system

Abstract

The system according to this embodiment aims to analyze the activity status and physical condition of pets in real time and provide them with an optimal environment. [Solution] The system according to the embodiment comprises an analysis unit, an environment optimization unit, a learning unit, and a nurturing unit. The analysis unit analyzes the pet's activity status and physical condition in real time. The environment optimization unit provides the optimal environment based on the data analyzed by the analysis unit. The learning unit learns the pet's behavior based on the video footage from a camera set up in the room. The nurturing unit allows the pet to live with you indefinitely on your smartphone.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the activity status and physical condition of a pet are not sufficiently analyzed in real time to provide an optimal environment.

[0005] The system according to the embodiment aims to analyze the activity status and physical condition of a pet in real time and provide an optimal environment.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, an environment optimization unit, a learning unit, and a nurturing unit. The analysis unit analyzes the pet's activity status and physical condition in real time. The environment optimization unit provides the optimal environment based on the data analyzed by the analysis unit. The learning unit learns the pet's behavior based on images from a camera set up in the room. The nurturing unit allows the pet to live with the user indefinitely on a smartphone. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the activity status and physical condition of pets in real time and provide an optimal environment. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The pet monitoring system according to an embodiment of the present invention is a system that analyzes the activity status and physical condition of a pet in real time, provides an optimal environment, learns the pet's behavior, and allows users to live with their pet on their smartphone. The pet monitoring system analyzes the activity status and physical condition of a pet in real time, provides an optimal environment, learns the pet's behavior, and allows users to live with their pet on their smartphone. For example, the pet monitoring system uses a sensor attached to the pet's collar to analyze the pet's activity status and physical condition in real time. Next, the pet monitoring system works in conjunction with IoT appliances to maintain the pet's comfort and provide an optimal environment. Furthermore, the pet monitoring system learns the pet's behavior based on video footage from a camera set up in the room. Finally, the pet monitoring system allows pets to live with their owners on their smartphones at all times. The pet monitoring system provides three components: a collar sensor, an AI-equipped camera, and a smartphone app. The collar sensor is equipped with GPS and a motion sensor, making it possible to record daily walks, track lost pets, and understand the pet's behavior. The AI-equipped high-definition camera monitors the pet's situation in real time and suggests an optimal environment. For example, if the room temperature exceeds 24 degrees Celsius, it will suggest that the optimal temperature is 22 degrees Celsius and prompt the air conditioner to turn on. It will also suggest whether to run automatic feeding if you anticipate being late returning home. Furthermore, a camera equipped with object detection capabilities will determine the furniture and layout of the room and record your pet's behavior. For example, it will record actions such as relaxing on the living room sofa, napping in the entryway after looking out the window, going back and forth between the bed and the floor in the bedroom, or two pets chasing each other. The pet monitoring system app combines three functions: camera monitoring, an AI chatbot, and a pet nurturing mode, allowing you to safely and securely monitor your pet with a single service. In pet nurturing mode, based on the behavior of your pet that the AI ​​has learned, your pet can live freely in a metaverse space. Even after your pet crosses the rainbow bridge, it can always be by your side.This allows the pet monitoring system to analyze the pet's activity and health in real time, provide an optimal environment, learn the pet's behavior, and essentially "live" with the pet through a smartphone.

[0029] The pet monitoring system according to this embodiment comprises an analysis unit, an environment optimization unit, a learning unit, and a training unit. The analysis unit analyzes the pet's activity status and physical condition in real time. The analysis unit analyzes data obtained from, for example, a sensor attached to the collar. The analysis unit grasps the pet's behavior using, for example, GPS and motion sensors. The analysis unit estimates the pet's emotions and adjusts the accuracy of the analysis based on the estimated emotions. The environment optimization unit provides an optimal environment based on the data analyzed by the analysis unit. The environment optimization unit maintains the pet's comfort by, for example, coordinating with IoT appliances. The environment optimization unit suggests, for example, room temperature and feeding timing. The environment optimization unit estimates the pet's emotions and adjusts the environmental settings based on the estimated emotions. The learning unit learns the pet's behavior based on images from a camera set up in the room. The learning unit determines the furniture and layout of the room using, for example, an object detection function. The learning unit estimates the pet's emotions and adjusts the learning algorithm based on the estimated emotions. The pet care unit allows pets to live with their owners indefinitely within a smartphone. For example, the pet care unit allows pets to live freely in a metaverse space. For example, the pet care unit estimates the pet's emotions and adjusts the care method based on the estimated emotions. As a result, the pet monitoring system according to this embodiment can analyze the pet's activity status and physical condition in real time, provide an optimal environment, learn the pet's behavior, and allow pets to live with their owners within a smartphone.

[0030] The analysis unit analyzes the pet's activity level and physical condition in real time. For example, the analysis unit analyzes data obtained from sensors attached to the collar. Specifically, sensors attached to the collar collect biometric data such as the pet's heart rate, body temperature, and respiratory rate. This data is transmitted to the analysis unit via wireless communication and analyzed in real time. The analysis unit uses, for example, GPS and motion sensors to understand the pet's behavior. GPS provides the pet's location information, and motion sensors detect the pet's movements and activity level. This allows for accurate understanding of where the pet is and what activities it is engaged in. Furthermore, the analysis unit estimates the pet's emotions and adjusts the accuracy of the analysis based on the estimated emotions. AI-based image analysis and voice analysis technologies are used for emotion estimation. For example, the analysis unit analyzes the pet's facial expressions and vocalizations to estimate emotions such as joy, sadness, and excitement. This allows the analysis unit to perform analysis that takes the pet's emotional state into account and provide more accurate data. The analysis unit comprehensively analyzes this data to understand the pet's health condition and behavioral patterns. For example, if a pet exhibits unusual behavior or shows changes in its health, an alert can be sent to the owner. This allows owners to more effectively manage their pet's health.

[0031] The Environmental Optimization Unit provides the optimal environment based on data analyzed by the Analysis Unit. Specifically, the Environmental Optimization Unit works in conjunction with IoT appliances to maintain the pet's comfort. For example, it can control smart air conditioners and humidifiers to adjust room temperature and humidity. If the pet is too hot, it will turn on the air conditioner; if it is too cold, it will turn on the heater. It also has a function to suggest feeding times, setting the optimal feeding time according to the pet's activity level and physical condition. Furthermore, the Environmental Optimization Unit estimates the pet's emotions and adjusts the environmental settings based on the estimated emotions. For example, if the pet is stressed, it can play relaxing music or adjust the brightness of the lighting. This ensures that the pet is always in a comfortable environment. The Environmental Optimization Unit receives data from the Analysis Unit in real time and can quickly change the environmental settings. For example, if the pet starts exercising, it will adjust the room temperature appropriately and provide a relaxing environment after exercise. In addition, the Environmental Optimization Unit learns the pet's preferences and past data to create more individualized environmental settings. This maximizes the pet's comfort.

[0032] The learning unit learns the pet's behavior based on video footage from cameras set up in the room. Specifically, the learning unit uses object detection to determine the furniture and layout of the room. This allows it to understand the environment in which the pet spends time and analyze its behavioral patterns. For example, if a pet frequently spends time in a particular spot, it may indicate that the spot is comfortable for the pet. Furthermore, the learning unit estimates the pet's emotions and adjusts its learning algorithm based on the estimated emotions. AI-based image analysis and voice analysis technologies are used for emotion estimation. For example, it analyzes the pet's facial expressions and vocalizations to estimate emotions such as joy, sadness, and excitement. This allows the learning unit to learn while considering the pet's emotional state, achieving more accurate behavioral analysis. Based on this data, the learning unit learns the pet's behavioral patterns and preferences and provides appropriate advice to the owner. For example, if a pet is active during a specific time of day, it is recommended to set playtime to coincide with that time. The learning unit can also accumulate pet behavioral data and analyze long-term trends. This allows for early detection of changes in a pet's health and behavior, enabling appropriate measures to be taken.

[0033] The Pet Care Department allows pets to live with their owners in a smartphone environment. Specifically, it enables pets to live freely in a metaverse space. This metaverse space is constructed using virtual reality technology, and pets can engage in various activities within this virtual space. For example, pets can play in a virtual garden or rest in a virtual home. The Pet Care Department estimates the pet's emotions and adjusts the care method based on these estimates. AI-based image and voice analysis technologies are used for emotion estimation. For example, it analyzes the pet's facial expressions and movements to estimate emotions such as joy, sadness, and excitement. This allows the Pet Care Department to provide a more appropriate environment by considering the pet's emotional state. The Pet Care Department optimizes activities within the virtual space based on the pet's behavioral data. For example, if a pet prefers a particular activity, it can be set to increase that activity. The Pet Care Department also monitors the pet's health and behavioral patterns and adjusts the virtual environment as needed. This ensures that pets can always live in a comfortable environment. Furthermore, the Pet Care Department allows owners to interact with their pets within the virtual space. For example, pet owners can play with their pets and feed them through their smartphones. This can deepen the bond between owners and their pets.

[0034] The analysis unit can analyze data obtained from sensors attached to the collar. For example, the analysis unit analyzes data obtained from sensors attached to the collar. For example, the analysis unit uses GPS and motion sensors to understand the pet's behavior. For example, the analysis unit estimates the pet's emotions and adjusts the accuracy of the analysis based on the estimated emotions of the pet. This allows for an accurate understanding of the pet's activity level and physical condition by analyzing data obtained from sensors attached to the collar. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data obtained from the collar sensors into a generating AI and have the generating AI perform the data analysis.

[0035] The environmental optimization unit can maintain the comfort of pets in cooperation with IoT appliances. For example, the environmental optimization unit can maintain the comfort of pets in cooperation with IoT appliances. For example, the environmental optimization unit can suggest room temperature and feeding timing. For example, the environmental optimization unit can estimate the pet's emotions and adjust the environmental settings based on the estimated emotions of the pet. In this way, by coordinating with IoT appliances, it can provide an optimal environment to maintain the comfort of pets. Some or all of the above processing in the environmental optimization unit may be performed using AI, for example, or without AI. For example, the environmental optimization unit can input data from IoT appliances into a generating AI and have the generating AI execute the optimal environmental settings.

[0036] The learning unit can learn the pet's behavior based on video footage from a camera set up in the room. For example, the learning unit learns the pet's behavior based on video footage from a camera set up in the room. For example, the learning unit uses object detection to determine the furniture and layout of the room. For example, the learning unit estimates the pet's emotions and adjusts the learning algorithm based on the estimated emotions of the pet. In this way, by learning the pet's behavior based on video footage from a camera set up in the room, the learning unit can understand the pet's behavior patterns. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input camera video data into a generating AI and have the generating AI perform the learning of the pet's behavior.

[0037] The training unit allows pets to live freely in the metaverse space. The training unit, for example, estimates the pet's emotions and adjusts the training method based on the estimated emotions. This allows pets to live freely in the metaverse space. Some or all of the above processing in the training unit may be performed using AI, for example, or without AI. For example, the training unit can input pet behavior data into a generating AI and have the generating AI perform training in the metaverse space.

[0038] The analysis unit can understand the pet's behavior using GPS and motion sensors. For example, the analysis unit uses GPS and motion sensors to understand the pet's behavior. For example, the analysis unit analyzes data obtained from sensors attached to the collar. For example, the analysis unit estimates the pet's emotions and adjusts the accuracy of the analysis based on the estimated emotions of the pet. This allows for accurate understanding of the pet's behavior using GPS and motion sensors. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input GPS and motion sensor data into a generating AI and have the generating AI perform the analysis of the pet's behavior.

[0039] The environmental optimization unit can suggest room temperature and feeding timing. For example, the environmental optimization unit suggests room temperature and feeding timing. For example, the environmental optimization unit maintains pet comfort by coordinating with IoT appliances. For example, the environmental optimization unit estimates the pet's emotions and adjusts the environmental settings based on the estimated emotions. This allows the unit to maintain pet comfort by suggesting room temperature and feeding timing. Some or all of the above processing in the environmental optimization unit may be performed using AI, for example, or without AI. For example, the environmental optimization unit can input room temperature and feeding data into a generating AI and have the generating AI suggest the optimal timing.

[0040] The learning unit can determine the furniture and layout of a room using object detection functionality. For example, the learning unit uses object detection functionality to determine the furniture and layout of a room. For example, the learning unit learns the behavior of a pet based on video footage from a camera set up in the room. For example, the learning unit estimates the pet's emotions and adjusts the learning algorithm based on the estimated emotions of the pet. This allows the furniture and layout of a room to be accurately determined using object detection functionality. Some or all of the above-described processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input camera video data into a generating AI and have the generating AI perform object detection.

[0041] The analysis unit can estimate the pet's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, the analysis unit estimates the pet's emotions and adjusts the accuracy of the analysis based on the estimated emotions. For example, the analysis unit analyzes data obtained from sensors attached to the collar. For example, the analysis unit uses GPS and motion sensors to understand the pet's behavior. This allows for more accurate analysis by adjusting the accuracy of the analysis based on the pet's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input pet emotion data into a generative AI and have the generative AI perform emotion estimation and accuracy adjustment of the analysis.

[0042] The analysis unit can incorporate data from the collar sensor, as well as the pet's eating and sleeping patterns, into its analysis. For example, the analysis unit can incorporate data from the collar sensor, as well as the pet's eating and sleeping patterns, into its analysis. The analysis unit can, for example, use GPS and motion sensors to understand the pet's behavior. The analysis unit can, for example, estimate the pet's emotions and adjust the accuracy of the analysis based on the estimated emotions. By incorporating the pet's eating and sleeping patterns into the analysis, a more comprehensive analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data from the collar sensor and data from the pet's eating and sleeping patterns into a generating AI and have the generating AI perform a comprehensive analysis.

[0043] The analysis unit can apply different analysis algorithms based on the pet's age and health condition during analysis. For example, the analysis unit applies different analysis algorithms based on the pet's age and health condition during analysis. For example, the analysis unit analyzes data obtained from sensors attached to the collar. For example, the analysis unit uses GPS and motion sensors to understand the pet's behavior. This allows for more appropriate analysis by applying different analysis algorithms based on the pet's age and health condition. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the pet's age and health condition into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0044] The analysis unit can estimate the pet's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, the analysis unit estimates the pet's emotions and adjusts the display method of the analysis results based on the estimated emotions. For example, the analysis unit analyzes data obtained from a sensor attached to the collar. For example, the analysis unit uses GPS and motion sensors to understand the pet's behavior. This allows for a more easily understandable display by adjusting the display method of the analysis results based on the pet's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input pet emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the display method.

[0045] The analysis unit can provide analysis results while considering the lifestyle patterns of the pet owner during analysis. For example, the analysis unit provides analysis results while considering the lifestyle patterns of the pet owner during analysis. For example, the analysis unit analyzes data obtained from sensors attached to the collar. For example, the analysis unit uses GPS and motion sensors to understand the pet's behavior. This allows for the provision of more appropriate analysis results by considering the lifestyle patterns of the pet owner. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the owner's lifestyle patterns into a generating AI and have the generating AI perform the task of providing analysis results.

[0046] The analysis unit can detect anomalies by comparing the pet's activity data with that of other pets during analysis. For example, the analysis unit can detect anomalies by comparing the pet's activity data with that of other pets during analysis. For example, the analysis unit can analyze data obtained from sensors attached to the collar. For example, the analysis unit can understand the pet's behavior using GPS and motion sensors. This allows for early detection of anomalies by comparing the pet's behavior with that of other pets. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the pet's activity data into a generating AI and have the generating AI perform comparison with other pets and anomaly detection.

[0047] The environment optimization unit can estimate the pet's emotions and adjust the environmental settings based on the estimated emotions. For example, the environment optimization unit estimates the pet's emotions and adjusts the environmental settings based on the estimated emotions. For example, the environment optimization unit can maintain the pet's comfort by coordinating with IoT appliances. For example, the environment optimization unit can suggest room temperature and feeding timing. By adjusting the environmental settings based on the pet's emotions, a more comfortable environment can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the environment optimization unit may be performed using AI, for example, or without AI. For example, the environment optimization unit can input pet emotion data into the generative AI and have the generative AI perform emotion estimation and adjustment of environmental settings.

[0048] The environment optimization unit can provide the optimal environment by referring to the pet's past environmental data during environment optimization. For example, the environment optimization unit can provide the optimal environment by referring to the pet's past environmental data during environment optimization. For example, the environment optimization unit can maintain the pet's comfort by coordinating with IoT appliances. For example, the environment optimization unit can suggest room temperature and feeding timing. By referring to the pet's past environmental data, a more appropriate environment can be provided. Some or all of the above processing in the environment optimization unit may be performed using AI, for example, or without AI. For example, the environment optimization unit can input the pet's past environmental data into a generating AI and have the generating AI perform the task of providing the optimal environment.

[0049] The environment optimization unit can apply different optimization algorithms depending on the type and characteristics of the pet during environment optimization. For example, the environment optimization unit applies different optimization algorithms depending on the type and characteristics of the pet during environment optimization. For example, the environment optimization unit maintains the pet's comfort in cooperation with IoT appliances. For example, the environment optimization unit suggests room temperature and feeding timing. By applying different optimization algorithms depending on the type and characteristics of the pet, a more appropriate environment can be provided. Some or all of the above processing in the environment optimization unit may be performed using AI, for example, or without AI. For example, the environment optimization unit can input data on the type and characteristics of the pet into a generating AI and have the generating AI execute the application of different optimization algorithms.

[0050] The environmental optimization unit can estimate the pet's emotions and determine the priority of environmental optimization based on the estimated emotions. For example, the environmental optimization unit can estimate the pet's emotions and determine the priority of environmental optimization based on the estimated emotions. For example, the environmental optimization unit can maintain the pet's comfort by coordinating with IoT appliances. For example, the environmental optimization unit can suggest room temperature and feeding timing. By determining the priority of environmental optimization based on the pet's emotions, more effective environmental optimization becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the environmental optimization unit may be performed using AI, for example, or without AI. For example, the environmental optimization unit can input pet emotion data into the generative AI and have the generative AI perform emotion estimation and priority determination.

[0051] The environment optimization unit can provide the optimal environment by taking into account the pet owner's preferences and lifestyle during environment optimization. For example, the environment optimization unit can provide the optimal environment by taking into account the pet owner's preferences and lifestyle during environment optimization. For example, the environment optimization unit can maintain the pet's comfort by coordinating with IoT appliances. For example, the environment optimization unit can suggest room temperature and feeding timing. In this way, a more appropriate environment can be provided by taking into account the pet owner's preferences and lifestyle. Some or all of the above processing in the environment optimization unit may be performed using AI, for example, or without using AI. For example, the environment optimization unit can input data on the owner's preferences and lifestyle into a generating AI and have the generating AI perform the task of providing the optimal environment.

[0052] The environmental optimization unit can adjust environmental settings according to the season and weather based on pet activity data during environmental optimization. For example, the environmental optimization unit adjusts environmental settings according to the season and weather based on pet activity data during environmental optimization. For example, the environmental optimization unit can maintain pet comfort by coordinating with IoT appliances. For example, the environmental optimization unit can suggest room temperature and feeding timing. In this way, by adjusting environmental settings according to the season and weather, it is possible to provide an environment in which pets can live comfortably. Some or all of the above processing in the environmental optimization unit may be performed using AI, for example, or without using AI. For example, the environmental optimization unit can input seasonal and weather data into a generating AI and have the generating AI execute the environmental settings.

[0053] The learning unit can estimate the pet's emotions and adjust the learning algorithm based on the estimated emotions. For example, the learning unit estimates the pet's emotions and adjusts the learning algorithm based on the estimated emotions. For example, the learning unit learns the pet's behavior based on video footage from a camera set up in the room. For example, the learning unit uses object detection to determine the furniture and layout of the room. This allows for more accurate learning by adjusting the learning algorithm based on the pet's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input pet emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the learning algorithm.

[0054] The learning unit can improve the accuracy of its learning by referring to past behavioral data of the pet during the learning process. For example, the learning unit can improve the accuracy of its learning by referring to past behavioral data of the pet during the learning process. For example, the learning unit learns the pet's behavior based on video footage from a camera set up in the room. For example, the learning unit can determine the furniture and layout of the room using an object detection function. This improves the accuracy of the learning process by referring to past behavioral data of the pet. Some or all of the above processing in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past behavioral data of the pet into a generating AI and have the generating AI perform the improvement of the learning accuracy.

[0055] The learning unit can apply different learning algorithms depending on the type and characteristics of the pet during learning. For example, the learning unit applies different learning algorithms depending on the type and characteristics of the pet during learning. For example, the learning unit learns the behavior of the pet based on the video footage from a camera set up in the room. For example, the learning unit uses object detection to determine the furniture and layout of the room. By applying different learning algorithms depending on the type and characteristics of the pet, more appropriate learning becomes possible. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input data on the type and characteristics of the pet into a generating AI and have the generating AI execute the application of different learning algorithms.

[0056] The learning unit can estimate the pet's emotions and adjust the display method of the learning results based on the estimated emotions. For example, the learning unit estimates the pet's emotions and adjusts the display method of the learning results based on the estimated emotions. For example, the learning unit learns the pet's behavior based on video footage from a camera set up in the room. For example, the learning unit uses object detection to determine the furniture and layout of the room. By adjusting the display method of the learning results based on the pet's emotions, a more easily understandable display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input pet emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the display method.

[0057] The learning unit can select training data while considering the lifestyle patterns of pet owners during training. For example, the learning unit selects training data while considering the lifestyle patterns of pet owners during training. For example, the learning unit learns the behavior of pets based on images from cameras set up in the room. For example, the learning unit uses object detection to determine the furniture and layout of the room. This allows for the selection of more appropriate training data by considering the lifestyle patterns of pet owners. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input data on the owner's lifestyle patterns into a generating AI and have the generating AI perform the selection of training data.

[0058] The learning unit can learn how to interact with other pets based on the pet's activity data during training. For example, the learning unit learns how to interact with other pets based on the pet's activity data during training. For example, the learning unit learns the pet's behavior based on the video footage from a camera set up in the room. For example, the learning unit uses object detection to determine the furniture and layout of the room. In this way, the pet's social skills can be improved by learning how to interact with other pets. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the pet's activity data into a generating AI and have the generating AI perform the learning of interactions with other pets.

[0059] The training unit can estimate the pet's emotions and adjust the training method based on the estimated emotions. For example, the training unit estimates the pet's emotions and adjusts the training method based on the estimated emotions. For example, the training unit allows the pet to live freely in a metaverse space. For example, the training unit estimates the pet's emotions and adjusts the training method based on the estimated emotions. This allows for more appropriate training by adjusting the training method based on the pet's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the training unit may be performed using AI, for example, or without AI. For example, the training unit can input pet emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the training method.

[0060] The training unit can provide the optimal training method by referring to the pet's past behavioral data during training. For example, the training unit can provide the optimal training method by referring to the pet's past behavioral data during training. For example, the training unit can allow the pet to live freely in a metaverse space. For example, the training unit can estimate the pet's emotions and adjust the training method based on the estimated emotions of the pet. In this way, the optimal training method can be provided by referring to the pet's past behavioral data. Some or all of the above processing in the training unit may be performed using AI, for example, or without using AI. For example, the training unit can input the pet's past behavioral data into a generating AI and have the generating AI perform the task of providing the optimal training method.

[0061] The training unit can apply different training algorithms depending on the type and characteristics of the pet during training. For example, the training unit applies different training algorithms depending on the type and characteristics of the pet during training. For example, the training unit allows the pet to live freely in a metaverse space. For example, the training unit estimates the pet's emotions and adjusts the training method based on the estimated emotions of the pet. This makes it possible to train pets more appropriately by applying different training algorithms depending on the type and characteristics of the pet. Some or all of the above processes in the training unit may be performed using AI, for example, or without AI. For example, the training unit can input data on the type and characteristics of the pet into a generating AI and have the generating AI execute the application of different training algorithms.

[0062] The training unit can estimate the pet's emotions and determine training priorities based on the estimated emotions. For example, the training unit can estimate the pet's emotions and determine training priorities based on the estimated emotions. For example, the training unit can allow the pet to live freely in a metaverse space. For example, the training unit can estimate the pet's emotions and adjust the training method based on the estimated emotions. This makes it possible to train more effectively by determining training priorities based on the pet's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the training unit may be performed using AI, for example, or without AI. For example, the training unit can input pet emotion data into a generative AI and have the generative AI perform emotion estimation and determine training priorities.

[0063] The training unit can provide the optimal training method during training, taking into account the preferences and lifestyle of the pet owner. For example, the training unit can provide the optimal training method during training, taking into account the preferences and lifestyle of the pet owner. For example, the training unit can allow pets to live freely in a metaverse space. For example, the training unit can estimate the pet's emotions and adjust the training method based on the estimated emotions of the pet. This allows for the provision of a more appropriate training method by taking into account the preferences and lifestyle of the pet owner. Some or all of the above processing in the training unit may be performed using AI, for example, or without AI. For example, the training unit can input data on the owner's preferences and lifestyle into a generating AI and have the generating AI perform the task of providing the optimal training method.

[0064] The training unit can incorporate interactions with other pets into the training process based on the pet's activity data. For example, the training unit can incorporate interactions with other pets into the training process based on the pet's activity data. For example, the training unit can allow pets to live freely in a metaverse space. For example, the training unit can estimate the pet's emotions and adjust the training method based on the estimated emotions. By incorporating interactions with other pets into the training process, the pet's social skills can be improved. Some or all of the above processes in the training unit may be performed using AI, for example, or without AI. For example, the training unit can input the pet's activity data into a generating AI and have the generating AI perform the training of interactions with other pets.

[0065] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0066] The pet monitoring system can input blood test data into its analysis unit to monitor the pet's health. For example, by inputting the results of blood tests regularly performed by a veterinarian into the system and having the analysis unit analyze them, a more detailed understanding of the pet's health can be obtained. Furthermore, by combining blood test data with daily activity data, the analysis unit can detect changes in health at an early stage. For example, if fluctuations in specific components in the blood are observed, the system can analyze how these fluctuations are affecting the pet's activity patterns. The analysis unit can also provide advice on the pet's diet and exercise based on the blood test data. This allows for more effective management of the pet's health.

[0067] The pet monitoring system can input dietary data into its analysis unit to support pet diet management. For example, it can record the type and amount of food a pet eats, and the analysis unit can analyze this data to understand the pet's nutritional status. Furthermore, the analysis unit can support pet weight management by combining dietary data with activity data. For example, it can analyze the balance between food intake and activity level and suggest appropriate food amounts. The analysis unit can also evaluate the impact of specific ingredients on pet health and suggest improvements to the diet. This allows for more effective pet diet management.

[0068] The pet monitoring system can input sleep data into an analysis unit to monitor the pet's sleep patterns. For example, it can record the amount of time the pet sleeps and the quality of its sleep, and the analysis unit can analyze this data to understand the pet's sleep patterns. Furthermore, the analysis unit can evaluate the pet's health by combining sleep data with activity data. For example, it can analyze how sleep deprivation affects the pet's activity and take appropriate measures. The analysis unit can also provide advice on improving the pet's sleep environment. This allows for more effective management of pet sleep.

[0069] A pet monitoring system can monitor a pet's activity level and provide an appropriate exercise plan. For example, the analysis unit can analyze the pet's activity data to understand its daily activity level and propose an appropriate exercise plan. Furthermore, the analysis unit can adjust the exercise plan based on the pet's health condition and age. For instance, it can suggest vigorous exercise for young pets and lighter exercise for older pets. The analysis unit can also provide advice on balancing the pet's exercise and food intake. This allows for more effective pet health management.

[0070] The pet monitoring system can input weight data into an analysis unit to support pet weight management. For example, by regularly measuring the pet's weight and having the analysis unit analyze it, the pet's weight fluctuations can be tracked. Furthermore, the analysis unit can provide weight management advice by combining weight data with diet data and exercise data. For example, if the pet's weight is increasing, it can suggest adjusting food intake or increasing exercise. The analysis unit can also provide a goal-setting function to support pet weight management. This makes pet weight management more effective.

[0071] The following briefly describes the processing flow for example form 1.

[0072] Step 1: The analysis unit analyzes the pet's activity level and physical condition in real time. For example, the analysis unit analyzes data obtained from sensors attached to the collar and uses GPS and motion sensors to understand the pet's behavior. It also estimates the pet's emotions and adjusts the accuracy of the analysis based on the estimated emotions. Step 2: The environment optimization unit provides the optimal environment based on the data analyzed by the analysis unit. For example, the environment optimization unit works in conjunction with IoT appliances to maintain the pet's comfort and suggests room temperature and feeding timing. It also estimates the pet's emotions and adjusts the environmental settings based on the estimated emotions. Step 3: The learning unit learns the pet's behavior based on the video footage from the camera set up in the room. For example, the learning unit uses object detection to determine the furniture and layout of the room, estimates the pet's emotions, and adjusts the learning algorithm accordingly. Step 4: The nurturing department ensures that the pet lives with you permanently within your smartphone. For example, the nurturing department allows the pet to live freely in a metaverse space, estimates the pet's emotions, and adjusts the nurturing method accordingly.

[0073] (Example of form 2) The pet monitoring system according to an embodiment of the present invention is a system that analyzes the activity status and physical condition of a pet in real time, provides an optimal environment, learns the pet's behavior, and allows users to live with their pet on their smartphone. The pet monitoring system analyzes the activity status and physical condition of a pet in real time, provides an optimal environment, learns the pet's behavior, and allows users to live with their pet on their smartphone. For example, the pet monitoring system uses a sensor attached to the pet's collar to analyze the pet's activity status and physical condition in real time. Next, the pet monitoring system works in conjunction with IoT appliances to maintain the pet's comfort and provide an optimal environment. Furthermore, the pet monitoring system learns the pet's behavior based on video footage from a camera set up in the room. Finally, the pet monitoring system allows pets to live with their owners on their smartphones at all times. The pet monitoring system provides three components: a collar sensor, an AI-equipped camera, and a smartphone app. The collar sensor is equipped with GPS and a motion sensor, making it possible to record daily walks, track lost pets, and understand the pet's behavior. The AI-equipped high-definition camera monitors the pet's situation in real time and suggests an optimal environment. For example, if the room temperature exceeds 24 degrees Celsius, it will suggest that the optimal temperature is 22 degrees Celsius and prompt the air conditioner to turn on. It will also suggest whether to run automatic feeding if you anticipate being late returning home. Furthermore, a camera equipped with object detection capabilities will determine the furniture and layout of the room and record your pet's behavior. For example, it will record actions such as relaxing on the living room sofa, napping in the entryway after looking out the window, going back and forth between the bed and the floor in the bedroom, or two pets chasing each other. The pet monitoring system app combines three functions: camera monitoring, an AI chatbot, and a pet nurturing mode, allowing you to safely and securely monitor your pet with a single service. In pet nurturing mode, based on the behavior of your pet that the AI ​​has learned, your pet can live freely in a metaverse space. Even after your pet crosses the rainbow bridge, it can always be by your side.This allows the pet monitoring system to analyze the pet's activity and health in real time, provide an optimal environment, learn the pet's behavior, and essentially "live" with the pet through a smartphone.

[0074] The pet monitoring system according to this embodiment comprises an analysis unit, an environment optimization unit, a learning unit, and a training unit. The analysis unit analyzes the pet's activity status and physical condition in real time. The analysis unit analyzes data obtained from, for example, a sensor attached to the collar. The analysis unit grasps the pet's behavior using, for example, GPS and motion sensors. The analysis unit estimates the pet's emotions and adjusts the accuracy of the analysis based on the estimated emotions. The environment optimization unit provides an optimal environment based on the data analyzed by the analysis unit. The environment optimization unit maintains the pet's comfort by, for example, coordinating with IoT appliances. The environment optimization unit suggests, for example, room temperature and feeding timing. The environment optimization unit estimates the pet's emotions and adjusts the environmental settings based on the estimated emotions. The learning unit learns the pet's behavior based on images from a camera set up in the room. The learning unit determines the furniture and layout of the room using, for example, an object detection function. The learning unit estimates the pet's emotions and adjusts the learning algorithm based on the estimated emotions. The pet care unit allows pets to live with their owners indefinitely within a smartphone. For example, the pet care unit allows pets to live freely in a metaverse space. For example, the pet care unit estimates the pet's emotions and adjusts the care method based on the estimated emotions. As a result, the pet monitoring system according to this embodiment can analyze the pet's activity status and physical condition in real time, provide an optimal environment, learn the pet's behavior, and allow pets to live with their owners within a smartphone.

[0075] The analysis unit analyzes the pet's activity level and physical condition in real time. For example, the analysis unit analyzes data obtained from sensors attached to the collar. Specifically, sensors attached to the collar collect biometric data such as the pet's heart rate, body temperature, and respiratory rate. This data is transmitted to the analysis unit via wireless communication and analyzed in real time. The analysis unit uses, for example, GPS and motion sensors to understand the pet's behavior. GPS provides the pet's location information, and motion sensors detect the pet's movements and activity level. This allows for accurate understanding of where the pet is and what activities it is engaged in. Furthermore, the analysis unit estimates the pet's emotions and adjusts the accuracy of the analysis based on the estimated emotions. AI-based image analysis and voice analysis technologies are used for emotion estimation. For example, the analysis unit analyzes the pet's facial expressions and vocalizations to estimate emotions such as joy, sadness, and excitement. This allows the analysis unit to perform analysis that takes the pet's emotional state into account and provide more accurate data. The analysis unit comprehensively analyzes this data to understand the pet's health condition and behavioral patterns. For example, if a pet exhibits unusual behavior or shows changes in its health, an alert can be sent to the owner. This allows owners to more effectively manage their pet's health.

[0076] The Environmental Optimization Unit provides the optimal environment based on data analyzed by the Analysis Unit. Specifically, the Environmental Optimization Unit works in conjunction with IoT appliances to maintain the pet's comfort. For example, it can control smart air conditioners and humidifiers to adjust room temperature and humidity. If the pet is too hot, it will turn on the air conditioner; if it is too cold, it will turn on the heater. It also has a function to suggest feeding times, setting the optimal feeding time according to the pet's activity level and physical condition. Furthermore, the Environmental Optimization Unit estimates the pet's emotions and adjusts the environmental settings based on the estimated emotions. For example, if the pet is stressed, it can play relaxing music or adjust the brightness of the lighting. This ensures that the pet is always in a comfortable environment. The Environmental Optimization Unit receives data from the Analysis Unit in real time and can quickly change the environmental settings. For example, if the pet starts exercising, it will adjust the room temperature appropriately and provide a relaxing environment after exercise. In addition, the Environmental Optimization Unit learns the pet's preferences and past data to create more individualized environmental settings. This maximizes the pet's comfort.

[0077] The learning unit learns the pet's behavior based on video footage from cameras set up in the room. Specifically, the learning unit uses object detection to determine the furniture and layout of the room. This allows it to understand the environment in which the pet spends time and analyze its behavioral patterns. For example, if a pet frequently spends time in a particular spot, it may indicate that the spot is comfortable for the pet. Furthermore, the learning unit estimates the pet's emotions and adjusts its learning algorithm based on the estimated emotions. AI-based image analysis and voice analysis technologies are used for emotion estimation. For example, it analyzes the pet's facial expressions and vocalizations to estimate emotions such as joy, sadness, and excitement. This allows the learning unit to learn while considering the pet's emotional state, achieving more accurate behavioral analysis. Based on this data, the learning unit learns the pet's behavioral patterns and preferences and provides appropriate advice to the owner. For example, if a pet is active during a specific time of day, it is recommended to set playtime to coincide with that time. The learning unit can also accumulate pet behavioral data and analyze long-term trends. This allows for early detection of changes in a pet's health and behavior, enabling appropriate measures to be taken.

[0078] The Pet Care Department allows pets to live with their owners in a smartphone environment. Specifically, it enables pets to live freely in a metaverse space. This metaverse space is constructed using virtual reality technology, and pets can engage in various activities within this virtual space. For example, pets can play in a virtual garden or rest in a virtual home. The Pet Care Department estimates the pet's emotions and adjusts the care method based on these estimates. AI-based image and voice analysis technologies are used for emotion estimation. For example, it analyzes the pet's facial expressions and movements to estimate emotions such as joy, sadness, and excitement. This allows the Pet Care Department to provide a more appropriate environment by considering the pet's emotional state. The Pet Care Department optimizes activities within the virtual space based on the pet's behavioral data. For example, if a pet prefers a particular activity, it can be set to increase that activity. The Pet Care Department also monitors the pet's health and behavioral patterns and adjusts the virtual environment as needed. This ensures that pets can always live in a comfortable environment. Furthermore, the Pet Care Department allows owners to interact with their pets within the virtual space. For example, pet owners can play with their pets and feed them through their smartphones. This can deepen the bond between owners and their pets.

[0079] The analysis unit can analyze data obtained from sensors attached to the collar. For example, the analysis unit analyzes data obtained from sensors attached to the collar. For example, the analysis unit uses GPS and motion sensors to understand the pet's behavior. For example, the analysis unit estimates the pet's emotions and adjusts the accuracy of the analysis based on the estimated emotions of the pet. This allows for an accurate understanding of the pet's activity level and physical condition by analyzing data obtained from sensors attached to the collar. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data obtained from the collar sensors into a generating AI and have the generating AI perform the data analysis.

[0080] The environmental optimization unit can maintain the comfort of pets in cooperation with IoT appliances. For example, the environmental optimization unit can maintain the comfort of pets in cooperation with IoT appliances. For example, the environmental optimization unit can suggest room temperature and feeding timing. For example, the environmental optimization unit can estimate the pet's emotions and adjust the environmental settings based on the estimated emotions of the pet. In this way, by coordinating with IoT appliances, it can provide an optimal environment to maintain the comfort of pets. Some or all of the above processing in the environmental optimization unit may be performed using AI, for example, or without AI. For example, the environmental optimization unit can input data from IoT appliances into a generating AI and have the generating AI execute the optimal environmental settings.

[0081] The learning unit can learn the pet's behavior based on video footage from a camera set up in the room. For example, the learning unit learns the pet's behavior based on video footage from a camera set up in the room. For example, the learning unit uses object detection to determine the furniture and layout of the room. For example, the learning unit estimates the pet's emotions and adjusts the learning algorithm based on the estimated emotions of the pet. In this way, by learning the pet's behavior based on video footage from a camera set up in the room, the learning unit can understand the pet's behavior patterns. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input camera video data into a generating AI and have the generating AI perform the learning of the pet's behavior.

[0082] The training unit allows pets to live freely in the metaverse space. The training unit, for example, estimates the pet's emotions and adjusts the training method based on the estimated emotions. This allows pets to live freely in the metaverse space. Some or all of the above processing in the training unit may be performed using AI, for example, or without AI. For example, the training unit can input pet behavior data into a generating AI and have the generating AI perform training in the metaverse space.

[0083] The analysis unit can understand the pet's behavior using GPS and motion sensors. For example, the analysis unit uses GPS and motion sensors to understand the pet's behavior. For example, the analysis unit analyzes data obtained from sensors attached to the collar. For example, the analysis unit estimates the pet's emotions and adjusts the accuracy of the analysis based on the estimated emotions of the pet. This allows for accurate understanding of the pet's behavior using GPS and motion sensors. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input GPS and motion sensor data into a generating AI and have the generating AI perform the analysis of the pet's behavior.

[0084] The environmental optimization unit can suggest room temperature and feeding timing. For example, the environmental optimization unit suggests room temperature and feeding timing. For example, the environmental optimization unit maintains pet comfort by coordinating with IoT appliances. For example, the environmental optimization unit estimates the pet's emotions and adjusts the environmental settings based on the estimated emotions. This allows the unit to maintain pet comfort by suggesting room temperature and feeding timing. Some or all of the above processing in the environmental optimization unit may be performed using AI, for example, or without AI. For example, the environmental optimization unit can input room temperature and feeding data into a generating AI and have the generating AI suggest the optimal timing.

[0085] The learning unit can determine the furniture and layout of a room using object detection functionality. For example, the learning unit uses object detection functionality to determine the furniture and layout of a room. For example, the learning unit learns the behavior of a pet based on video footage from a camera set up in the room. For example, the learning unit estimates the pet's emotions and adjusts the learning algorithm based on the estimated emotions of the pet. This allows the furniture and layout of a room to be accurately determined using object detection functionality. Some or all of the above-described processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input camera video data into a generating AI and have the generating AI perform object detection.

[0086] The analysis unit can estimate the pet's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, the analysis unit estimates the pet's emotions and adjusts the accuracy of the analysis based on the estimated emotions. For example, the analysis unit analyzes data obtained from sensors attached to the collar. For example, the analysis unit uses GPS and motion sensors to understand the pet's behavior. This allows for more accurate analysis by adjusting the accuracy of the analysis based on the pet's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input pet emotion data into a generative AI and have the generative AI perform emotion estimation and accuracy adjustment of the analysis.

[0087] The analysis unit can incorporate data from the collar sensor, as well as the pet's eating and sleeping patterns, into its analysis. For example, the analysis unit can incorporate data from the collar sensor, as well as the pet's eating and sleeping patterns, into its analysis. The analysis unit can, for example, use GPS and motion sensors to understand the pet's behavior. The analysis unit can, for example, estimate the pet's emotions and adjust the accuracy of the analysis based on the estimated emotions. By incorporating the pet's eating and sleeping patterns into the analysis, a more comprehensive analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data from the collar sensor and data from the pet's eating and sleeping patterns into a generating AI and have the generating AI perform a comprehensive analysis.

[0088] The analysis unit can apply different analysis algorithms based on the pet's age and health condition during analysis. For example, the analysis unit applies different analysis algorithms based on the pet's age and health condition during analysis. For example, the analysis unit analyzes data obtained from sensors attached to the collar. For example, the analysis unit uses GPS and motion sensors to understand the pet's behavior. This allows for more appropriate analysis by applying different analysis algorithms based on the pet's age and health condition. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the pet's age and health condition into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0089] The analysis unit can estimate the pet's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, the analysis unit estimates the pet's emotions and adjusts the display method of the analysis results based on the estimated emotions. For example, the analysis unit analyzes data obtained from a sensor attached to the collar. For example, the analysis unit uses GPS and motion sensors to understand the pet's behavior. This allows for a more easily understandable display by adjusting the display method of the analysis results based on the pet's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input pet emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the display method.

[0090] The analysis unit can provide analysis results while considering the lifestyle patterns of the pet owner during analysis. For example, the analysis unit provides analysis results while considering the lifestyle patterns of the pet owner during analysis. For example, the analysis unit analyzes data obtained from sensors attached to the collar. For example, the analysis unit uses GPS and motion sensors to understand the pet's behavior. This allows for the provision of more appropriate analysis results by considering the lifestyle patterns of the pet owner. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the owner's lifestyle patterns into a generating AI and have the generating AI perform the task of providing analysis results.

[0091] The analysis unit can detect anomalies by comparing the pet's activity data with that of other pets during analysis. For example, the analysis unit can detect anomalies by comparing the pet's activity data with that of other pets during analysis. For example, the analysis unit can analyze data obtained from sensors attached to the collar. For example, the analysis unit can understand the pet's behavior using GPS and motion sensors. This allows for early detection of anomalies by comparing the pet's behavior with that of other pets. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the pet's activity data into a generating AI and have the generating AI perform comparison with other pets and anomaly detection.

[0092] The environment optimization unit can estimate the pet's emotions and adjust the environmental settings based on the estimated emotions. For example, the environment optimization unit estimates the pet's emotions and adjusts the environmental settings based on the estimated emotions. For example, the environment optimization unit can maintain the pet's comfort by coordinating with IoT appliances. For example, the environment optimization unit can suggest room temperature and feeding timing. By adjusting the environmental settings based on the pet's emotions, a more comfortable environment can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the environment optimization unit may be performed using AI, for example, or without AI. For example, the environment optimization unit can input pet emotion data into the generative AI and have the generative AI perform emotion estimation and adjustment of environmental settings.

[0093] The environment optimization unit can provide the optimal environment by referring to the pet's past environmental data during environment optimization. For example, the environment optimization unit can provide the optimal environment by referring to the pet's past environmental data during environment optimization. For example, the environment optimization unit can maintain the pet's comfort by coordinating with IoT appliances. For example, the environment optimization unit can suggest room temperature and feeding timing. By referring to the pet's past environmental data, a more appropriate environment can be provided. Some or all of the above processing in the environment optimization unit may be performed using AI, for example, or without AI. For example, the environment optimization unit can input the pet's past environmental data into a generating AI and have the generating AI perform the task of providing the optimal environment.

[0094] The environment optimization unit can apply different optimization algorithms depending on the type and characteristics of the pet during environment optimization. For example, the environment optimization unit applies different optimization algorithms depending on the type and characteristics of the pet during environment optimization. For example, the environment optimization unit maintains the pet's comfort in cooperation with IoT appliances. For example, the environment optimization unit suggests room temperature and feeding timing. By applying different optimization algorithms depending on the type and characteristics of the pet, a more appropriate environment can be provided. Some or all of the above processing in the environment optimization unit may be performed using AI, for example, or without AI. For example, the environment optimization unit can input data on the type and characteristics of the pet into a generating AI and have the generating AI execute the application of different optimization algorithms.

[0095] The environmental optimization unit can estimate the pet's emotions and determine the priority of environmental optimization based on the estimated emotions. For example, the environmental optimization unit can estimate the pet's emotions and determine the priority of environmental optimization based on the estimated emotions. For example, the environmental optimization unit can maintain the pet's comfort by coordinating with IoT appliances. For example, the environmental optimization unit can suggest room temperature and feeding timing. By determining the priority of environmental optimization based on the pet's emotions, more effective environmental optimization becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the environmental optimization unit may be performed using AI, for example, or without AI. For example, the environmental optimization unit can input pet emotion data into the generative AI and have the generative AI perform emotion estimation and priority determination.

[0096] The environment optimization unit can provide the optimal environment by taking into account the pet owner's preferences and lifestyle during environment optimization. For example, the environment optimization unit can provide the optimal environment by taking into account the pet owner's preferences and lifestyle during environment optimization. For example, the environment optimization unit can maintain the pet's comfort by coordinating with IoT appliances. For example, the environment optimization unit can suggest room temperature and feeding timing. In this way, a more appropriate environment can be provided by taking into account the pet owner's preferences and lifestyle. Some or all of the above processing in the environment optimization unit may be performed using AI, for example, or without using AI. For example, the environment optimization unit can input data on the owner's preferences and lifestyle into a generating AI and have the generating AI perform the task of providing the optimal environment.

[0097] The environmental optimization unit can adjust environmental settings according to the season and weather based on pet activity data during environmental optimization. For example, the environmental optimization unit adjusts environmental settings according to the season and weather based on pet activity data during environmental optimization. For example, the environmental optimization unit can maintain pet comfort by coordinating with IoT appliances. For example, the environmental optimization unit can suggest room temperature and feeding timing. In this way, by adjusting environmental settings according to the season and weather, it is possible to provide an environment in which pets can live comfortably. Some or all of the above processing in the environmental optimization unit may be performed using AI, for example, or without using AI. For example, the environmental optimization unit can input seasonal and weather data into a generating AI and have the generating AI execute the environmental settings.

[0098] The learning unit can estimate the pet's emotions and adjust the learning algorithm based on the estimated emotions. For example, the learning unit estimates the pet's emotions and adjusts the learning algorithm based on the estimated emotions. For example, the learning unit learns the pet's behavior based on video footage from a camera set up in the room. For example, the learning unit uses object detection to determine the furniture and layout of the room. This allows for more accurate learning by adjusting the learning algorithm based on the pet's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input pet emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the learning algorithm.

[0099] The learning unit can improve the accuracy of its learning by referring to past behavioral data of the pet during the learning process. For example, the learning unit can improve the accuracy of its learning by referring to past behavioral data of the pet during the learning process. For example, the learning unit learns the pet's behavior based on video footage from a camera set up in the room. For example, the learning unit can determine the furniture and layout of the room using an object detection function. This improves the accuracy of the learning process by referring to past behavioral data of the pet. Some or all of the above processing in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past behavioral data of the pet into a generating AI and have the generating AI perform the improvement of the learning accuracy.

[0100] The learning unit can apply different learning algorithms depending on the type and characteristics of the pet during learning. For example, the learning unit applies different learning algorithms depending on the type and characteristics of the pet during learning. For example, the learning unit learns the behavior of the pet based on the video footage from a camera set up in the room. For example, the learning unit uses object detection to determine the furniture and layout of the room. By applying different learning algorithms depending on the type and characteristics of the pet, more appropriate learning becomes possible. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input data on the type and characteristics of the pet into a generating AI and have the generating AI execute the application of different learning algorithms.

[0101] The learning unit can estimate the pet's emotions and adjust the display method of the learning results based on the estimated emotions. For example, the learning unit estimates the pet's emotions and adjusts the display method of the learning results based on the estimated emotions. For example, the learning unit learns the pet's behavior based on video footage from a camera set up in the room. For example, the learning unit uses object detection to determine the furniture and layout of the room. By adjusting the display method of the learning results based on the pet's emotions, a more easily understandable display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input pet emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the display method.

[0102] The learning unit can select training data while considering the lifestyle patterns of pet owners during training. For example, the learning unit selects training data while considering the lifestyle patterns of pet owners during training. For example, the learning unit learns the behavior of pets based on images from cameras set up in the room. For example, the learning unit uses object detection to determine the furniture and layout of the room. This allows for the selection of more appropriate training data by considering the lifestyle patterns of pet owners. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input data on the owner's lifestyle patterns into a generating AI and have the generating AI perform the selection of training data.

[0103] The learning unit can learn how to interact with other pets based on the pet's activity data during training. For example, the learning unit learns how to interact with other pets based on the pet's activity data during training. For example, the learning unit learns the pet's behavior based on the video footage from a camera set up in the room. For example, the learning unit uses object detection to determine the furniture and layout of the room. In this way, the pet's social skills can be improved by learning how to interact with other pets. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the pet's activity data into a generating AI and have the generating AI perform the learning of interactions with other pets.

[0104] The training unit can estimate the pet's emotions and adjust the training method based on the estimated emotions. For example, the training unit estimates the pet's emotions and adjusts the training method based on the estimated emotions. For example, the training unit allows the pet to live freely in a metaverse space. For example, the training unit estimates the pet's emotions and adjusts the training method based on the estimated emotions. This allows for more appropriate training by adjusting the training method based on the pet's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the training unit may be performed using AI, for example, or without AI. For example, the training unit can input pet emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the training method.

[0105] The training unit can provide the optimal training method by referring to the pet's past behavioral data during training. For example, the training unit can provide the optimal training method by referring to the pet's past behavioral data during training. For example, the training unit can allow the pet to live freely in a metaverse space. For example, the training unit can estimate the pet's emotions and adjust the training method based on the estimated emotions of the pet. In this way, the optimal training method can be provided by referring to the pet's past behavioral data. Some or all of the above processing in the training unit may be performed using AI, for example, or without using AI. For example, the training unit can input the pet's past behavioral data into a generating AI and have the generating AI perform the task of providing the optimal training method.

[0106] The training unit can apply different training algorithms depending on the type and characteristics of the pet during training. For example, the training unit applies different training algorithms depending on the type and characteristics of the pet during training. For example, the training unit allows the pet to live freely in a metaverse space. For example, the training unit estimates the pet's emotions and adjusts the training method based on the estimated emotions of the pet. This makes it possible to train pets more appropriately by applying different training algorithms depending on the type and characteristics of the pet. Some or all of the above processes in the training unit may be performed using AI, for example, or without AI. For example, the training unit can input data on the type and characteristics of the pet into a generating AI and have the generating AI execute the application of different training algorithms.

[0107] The training unit can estimate the pet's emotions and determine training priorities based on the estimated emotions. For example, the training unit can estimate the pet's emotions and determine training priorities based on the estimated emotions. For example, the training unit can allow the pet to live freely in a metaverse space. For example, the training unit can estimate the pet's emotions and adjust the training method based on the estimated emotions. This makes it possible to train more effectively by determining training priorities based on the pet's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the training unit may be performed using AI, for example, or without AI. For example, the training unit can input pet emotion data into a generative AI and have the generative AI perform emotion estimation and determine training priorities.

[0108] The training unit can provide the optimal training method during training, taking into account the preferences and lifestyle of the pet owner. For example, the training unit can provide the optimal training method during training, taking into account the preferences and lifestyle of the pet owner. For example, the training unit can allow pets to live freely in a metaverse space. For example, the training unit can estimate the pet's emotions and adjust the training method based on the estimated emotions of the pet. This allows for the provision of a more appropriate training method by taking into account the preferences and lifestyle of the pet owner. Some or all of the above processing in the training unit may be performed using AI, for example, or without AI. For example, the training unit can input data on the owner's preferences and lifestyle into a generating AI and have the generating AI perform the task of providing the optimal training method.

[0109] The training unit can incorporate interactions with other pets into the training process based on the pet's activity data. For example, the training unit can incorporate interactions with other pets into the training process based on the pet's activity data. For example, the training unit can allow pets to live freely in a metaverse space. For example, the training unit can estimate the pet's emotions and adjust the training method based on the estimated emotions. By incorporating interactions with other pets into the training process, the pet's social skills can be improved. Some or all of the above processes in the training unit may be performed using AI, for example, or without AI. For example, the training unit can input the pet's activity data into a generating AI and have the generating AI perform the training of interactions with other pets.

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

[0111] The pet monitoring system can input blood test data into its analysis unit to monitor the pet's health. For example, by inputting the results of blood tests regularly performed by a veterinarian into the system and having the analysis unit analyze them, a more detailed understanding of the pet's health can be obtained. Furthermore, by combining blood test data with daily activity data, the analysis unit can detect changes in health at an early stage. For example, if fluctuations in specific components in the blood are observed, the system can analyze how these fluctuations are affecting the pet's activity patterns. The analysis unit can also provide advice on the pet's diet and exercise based on the blood test data. This allows for more effective management of the pet's health.

[0112] The pet monitoring system can input heart rate and respiratory rate data into its analysis unit to monitor the pet's stress level. For example, a sensor attached to the collar can measure the pet's heart rate and respiratory rate in real time, and the analysis unit can analyze this data to estimate the pet's stress level. Furthermore, the analysis unit can identify the cause of stress by combining heart rate and respiratory rate data with other activity data. For example, if the heart rate increases during a specific time of day or under specific environmental conditions, the cause can be identified, and the environment optimization unit can take appropriate measures. The analysis unit can also suggest ways for the pet to relax based on its stress level. This allows for more effective stress management for pets.

[0113] The pet monitoring system can incorporate interaction data with other pets into its learning unit to improve the pet's socialization. For example, it can film a pet playing with other pets, and the learning unit can analyze the footage to learn the pet's social behavior. Furthermore, the learning unit can provide advice to improve the pet's socialization based on the interaction data with other pets. For instance, if a specific behavior improves relationships with other pets, it can provide advice on how to promote that behavior. The learning unit can also evaluate the pet's social behavior and provide feedback to the owner. This helps improve the pet's socialization and build better relationships.

[0114] The pet monitoring system can input dietary data into its analysis unit to support pet diet management. For example, it can record the type and amount of food a pet eats, and the analysis unit can analyze this data to understand the pet's nutritional status. Furthermore, the analysis unit can support pet weight management by combining dietary data with activity data. For example, it can analyze the balance between food intake and activity level and suggest appropriate food amounts. The analysis unit can also evaluate the impact of specific ingredients on pet health and suggest improvements to the diet. This allows for more effective pet diet management.

[0115] The pet monitoring system can input sleep data into an analysis unit to monitor the pet's sleep patterns. For example, it can record the amount of time the pet sleeps and the quality of its sleep, and the analysis unit can analyze this data to understand the pet's sleep patterns. Furthermore, the analysis unit can evaluate the pet's health by combining sleep data with activity data. For example, it can analyze how sleep deprivation affects the pet's activity and take appropriate measures. The analysis unit can also provide advice on improving the pet's sleep environment. This allows for more effective management of pet sleep.

[0116] A pet monitoring system can estimate a pet's emotions and support communication with the pet based on those estimates. For example, the analysis unit can analyze the pet's behavioral data and estimate its emotions, informing the owner of the pet's emotional state. Furthermore, the analysis unit can suggest appropriate communication methods to the owner based on the pet's emotions. For instance, if the pet is stressed, it can suggest ways to help it relax. The analysis unit can also adjust playtime and training methods based on the pet's emotions. This allows for more effective communication with pets.

[0117] A pet monitoring system can monitor a pet's activity level and provide an appropriate exercise plan. For example, the analysis unit can analyze the pet's activity data to understand its daily activity level and propose an appropriate exercise plan. Furthermore, the analysis unit can adjust the exercise plan based on the pet's health condition and age. For instance, it can suggest vigorous exercise for young pets and lighter exercise for older pets. The analysis unit can also provide advice on balancing the pet's exercise and food intake. This allows for more effective pet health management.

[0118] A pet monitoring system can analyze a pet's behavioral patterns and detect abnormal behavior early. For example, the analysis unit can analyze the pet's behavioral data and compare it to normal behavioral patterns to detect abnormal behavior. Furthermore, the analysis unit can identify the cause of the abnormal behavior and take appropriate measures. For instance, if abnormal behavior is caused by stress or illness, the analysis unit can identify the cause and the environment optimization unit can take appropriate measures. The analysis unit can also notify the owner when abnormal behavior occurs and provide advice for early intervention. This allows for more effective pet health management.

[0119] The pet monitoring system can input weight data into an analysis unit to support pet weight management. For example, by regularly measuring the pet's weight and having the analysis unit analyze it, the pet's weight fluctuations can be tracked. Furthermore, the analysis unit can provide weight management advice by combining weight data with diet data and exercise data. For example, if the pet's weight is increasing, it can suggest adjusting food intake or increasing exercise. The analysis unit can also provide a goal-setting function to support pet weight management. This makes pet weight management more effective.

[0120] A pet monitoring system can estimate a pet's emotions and suggest ways to play based on those emotions. For example, the analysis unit can analyze the pet's behavioral data and estimate its emotions to identify the types of play the pet enjoys. Furthermore, the analysis unit can suggest appropriate ways to play to the owner based on the pet's emotions. For instance, if the pet is excited, it can suggest ways to help the pet release its energy. The analysis unit can also adjust the frequency and duration of play based on the pet's emotions, making playtime with pets more effective.

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

[0122] Step 1: The analysis unit analyzes the pet's activity level and physical condition in real time. For example, the analysis unit analyzes data obtained from sensors attached to the collar and uses GPS and motion sensors to understand the pet's behavior. It also estimates the pet's emotions and adjusts the accuracy of the analysis based on the estimated emotions. Step 2: The environment optimization unit provides the optimal environment based on the data analyzed by the analysis unit. For example, the environment optimization unit works in conjunction with IoT appliances to maintain the pet's comfort and suggests room temperature and feeding timing. It also estimates the pet's emotions and adjusts the environmental settings based on the estimated emotions. Step 3: The learning unit learns the pet's behavior based on the video footage from the camera set up in the room. For example, the learning unit uses object detection to determine the furniture and layout of the room, estimates the pet's emotions, and adjusts the learning algorithm accordingly. Step 4: The nurturing department ensures that the pet lives with you permanently within your smartphone. For example, the nurturing department allows the pet to live freely in a metaverse space, estimates the pet's emotions, and adjusts the nurturing method accordingly.

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

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

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

[0126] Each of the multiple elements described above, including the analysis unit, environment optimization unit, learning unit, and nurturing unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented as a processing unit that analyzes data obtained from a sensor attached to the collar of the smart device 14. The environment optimization unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and works in conjunction with IoT home appliances to maintain the pet's comfort. The learning unit is implemented, for example, as a processing unit that learns the pet's behavior based on the images from the camera of the smart device 14. The nurturing unit is implemented, for example, by an application on the smart device 14, allowing the pet to live freely in a metaverse space. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0142] Each of the multiple elements described above, including the analysis unit, environment optimization unit, learning unit, and nurturing unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented as a processing unit that analyzes data obtained from a sensor attached to the collar of the smart glasses 214. The environment optimization unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and works in conjunction with IoT home appliances to maintain the pet's comfort. The learning unit is implemented, for example, as a processing unit that learns the pet's behavior based on the images from the camera of the smart glasses 214. The nurturing unit is implemented, for example, by an application of the smart glasses 214, allowing the pet to live freely in a metaverse space. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0158] Each of the multiple elements described above, including the analysis unit, environment optimization unit, learning unit, and nurturing unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented as a processing unit that analyzes data obtained from a sensor attached to the collar of the headset terminal 314. The environment optimization unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and works in conjunction with IoT home appliances to maintain the pet's comfort. The learning unit is implemented, for example, as a processing unit that learns the pet's behavior based on the video footage from the camera of the headset terminal 314. The nurturing unit is implemented, for example, by an application on the headset terminal 314, allowing the pet to live freely in a metaverse space. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0175] Each of the multiple elements described above, including the analysis unit, environment optimization unit, learning unit, and nurturing unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented as a processing unit that analyzes data obtained from a sensor attached to the collar of the robot 414. The environment optimization unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and works in conjunction with IoT home appliances to maintain the pet's comfort. The learning unit is implemented, for example, as a processing unit that learns the pet's behavior based on the images from the robot 414's camera. The nurturing unit is implemented, for example, by an application of the robot 414, allowing the pet to live freely in a metaverse space. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0194] (Note 1) The analysis unit analyzes the pet's activity level and health in real time, An environment optimization unit provides an optimal environment based on the data analyzed by the aforementioned analysis unit, The learning section uses footage from cameras set up in the room to learn about the pet's behavior, It includes a pet-raising section where you can live with your pet forever on your smartphone. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Analyze the data obtained from the sensor attached to the collar. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned environment optimization unit, Connect with IoT appliances to maintain your pet's comfort. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned learning unit, The system learns the pet's behavior based on footage from cameras set up in the room. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned training department, Pets live freely and carefree in the metaverse space. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, Use GPS and motion sensors to understand your pet's behavior. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned environment optimization unit, Suggests room temperature and feeding timing. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned learning unit, Object detection functionality is used to determine the furniture and layout of the room. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, It estimates the pet's emotions and adjusts the accuracy of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, In addition to data obtained from collar sensors, the analysis incorporates the pet's eating and sleeping patterns. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, During analysis, different analysis algorithms are applied based on the pet's age and health condition. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, It estimates the pet's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During the analysis, the analysis results are provided while taking into account the lifestyle patterns of pet owners. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the system detects anomalies by comparing the pet's activity data with that of other pets. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned environment optimization unit, It estimates the pet's emotions and adjusts the environmental settings based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned environment optimization unit, When optimizing the environment, we refer to the pet's past environmental data to provide the optimal environment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned environment optimization unit, When optimizing the environment, different optimization algorithms are applied depending on the type and characteristics of the pet. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned environment optimization unit, It estimates the pet's emotions and determines the priority of environmental optimization based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned environment optimization unit, When optimizing the environment, we provide the best possible environment by taking into account the pet owner's preferences and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned environment optimization unit, During environment optimization, the environment settings are adjusted according to the season and weather based on pet activity data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned learning unit, It estimates the pet's emotions and adjusts the learning algorithm based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned learning unit, During training, the system improves learning accuracy by referencing past behavioral data of pets. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned learning unit, During training, different learning algorithms are applied depending on the type and characteristics of the pet. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned learning unit, It estimates the pet's emotions and adjusts how the learning results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned learning unit, During the learning process, the learning data is selected considering the lifestyle patterns of pet owners. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned learning unit, During training, the system learns how to interact with other pets based on the activity data of the pet. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned training department, It estimates the pet's emotions and adjusts the training method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned training department, During training, the system provides the optimal training method by referring to the pet's past behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned training department, During training, different training algorithms are applied depending on the type and characteristics of the pet. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned training department, It estimates the pet's emotions and determines priorities for raising the pet based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned training department, During the rearing process, we provide the optimal rearing method, taking into account the pet owner's preferences and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned training department, During training, interaction with other pets is incorporated into the training process based on the pet's activity data. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

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

Claims

1. The analysis unit analyzes the pet's activity level and health in real time, An environment optimization unit provides an optimal environment based on the data analyzed by the aforementioned analysis unit, The learning section uses footage from cameras set up in the room to learn about the pet's behavior, It includes a pet-raising section where you can live with your pet forever on your smartphone. A system characterized by the following features.

2. The aforementioned analysis unit, Analyze the data obtained from the sensor attached to the collar. The system according to feature 1.

3. The aforementioned environment optimization unit, Connect with IoT appliances to maintain your pet's comfort. The system according to feature 1.

4. The aforementioned learning unit, The system learns the pet's behavior based on footage from cameras set up in the room. The system according to feature 1.

5. The aforementioned training department, Pets live freely and carefree in the metaverse space. The system according to feature 1.

6. The aforementioned analysis unit, Use GPS and motion sensors to understand your pet's behavior. The system according to feature 1.

7. The aforementioned environment optimization unit, Suggests room temperature and feeding timing. The system according to feature 1.

8. The aforementioned learning unit, Object detection functionality is used to determine the furniture and layout of the room. The system according to feature 1.

Images