet for cats. The nutrition management department also develops an AI model that provides nutritional management plans for each type of pet to accommodate different types of pets. For example, it might suggest a vitamin-rich diet for birds. The nutrition management department also develops a model that provides nutritional management plans for each type of pet based on the nutritional data for different types of pets. For example, it might suggest a high-protein and low-carbohydrate diet for cats. This improves the accuracy of nutritional management by developing an AI model that provides nutritional management plans for each type of pet to accommodate different types of pets.

[0042] The emergency call unit can detect abnormalities at an earlier stage by referencing data on past emergency situations involving pets. For example, the emergency call unit stores data on past emergency situations involving pets in a database and detects abnormalities based on that data. For example, if a pet that suddenly collapsed in the past exhibits similar behavior again, it is immediately detected as an abnormality. The emergency call unit also collects data on past emergency situations involving pets over a long period of time and detects abnormalities at an earlier stage based on that data. For example, if a pet that previously exhibited difficulty breathing exhibits similar behavior again, it is immediately detected as an abnormality. The emergency call unit also builds a system that detects emergency behavior at an earlier stage based on the emergency data. For example, if a pet that previously had a seizure exhibits similar behavior again, it is immediately detected as an abnormality. In this way, abnormalities can be detected at an earlier stage by referencing data on past emergency situations.

[0043] The emergency call unit monitors a pet's biological data in real time and can immediately detect any abnormalities. The emergency call unit monitors biological data such as heart rate and breathing rate in real time and immediately detects any abnormalities. For example, a sudden increase in heart rate is immediately detected as an abnormality. The emergency call unit will also collect biological data in real time and build a system that immediately detects emergency behavior based on that data. For example, a sudden drop in breathing rate is immediately detected as an abnormality. The emergency call unit will also develop a system that immediately detects emergency behavior based on biological data such as heart rate and breathing rate. For example, an irregular heart rate is immediately detected as an abnormality. This allows for immediate detection of abnormalities by monitoring biological data in real time.

[0044] The emergency call unit can be expanded to include a system that automatically sends emergency calls not only to the pet owner but also to nearby veterinary clinics and pet sitters. For example, the emergency call unit will build a system that automatically sends emergency calls not only to the pet owner but also to nearby veterinary clinics when emergency behavior is detected. For example, if a pet suddenly collapses, the veterinary clinic will be immediately notified. The emergency call unit will also develop a system that automatically sends emergency calls not only to the pet owner but also to the pet sitter. For example, if a pet exhibits abnormal behavior, the pet sitter will be immediately notified. The emergency call unit will also build a system that automatically sends emergency calls to multiple contacts when emergency behavior is detected. For example, if a pet suddenly exhibits difficulty breathing, the owner, veterinary clinic, and pet sitter will be notified simultaneously. Expanding the scope of emergency call notifications enables faster response.

[0045] The emergency call department can develop an AI model that provides specialized emergency response plans to respond to different emergencies. The emergency call department develops an AI model that provides specialized emergency response plans to respond to different emergencies, such as injury, illness, and getting lost. For example, it proposes first aid methods in the event of an injured pet. The emergency call department also develops multiple AI models that provide specialized emergency response plans to respond to different emergencies, with each model responding to a specific emergency. For example, it proposes how to respond if a pet shows signs of illness. The emergency call department also develops an AI model specialized for each emergency, with each model responding to a specific emergency with high accuracy. For example, it proposes how to search for a lost pet. In this way, by developing AI models that provide specialized emergency response plans to respond to different emergencies, the accuracy of emergency responses is improved.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The behavior analysis unit analyzes video data acquired by a webcam that monitors pet behavior. For example, the behavior analysis unit learns a pet's normal behavioral patterns and detects abnormal behavior. The behavior analysis unit can also detect itchy or painful behavior based on the pet's behavioral data. The health detection unit detects health problems based on the data analyzed by the behavior analysis unit. For example, if a pet frequently shows signs of itching, the health detection unit detects this as a skin problem. The health detection unit can also detect a loss of appetite or abnormal weight fluctuations as a health problem. The nutrition management unit supports daily nutritional management based on health problems detected by the health detection unit. For example, the nutrition management unit monitors whether a pet is eating an appropriate amount of food and provides advice to the owner as needed. The nutrition management unit can also analyze a pet's dietary data and suggest nutritional balance. The emergency call unit automatically makes an emergency call in the event of an emergency. For example, if a pet suddenly collapses or exhibits abnormal behavior, the emergency call unit automatically sends an emergency call to the pet's owner or an emergency contact. This allows the pet health management system according to the embodiment to efficiently manage pet health and respond to emergencies. For example, even when the owner is out, the pet's health condition can be monitored in real time, and any abnormalities can be dealt with immediately. Furthermore, by supporting daily nutritional management, the pet's health can be maintained. Furthermore, by automatically sending an emergency call in the event of an emergency, a prompt response is possible.

[0048] The behavior analysis unit takes into account the individual personality and past behavioral history of each pet, enabling more accurate detection of abnormal behavior. The behavior analysis unit, for example, stores the individual personality and past behavioral history of each pet in a database and detects abnormal behavior based on that information. For example, if a particular pet is usually active but suddenly stops moving, this is detected as abnormal behavior. The behavior analysis unit also learns the pet's normal behavior patterns by collecting the pet's behavioral history over a long period of time and analyzing that data. This makes it possible to detect even subtle abnormal behavior. For example, if a pet does not behave in a specific manner during a specific time period, this is detected as an abnormality. The behavior analysis unit also analyzes behavior based on the pet's personality data. For example, because the criteria for abnormal behavior differ for introverted and extroverted pets, criteria for abnormal behavior are set according to each personality. This improves the accuracy of abnormal behavior detection by taking into account the individual personality and past behavioral history of each pet.

[0049] The behavior analysis unit can simultaneously analyze biometric data such as a pet's body temperature and heart rate to identify the cause of abnormal behavior. The behavior analysis unit collects biometric data using, for example, a body temperature sensor or heart rate monitor and integrates it with behavioral data for analysis. For example, if a pet moves around abnormally, it can check for rising body temperature and fluctuations in heart rate to detect the possibility of heatstroke. The behavior analysis unit also collects biometric data in real time and analyzes that data to correlate abnormal behavior with abnormalities in the body. For example, if a pet frequently shows signs of itching, it can check for rising skin temperature and detect the possibility of dermatitis. The behavior analysis unit also collects behavioral data and biometric data of a pet over a long period of time and analyzes that data to identify the cause of abnormal behavior. For example, if a pet shows a loss of appetite, it can check for a drop in heart rate and fluctuations in body temperature to detect digestive problems. This allows the analysis of biometric data to identify the cause of abnormal behavior.

[0050] The behavior analysis unit uses multiple cameras and sensors to perform multifaceted behavior analysis, allowing for a more detailed understanding of behavioral patterns. For example, the behavior analysis unit installs multiple cameras and captures images of a pet's behavior from different angles to analyze behavioral patterns in more detail. For example, if a pet is behaving abnormally in the corner of a room, the unit integrates and analyzes footage from multiple cameras. The behavior analysis unit also combines not only cameras but also motion sensors and audio sensors to analyze a pet's behavior in a multifaceted manner. For example, if a pet makes an abnormal noise, the unit associates the source of the sound with the pet's behavior for analysis. The behavior analysis unit also collects behavioral data of a pet using multiple sensors and integrates and analyzes the data. For example, if a pet is behaving abnormally in a specific location, the temperature and humidity data of that location are also included in the analysis. In this way, by using multiple cameras and sensors, a more detailed understanding of behavioral patterns can be obtained.

[0051] The behavior analysis unit can develop an AI model that learns the behavioral patterns of each type of pet to accommodate different types of pets. The behavior analysis unit collects behavioral data of different types of pets, such as dogs, cats, and birds, and learns the behavioral patterns of each type. For example, it compares a specific behavioral pattern of a dog with that of a cat to detect abnormal behavior in each type. The behavior analysis unit also develops an AI model that learns the behavioral patterns of each type of pet to accommodate different types of pets. For example, if a bird exhibits abnormal behavior of spreading its wings, it detects that behavior as abnormal. The behavior analysis unit also analyzes the behavioral patterns of each type of pet based on the behavioral data of different types of pets and detects abnormal behavior. For example, if a cat frequently sharpens its claws, it detects that behavior as abnormal. In this way, by developing an AI model that learns the behavioral patterns of each type of pet to accommodate different types of pets, the accuracy of detecting abnormal behavior is improved.

[0052] The health detection unit can detect health problems with higher accuracy by referring to the pet's past health checkup data and medical history. The health detection unit, for example, stores the pet's past health checkup data and medical history in a database and detects health problems based on that. For example, if a pet that previously suffered from a skin disease shows signs of itching again, it detects this as a recurrence of the skin disease. The health detection unit also collects the pet's past health checkup data over a long period of time and analyzes that data to detect health problems with high accuracy. For example, it detects abnormal weight fluctuations based on past weight fluctuation data. The health detection unit also detects health problems based on the pet's medical history data. For example, if a pet that previously had a digestive problem shows a loss of appetite, it detects this as a recurrence of the problem. In this way, by referring to the past health checkup data and medical history, the accuracy of health problem detection is improved.

[0053] The health detection unit can identify health problems caused by environmental factors by combining and analyzing pet behavioral data and environmental data. For example, the health detection unit integrates and analyzes behavioral data and environmental data. For example, if a pet coughs frequently, air quality data can be analyzed to detect possible allergies. The health detection unit also collects environmental data in real time and combines and analyzes that data with behavioral data to identify health problems caused by environmental factors. For example, if a pet becomes abnormally immobile in a high-temperature environment, it can detect possible heatstroke. The health detection unit also collects pet behavioral data and environmental data over a long period of time and analyzes that data to identify health problems caused by environmental factors. For example, if a pet exhibits skin problems in a high-humidity environment, the environmental factors can be identified. In this way, health problems caused by environmental factors can be identified by combining and analyzing behavioral data and environmental data.

[0054] The health detection unit comprehensively monitors the entire pet rearing environment and can detect health problems. The health detection unit, for example, builds a system that integrates and analyzes data such as diet, exercise, and sleep. For example, if a pet shows a loss of appetite, the amount of exercise and sleep time are also analyzed to identify the cause. The health detection unit also develops a system that monitors the entire pet rearing environment and detects health problems based on the data. For example, if a pet becomes abnormally motionless, the diet data and sleep data are analyzed to identify the cause. The health detection unit also collects data such as diet, exercise, and sleep in real time and integrates and analyzes the data to build a system that comprehensively detects health problems. For example, if a pet frequently shows signs of itching, the diet data and exercise data are analyzed to identify the cause. In this way, health problems can be comprehensively detected by monitoring the entire pet rearing environment.

[0055] The health detection unit can develop specialized AI models to address different health issues. The health detection unit develops specialized AI models to address different health issues, such as skin diseases, digestive system problems, and musculoskeletal problems. For example, an AI model is developed to detect signs of skin diseases. The health detection unit also develops multiple specialized AI models to address different health issues, with each model detecting a specific health issue. For example, an AI model is developed to detect digestive system problems. The health detection unit also develops AI models specialized for each health issue, with each model detecting a specific health issue with high accuracy. For example, an AI model is developed to detect musculoskeletal problems. In this way, by developing specialized AI models to address different health issues, the accuracy of health problem detection is improved.

[0056] The nutrition management unit can propose an optimal nutritional balance by taking into account individual health data such as the pet's age, weight, and activity level. For example, when analyzing a pet's dietary data, the nutrition management unit proposes an optimal nutritional balance based on individual health data such as age, weight, and activity level. For example, a high-protein diet is proposed for a growing pet. The nutrition management unit also analyzes dietary data based on the pet's individual health data and proposes an optimal nutritional balance. For example, a low-calorie diet is proposed for a pet that is gaining weight. The nutrition management unit also analyzes dietary data based on data such as age, weight, and activity level and proposes an optimal nutritional balance. For example, an easy-to-digest diet is proposed for an elderly pet. In this way, the optimal nutritional balance can be proposed by taking into account individual health data.

[0057] The nutritional management unit can learn a pet's dietary history and preferences and perform nutritional management according to the dietary preferences. For example, when analyzing a pet's dietary data, the nutritional management unit learns the dietary history and preferences and performs nutritional management according to the preferences. For example, for a pet that prefers a particular food, it will suggest a nutritional balance based on that food. The nutritional management unit also collects a pet's dietary history over a long period of time and performs nutritional management according to the preferences based on that data. For example, for a pet that prefers a particular ingredient, it will suggest a meal that includes that ingredient. The nutritional management unit also develops an AI model that learns the dietary history and preferences and performs nutritional management according to the pet's preferences. For example, for a pet that prefers a particular flavor, it will suggest a meal based on that flavor. In this way, by learning the dietary history and preferences, it is possible to perform nutritional management according to the preferences.

[0058] The nutrition management department can expand daily nutrition management into a system that links pet exercise data and sleep data to provide comprehensive health management. For example, the nutrition management department will build a system that integrates exercise data and sleep data when analyzing pet dietary data. For example, if a pet is not getting enough exercise, the amount of food will be adjusted based on that data. The nutrition management department will also collect pet exercise and sleep data in real time and develop a system that provides comprehensive health management based on that data. For example, if a pet is not getting enough sleep, the nutritional balance will be adjusted based on that data. The nutrition management department will also develop an AI model that integrates and analyzes dietary data, exercise data, and sleep data to build a system that provides comprehensive health management. For example, if a pet exercises a lot, the data will be used to suggest high-calorie meals. This will enable comprehensive health management by linking it with exercise and sleep data.

[0059] The nutrition management department can develop an AI model that provides nutritional management plans for each type of pet to accommodate different types of pets. The nutrition management department collects nutritional data for different types of pets, such as dogs, cats, and birds, and develops a model that provides nutritional management plans for each type. For example, it might suggest a high-protein diet for dogs and a high-fat diet for cats. The nutrition management department also develops an AI model that provides nutritional management plans for each type of pet to accommodate different types of pets. For example, it might suggest a vitamin-rich diet for birds. The nutrition management department also develops a model that provides nutritional management plans for each type of pet based on the nutritional data for different types of pets. For example, it might suggest a high-protein and low-carbohydrate diet for cats. This improves the accuracy of nutritional management by developing an AI model that provides nutritional management plans for each type of pet to accommodate different types of pets.

[0060] The emergency call unit can detect abnormalities at an earlier stage by referencing data on past emergency situations involving pets. For example, the emergency call unit stores data on past emergency situations involving pets in a database and detects abnormalities based on that data. For example, if a pet that suddenly collapsed in the past exhibits similar behavior again, it is immediately detected as an abnormality. The emergency call unit also collects data on past emergency situations involving pets over a long period of time and detects abnormalities at an earlier stage based on that data. For example, if a pet that previously exhibited difficulty breathing exhibits similar behavior again, it is immediately detected as an abnormality. The emergency call unit also builds a system that detects emergency behavior at an earlier stage based on the emergency data. For example, if a pet that previously had a seizure exhibits similar behavior again, it is immediately detected as an abnormality. In this way, abnormalities can be detected at an earlier stage by referencing data on past emergency situations.

[0061] The emergency call unit monitors a pet's biological data in real time and can immediately detect any abnormalities. The emergency call unit monitors biological data such as heart rate and breathing rate in real time and immediately detects any abnormalities. For example, a sudden increase in heart rate is immediately detected as an abnormality. The emergency call unit will also collect biological data in real time and build a system that immediately detects emergency behavior based on that data. For example, a sudden drop in breathing rate is immediately detected as an abnormality. The emergency call unit will also develop a system that immediately detects emergency behavior based on biological data such as heart rate and breathing rate. For example, an irregular heart rate is immediately detected as an abnormality. This allows for immediate detection of abnormalities by monitoring biological data in real time.

[0062] The emergency call unit can be expanded to include a system that automatically sends emergency calls not only to the pet owner but also to nearby veterinary clinics and pet sitters. For example, the emergency call unit will build a system that automatically sends emergency calls not only to the pet owner but also to nearby veterinary clinics when emergency behavior is detected. For example, if a pet suddenly collapses, the veterinary clinic will be immediately notified. The emergency call unit will also develop a system that automatically sends emergency calls not only to the pet owner but also to the pet sitter. For example, if a pet exhibits abnormal behavior, the pet sitter will be immediately notified. The emergency call unit will also build a system that automatically sends emergency calls to multiple contacts when emergency behavior is detected. For example, if a pet suddenly exhibits difficulty breathing, the owner, veterinary clinic, and pet sitter will be notified simultaneously. Expanding the scope of emergency call notifications enables faster response.

[0063] The emergency call department can develop an AI model that provides specialized emergency response plans to respond to different emergencies. The emergency call department develops an AI model that provides specialized emergency response plans to respond to different emergencies, such as injury, illness, and getting lost. For example, it proposes first aid methods in the event of an injured pet. The emergency call department also develops multiple AI models that provide specialized emergency response plans to respond to different emergencies, with each model responding to a specific emergency. For example, it proposes how to respond if a pet shows signs of illness. The emergency call department also develops an AI model specialized for each emergency, with each model responding to a specific emergency with high accuracy. For example, it proposes how to search for a lost pet. In this way, by developing AI models that provide specialized emergency response plans to respond to different emergencies, the accuracy of emergency responses is improved.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The webcam monitors your pet's behavior and captures video data, allowing you to record your pet's behavior in real time. Step 2: The behavior analysis unit analyzes the video data captured by the webcam. For example, it learns the pet's normal behavior patterns and detects abnormal behavior. It can also detect behaviors that indicate itching or pain. Step 3: The health detection unit detects health problems based on the data analyzed by the behavior analysis unit. For example, if the animal frequently shows signs of itching, it will be detected as a skin problem. It will also detect loss of appetite or abnormal weight fluctuations as health problems. Step 4: The nutrition management unit supports daily nutritional management based on health problems detected by the health detection unit. For example, it monitors whether the pet is eating an appropriate amount of food and provides advice to the owner as needed. It can also analyze the pet's dietary data and suggest nutritional balance. Step 5: The emergency call function automatically sends an emergency call in the event of an emergency. For example, if a pet suddenly collapses or shows abnormal behavior, an emergency call will be automatically sent to the owner or emergency contact.

[0066] (Example 2) A pet health management system according to an embodiment of the present invention supports pet health management and emergency response by linking a webcam that monitors pet behavior with AI. This allows the pet health management system to analyze pet behavior, detect abnormal behavior and health problems, and support daily nutritional management. It can also automatically make an emergency call in the event of an emergency.

[0067] A pet health management system according to an embodiment includes a behavior analysis unit, a health detection unit, a nutrition management unit, and an emergency call unit. The behavior analysis unit analyzes video data acquired by a webcam that monitors the pet's behavior. For example, the behavior analysis unit learns the pet's normal behavioral patterns and detects abnormal behavior. The behavior analysis unit can also detect itchy or painful behaviors based on the pet's behavioral data. The health detection unit detects health problems based on the data analyzed by the behavior analysis unit. For example, if the pet frequently shows signs of itching, the health detection unit detects a skin problem. The health detection unit can also detect a loss of appetite or abnormal weight fluctuations as a health problem. The nutrition management unit supports daily nutritional management based on the health problems detected by the health detection unit. For example, the nutrition management unit monitors whether the pet is consuming an appropriate amount of food and provides advice to the owner as needed. The nutrition management unit can also analyze the pet's dietary data and suggest nutritional balance. The emergency call unit automatically sends an emergency call in the event of an emergency. For example, if a pet suddenly collapses or exhibits abnormal behavior, the emergency call unit automatically sends an emergency call to the owner or an emergency contact. This allows the pet health management system according to the embodiment to efficiently manage the health of pets and respond to emergencies. For example, even when the owner is out, the pet's health condition can be monitored in real time, and any abnormalities can be dealt with immediately. In addition, by supporting daily nutritional management, the pet's health can be maintained. Furthermore, by automatically sending an emergency call in the event of an emergency, a prompt response is possible.

[0068] The behavior analysis unit takes into account the individual personality and past behavioral history of each pet, enabling more accurate detection of abnormal behavior. The behavior analysis unit, for example, stores the individual personality and past behavioral history of each pet in a database and detects abnormal behavior based on that information. For example, if a particular pet is usually active but suddenly stops moving, this is detected as abnormal behavior. The behavior analysis unit also learns the pet's normal behavior patterns by collecting the pet's behavioral history over a long period of time and analyzing that data. This makes it possible to detect even subtle abnormal behavior. For example, if a pet does not behave in a specific manner during a specific time period, this is detected as an abnormality. The behavior analysis unit also analyzes behavior based on the pet's personality data. For example, because the criteria for abnormal behavior differ for introverted and extroverted pets, criteria for abnormal behavior are set according to each personality. This improves the accuracy of abnormal behavior detection by taking into account the individual personality and past behavioral history of each pet.

[0069] The behavior analysis unit can simultaneously analyze biometric data such as a pet's body temperature and heart rate to identify the cause of abnormal behavior. The behavior analysis unit collects biometric data using, for example, a body temperature sensor or heart rate monitor and integrates it with behavioral data for analysis. For example, if a pet moves around abnormally, it can check for rising body temperature and fluctuations in heart rate to detect the possibility of heatstroke. The behavior analysis unit also collects biometric data in real time and analyzes that data to correlate abnormal behavior with abnormalities in the body. For example, if a pet frequently shows signs of itching, it can check for rising skin temperature and detect the possibility of dermatitis. The behavior analysis unit also collects behavioral data and biometric data of a pet over a long period of time and analyzes that data to identify the cause of abnormal behavior. For example, if a pet shows a loss of appetite, it can check for a drop in heart rate and fluctuations in body temperature to detect digestive problems. This allows the analysis of biometric data to identify the cause of abnormal behavior.

[0070] The behavior analysis unit uses the emotion estimation function to estimate the emotional state from the pet's behavior and detect signs of stress or anxiety. The behavior analysis unit estimates the pet's emotional state using, for example, an emotion estimation algorithm. For example, if the pet barks frequently, this is detected as a sign of stress or anxiety. The behavior analysis unit also estimates the emotional state based on the pet's behavior data and detects signs of stress or anxiety. For example, if the pet avoids a particular place, this is detected as anxiety about that place. The behavior analysis unit also uses the emotion estimation function to analyze the emotional state from the pet's behavior in real time and detect signs of stress or anxiety. For example, if the pet scratches its body frequently, this is detected as a sign of stress. In this way, the emotion estimation function can be used to detect signs of stress or anxiety in the pet.

[0071] The behavior analysis unit uses multiple cameras and sensors to perform multifaceted behavior analysis, allowing for a more detailed understanding of behavioral patterns. For example, the behavior analysis unit installs multiple cameras and captures images of a pet's behavior from different angles to analyze behavioral patterns in more detail. For example, if a pet is behaving abnormally in the corner of a room, the unit integrates and analyzes footage from multiple cameras. The behavior analysis unit also combines not only cameras but also motion sensors and audio sensors to analyze a pet's behavior in a multifaceted manner. For example, if a pet makes an abnormal noise, the unit associates the source of the sound with the pet's behavior for analysis. The behavior analysis unit also collects behavioral data of a pet using multiple sensors and integrates and analyzes the data. For example, if a pet is behaving abnormally in a specific location, the temperature and humidity data of that location are also included in the analysis. In this way, by using multiple cameras and sensors, a more detailed understanding of behavioral patterns can be obtained.

[0072] The behavior analysis unit can develop an AI model that learns the behavioral patterns of each type of pet to accommodate different types of pets. The behavior analysis unit collects behavioral data of different types of pets, such as dogs, cats, and birds, and learns the behavioral patterns of each type. For example, it compares a specific behavioral pattern of a dog with that of a cat to detect abnormal behavior in each type. The behavior analysis unit also develops an AI model that learns the behavioral patterns of each type of pet to accommodate different types of pets. For example, if a bird exhibits abnormal behavior of spreading its wings, it detects that behavior as abnormal. The behavior analysis unit also analyzes the behavioral patterns of each type of pet based on the behavioral data of different types of pets and detects abnormal behavior. For example, if a cat frequently sharpens its claws, it detects that behavior as abnormal. In this way, by developing an AI model that learns the behavioral patterns of each type of pet to accommodate different types of pets, the accuracy of detecting abnormal behavior is improved.

[0073] The behavior analysis unit can use the emotion estimation function to feed back the results of the pet's behavior analysis to the owner and suggest a care method according to the pet's emotional state. The behavior analysis unit, for example, analyzes the pet's behavior and uses the emotion estimation function to estimate the pet's emotional state, feeding back the results to the owner. For example, if the pet is feeling stressed, it suggests the cause and countermeasures. The behavior analysis unit also uses the emotion estimation function to suggest specific care methods to the owner based on the results of the pet's behavior analysis. For example, if the pet is feeling anxious, it suggests environmental improvements to reassure the pet. The behavior analysis unit also suggests care methods according to the pet's emotional state to the owner based on the results of the pet's behavior analysis and the emotion estimation data. For example, if the pet is feeling lonely, it suggests increasing play time. In this way, the emotion estimation function can be used to suggest care methods according to the pet's emotional state.

[0074] The health detection unit can detect health problems with higher accuracy by referring to the pet's past health checkup data and medical history. The health detection unit, for example, stores the pet's past health checkup data and medical history in a database and detects health problems based on that. For example, if a pet that previously suffered from a skin disease shows signs of itching again, it detects this as a recurrence of the skin disease. The health detection unit also collects the pet's past health checkup data over a long period of time and analyzes that data to detect health problems with high accuracy. For example, it detects abnormal weight fluctuations based on past weight fluctuation data. The health detection unit also detects health problems based on the pet's medical history data. For example, if a pet that previously had a digestive problem shows a loss of appetite, it detects this as a recurrence of the problem. In this way, by referring to the past health checkup data and medical history, the accuracy of health problem detection is improved.

[0075] The health detection unit can identify health problems caused by environmental factors by combining and analyzing pet behavioral data and environmental data. For example, the health detection unit integrates and analyzes behavioral data and environmental data. For example, if a pet coughs frequently, air quality data can be analyzed to detect possible allergies. The health detection unit also collects environmental data in real time and combines and analyzes that data with behavioral data to identify health problems caused by environmental factors. For example, if a pet becomes abnormally immobile in a high-temperature environment, it can detect possible heatstroke. The health detection unit also collects pet behavioral data and environmental data over a long period of time and analyzes that data to identify health problems caused by environmental factors. For example, if a pet exhibits skin problems in a high-humidity environment, the environmental factors can be identified. In this way, health problems caused by environmental factors can be identified by combining and analyzing behavioral data and environmental data.

[0076] The health detection unit can use the emotion estimation function to estimate the emotional state of a pet from its behavior and detect the impact of emotional stress on its health. For example, the health detection unit detects the impact of emotional stress on its health by estimating the emotional state of a pet using the emotion estimation function. For example, if a pet barks frequently, it detects the possibility that stress is causing health problems. The health detection unit also uses the emotion estimation function to estimate the emotional state based on the pet's behavioral data and identify the impact of emotional stress on its health. For example, if a pet feels anxious, it detects the possibility that the anxiety is leading to a loss of appetite. The health detection unit also analyzes the impact of emotional stress on its health based on the pet's behavioral data and emotion estimation data. For example, if a pet feels lonely, it detects the possibility that the loneliness is leading to weight loss. In this way, the emotion estimation function can be used to detect the impact of emotional stress on its health.

[0077] The health detection unit comprehensively monitors the entire pet rearing environment and can detect health problems. The health detection unit, for example, builds a system that integrates and analyzes data such as diet, exercise, and sleep. For example, if a pet shows a loss of appetite, the amount of exercise and sleep time are also analyzed to identify the cause. The health detection unit also develops a system that monitors the entire pet rearing environment and detects health problems based on the data. For example, if a pet becomes abnormally motionless, the diet data and sleep data are analyzed to identify the cause. The health detection unit also collects data such as diet, exercise, and sleep in real time and integrates and analyzes the data to build a system that comprehensively detects health problems. For example, if a pet frequently shows signs of itching, the diet data and exercise data are analyzed to identify the cause. In this way, health problems can be comprehensively detected by monitoring the entire pet rearing environment.

[0078] The health detection unit can develop specialized AI models to address different health issues. The health detection unit develops specialized AI models to address different health issues, such as skin diseases, digestive system problems, and musculoskeletal problems. For example, an AI model is developed to detect signs of skin diseases. The health detection unit also develops multiple specialized AI models to address different health issues, with each model detecting a specific health issue. For example, an AI model is developed to detect digestive system problems. The health detection unit also develops AI models specialized for each health issue, with each model detecting a specific health issue with high accuracy. For example, an AI model is developed to detect musculoskeletal problems. In this way, by developing specialized AI models to address different health issues, the accuracy of health problem detection is improved.

[0079] The health detection unit can use the emotion estimation function to propose an emotional care plan according to the pet's health condition and provide the owner with a specific care method. The health detection unit, for example, analyzes the pet's health condition and uses the emotion estimation function to propose an emotional care plan. For example, if the pet is feeling stressed, the health detection unit provides the owner with a specific care method to reduce the stress. The health detection unit also uses the emotion estimation function to propose an emotional care plan according to the pet's health condition and provide the owner with a specific care method. For example, if the pet is feeling anxious, the health detection unit suggests environmental improvements to alleviate the anxiety. The health detection unit also proposes an emotional care plan based on the pet's health condition and emotional state and provides the owner with a specific care method. For example, if the pet is feeling lonely, the health detection unit suggests increasing play time to alleviate the loneliness. In this way, the emotion estimation function can be used to propose an emotional care plan according to the pet's health condition.

[0080] The nutrition management unit can propose an optimal nutritional balance by taking into account individual health data such as the pet's age, weight, and activity level. For example, when analyzing a pet's dietary data, the nutrition management unit proposes an optimal nutritional balance based on individual health data such as age, weight, and activity level. For example, a high-protein diet is proposed for a growing pet. The nutrition management unit also analyzes dietary data based on the pet's individual health data and proposes an optimal nutritional balance. For example, a low-calorie diet is proposed for a pet that is gaining weight. The nutrition management unit also analyzes dietary data based on data such as age, weight, and activity level and proposes an optimal nutritional balance. For example, an easy-to-digest diet is proposed for an elderly pet. In this way, the optimal nutritional balance can be proposed by taking into account individual health data.

[0081] The nutritional management unit can learn a pet's dietary history and preferences and perform nutritional management according to the dietary preferences. For example, when analyzing a pet's dietary data, the nutritional management unit learns the dietary history and preferences and performs nutritional management according to the preferences. For example, for a pet that prefers a particular food, it will suggest a nutritional balance based on that food. The nutritional management unit also collects a pet's dietary history over a long period of time and performs nutritional management according to the preferences based on that data. For example, for a pet that prefers a particular ingredient, it will suggest a meal that includes that ingredient. The nutritional management unit also develops an AI model that learns the dietary history and preferences and performs nutritional management according to the pet's preferences. For example, for a pet that prefers a particular flavor, it will suggest a meal based on that flavor. In this way, by learning the dietary history and preferences, it is possible to perform nutritional management according to the preferences.

[0082] The nutrition management unit uses the emotion estimation function to analyze the emotional state of the pet while eating and evaluate the impact of stress and anxiety on eating. For example, the nutrition management unit analyzes the pet's behavior while eating and estimates the emotional state using the emotion estimation function to evaluate the impact of stress and anxiety on eating. For example, if the pet feels anxious while eating, the cause is identified. The nutrition management unit also uses the emotion estimation function to analyze the pet's emotional state while eating and evaluate the impact of stress and anxiety on eating. For example, if the pet feels stressed while eating, the stress is evaluated as possibly leading to a loss of appetite. The nutrition management unit also analyzes the impact of stress and anxiety on eating based on the behavior data and emotion estimation data of the pet while eating. For example, if the pet feels lonely while eating, the loneliness is evaluated as possibly leading to a decrease in food intake. In this way, the emotion estimation function can be used to evaluate the impact of stress and anxiety on eating.

[0083] The nutrition management department can expand daily nutrition management into a system that links pet exercise data and sleep data to provide comprehensive health management. For example, the nutrition management department will build a system that integrates exercise data and sleep data when analyzing pet dietary data. For example, if a pet is not getting enough exercise, the amount of food will be adjusted based on that data. The nutrition management department will also collect pet exercise and sleep data in real time and develop a system that provides comprehensive health management based on that data. For example, if a pet is not getting enough sleep, the nutritional balance will be adjusted based on that data. The nutrition management department will also develop an AI model that integrates and analyzes dietary data, exercise data, and sleep data to build a system that provides comprehensive health management. For example, if a pet exercises a lot, the data will be used to suggest high-calorie meals. This will enable comprehensive health management by linking it with exercise and sleep data.

[0084] The nutrition management department can develop an AI model that provides nutritional management plans for each type of pet to accommodate different types of pets. The nutrition management department collects nutritional data for different types of pets, such as dogs, cats, and birds, and develops a model that provides nutritional management plans for each type. For example, it might suggest a high-protein diet for dogs and a high-fat diet for cats. The nutrition management department also develops an AI model that provides nutritional management plans for each type of pet to accommodate different types of pets. For example, it might suggest a vitamin-rich diet for birds. The nutrition management department also develops a model that provides nutritional management plans for each type of pet based on the nutritional data for different types of pets. For example, it might suggest a high-protein and low-carbohydrate diet for cats. This improves the accuracy of nutritional management by developing an AI model that provides nutritional management plans for each type of pet to accommodate different types of pets.

[0085] The nutrition management unit uses the emotion estimation function to make suggestions for improving the eating environment in accordance with the emotional state of the pet while eating, thereby improving the quality of the meal. The nutrition management unit, for example, analyzes the pet's behavior while eating and estimates the emotional state using the emotion estimation function to make suggestions for improving the eating environment. For example, if the pet feels anxious while eating, the cause is identified and suggestions for improvement are made. The nutrition management unit also uses the emotion estimation function to analyze the pet's emotional state while eating and makes suggestions for improving the eating environment. For example, if the pet feels stressed while eating, the nutrition management unit suggests environmental improvements to reduce the stress. The nutrition management unit also makes suggestions for improving the eating environment based on the behavior data and emotion estimation data of the pet while eating, thereby improving the quality of the meal. For example, if the pet feels lonely while eating, the nutrition management unit suggests environmental improvements to alleviate the loneliness. In this way, the emotion estimation function can be used to make suggestions for improving the eating environment and improve the quality of the meal.

[0086] The emergency call unit can detect abnormalities at an earlier stage by referencing data on past emergency situations involving pets. For example, the emergency call unit stores data on past emergency situations involving pets in a database and detects abnormalities based on that data. For example, if a pet that suddenly collapsed in the past exhibits similar behavior again, it is immediately detected as an abnormality. The emergency call unit also collects data on past emergency situations involving pets over a long period of time and detects abnormalities at an earlier stage based on that data. For example, if a pet that previously exhibited difficulty breathing exhibits similar behavior again, it is immediately detected as an abnormality. The emergency call unit also builds a system that detects emergency behavior at an earlier stage based on the emergency data. For example, if a pet that previously had a seizure exhibits similar behavior again, it is immediately detected as an abnormality. In this way, abnormalities can be detected at an earlier stage by referencing data on past emergency situations.

[0087] The emergency call unit monitors a pet's biological data in real time and can immediately detect any abnormalities. The emergency call unit monitors biological data such as heart rate and breathing rate in real time and immediately detects any abnormalities. For example, a sudden increase in heart rate is immediately detected as an abnormality. The emergency call unit will also collect biological data in real time and build a system that immediately detects emergency behavior based on that data. For example, a sudden drop in breathing rate is immediately detected as an abnormality. The emergency call unit will also develop a system that immediately detects emergency behavior based on biological data such as heart rate and breathing rate. For example, an irregular heart rate is immediately detected as an abnormality. This allows for immediate detection of abnormalities by monitoring biological data in real time.

[0088] The emergency call unit can use the emotion estimation function to analyze the emotional state of the pet during emergency behavior and determine the priority of the emergency response. The emergency call unit, for example, analyzes the pet's emergency behavior and estimates the emotional state using the emotion estimation function to determine the priority of the emergency response. For example, if the pet is feeling extremely anxious, the priority is set high. The emergency call unit also uses the emotion estimation function to analyze the pet's emotional state during emergency behavior and determines the priority of the emergency response. For example, if the pet is feeling extremely fear, the priority is set high. The emergency call unit also builds a system that determines the priority of the emergency response based on the pet's emergency behavior data and emotion estimation data. For example, if the pet is feeling extremely stressed, the priority is set high. In this way, the emotion estimation function can be used to determine the priority of the emergency response.

[0089] The emergency call unit can be expanded to include a system that automatically sends emergency calls not only to the pet owner but also to nearby veterinary clinics and pet sitters. For example, the emergency call unit will build a system that automatically sends emergency calls not only to the pet owner but also to nearby veterinary clinics when emergency behavior is detected. For example, if a pet suddenly collapses, the veterinary clinic will be immediately notified. The emergency call unit will also develop a system that automatically sends emergency calls not only to the pet owner but also to the pet sitter. For example, if a pet exhibits abnormal behavior, the pet sitter will be immediately notified. The emergency call unit will also build a system that automatically sends emergency calls to multiple contacts when emergency behavior is detected. For example, if a pet suddenly exhibits difficulty breathing, the owner, veterinary clinic, and pet sitter will be notified simultaneously. Expanding the scope of emergency call notifications enables faster response.

[0090] The emergency call department can develop an AI model that provides specialized emergency response plans to respond to different emergencies. The emergency call department develops an AI model that provides specialized emergency response plans to respond to different emergencies, such as injury, illness, and getting lost. For example, it proposes first aid methods in the event of an injured pet. The emergency call department also develops multiple AI models that provide specialized emergency response plans to respond to different emergencies, with each model responding to a specific emergency. For example, it proposes how to respond if a pet shows signs of illness. The emergency call department also develops an AI model specialized for each emergency, with each model responding to a specific emergency with high accuracy. For example, it proposes how to search for a lost pet. In this way, by developing AI models that provide specialized emergency response plans to respond to different emergencies, the accuracy of emergency responses is improved.

[0091] The emergency call unit uses the emotion estimation function to propose an emergency response method according to the emotional state of the pet during emergency behavior, thereby supporting a prompt and appropriate response. The emergency call unit proposes an emergency response method, for example, by analyzing the pet's emergency behavior and estimating the emotional state using the emotion estimation function. For example, if the pet is feeling extremely anxious, it proposes a response method to alleviate that anxiety. The emergency call unit also uses the emotion estimation function to analyze the pet's emotional state during emergency behavior and proposes a prompt and appropriate response method. For example, if the pet is feeling extremely fear, it proposes a response method to alleviate that fear. The emergency call unit also proposes an emergency response method based on the pet's emergency behavior data and emotion estimation data, thereby building a system to support a prompt and appropriate response. For example, if the pet is feeling extremely stressed, it proposes a response method to reduce that stress. As a result, the emotion estimation function can be used to propose an emergency response method according to the pet's emotional state during emergency behavior, thereby supporting a prompt and appropriate response.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The behavior analysis unit analyzes video data acquired by a webcam that monitors pet behavior. For example, the behavior analysis unit learns a pet's normal behavioral patterns and detects abnormal behavior. The behavior analysis unit can also detect itchy or painful behavior based on the pet's behavioral data. The health detection unit detects health problems based on the data analyzed by the behavior analysis unit. For example, if a pet frequently shows signs of itching, the health detection unit detects this as a skin problem. The health detection unit can also detect a loss of appetite or abnormal weight fluctuations as a health problem. The nutrition management unit supports daily nutritional management based on health problems detected by the health detection unit. For example, the nutrition management unit monitors whether a pet is eating an appropriate amount of food and provides advice to the owner as needed. The nutrition management unit can also analyze a pet's dietary data and suggest nutritional balance. The emergency call unit automatically makes an emergency call in the event of an emergency. For example, if a pet suddenly collapses or exhibits abnormal behavior, the emergency call unit automatically sends an emergency call to the pet's owner or an emergency contact. This allows the pet health management system according to the embodiment to efficiently manage pet health and respond to emergencies. For example, even when the owner is out, the pet's health condition can be monitored in real time, and any abnormalities can be dealt with immediately. Furthermore, by supporting daily nutritional management, the pet's health can be maintained. Furthermore, by automatically sending an emergency call in the event of an emergency, a prompt response is possible.

[0094] The behavior analysis unit takes into account the individual personality and past behavioral history of each pet, enabling more accurate detection of abnormal behavior. The behavior analysis unit, for example, stores the individual personality and past behavioral history of each pet in a database and detects abnormal behavior based on that information. For example, if a particular pet is usually active but suddenly stops moving, this is detected as abnormal behavior. The behavior analysis unit also learns the pet's normal behavior patterns by collecting the pet's behavioral history over a long period of time and analyzing that data. This makes it possible to detect even subtle abnormal behavior. For example, if a pet does not behave in a specific manner during a specific time period, this is detected as an abnormality. The behavior analysis unit also analyzes behavior based on the pet's personality data. For example, because the criteria for abnormal behavior differ for introverted and extroverted pets, criteria for abnormal behavior are set according to each personality. This improves the accuracy of abnormal behavior detection by taking into account the individual personality and past behavioral history of each pet.

[0095] The behavior analysis unit can simultaneously analyze biometric data such as a pet's body temperature and heart rate to identify the cause of abnormal behavior. The behavior analysis unit collects biometric data using, for example, a body temperature sensor or heart rate monitor and integrates it with behavioral data for analysis. For example, if a pet moves around abnormally, it can check for rising body temperature and fluctuations in heart rate to detect the possibility of heatstroke. The behavior analysis unit also collects biometric data in real time and analyzes that data to correlate abnormal behavior with abnormalities in the body. For example, if a pet frequently shows signs of itching, it can check for rising skin temperature and detect the possibility of dermatitis. The behavior analysis unit also collects behavioral data and biometric data of a pet over a long period of time and analyzes that data to identify the cause of abnormal behavior. For example, if a pet shows a loss of appetite, it can check for a drop in heart rate and fluctuations in body temperature to detect digestive problems. This allows the analysis of biometric data to identify the cause of abnormal behavior.

[0096] The behavior analysis unit uses the emotion estimation function to estimate the emotional state from the pet's behavior and detect signs of stress or anxiety. The behavior analysis unit estimates the pet's emotional state using, for example, an emotion estimation algorithm. For example, if the pet barks frequently, this is detected as a sign of stress or anxiety. The behavior analysis unit also estimates the emotional state based on the pet's behavior data and detects signs of stress or anxiety. For example, if the pet avoids a particular place, this is detected as anxiety about that place. The behavior analysis unit also uses the emotion estimation function to analyze the emotional state from the pet's behavior in real time and detect signs of stress or anxiety. For example, if the pet scratches its body frequently, this is detected as a sign of stress. In this way, the emotion estimation function can be used to detect signs of stress or anxiety in the pet.

[0097] The behavior analysis unit uses multiple cameras and sensors to perform multifaceted behavior analysis, allowing for a more detailed understanding of behavioral patterns. For example, the behavior analysis unit installs multiple cameras and captures images of a pet's behavior from different angles to analyze behavioral patterns in more detail. For example, if a pet is behaving abnormally in the corner of a room, the unit integrates and analyzes footage from multiple cameras. The behavior analysis unit also combines not only cameras but also motion sensors and audio sensors to analyze a pet's behavior in a multifaceted manner. For example, if a pet makes an abnormal noise, the unit associates the source of the sound with the pet's behavior for analysis. The behavior analysis unit also collects behavioral data of a pet using multiple sensors and integrates and analyzes the data. For example, if a pet is behaving abnormally in a specific location, the temperature and humidity data of that location are also included in the analysis. In this way, by using multiple cameras and sensors, a more detailed understanding of behavioral patterns can be obtained.

[0098] The behavior analysis unit can develop an AI model that learns the behavioral patterns of each type of pet to accommodate different types of pets. The behavior analysis unit collects behavioral data of different types of pets, such as dogs, cats, and birds, and learns the behavioral patterns of each type. For example, it compares a specific behavioral pattern of a dog with that of a cat to detect abnormal behavior in each type. The behavior analysis unit also develops an AI model that learns the behavioral patterns of each type of pet to accommodate different types of pets. For example, if a bird exhibits abnormal behavior of spreading its wings, it detects that behavior as abnormal. The behavior analysis unit also analyzes the behavioral patterns of each type of pet based on the behavioral data of different types of pets and detects abnormal behavior. For example, if a cat frequently sharpens its claws, it detects that behavior as abnormal. In this way, by developing an AI model that learns the behavioral patterns of each type of pet to accommodate different types of pets, the accuracy of detecting abnormal behavior is improved.

[0099] The behavior analysis unit can use the emotion estimation function to feed back the results of the pet's behavior analysis to the owner and suggest a care method according to the pet's emotional state. The behavior analysis unit, for example, analyzes the pet's behavior and uses the emotion estimation function to estimate the pet's emotional state, feeding back the results to the owner. For example, if the pet is feeling stressed, it suggests the cause and countermeasures. The behavior analysis unit also uses the emotion estimation function to suggest specific care methods to the owner based on the results of the pet's behavior analysis. For example, if the pet is feeling anxious, it suggests environmental improvements to reassure the pet. The behavior analysis unit also suggests care methods according to the pet's emotional state to the owner based on the results of the pet's behavior analysis and the emotion estimation data. For example, if the pet is feeling lonely, it suggests increasing play time. In this way, the emotion estimation function can be used to suggest care methods according to the pet's emotional state.

[0100] The health detection unit can detect health problems with higher accuracy by referring to the pet's past health checkup data and medical history. The health detection unit, for example, stores the pet's past health checkup data and medical history in a database and detects health problems based on that. For example, if a pet that previously suffered from a skin disease shows signs of itching again, it detects this as a recurrence of the skin disease. The health detection unit also collects the pet's past health checkup data over a long period of time and analyzes that data to detect health problems with high accuracy. For example, it detects abnormal weight fluctuations based on past weight fluctuation data. The health detection unit also detects health problems based on the pet's medical history data. For example, if a pet that previously had a digestive problem shows a loss of appetite, it detects this as a recurrence of the problem. In this way, by referring to the past health checkup data and medical history, the accuracy of health problem detection is improved.

[0101] The health detection unit can identify health problems caused by environmental factors by combining and analyzing pet behavioral data and environmental data. For example, the health detection unit integrates and analyzes behavioral data and environmental data. For example, if a pet coughs frequently, air quality data can be analyzed to detect possible allergies. The health detection unit also collects environmental data in real time and combines and analyzes that data with behavioral data to identify health problems caused by environmental factors. For example, if a pet becomes abnormally immobile in a high-temperature environment, it can detect possible heatstroke. The health detection unit also collects pet behavioral data and environmental data over a long period of time and analyzes that data to identify health problems caused by environmental factors. For example, if a pet exhibits skin problems in a high-humidity environment, the environmental factors can be identified. In this way, health problems caused by environmental factors can be identified by combining and analyzing behavioral data and environmental data.

[0102] The health detection unit can use the emotion estimation function to estimate the emotional state of a pet from its behavior and detect the impact of emotional stress on its health. For example, the health detection unit detects the impact of emotional stress on its health by estimating the emotional state of a pet using the emotion estimation function. For example, if a pet barks frequently, it detects the possibility that stress is causing health problems. The health detection unit also uses the emotion estimation function to estimate the emotional state based on the pet's behavioral data and identify the impact of emotional stress on its health. For example, if a pet feels anxious, it detects the possibility that the anxiety is leading to a loss of appetite. The health detection unit also analyzes the impact of emotional stress on its health based on the pet's behavioral data and emotion estimation data. For example, if a pet feels lonely, it detects the possibility that the loneliness is leading to weight loss. In this way, the emotion estimation function can be used to detect the impact of emotional stress on its health.

[0103] The health detection unit comprehensively monitors the entire pet rearing environment and can detect health problems. The health detection unit, for example, builds a system that integrates and analyzes data such as diet, exercise, and sleep. For example, if a pet shows a loss of appetite, the amount of exercise and sleep time are also analyzed to identify the cause. The health detection unit also develops a system that monitors the entire pet rearing environment and detects health problems based on the data. For example, if a pet becomes abnormally motionless, the diet data and sleep data are analyzed to identify the cause. The health detection unit also collects data such as diet, exercise, and sleep in real time and integrates and analyzes the data to build a system that comprehensively detects health problems. For example, if a pet frequently shows signs of itching, the diet data and exercise data are analyzed to identify the cause. In this way, health problems can be comprehensively detected by monitoring the entire pet rearing environment.

[0104] The health detection unit can develop specialized AI models to address different health issues. The health detection unit develops specialized AI models to address different health issues, such as skin diseases, digestive system problems, and musculoskeletal problems. For example, an AI model is developed to detect signs of skin diseases. The health detection unit also develops multiple specialized AI models to address different health issues, with each model detecting a specific health issue. For example, an AI model is developed to detect digestive system problems. The health detection unit also develops AI models specialized for each health issue, with each model detecting a specific health issue with high accuracy. For example, an AI model is developed to detect musculoskeletal problems. In this way, by developing specialized AI models to address different health issues, the accuracy of health problem detection is improved.

[0105] The health detection unit can use the emotion estimation function to propose an emotional care plan according to the pet's health condition and provide the owner with a specific care method. The health detection unit, for example, analyzes the pet's health condition and uses the emotion estimation function to propose an emotional care plan. For example, if the pet is feeling stressed, the health detection unit provides the owner with a specific care method to reduce the stress. The health detection unit also uses the emotion estimation function to propose an emotional care plan according to the pet's health condition and provide the owner with a specific care method. For example, if the pet is feeling anxious, the health detection unit suggests environmental improvements to alleviate the anxiety. The health detection unit also proposes an emotional care plan based on the pet's health condition and emotional state and provides the owner with a specific care method. For example, if the pet is feeling lonely, the health detection unit suggests increasing play time to alleviate the loneliness. In this way, the emotion estimation function can be used to propose an emotional care plan according to the pet's health condition.

[0106] The nutrition management unit can propose an optimal nutritional balance by taking into account individual health data such as the pet's age, weight, and activity level. For example, when analyzing a pet's dietary data, the nutrition management unit proposes an optimal nutritional balance based on individual health data such as age, weight, and activity level. For example, a high-protein diet is proposed for a growing pet. The nutrition management unit also analyzes dietary data based on the pet's individual health data and proposes an optimal nutritional balance. For example, a low-calorie diet is proposed for a pet that is gaining weight. The nutrition management unit also analyzes dietary data based on data such as age, weight, and activity level and proposes an optimal nutritional balance. For example, an easy-to-digest diet is proposed for an elderly pet. In this way, the optimal nutritional balance can be proposed by taking into account individual health data.

[0107] The nutritional management unit can learn a pet's dietary history and preferences and perform nutritional management according to the dietary preferences. For example, when analyzing a pet's dietary data, the nutritional management unit learns the dietary history and preferences and performs nutritional management according to the preferences. For example, for a pet that prefers a particular food, it will suggest a nutritional balance based on that food. The nutritional management unit also collects a pet's dietary history over a long period of time and performs nutritional management according to the preferences based on that data. For example, for a pet that prefers a particular ingredient, it will suggest a meal that includes that ingredient. The nutritional management unit also develops an AI model that learns the dietary history and preferences and performs nutritional management according to the pet's preferences. For example, for a pet that prefers a particular flavor, it will suggest a meal based on that flavor. In this way, by learning the dietary history and preferences, it is possible to perform nutritional management according to the preferences.

[0108] The nutrition management unit uses the emotion estimation function to analyze the emotional state of the pet while eating and evaluate the impact of stress and anxiety on eating. For example, the nutrition management unit analyzes the pet's behavior while eating and estimates the emotional state using the emotion estimation function to evaluate the impact of stress and anxiety on eating. For example, if the pet feels anxious while eating, the cause is identified. The nutrition management unit also uses the emotion estimation function to analyze the pet's emotional state while eating and evaluate the impact of stress and anxiety on eating. For example, if the pet feels stressed while eating, the stress is evaluated as possibly leading to a loss of appetite. The nutrition management unit also analyzes the impact of stress and anxiety on eating based on the behavior data and emotion estimation data of the pet while eating. For example, if the pet feels lonely while eating, the loneliness is evaluated as possibly leading to a decrease in food intake. In this way, the emotion estimation function can be used to evaluate the impact of stress and anxiety on eating.

[0109] The nutrition management department can expand daily nutrition management into a system that links pet exercise data and sleep data to provide comprehensive health management. For example, the nutrition management department will build a system that integrates exercise data and sleep data when analyzing pet dietary data. For example, if a pet is not getting enough exercise, the amount of food will be adjusted based on that data. The nutrition management department will also collect pet exercise and sleep data in real time and develop a system that provides comprehensive health management based on that data. For example, if a pet is not getting enough sleep, the nutritional balance will be adjusted based on that data. The nutrition management department will also develop an AI model that integrates and analyzes dietary data, exercise data, and sleep data to build a system that provides comprehensive health management. For example, if a pet exercises a lot, the data will be used to suggest high-calorie meals. This will enable comprehensive health management by linking it with exercise and sleep data.

[0110] The nutrition management department can develop an AI model that provides nutritional management plans for each type of pet to accommodate different types of pets. The nutrition management department collects nutritional data for different types of pets, such as dogs, cats, and birds, and develops a model that provides nutritional management plans for each type. For example, it might suggest a high-protein diet for dogs and a high-fat diet for cats. The nutrition management department also develops an AI model that provides nutritional management plans for each type of pet to accommodate different types of pets. For example, it might suggest a vitamin-rich diet for birds. The nutrition management department also develops a model that provides nutritional management plans for each type of pet based on the nutritional data for different types of pets. For example, it might suggest a high-protein and low-carbohydrate diet for cats. This improves the accuracy of nutritional management by developing an AI model that provides nutritional management plans for each type of pet to accommodate different types of pets.

[0111] The nutrition management unit uses the emotion estimation function to make suggestions for improving the eating environment in accordance with the emotional state of the pet while eating, thereby improving the quality of the meal. The nutrition management unit, for example, analyzes the pet's behavior while eating and estimates the emotional state using the emotion estimation function to make suggestions for improving the eating environment. For example, if the pet feels anxious while eating, the cause is identified and suggestions for improvement are made. The nutrition management unit also uses the emotion estimation function to analyze the pet's emotional state while eating and makes suggestions for improving the eating environment. For example, if the pet feels stressed while eating, the nutrition management unit suggests environmental improvements to reduce the stress. The nutrition management unit also makes suggestions for improving the eating environment based on the behavior data and emotion estimation data of the pet while eating, thereby improving the quality of the meal. For example, if the pet feels lonely while eating, the nutrition management unit suggests environmental improvements to alleviate the loneliness. In this way, the emotion estimation function can be used to make suggestions for improving the eating environment and improve the quality of the meal.

[0112] The emergency call unit can detect abnormalities at an earlier stage by referencing data on past emergency situations involving pets. For example, the emergency call unit stores data on past emergency situations involving pets in a database and detects abnormalities based on that data. For example, if a pet that suddenly collapsed in the past exhibits similar behavior again, it is immediately detected as an abnormality. The emergency call unit also collects data on past emergency situations involving pets over a long period of time and detects abnormalities at an earlier stage based on that data. For example, if a pet that previously exhibited difficulty breathing exhibits similar behavior again, it is immediately detected as an abnormality. The emergency call unit also builds a system that detects emergency behavior at an earlier stage based on the emergency data. For example, if a pet that previously had a seizure exhibits similar behavior again, it is immediately detected as an abnormality. In this way, abnormalities can be detected at an earlier stage by referencing data on past emergency situations.

[0113] The emergency call unit monitors a pet's biological data in real time and can immediately detect any abnormalities. The emergency call unit monitors biological data such as heart rate and breathing rate in real time and immediately detects any abnormalities. For example, a sudden increase in heart rate is immediately detected as an abnormality. The emergency call unit will also collect biological data in real time and build a system that immediately detects emergency behavior based on that data. For example, a sudden drop in breathing rate is immediately detected as an abnormality. The emergency call unit will also develop a system that immediately detects emergency behavior based on biological data such as heart rate and breathing rate. For example, an irregular heart rate is immediately detected as an abnormality. This allows for immediate detection of abnormalities by monitoring biological data in real time.

[0114] The emergency call unit can use the emotion estimation function to analyze the emotional state of the pet during emergency behavior and determine the priority of the emergency response. The emergency call unit, for example, analyzes the pet's emergency behavior and estimates the emotional state using the emotion estimation function to determine the priority of the emergency response. For example, if the pet is feeling extremely anxious, the priority is set high. The emergency call unit also uses the emotion estimation function to analyze the pet's emotional state during emergency behavior and determines the priority of the emergency response. For example, if the pet is feeling extremely fear, the priority is set high. The emergency call unit also builds a system that determines the priority of the emergency response based on the pet's emergency behavior data and emotion estimation data. For example, if the pet is feeling extremely stressed, the priority is set high. In this way, the emotion estimation function can be used to determine the priority of the emergency response.

[0115] The emergency call unit can be expanded to include a system that automatically sends emergency calls not only to the pet owner but also to nearby veterinary clinics and pet sitters. For example, the emergency call unit will build a system that automatically sends emergency calls not only to the pet owner but also to nearby veterinary clinics when emergency behavior is detected. For example, if a pet suddenly collapses, the veterinary clinic will be immediately notified. The emergency call unit will also develop a system that automatically sends emergency calls not only to the pet owner but also to the pet sitter. For example, if a pet exhibits abnormal behavior, the pet sitter will be immediately notified. The emergency call unit will also build a system that automatically sends emergency calls to multiple contacts when emergency behavior is detected. For example, if a pet suddenly exhibits difficulty breathing, the owner, veterinary clinic, and pet sitter will be notified simultaneously. Expanding the scope of emergency call notifications enables faster response.

[0116] The emergency call department can develop an AI model that provides specialized emergency response plans to respond to different emergencies. The emergency call department develops an AI model that provides specialized emergency response plans to respond to different emergencies, such as injury, illness, and getting lost. For example, it proposes first aid methods in the event of an injured pet. The emergency call department also develops multiple AI models that provide specialized emergency response plans to respond to different emergencies, with each model responding to a specific emergency. For example, it proposes how to respond if a pet shows signs of illness. The emergency call department also develops an AI model specialized for each emergency, with each model responding to a specific emergency with high accuracy. For example, it proposes how to search for a lost pet. In this way, by developing AI models that provide specialized emergency response plans to respond to different emergencies, the accuracy of emergency responses is improved.

[0117] The emergency call unit uses the emotion estimation function to propose an emergency response method according to the emotional state of the pet during emergency behavior, thereby supporting a prompt and appropriate response. The emergency call unit proposes an emergency response method, for example, by analyzing the pet's emergency behavior and estimating the emotional state using the emotion estimation function. For example, if the pet is feeling extremely anxious, it proposes a response method to alleviate that anxiety. The emergency call unit also uses the emotion estimation function to analyze the pet's emotional state during emergency behavior and proposes a prompt and appropriate response method. For example, if the pet is feeling extremely fear, it proposes a response method to alleviate that fear. The emergency call unit also proposes an emergency response method based on the pet's emergency behavior data and emotion estimation data, thereby building a system to support a prompt and appropriate response. For example, if the pet is feeling extremely stressed, it proposes a response method to reduce that stress. As a result, the emotion estimation function can be used to propose an emergency response method according to the pet's emotional state during emergency behavior, thereby supporting a prompt and appropriate response.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The webcam monitors your pet's behavior and captures video data, allowing you to record your pet's behavior in real time. Step 2: The behavior analysis unit analyzes the video data captured by the webcam. For example, it learns the pet's normal behavior patterns and detects abnormal behavior. It can also detect behaviors that indicate itching or pain. Step 3: The health detection unit detects health problems based on the data analyzed by the behavior analysis unit. For example, if the animal frequently shows signs of itching, it will be detected as a skin problem. It will also detect loss of appetite or abnormal weight fluctuations as health problems. Step 4: The nutrition management unit supports daily nutritional management based on health problems detected by the health detection unit. For example, it monitors whether the pet is eating an appropriate amount of food and provides advice to the owner as needed. It can also analyze the pet's dietary data and suggest nutritional balance. Step 5: The emergency call function automatically sends an emergency call in the event of an emergency. For example, if a pet suddenly collapses or shows abnormal behavior, an emergency call will be automatically sent to the owner or emergency contact.

[0120] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0121] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0140] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0160] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0161] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0162] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0163] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0164] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0165] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0166] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0167] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0169] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0170] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0171] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0172] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0173] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0174] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0175] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0176] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0177] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0178] 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.

[0179] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0180] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0181] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0182] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0183] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0184] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0185] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0186] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A webcam to monitor your pet's behavior, a behavior analysis unit that analyzes video data acquired by the web camera; a health detection unit that detects health problems based on the data analyzed by the behavior analysis unit; a nutrition management unit that supports daily nutrition management based on the health problems detected by the health detection unit; An emergency call unit that automatically issues an emergency call in the event of an emergency. A system characterized by:

2. The behavior analysis unit Taking into account the individual characteristics and past behavioral history of the pet, more accurate detection of abnormal behavior is performed.

2. The system of claim 1.

3. The behavior analysis unit The pet's biological data, such as body temperature and heart rate, is also analyzed at the same time to identify the cause of abnormal behavior.

2. The system of claim 1.

4. The behavior analysis unit Inferring the pet's emotional state from its behavior and detecting signs of stress or anxiety 2. The system of claim 1.

5. The behavior analysis unit Using multiple cameras and sensors, we analyze behavior from multiple angles to understand more detailed behavioral patterns.

2. The system of claim 1.

6. The behavior analysis unit Developing the AI ​​model to learn the behavioral patterns of different types of pets 2. The system of claim 1.

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