system

Abstract

The system according to this embodiment aims to provide appropriate matching of horses with suitable adoptive homes, multidimensional analysis of horses, and education and support for horse owners. [Solution] The system according to the embodiment comprises a matching unit, an analysis unit, an education unit, and a management unit. The matching unit automatically matches horses with potential adopters. The analysis unit performs multidimensional analysis of the horses matched by the matching unit. The education unit provides education and support to horse owners based on the results of the analysis performed by the analysis unit. The management unit performs behavioral prediction, nutritional management, and stress level management based on the information provided by the education unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including 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, appropriate matching with the destination of horse adoption, multi-dimensional analysis of horses, and education and support for horse owners have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to provide appropriate matching with the destination of horse adoption, multi-dimensional analysis of horses, and education and support for horse owners.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a matching unit, an analysis unit, an education unit, and a management unit. The matching unit automatically matches horses with potential adopters. The analysis unit performs multidimensional analysis of the horses matched by the matching unit. The education unit provides education and support to horse owners based on the results of the analysis performed by the analysis unit. The management unit performs behavioral prediction, nutritional management, and stress level management based on the information provided by the education unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide appropriate matching with a suitable recipient for a horse, multidimensional analysis of the horse, and education and support for the horse owner. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applicable 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 reception 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 reception 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 AI ​​agent service according to an embodiment of the present invention is a system for preventing the euthanasia of horses. This system automatically matches horses with potential adopters, performs multidimensional analysis on the horses, and provides education and support to horse owners. Furthermore, the system has unique behavior prediction, nutrition management, and stress level management functions. For example, the system performs optimal matching considering the horse's characteristics and the conditions of the adopter. For example, it suggests a suitable adopter based on information such as the horse's age, health condition, and temperament. Next, the system analyzes the horse's behavioral data, health data, environmental data, etc., to comprehensively evaluate the horse's condition. For example, it analyzes the horse's exercise level, diet, stress level, etc., to understand the horse's health condition. Furthermore, the system provides education and support to horse owners. The system provides information on horse management and training, and educates and supports horse owners. For example, it provides information on horse health management methods, nutrition management methods, and stress management methods. The system also has unique behavior prediction, nutrition management, and stress level management functions. The system predicts behavior based on the horse's past data and real-time information, and uses this to help with stress management and health maintenance. For example, it can analyze the horse's behavior patterns and take appropriate measures before stress levels rise. Furthermore, the system provides an optimal nutritional plan based on each horse's individual health condition and activity level, supporting their health. This allows the system to prevent horses from being euthanized and to provide education and support to horse owners. For example, by monitoring a horse's health in real time and quickly detecting and responding to problems, the system can maintain the horse's health. Also, by enabling horse owners to learn proper management methods, horse health management can be improved, reducing the risk of euthanasia. In this way, the AI ​​agent service can prevent horses from being euthanized and provide education and support to horse owners.

[0029] The AI ​​agent service according to this embodiment comprises a matching unit, an analysis unit, an education unit, and a management unit. The matching unit automatically matches horses with potential adopters. The matching unit performs optimal matching by considering, for example, the horse's characteristics and the conditions of the adopters. For example, the matching unit proposes a suitable adopter based on information such as the horse's age, health condition, and temperament. The analysis unit performs multidimensional analysis of the horses matched by the matching unit. The analysis unit comprehensively evaluates the horse's condition by analyzing, for example, the horse's behavioral data, health data, and environmental data. For example, the analysis unit analyzes the horse's exercise level, diet, and stress level to understand the horse's health condition. The education unit provides education and support to horse owners based on the results analyzed by the analysis unit. For example, the education unit provides information on horse management and training to educate and support horse owners. For example, the education unit provides information on horse health management methods, nutrition management methods, and stress management methods. The management unit performs behavioral prediction, nutrition management, and stress level management based on the information provided by the education unit. The management department predicts horse behavior based on past data and real-time information, for example, to help manage stress and maintain health. For instance, it can analyze horse behavior patterns and take appropriate measures before stress levels rise. Furthermore, the management department provides optimal nutritional plans based on each horse's individual health status and activity level, supporting their well-being. This allows the AI ​​agent service, according to this embodiment, to prevent horse euthanasia and provide education and support to horse owners.

[0030] The matching unit automatically matches horses with potential adopters. For example, it considers the horse's characteristics and the adopter's requirements to achieve the optimal match. Specifically, the matching unit collects detailed information such as the horse's age, health, temperament, past training history, and competition results, and stores this information in a database. Adopter requirements include facility size, living environment, experience in horse care, and purpose (competition, breeding, pet, etc.). Based on this information, an AI algorithm performs the optimal match. For example, a facility with a large exercise area is suitable for a young, active horse, while a quiet environment is preferable for an older horse. The AI ​​comprehensively evaluates these conditions and proposes the most suitable adopter. Furthermore, the matching unit learns from past matching results and feedback to improve matching accuracy. For example, it can analyze patterns of successful past matches to improve matching accuracy under similar conditions. This allows the matching unit to provide the optimal environment for both the horse and the adopter, thereby improving the horse's welfare.

[0031] The analysis unit performs multidimensional analysis of horses matched by the matching unit. The analysis unit comprehensively evaluates the horse's condition by analyzing data such as behavioral data, health data, and environmental data. Specifically, it collects data such as the horse's exercise level, diet, stress level, sleep patterns, heart rate, and body temperature, and analyzes it using AI. For example, by analyzing the horse's exercise level, it evaluates whether appropriate exercise is being performed and suggests adjustments if there is an excess or deficiency. In the analysis of diet, it evaluates whether nutritional balance is maintained and suggests nutritional supplements as needed. In the analysis of stress levels, it monitors changes in heart rate and behavioral patterns to identify the cause of stress. This allows the analysis unit to grasp the horse's health status in real time and detect abnormalities early. Furthermore, by comparing with past data, the analysis unit can evaluate long-term health trends and predict future risks. For example, based on past data, it can predict health risks under specific seasons or environmental conditions and take preventative measures. This allows the analysis unit to comprehensively support horse health management and improve horse welfare.

[0032] The Education Department provides education and support to horse owners based on the results analyzed by the Analysis Department. For example, the Education Department provides information on horse management and training, offering educational support to owners. Specifically, it provides information on horse health management methods, nutrition management methods, stress management methods, and training methods. For instance, regarding horse health management, it explains the importance of regular health checks and how to deal with any abnormalities. For nutrition management, it proposes appropriate meal plans based on the horse's age and activity level, and provides specific advice on maintaining nutritional balance. Regarding stress management, it introduces key points for recognizing stress signs in horses and measures to improve the environment to reduce stress. For training methods, it provides training plans tailored to the horse's physical strength and abilities, recommending training within reasonable limits. Furthermore, the Education Department holds online seminars and workshops to provide owners with opportunities to learn the latest knowledge and techniques. This allows owners to deepen their knowledge of horse management and training, and provide a better living environment. The Education Department emphasizes communication with horse owners, addressing individual consultations and questions to alleviate owners' anxieties and doubts, and improve the welfare of the horses.

[0033] The Management Department performs behavioral prediction, nutritional management, and stress level management based on information provided by the Education Department. For example, the Management Department predicts behavior based on past data and real-time information of horses, and uses this information to manage stress and maintain health. Specifically, it can analyze horse behavior patterns and take appropriate measures before stress levels rise. For example, it monitors horse behavior patterns, identifies the cause of stress if abnormal behavior is observed, and improves the environment or adjusts training. The Management Department also provides optimal nutritional plans based on each horse's health condition and activity level to support their health. For example, it creates meal plans according to the horse's weight and activity level and provides specific advice to maintain nutritional balance. Furthermore, the Management Department can monitor the horses' health in real time and respond quickly if abnormalities are observed. For example, it monitors changes in the horse's body temperature and heart rate, and if abnormalities are found, it works with veterinarians to take appropriate measures. In this way, the Management Department can comprehensively support the health management of horses and improve their welfare. Furthermore, the management department prioritizes communication with horse owners, regularly reporting on the horses' health and management methods, and addressing any concerns or questions they may have. This allows the management department to work with owners to manage the horses' health and improve their welfare.

[0034] The data collection unit can collect horse behavior data. For example, the data collection unit collects horse walking data, eating data, resting data, etc. For example, the data collection unit collects horse walking data using sensors and stores it in a database. For example, the data collection unit captures horse eating data with a camera and extracts the data using image analysis technology. For example, the data collection unit collects horse resting data using motion sensors and stores it in a database. In this way, the data collection unit can more accurately understand the horse's condition by collecting horse behavior data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input horse behavior data into AI, and the AI ​​can analyze the data and extract behavior patterns.

[0035] The data collection unit can collect health data from horses. For example, the data collection unit can collect health data such as the horse's body temperature, heart rate, and blood test results. For example, the data collection unit can measure the horse's body temperature with a temperature sensor and store it in a database. For example, the data collection unit can measure the horse's heart rate with a heart rate sensor and store it in a database. For example, the data collection unit can obtain the horse's blood test results from a laboratory and store them in a database. In this way, the data collection unit can more accurately understand the horse's health status by collecting health data from horses. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the horse's health data into AI, and the AI ​​can analyze the data and evaluate the health status.

[0036] The monitoring unit can monitor the stress level of horses. The monitoring unit monitors stress levels, for example, by measuring changes in the horse's behavior patterns or hormone levels. For example, the monitoring unit detects changes in the horse's behavior patterns using a motion sensor and stores the data in a database. For example, the monitoring unit measures the horse's hormone levels through a blood test and stores the data in a database. For example, the monitoring unit measures fluctuations in the horse's heart rate using a heart rate sensor and stores the data in a database. This allows the monitoring unit to manage stress by monitoring the horse's stress level. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input data on the horse's stress level into an AI, which can analyze the data and evaluate the stress level.

[0037] The service provider can provide a nutrition plan for horses. For example, the service provider can provide an optimal nutrition plan based on the individual health status and activity level of each horse. For example, the service provider can assess the health status of horses, calculate the necessary nutrients, and create a nutrition plan. For example, the service provider can monitor the activity level of horses and adjust the nutrition plan based on calorie expenditure. For example, the service provider can record the contents of horses' meals, evaluate the nutritional balance, and provide a nutrition plan. In this way, the service provider can support the health of horses by providing a nutrition plan for them. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input horse health data and activity data into AI, and the AI ​​can generate an optimal nutrition plan.

[0038] The data collection unit can collect data on the horse's exercise level, diet, and stress level. For example, the data collection unit can measure the horse's exercise level using a pedometer or exercise sensor and store it in a database. For example, the data collection unit can photograph the horse's diet with a camera and extract the data using image analysis technology. For example, the data collection unit can evaluate the horse's stress level using a heart rate sensor or hormone level measurement and store it in a database. In this way, the data collection unit can comprehensively understand the horse's condition by collecting data such as the horse's exercise level, diet, and stress level. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the horse's exercise data into AI, which can analyze the data and extract exercise patterns.

[0039] The analysis unit can analyze the collected data and evaluate the horse's health. For example, the analysis unit can analyze data such as the horse's exercise level, diet, and stress level to comprehensively evaluate the horse's health. For example, the analysis unit can analyze the horse's exercise data to assess the risk of insufficient or excessive exercise. For example, the analysis unit can analyze the horse's diet data to assess imbalances in nutrition. For example, the analysis unit can analyze the horse's stress level data to identify the cause of stress. In this way, the analysis unit can evaluate the horse's health by analyzing the collected data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the horse's health data into AI, which can then analyze the data and evaluate the health status.

[0040] The management department can analyze the horses' behavioral patterns and take appropriate measures before stress levels rise. For example, the management department can monitor the horses' behavioral patterns and detect signs of stress early. For example, the management department can analyze changes in the horses' behavioral patterns and identify the causes of stress. For example, the management department can analyze the horses' behavioral patterns and propose measures to reduce stress. In this way, by analyzing the horses' behavioral patterns, the management department can take appropriate measures before stress levels rise. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input horse behavioral data into AI, which can analyze the data to detect signs of stress and propose countermeasures.

[0041] The service provider can provide an optimal nutrition plan based on the individual health status and activity level of each horse. For example, the service provider can assess the horse's health status, calculate the necessary nutrients, and create a nutrition plan. For example, the service provider can monitor the horse's activity level and adjust the nutrition plan based on calorie expenditure. For example, the service provider can record the horse's diet, evaluate the nutritional balance, and provide a nutrition plan. In this way, the service provider can support the horse's health by providing an optimal nutrition plan based on the individual health status and activity level of each horse. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input horse health data and activity data into AI, and the AI ​​can generate an optimal nutrition plan.

[0042] The matching unit can estimate the horse's emotions and adjust the selection criteria for adoptive homes based on the estimated emotions. For example, if the horse's emotions are unstable, the matching unit will prioritize adoptive homes with a quiet and calm environment. If the horse's emotions are relaxed, the matching unit will determine that the horse can adapt to a noisy environment and will relax the selection criteria. If the horse's emotions are stressed, the matching unit will select homes based on whether the staff at the adoptive home are skilled in managing stress in horses. In this way, the matching unit can select an adoptive home suitable for the horse by adjusting the selection criteria based on the horse's emotions. Some or all of the above processing in the matching unit may be performed using AI, for example, or not. For example, the matching unit can input the horse's emotional data into an AI, which can analyze the data and adjust the selection criteria for adoptive homes.

[0043] The matching unit can analyze the past acceptance history of the receiving facility and select the optimal matching method. For example, the matching unit can analyze the health and adaptability of horses previously accepted by the receiving facility and prioritize matching horses with similar conditions. For example, the matching unit can consider the age and temperament of horses previously accepted by the receiving facility and select horses with similar characteristics. For example, the matching unit can analyze the breeding environment and management methods of horses previously accepted by the receiving facility and select suitable horses. In this way, the matching unit can select the optimal matching method by analyzing the past acceptance history of the receiving facility. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input the receiving facility's acceptance history data into AI, which can analyze the data and select the optimal matching method.

[0044] The matching unit can improve the accuracy of matching by evaluating the horse's adaptability based on the environment and conditions of the receiving location. For example, the matching unit considers the climate conditions of the receiving location and selects an environment to which the horse can easily adapt. For example, the matching unit evaluates the size of the living space and exercise area at the receiving location and selects an environment suitable for the horse's exercise needs. For example, the matching unit evaluates the experience and skills of the staff at the receiving location and determines whether they can manage the horse in a way that suits its characteristics. In this way, the matching unit can improve the accuracy of matching by evaluating the horse's adaptability based on the environment and conditions of the receiving location. Some or all of the above processes in the matching unit may be performed using AI, for example, or not using AI. For example, the matching unit can input environmental data of the receiving location into AI, and the AI ​​can analyze the data to evaluate the horse's adaptability.

[0045] The matching unit can estimate a horse's emotions and determine the priority of potential adopters based on the estimated emotions. For example, if a horse's emotions are unstable, the matching unit will prioritize adopters with a quiet and calm environment. If a horse's emotions are relaxed, the matching unit will determine that the horse can adapt to a noisy environment and will relax the selection criteria. If a horse's emotions are stressed, the matching unit will select adopters based on whether the staff at the adopter are skilled in managing stress in horses. In this way, the matching unit can select an adopter suitable for the horse by determining the priority of adopters based on the horse's emotions. Some or all of the above processing in the matching unit may be performed using AI, for example, or not. For example, the matching unit can input horse emotion data into an AI, which can analyze the data to determine the priority of adopters.

[0046] The matching unit can prioritize selecting highly relevant adoptive homes by considering the geographical location information of the adoptive homes. For example, the matching unit will prioritize selecting an adoptive home if it is close to the horse's current location, as this reduces the burden of travel. For example, the matching unit will prioritize selecting an adoptive home if it is located in an area with climatic conditions similar to the horse's previous living environment, as this makes it easier for the horse to adapt. For example, the matching unit will prioritize selecting an adoptive home if it has conditions similar to the horse's past living environment, as this makes it easier for the horse to adapt. In this way, the matching unit can prioritize selecting highly relevant adoptive homes by considering the geographical location information of the adoptive homes. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input the geographical location information of the adoptive homes into AI, and the AI ​​can analyze the data to select highly relevant adoptive homes.

[0047] The matching unit can analyze the social media activity of potential adopters and select suitable adopters. For example, if an adopter frequently posts information about horse care on social media, the matching unit will select them, judging that they are proactive in horse management. For example, if an adopter frequently interacts with other horse owners on social media, the matching unit will select them, judging that they value the socialization of horses. For example, if an adopter shares information about horse health management on social media, the matching unit will select them, judging that they have a strong interest in horse health management. In this way, the matching unit can select suitable adopters by analyzing the social media activity of potential adopters. Some or all of the above processing in the matching unit may be performed using AI, for example, or not using AI. For example, the matching unit can input the social media data of potential adopters into AI, and the AI ​​can analyze the data to select suitable adopters.

[0048] The analysis unit can estimate the horse's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the horse's emotions are unstable, the analysis unit provides the analysis results in a simple and easy-to-understand format. For example, if the horse's emotions are relaxed, the analysis unit provides detailed analysis results that are easy for the owner to understand. For example, if the horse's emotions are stressed, the analysis unit includes specific advice for stress reduction in the analysis results. In this way, the analysis unit can provide analysis results that are easy for the owner to understand by adjusting the presentation of the analysis based on the horse's emotions. 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 horse emotion data into AI, which can analyze the data and adjust the presentation of the analysis results.

[0049] The analysis unit can adjust the level of detail of the analysis based on the horse's health condition during the analysis. For example, if the horse's health condition is good, the analysis unit provides detailed analysis results so that the owner can continue health management. For example, if the horse's health condition is deteriorating, the analysis unit summarizes the analysis results concisely so that a quick response can be taken. For example, if the horse's health condition is fluctuating, the analysis unit includes the factors causing the fluctuation in the health condition in the analysis results so that the owner can take appropriate measures. In this way, the analysis unit can adjust the level of detail of the analysis based on the horse's health condition so that the owner can take appropriate action. 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 horse health data into AI, and the AI ​​can analyze the data and adjust the level of detail of the analysis.

[0050] The analysis unit can apply different analysis algorithms depending on the horse's category during analysis. For example, for young horses, the analysis unit applies an analysis algorithm that emphasizes growth-related data. For older horses, the analysis unit applies an analysis algorithm that emphasizes health maintenance-related data. For racehorses, the analysis unit applies an analysis algorithm that emphasizes athletic ability and performance-related data. By applying different analysis algorithms depending on the horse's category, the analysis unit can provide more accurate analysis results. 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 horse category data into AI, which can analyze the data and apply an appropriate analysis algorithm.

[0051] The analysis unit can estimate the horse's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the horse's emotions are unstable, the analysis unit can summarize the results briefly so that the owner can understand them quickly. For example, if the horse's emotions are relaxed, the analysis unit can provide detailed results so that the owner can understand them thoroughly. For example, if the horse's emotions are stressed, the analysis unit can summarize the results concisely and include specific advice for stress reduction. In this way, the analysis unit can provide analysis results that are easy for the owner to understand by adjusting the length of the analysis based on the horse's emotions. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input horse emotion data into AI, and the AI ​​can analyze the data and adjust the length of the analysis.

[0052] The analysis unit can determine the priority of analysis based on the timing of data collection for the horses during the analysis process. For example, the analysis unit may prioritize the analysis of recently collected data to understand the horses' current health status. For example, the analysis unit may refer to past data to analyze long-term changes in health status. For example, the analysis unit may prioritize the analysis of data collected before and after a specific event (such as a competition) to understand fluctuations in performance. In this way, the analysis unit can understand the horses' current health status by determining the priority of analysis based on the timing of data collection. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input horse data collection timing data into AI, which can then analyze the data and determine the priority of analysis.

[0053] The analysis unit can adjust the order of analysis based on relevant horse data during the analysis. For example, the analysis unit may prioritize analyzing horse health data to understand the horse's health status. Next, it may analyze horse exercise data to evaluate exercise volume and performance. Finally, it may analyze horse environmental data to evaluate the impact of environmental factors on health and performance. This allows the analysis unit to perform efficient analysis by adjusting the order of analysis based on relevant horse data. 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 relevant horse data into AI, which can analyze the data and adjust the order of analysis.

[0054] The Ministry of Education can estimate the emotions of horse owners and adjust the presentation of educational content based on those estimated emotions. For example, if a horse owner is stressed, the Ministry of Education will provide simple and easy-to-understand educational content. If a horse owner is relaxed, the Ministry of Education will provide detailed educational content to allow the horse owner to learn at their own pace. If a horse owner is excited, the Ministry of Education will provide visually stimulating educational content to capture their interest. In this way, the Ministry of Education can provide educational content that is easy for horse owners to understand by adjusting the presentation of the content based on their emotions. Some or all of the above processing by the Ministry of Education may be performed using AI, for example, or not. For example, the Ministry of Education can input the horse owner's emotional data into an AI, which can then analyze the data and adjust the presentation of the educational content.

[0055] The Ministry of Education can adjust the level of detail in educational content based on the horse's health condition during training. For example, if the horse is in good health, the Ministry of Education can provide detailed health management methods to enable the owner to continue managing the horse. If the horse's health is deteriorating, the Ministry of Education can provide concise health management methods to enable a quick response. If the horse's health is fluctuating, the Ministry of Education can provide educational content that includes the factors causing the fluctuations in health, enabling the owner to take appropriate measures. In this way, the Ministry of Education can adjust the level of detail in educational content based on the horse's health condition, allowing the owner to take appropriate action. Some or all of the above processes by the Ministry of Education may be performed using AI, for example, or not using AI. For example, the Ministry of Education can input horse health data into AI, which can then analyze the data and adjust the level of detail in educational content.

[0056] The Ministry of Education can select the most suitable educational method for horse owners based on their past learning history. For example, the Ministry of Education can provide educational methods that allow horse owners to review and apply what they have learned in the past. For example, the Ministry of Education can provide educational methods that allow horse owners to focus on topics they have struggled with in the past. For example, the Ministry of Education can provide educational methods that capture the horse owner's interest based on topics they have enjoyed learning in the past. This enables effective education by allowing the Ministry of Education to select the most suitable educational method based on the horse owner's past learning history. Some or all of the above processes by the Ministry of Education may be performed using AI, for example, or not. For example, the Ministry of Education can input the horse owner's learning history data into an AI, which can then analyze the data and select the most suitable educational method.

[0057] The Ministry of Education can estimate the emotions of horse owners and prioritize educational content based on those emotions. For example, if a horse owner is stressed, the Ministry of Education will prioritize providing important content to enable quick learning. If a horse owner is relaxed, the Ministry of Education will provide detailed content to enable thorough learning. If a horse owner is excited, the Ministry of Education will prioritize providing engaging content to increase motivation to learn. In this way, the Ministry of Education can enable horse owners to learn efficiently by prioritizing educational content based on their emotions. Some or all of the above processes by the Ministry of Education may be performed using AI, for example, or not. For example, the Ministry of Education can input horse owner emotion data into an AI, which can then analyze the data to determine the priority of educational content.

[0058] The Ministry of Education can provide highly relevant educational content during training, taking into account the geographical location of the horse owner. For example, the Ministry of Education can provide appropriate health management methods based on the climate conditions of the horse owner's region. For example, the Ministry of Education can provide appropriate breeding methods based on the breeding environment of the horse owner's region. For example, the Ministry of Education can provide relevant educational content based on competitions and events in the horse owner's region. In this way, the Ministry of Education can provide highly relevant educational content by taking into account the geographical location of the horse owner. Some or all of the above processing by the Ministry of Education may be performed using AI, for example, or not using AI. For example, the Ministry of Education can input the geographical location of the horse owner into AI, and the AI ​​can analyze the data to provide highly relevant educational content.

[0059] The Ministry of Education can analyze the social media activities of horse owners during training and provide relevant educational content. For example, the Ministry of Education can provide relevant educational content based on topics that horse owners show interest in on social media. For example, the Ministry of Education can provide relevant educational content based on the content of interactions between horse owners on social media. For example, the Ministry of Education can provide engaging educational content based on information that horse owners share on social media. In this way, the Ministry of Education can provide relevant educational content by analyzing the social media activities of horse owners. Some or all of the above processing by the Ministry of Education may be performed using AI, for example, or not using AI. For example, the Ministry of Education can input the social media data of horse owners into AI, and the AI ​​can analyze the data and provide relevant educational content.

[0060] The management department can estimate the horse's emotions and adjust management methods based on the estimated emotions. For example, if the horse's emotions are unstable, the management department will prioritize stress reduction management methods. For example, if the horse's emotions are relaxed, the management department will continue with normal management methods. For example, if the horse's emotions are stressed, the management department will identify the stressors and take appropriate measures. In this way, the management department can reduce the horse's stress by adjusting management methods based on the horse's emotions. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input horse emotion data into AI, and the AI ​​can analyze the data and adjust management methods.

[0061] The management department can analyze the horse's past behavioral data to select the optimal management method during management. For example, the management department can select a management method to reduce stress based on the horse's past behavioral data. For example, the management department can select a management method to maintain health based on the horse's past behavioral data. For example, the management department can select a management method to improve performance based on the horse's past behavioral data. In this way, the management department can select the optimal management method by analyzing the horse's past behavioral data. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input the horse's past behavioral data into AI, and the AI ​​can analyze the data to select the optimal management method.

[0062] The management department can customize management methods based on the horse's current health condition during management. For example, if the horse is in good health, the management department will continue with normal management methods. If the horse's health condition is deteriorating, the management department will prioritize implementing management methods for health recovery. If the horse's health condition is fluctuating, the management department will customize management methods according to the health condition. This allows the management department to provide appropriate management by customizing management methods based on the horse's current health condition. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input horse health data into AI, which can then analyze the data and customize the management methods.

[0063] The management department can estimate the horse's emotions and determine management priorities based on the estimated emotions. For example, if the horse's emotions are unstable, the management department will prioritize stress reduction management. For example, if the horse's emotions are relaxed, the management department will continue with normal management. For example, if the horse's emotions are stressed, the management department will identify the stressors and take appropriate measures. In this way, the management department can reduce the horse's stress by determining management priorities based on the horse's emotions. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input horse emotion data into AI, and the AI ​​can analyze the data to determine management priorities.

[0064] The management department can select the optimal management method when managing horses, taking into account the horses' geographical location. For example, the management department can select an appropriate management method by considering the climatic conditions of the horses' current location. For example, the management department can select an appropriate management method by considering the breeding environment of the horses' current location. For example, the management department can select an appropriate management method by considering competitions or events in the horses' current location. In this way, the management department can select the optimal management method by taking into account the horses' geographical location. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input the horses' geographical location information into AI, and the AI ​​can analyze the data to select the optimal management method.

[0065] The management department can analyze the horses' social media activity during management and propose management measures. For example, the management department can propose management measures for stress reduction based on the horses' social media activity. For example, the management department can propose management measures for maintaining health based on the horses' social media activity. For example, the management department can propose management measures for improving performance based on the horses' social media activity. In this way, the management department can propose appropriate management measures by analyzing the horses' social media activity. Some or all of the above processing in the management department may be performed using AI, for example, or without AI. For example, the management department can input the horses' social media data into AI, and the AI ​​can analyze the data and propose management measures.

[0066] The data collection unit can estimate the horse's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the horse's emotions are unstable, the data collection unit will prioritize data collection for stress reduction. For example, if the horse's emotions are relaxed, the data collection unit will continue with normal data collection. For example, if the horse's emotions are stressed, the data collection unit will collect data to identify the stressors. This allows the data collection unit to collect appropriate data by adjusting the timing of data collection based on the horse's emotions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input horse emotion data into AI, which can analyze the data and adjust the timing of data collection.

[0067] The data collection unit can analyze the horse's past data collection history and select the optimal data collection method. For example, the data collection unit can select a data collection method for stress reduction based on the horse's past data collection history. For example, the data collection unit can select a data collection method for maintaining health based on the horse's past data collection history. For example, the data collection unit can select a data collection method for improving performance based on the horse's past data collection history. In this way, the data collection unit can select the optimal data collection method by analyzing the horse's past data collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the horse's past data collection history into AI, and the AI ​​can analyze the data and select the optimal data collection method.

[0068] The data collection unit can estimate the horse's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the horse's emotions are unstable, the data collection unit will prioritize collecting data for stress reduction. For example, if the horse's emotions are relaxed, the data collection unit will collect normal data. For example, if the horse's emotions are stressed, the data collection unit will prioritize collecting data to identify stressors. In this way, the data collection unit can prioritize the collection of important data by determining the priority of data to collect based on the horse's emotions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input horse emotion data into AI, and the AI ​​can analyze the data and determine the priority of data to collect.

[0069] The data collection unit can prioritize the collection of highly relevant data by considering the horse's geographical location during data collection. For example, the data collection unit can prioritize the collection of relevant data by considering the climatic conditions of the horse's current location. For example, the data collection unit can prioritize the collection of relevant data by considering the horse's breeding environment at its current location. For example, the data collection unit can prioritize the collection of relevant data by considering competitions and events at the horse's current location. In this way, the data collection unit can prioritize the collection of highly relevant data by considering the horse's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the horse's geographical location information into AI, and the AI ​​can analyze the data and prioritize the collection of highly relevant data.

[0070] The monitoring unit can estimate the horse's emotions and adjust the monitoring method based on the estimated emotions. For example, if the horse's emotions are unstable, the monitoring unit will prioritize monitoring methods to reduce stress. For example, if the horse's emotions are relaxed, the monitoring unit will continue with the normal monitoring method. For example, if the horse's emotions are stressed, the monitoring unit will implement monitoring methods to identify stressors. This allows the monitoring unit to perform appropriate monitoring by adjusting the monitoring method based on the horse's emotions. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input horse emotion data into AI, which can then analyze the data and adjust the monitoring method.

[0071] The monitoring unit can analyze the horse's past stress levels during monitoring and select the optimal monitoring method. For example, the monitoring unit can select a monitoring method for stress reduction based on the horse's past stress levels. For example, the monitoring unit can select a monitoring method for maintaining health based on the horse's past stress levels. For example, the monitoring unit can select a monitoring method for improving performance based on the horse's past stress levels. In this way, the monitoring unit can select the optimal monitoring method by analyzing the horse's past stress levels. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the horse's past stress level data into AI, and the AI ​​can analyze the data and select the optimal monitoring method.

[0072] The monitoring unit can estimate the horse's emotions and determine monitoring priorities based on the estimated emotions. For example, if the horse's emotions are unstable, the monitoring unit will prioritize monitoring to reduce stress. For example, if the horse's emotions are relaxed, the monitoring unit will continue normal monitoring. For example, if the horse's emotions are stressed, the monitoring unit will prioritize monitoring to identify stressors. In this way, the monitoring unit can prioritize important monitoring by determining monitoring priorities based on the horse's emotions. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input horse emotion data into AI, and the AI ​​can analyze the data to determine monitoring priorities.

[0073] The monitoring unit can select the optimal monitoring method while considering the horse's geographical location information. For example, the monitoring unit can select an appropriate monitoring method by considering the climatic conditions of the horse's current location. For example, the monitoring unit can select an appropriate monitoring method by considering the horse's living environment at its current location. For example, the monitoring unit can select an appropriate monitoring method by considering competitions or events at the horse's current location. In this way, the monitoring unit can select the optimal monitoring method by considering the horse's geographical location information. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the horse's geographical location information into AI, and the AI ​​can analyze the data to select the optimal monitoring method.

[0074] The service provider can estimate the horse's emotions and adjust the method of providing the nutrition plan based on the estimated emotions. For example, if the horse's emotions are unstable, the service provider will prioritize providing a stress-reducing nutrition plan. For example, if the horse's emotions are relaxed, the service provider will provide a normal nutrition plan. For example, if the horse's emotions are stressed, the service provider will identify the stressors and provide an appropriate nutrition plan. In this way, the service provider can provide an appropriate nutrition plan by adjusting the method of providing the nutrition plan based on the horse's emotions. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input horse emotion data into AI, and the AI ​​can analyze the data and adjust the method of providing the nutrition plan.

[0075] The service provider can select the optimal nutrition plan by analyzing the horse's past health data when providing a nutrition plan. For example, the service provider can select a nutrition plan for maintaining health based on the horse's past health data. For example, the service provider can select a nutrition plan for improving performance based on the horse's past health data. For example, the service provider can select a nutrition plan for reducing stress based on the horse's past health data. In this way, the service provider can select the optimal nutrition plan by analyzing the horse's past health data. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the horse's past health data into AI, and the AI ​​can analyze the data to select the optimal nutrition plan.

[0076] The service provider can estimate the horse's emotions and determine the priority of nutritional plans based on the estimated emotions. For example, if the horse's emotions are unstable, the service provider will prioritize providing a stress-reducing nutritional plan. For example, if the horse's emotions are relaxed, the service provider will provide a normal nutritional plan. For example, if the horse's emotions are stressed, the service provider will identify the stressors and provide an appropriate nutritional plan. In this way, the service provider can prioritize important nutritional plans by determining the priority of nutritional plans based on the horse's emotions. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input horse emotion data into AI, and the AI ​​can analyze the data to determine the priority of nutritional plans.

[0077] The service provider can provide an optimal nutrition plan by considering the horse's geographical location when providing a nutrition plan. For example, the service provider can provide an appropriate nutrition plan by considering the climatic conditions of the horse's current location. For example, the service provider can provide an appropriate nutrition plan by considering the breeding environment of the horse's current location. For example, the service provider can provide an appropriate nutrition plan by considering competitions or events in the horse's current location. In this way, the service provider can provide an optimal nutrition plan by considering the horse's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input the horse's geographical location information into AI, and the AI ​​can analyze the data to provide an optimal nutrition plan.

[0078] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0079] The analysis unit can improve the accuracy of its analysis by referring to the horse's past health data when analyzing the horse's health data. For example, it can more accurately assess the horse's current health status based on its past health data. It can analyze fluctuations in health status based on the horse's past health data and create a long-term health management plan. It can also detect specific health problems early based on the horse's past health data. In this way, the analysis unit can improve the accuracy of its analysis by referring to the horse's past health data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the horse's past health data into an AI, which can then analyze the data to improve the accuracy of the analysis.

[0080] The Ministry of Education can analyze a horse owner's past learning history and select the most suitable educational method. For example, it can provide educational methods that allow for review and application based on what the horse owner has learned in the past. It can also provide educational methods that focus on topics the horse owner has struggled with in the past. Furthermore, it can provide educational methods that engage the horse owner's interest based on topics they have enjoyed learning in the past. This allows the Ministry of Education to select the most suitable educational method based on the horse owner's past learning history, thereby enabling effective education. Some or all of the above processes by the Ministry of Education may be performed using AI, or not. For example, the Ministry of Education can input the horse owner's learning history data into an AI, which can then analyze the data and select the most suitable educational method.

[0081] The management department can analyze a horse's past behavioral data to select the optimal management method. For example, it can select a management method to reduce stress based on the horse's past behavioral data. It can also select a management method to maintain health based on the horse's past behavioral data. In this way, the management department can select the optimal management method by analyzing the horse's past behavioral data. Some or all of the above processes in the management department may be performed using AI, or not. For example, the management department can input the horse's past behavioral data into an AI, which can then analyze the data and select the optimal management method.

[0082] The data collection unit can analyze the horse's past data collection history and select the optimal data collection method. For example, it can select a data collection method for stress reduction based on the horse's past data collection history. It can also select a data collection method for maintaining health based on the horse's past data collection history. In this way, the data collection unit can select the optimal data collection method by analyzing the horse's past data collection history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the horse's past data collection history into an AI, which can then analyze the data and select the optimal data collection method.

[0083] The monitoring unit can analyze a horse's past stress levels and select the optimal monitoring method. For example, it can select a monitoring method to reduce stress based on the horse's past stress levels. It can also select a monitoring method to maintain health based on the horse's past stress levels. It can also select a monitoring method to improve performance based on the horse's past stress levels. In this way, the monitoring unit can select the optimal monitoring method by analyzing the horse's past stress levels. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input the horse's past stress level data into AI, and the AI ​​can analyze the data and select the optimal monitoring method.

[0084] The following briefly describes the processing flow for example form 1.

[0085] Step 1: The matching unit automatically matches horses with potential adopters. For example, it considers the horse's characteristics and the adopter's requirements to make the best possible match. Specifically, it suggests suitable adopters based on information such as the horse's age, health condition, and temperament. Step 2: The analysis unit performs multidimensional analysis of the horses matched by the matching unit. For example, it analyzes the horses' behavioral data, health data, environmental data, etc., to comprehensively evaluate the horses' condition. Specifically, it analyzes the horses' exercise levels, diet, stress levels, etc., to understand the horses' health status. Step 3: The Education Department provides education and support to horse owners based on the results analyzed by the Analysis Department. For example, it provides information on horse management and training to educate and support horse owners. Specifically, it provides information on horse health management methods, nutrition management methods, stress management methods, etc. Step 4: The management department performs behavioral prediction, nutritional management, and stress level management based on the information provided by the education department. For example, they predict behavior based on the horses' past data and real-time information to help manage stress and maintain health. Specifically, they analyze the horses' behavioral patterns and take appropriate measures before stress levels rise. They also provide optimal nutritional plans based on each horse's individual health condition and activity level to support their health.

[0086] (Example of form 2) The AI ​​agent service according to an embodiment of the present invention is a system for preventing the euthanasia of horses. This system automatically matches horses with potential adopters, performs multidimensional analysis on the horses, and provides education and support to horse owners. Furthermore, the system has unique behavior prediction, nutrition management, and stress level management functions. For example, the system performs optimal matching considering the horse's characteristics and the conditions of the adopter. For example, it suggests a suitable adopter based on information such as the horse's age, health condition, and temperament. Next, the system analyzes the horse's behavioral data, health data, environmental data, etc., to comprehensively evaluate the horse's condition. For example, it analyzes the horse's exercise level, diet, stress level, etc., to understand the horse's health condition. Furthermore, the system provides education and support to horse owners. The system provides information on horse management and training, and educates and supports horse owners. For example, it provides information on horse health management methods, nutrition management methods, and stress management methods. The system also has unique behavior prediction, nutrition management, and stress level management functions. The system predicts behavior based on the horse's past data and real-time information, and uses this to help with stress management and health maintenance. For example, it can analyze the horse's behavior patterns and take appropriate measures before stress levels rise. Furthermore, the system provides an optimal nutritional plan based on each horse's individual health condition and activity level, supporting their health. This allows the system to prevent horses from being euthanized and to provide education and support to horse owners. For example, by monitoring a horse's health in real time and quickly detecting and responding to problems, the system can maintain the horse's health. Also, by enabling horse owners to learn proper management methods, horse health management can be improved, reducing the risk of euthanasia. In this way, the AI ​​agent service can prevent horses from being euthanized and provide education and support to horse owners.

[0087] The AI ​​agent service according to this embodiment comprises a matching unit, an analysis unit, an education unit, and a management unit. The matching unit automatically matches horses with potential adopters. The matching unit performs optimal matching by considering, for example, the horse's characteristics and the conditions of the adopters. For example, the matching unit proposes a suitable adopter based on information such as the horse's age, health condition, and temperament. The analysis unit performs multidimensional analysis of the horses matched by the matching unit. The analysis unit comprehensively evaluates the horse's condition by analyzing, for example, the horse's behavioral data, health data, and environmental data. For example, the analysis unit analyzes the horse's exercise level, diet, and stress level to understand the horse's health condition. The education unit provides education and support to horse owners based on the results analyzed by the analysis unit. For example, the education unit provides information on horse management and training to educate and support horse owners. For example, the education unit provides information on horse health management methods, nutrition management methods, and stress management methods. The management unit performs behavioral prediction, nutrition management, and stress level management based on the information provided by the education unit. The management department predicts horse behavior based on past data and real-time information, for example, to help manage stress and maintain health. For instance, it can analyze horse behavior patterns and take appropriate measures before stress levels rise. Furthermore, the management department provides optimal nutritional plans based on each horse's individual health status and activity level, supporting their well-being. This allows the AI ​​agent service, according to this embodiment, to prevent horse euthanasia and provide education and support to horse owners.

[0088] The matching unit automatically matches horses with potential adopters. For example, it considers the horse's characteristics and the adopter's requirements to achieve the optimal match. Specifically, the matching unit collects detailed information such as the horse's age, health, temperament, past training history, and competition results, and stores this information in a database. Adopter requirements include facility size, living environment, experience in horse care, and purpose (competition, breeding, pet, etc.). Based on this information, an AI algorithm performs the optimal match. For example, a facility with a large exercise area is suitable for a young, active horse, while a quiet environment is preferable for an older horse. The AI ​​comprehensively evaluates these conditions and proposes the most suitable adopter. Furthermore, the matching unit learns from past matching results and feedback to improve matching accuracy. For example, it can analyze patterns of successful past matches to improve matching accuracy under similar conditions. This allows the matching unit to provide the optimal environment for both the horse and the adopter, thereby improving the horse's welfare.

[0089] The analysis unit performs multidimensional analysis of horses matched by the matching unit. The analysis unit comprehensively evaluates the horse's condition by analyzing data such as behavioral data, health data, and environmental data. Specifically, it collects data such as the horse's exercise level, diet, stress level, sleep patterns, heart rate, and body temperature, and analyzes it using AI. For example, by analyzing the horse's exercise level, it evaluates whether appropriate exercise is being performed and suggests adjustments if there is an excess or deficiency. In the analysis of diet, it evaluates whether nutritional balance is maintained and suggests nutritional supplements as needed. In the analysis of stress levels, it monitors changes in heart rate and behavioral patterns to identify the cause of stress. This allows the analysis unit to grasp the horse's health status in real time and detect abnormalities early. Furthermore, by comparing with past data, the analysis unit can evaluate long-term health trends and predict future risks. For example, based on past data, it can predict health risks under specific seasons or environmental conditions and take preventative measures. This allows the analysis unit to comprehensively support horse health management and improve horse welfare.

[0090] The Education Department provides education and support to horse owners based on the results analyzed by the Analysis Department. For example, the Education Department provides information on horse management and training, offering educational support to owners. Specifically, it provides information on horse health management methods, nutrition management methods, stress management methods, and training methods. For instance, regarding horse health management, it explains the importance of regular health checks and how to deal with any abnormalities. For nutrition management, it proposes appropriate meal plans based on the horse's age and activity level, and provides specific advice on maintaining nutritional balance. Regarding stress management, it introduces key points for recognizing stress signs in horses and measures to improve the environment to reduce stress. For training methods, it provides training plans tailored to the horse's physical strength and abilities, recommending training within reasonable limits. Furthermore, the Education Department holds online seminars and workshops to provide owners with opportunities to learn the latest knowledge and techniques. This allows owners to deepen their knowledge of horse management and training, and provide a better living environment. The Education Department emphasizes communication with horse owners, addressing individual consultations and questions to alleviate owners' anxieties and doubts, and improve the welfare of the horses.

[0091] The Management Department performs behavioral prediction, nutritional management, and stress level management based on information provided by the Education Department. For example, the Management Department predicts behavior based on past data and real-time information of horses, and uses this information to manage stress and maintain health. Specifically, it can analyze horse behavior patterns and take appropriate measures before stress levels rise. For example, it monitors horse behavior patterns, identifies the cause of stress if abnormal behavior is observed, and improves the environment or adjusts training. The Management Department also provides optimal nutritional plans based on each horse's health condition and activity level to support their health. For example, it creates meal plans according to the horse's weight and activity level and provides specific advice to maintain nutritional balance. Furthermore, the Management Department can monitor the horses' health in real time and respond quickly if abnormalities are observed. For example, it monitors changes in the horse's body temperature and heart rate, and if abnormalities are found, it works with veterinarians to take appropriate measures. In this way, the Management Department can comprehensively support the health management of horses and improve their welfare. Furthermore, the management department prioritizes communication with horse owners, regularly reporting on the horses' health and management methods, and addressing any concerns or questions they may have. This allows the management department to work with owners to manage the horses' health and improve their welfare.

[0092] The data collection unit can collect horse behavior data. For example, the data collection unit collects horse walking data, eating data, resting data, etc. For example, the data collection unit collects horse walking data using sensors and stores it in a database. For example, the data collection unit captures horse eating data with a camera and extracts the data using image analysis technology. For example, the data collection unit collects horse resting data using motion sensors and stores it in a database. In this way, the data collection unit can more accurately understand the horse's condition by collecting horse behavior data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input horse behavior data into AI, and the AI ​​can analyze the data and extract behavior patterns.

[0093] The data collection unit can collect health data from horses. For example, the data collection unit can collect health data such as the horse's body temperature, heart rate, and blood test results. For example, the data collection unit can measure the horse's body temperature with a temperature sensor and store it in a database. For example, the data collection unit can measure the horse's heart rate with a heart rate sensor and store it in a database. For example, the data collection unit can obtain the horse's blood test results from a laboratory and store them in a database. In this way, the data collection unit can more accurately understand the horse's health status by collecting health data from horses. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the horse's health data into AI, and the AI ​​can analyze the data and evaluate the health status.

[0094] The monitoring unit can monitor the stress level of horses. The monitoring unit monitors stress levels, for example, by measuring changes in the horse's behavior patterns or hormone levels. For example, the monitoring unit detects changes in the horse's behavior patterns using a motion sensor and stores the data in a database. For example, the monitoring unit measures the horse's hormone levels through a blood test and stores the data in a database. For example, the monitoring unit measures fluctuations in the horse's heart rate using a heart rate sensor and stores the data in a database. This allows the monitoring unit to manage stress by monitoring the horse's stress level. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input data on the horse's stress level into an AI, which can analyze the data and evaluate the stress level.

[0095] The service provider can provide a nutrition plan for horses. For example, the service provider can provide an optimal nutrition plan based on the individual health status and activity level of each horse. For example, the service provider can assess the health status of horses, calculate the necessary nutrients, and create a nutrition plan. For example, the service provider can monitor the activity level of horses and adjust the nutrition plan based on calorie expenditure. For example, the service provider can record the contents of horses' meals, evaluate the nutritional balance, and provide a nutrition plan. In this way, the service provider can support the health of horses by providing a nutrition plan for them. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input horse health data and activity data into AI, and the AI ​​can generate an optimal nutrition plan.

[0096] The data collection unit can collect data on the horse's exercise level, diet, and stress level. For example, the data collection unit can measure the horse's exercise level using a pedometer or exercise sensor and store it in a database. For example, the data collection unit can photograph the horse's diet with a camera and extract the data using image analysis technology. For example, the data collection unit can evaluate the horse's stress level using a heart rate sensor or hormone level measurement and store it in a database. In this way, the data collection unit can comprehensively understand the horse's condition by collecting data such as the horse's exercise level, diet, and stress level. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the horse's exercise data into AI, which can analyze the data and extract exercise patterns.

[0097] The analysis unit can analyze the collected data and evaluate the horse's health. For example, the analysis unit can analyze data such as the horse's exercise level, diet, and stress level to comprehensively evaluate the horse's health. For example, the analysis unit can analyze the horse's exercise data to assess the risk of insufficient or excessive exercise. For example, the analysis unit can analyze the horse's diet data to assess imbalances in nutrition. For example, the analysis unit can analyze the horse's stress level data to identify the cause of stress. In this way, the analysis unit can evaluate the horse's health by analyzing the collected data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the horse's health data into AI, which can then analyze the data and evaluate the health status.

[0098] The management department can analyze the horses' behavioral patterns and take appropriate measures before stress levels rise. For example, the management department can monitor the horses' behavioral patterns and detect signs of stress early. For example, the management department can analyze changes in the horses' behavioral patterns and identify the causes of stress. For example, the management department can analyze the horses' behavioral patterns and propose measures to reduce stress. In this way, by analyzing the horses' behavioral patterns, the management department can take appropriate measures before stress levels rise. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input horse behavioral data into AI, which can analyze the data to detect signs of stress and propose countermeasures.

[0099] The service provider can provide an optimal nutrition plan based on the individual health status and activity level of each horse. For example, the service provider can assess the horse's health status, calculate the necessary nutrients, and create a nutrition plan. For example, the service provider can monitor the horse's activity level and adjust the nutrition plan based on calorie expenditure. For example, the service provider can record the horse's diet, evaluate the nutritional balance, and provide a nutrition plan. In this way, the service provider can support the horse's health by providing an optimal nutrition plan based on the individual health status and activity level of each horse. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input horse health data and activity data into AI, and the AI ​​can generate an optimal nutrition plan.

[0100] The matching unit can estimate the horse's emotions and adjust the selection criteria for adoptive homes based on the estimated emotions. For example, if the horse's emotions are unstable, the matching unit will prioritize adoptive homes with a quiet and calm environment. If the horse's emotions are relaxed, the matching unit will determine that the horse can adapt to a noisy environment and will relax the selection criteria. If the horse's emotions are stressed, the matching unit will select homes based on whether the staff at the adoptive home are skilled in managing stress in horses. In this way, the matching unit can select an adoptive home suitable for the horse by adjusting the selection criteria based on the horse's emotions. Some or all of the above processing in the matching unit may be performed using AI, for example, or not. For example, the matching unit can input the horse's emotional data into an AI, which can analyze the data and adjust the selection criteria for adoptive homes.

[0101] The matching unit can analyze the past acceptance history of the receiving facility and select the optimal matching method. For example, the matching unit can analyze the health and adaptability of horses previously accepted by the receiving facility and prioritize matching horses with similar conditions. For example, the matching unit can consider the age and temperament of horses previously accepted by the receiving facility and select horses with similar characteristics. For example, the matching unit can analyze the breeding environment and management methods of horses previously accepted by the receiving facility and select suitable horses. In this way, the matching unit can select the optimal matching method by analyzing the past acceptance history of the receiving facility. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input the receiving facility's acceptance history data into AI, which can analyze the data and select the optimal matching method.

[0102] The matching unit can improve the accuracy of matching by evaluating the horse's adaptability based on the environment and conditions of the receiving location. For example, the matching unit considers the climate conditions of the receiving location and selects an environment to which the horse can easily adapt. For example, the matching unit evaluates the size of the living space and exercise area at the receiving location and selects an environment suitable for the horse's exercise needs. For example, the matching unit evaluates the experience and skills of the staff at the receiving location and determines whether they can manage the horse in a way that suits its characteristics. In this way, the matching unit can improve the accuracy of matching by evaluating the horse's adaptability based on the environment and conditions of the receiving location. Some or all of the above processes in the matching unit may be performed using AI, for example, or not using AI. For example, the matching unit can input environmental data of the receiving location into AI, and the AI ​​can analyze the data to evaluate the horse's adaptability.

[0103] The matching unit can estimate a horse's emotions and determine the priority of potential adopters based on the estimated emotions. For example, if a horse's emotions are unstable, the matching unit will prioritize adopters with a quiet and calm environment. If a horse's emotions are relaxed, the matching unit will determine that the horse can adapt to a noisy environment and will relax the selection criteria. If a horse's emotions are stressed, the matching unit will select adopters based on whether the staff at the adopter are skilled in managing stress in horses. In this way, the matching unit can select an adopter suitable for the horse by determining the priority of adopters based on the horse's emotions. Some or all of the above processing in the matching unit may be performed using AI, for example, or not. For example, the matching unit can input horse emotion data into an AI, which can analyze the data to determine the priority of adopters.

[0104] The matching unit can prioritize selecting highly relevant adoptive homes by considering the geographical location information of the adoptive homes. For example, the matching unit will prioritize selecting an adoptive home if it is close to the horse's current location, as this reduces the burden of travel. For example, the matching unit will prioritize selecting an adoptive home if it is located in an area with climatic conditions similar to the horse's previous living environment, as this makes it easier for the horse to adapt. For example, the matching unit will prioritize selecting an adoptive home if it has conditions similar to the horse's past living environment, as this makes it easier for the horse to adapt. In this way, the matching unit can prioritize selecting highly relevant adoptive homes by considering the geographical location information of the adoptive homes. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input the geographical location information of the adoptive homes into AI, and the AI ​​can analyze the data to select highly relevant adoptive homes.

[0105] The matching unit can analyze the social media activity of potential adopters and select suitable adopters. For example, if an adopter frequently posts information about horse care on social media, the matching unit will select them, judging that they are proactive in horse management. For example, if an adopter frequently interacts with other horse owners on social media, the matching unit will select them, judging that they value the socialization of horses. For example, if an adopter shares information about horse health management on social media, the matching unit will select them, judging that they have a strong interest in horse health management. In this way, the matching unit can select suitable adopters by analyzing the social media activity of potential adopters. Some or all of the above processing in the matching unit may be performed using AI, for example, or not using AI. For example, the matching unit can input the social media data of potential adopters into AI, and the AI ​​can analyze the data to select suitable adopters.

[0106] The analysis unit can estimate the horse's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the horse's emotions are unstable, the analysis unit provides the analysis results in a simple and easy-to-understand format. For example, if the horse's emotions are relaxed, the analysis unit provides detailed analysis results that are easy for the owner to understand. For example, if the horse's emotions are stressed, the analysis unit includes specific advice for stress reduction in the analysis results. In this way, the analysis unit can provide analysis results that are easy for the owner to understand by adjusting the presentation of the analysis based on the horse's emotions. 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 horse emotion data into AI, which can analyze the data and adjust the presentation of the analysis results.

[0107] The analysis unit can adjust the level of detail of the analysis based on the horse's health condition during the analysis. For example, if the horse's health condition is good, the analysis unit provides detailed analysis results so that the owner can continue health management. For example, if the horse's health condition is deteriorating, the analysis unit summarizes the analysis results concisely so that a quick response can be taken. For example, if the horse's health condition is fluctuating, the analysis unit includes the factors causing the fluctuation in the health condition in the analysis results so that the owner can take appropriate measures. In this way, the analysis unit can adjust the level of detail of the analysis based on the horse's health condition so that the owner can take appropriate action. 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 horse health data into AI, and the AI ​​can analyze the data and adjust the level of detail of the analysis.

[0108] The analysis unit can apply different analysis algorithms depending on the horse's category during analysis. For example, for young horses, the analysis unit applies an analysis algorithm that emphasizes growth-related data. For older horses, the analysis unit applies an analysis algorithm that emphasizes health maintenance-related data. For racehorses, the analysis unit applies an analysis algorithm that emphasizes athletic ability and performance-related data. By applying different analysis algorithms depending on the horse's category, the analysis unit can provide more accurate analysis results. 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 horse category data into AI, which can analyze the data and apply an appropriate analysis algorithm.

[0109] The analysis unit can estimate the horse's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the horse's emotions are unstable, the analysis unit can summarize the results briefly so that the owner can understand them quickly. For example, if the horse's emotions are relaxed, the analysis unit can provide detailed results so that the owner can understand them thoroughly. For example, if the horse's emotions are stressed, the analysis unit can summarize the results concisely and include specific advice for stress reduction. In this way, the analysis unit can provide analysis results that are easy for the owner to understand by adjusting the length of the analysis based on the horse's emotions. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input horse emotion data into AI, and the AI ​​can analyze the data and adjust the length of the analysis.

[0110] The analysis unit can determine the priority of analysis based on the timing of data collection for the horses during the analysis process. For example, the analysis unit may prioritize the analysis of recently collected data to understand the horses' current health status. For example, the analysis unit may refer to past data to analyze long-term changes in health status. For example, the analysis unit may prioritize the analysis of data collected before and after a specific event (such as a competition) to understand fluctuations in performance. In this way, the analysis unit can understand the horses' current health status by determining the priority of analysis based on the timing of data collection. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input horse data collection timing data into AI, which can then analyze the data and determine the priority of analysis.

[0111] The analysis unit can adjust the order of analysis based on relevant horse data during the analysis. For example, the analysis unit may prioritize analyzing horse health data to understand the horse's health status. Next, it may analyze horse exercise data to evaluate exercise volume and performance. Finally, it may analyze horse environmental data to evaluate the impact of environmental factors on health and performance. This allows the analysis unit to perform efficient analysis by adjusting the order of analysis based on relevant horse data. 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 relevant horse data into AI, which can analyze the data and adjust the order of analysis.

[0112] The Ministry of Education can estimate the emotions of horse owners and adjust the presentation of educational content based on those estimated emotions. For example, if a horse owner is stressed, the Ministry of Education will provide simple and easy-to-understand educational content. If a horse owner is relaxed, the Ministry of Education will provide detailed educational content to allow the horse owner to learn at their own pace. If a horse owner is excited, the Ministry of Education will provide visually stimulating educational content to capture their interest. In this way, the Ministry of Education can provide educational content that is easy for horse owners to understand by adjusting the presentation of the content based on their emotions. Some or all of the above processing by the Ministry of Education may be performed using AI, for example, or not. For example, the Ministry of Education can input the horse owner's emotional data into an AI, which can then analyze the data and adjust the presentation of the educational content.

[0113] The Ministry of Education can adjust the level of detail in educational content based on the horse's health condition during training. For example, if the horse is in good health, the Ministry of Education can provide detailed health management methods to enable the owner to continue managing the horse. If the horse's health is deteriorating, the Ministry of Education can provide concise health management methods to enable a quick response. If the horse's health is fluctuating, the Ministry of Education can provide educational content that includes the factors causing the fluctuations in health, enabling the owner to take appropriate measures. In this way, the Ministry of Education can adjust the level of detail in educational content based on the horse's health condition, allowing the owner to take appropriate action. Some or all of the above processes by the Ministry of Education may be performed using AI, for example, or not using AI. For example, the Ministry of Education can input horse health data into AI, which can then analyze the data and adjust the level of detail in educational content.

[0114] The Ministry of Education can select the most suitable educational method for horse owners based on their past learning history. For example, the Ministry of Education can provide educational methods that allow horse owners to review and apply what they have learned in the past. For example, the Ministry of Education can provide educational methods that allow horse owners to focus on topics they have struggled with in the past. For example, the Ministry of Education can provide educational methods that capture the horse owner's interest based on topics they have enjoyed learning in the past. This enables effective education by allowing the Ministry of Education to select the most suitable educational method based on the horse owner's past learning history. Some or all of the above processes by the Ministry of Education may be performed using AI, for example, or not. For example, the Ministry of Education can input the horse owner's learning history data into an AI, which can then analyze the data and select the most suitable educational method.

[0115] The Ministry of Education can estimate the emotions of horse owners and prioritize educational content based on those emotions. For example, if a horse owner is stressed, the Ministry of Education will prioritize providing important content to enable quick learning. If a horse owner is relaxed, the Ministry of Education will provide detailed content to enable thorough learning. If a horse owner is excited, the Ministry of Education will prioritize providing engaging content to increase motivation to learn. In this way, the Ministry of Education can enable horse owners to learn efficiently by prioritizing educational content based on their emotions. Some or all of the above processes by the Ministry of Education may be performed using AI, for example, or not. For example, the Ministry of Education can input horse owner emotion data into an AI, which can then analyze the data to determine the priority of educational content.

[0116] The Ministry of Education can provide highly relevant educational content during training, taking into account the geographical location of the horse owner. For example, the Ministry of Education can provide appropriate health management methods based on the climate conditions of the horse owner's region. For example, the Ministry of Education can provide appropriate breeding methods based on the breeding environment of the horse owner's region. For example, the Ministry of Education can provide relevant educational content based on competitions and events in the horse owner's region. In this way, the Ministry of Education can provide highly relevant educational content by taking into account the geographical location of the horse owner. Some or all of the above processing by the Ministry of Education may be performed using AI, for example, or not using AI. For example, the Ministry of Education can input the geographical location of the horse owner into AI, and the AI ​​can analyze the data to provide highly relevant educational content.

[0117] The Ministry of Education can analyze the social media activities of horse owners during training and provide relevant educational content. For example, the Ministry of Education can provide relevant educational content based on topics that horse owners show interest in on social media. For example, the Ministry of Education can provide relevant educational content based on the content of interactions between horse owners on social media. For example, the Ministry of Education can provide engaging educational content based on information that horse owners share on social media. In this way, the Ministry of Education can provide relevant educational content by analyzing the social media activities of horse owners. Some or all of the above processing by the Ministry of Education may be performed using AI, for example, or not using AI. For example, the Ministry of Education can input the social media data of horse owners into AI, and the AI ​​can analyze the data and provide relevant educational content.

[0118] The management department can estimate the horse's emotions and adjust management methods based on the estimated emotions. For example, if the horse's emotions are unstable, the management department will prioritize stress reduction management methods. For example, if the horse's emotions are relaxed, the management department will continue with normal management methods. For example, if the horse's emotions are stressed, the management department will identify the stressors and take appropriate measures. In this way, the management department can reduce the horse's stress by adjusting management methods based on the horse's emotions. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input horse emotion data into AI, and the AI ​​can analyze the data and adjust management methods.

[0119] The management department can analyze the horse's past behavioral data to select the optimal management method during management. For example, the management department can select a management method to reduce stress based on the horse's past behavioral data. For example, the management department can select a management method to maintain health based on the horse's past behavioral data. For example, the management department can select a management method to improve performance based on the horse's past behavioral data. In this way, the management department can select the optimal management method by analyzing the horse's past behavioral data. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input the horse's past behavioral data into AI, and the AI ​​can analyze the data to select the optimal management method.

[0120] The management department can customize management methods based on the horse's current health condition during management. For example, if the horse is in good health, the management department will continue with normal management methods. If the horse's health condition is deteriorating, the management department will prioritize implementing management methods for health recovery. If the horse's health condition is fluctuating, the management department will customize management methods according to the health condition. This allows the management department to provide appropriate management by customizing management methods based on the horse's current health condition. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input horse health data into AI, which can then analyze the data and customize the management methods.

[0121] The management department can estimate the horse's emotions and determine management priorities based on the estimated emotions. For example, if the horse's emotions are unstable, the management department will prioritize stress reduction management. For example, if the horse's emotions are relaxed, the management department will continue with normal management. For example, if the horse's emotions are stressed, the management department will identify the stressors and take appropriate measures. In this way, the management department can reduce the horse's stress by determining management priorities based on the horse's emotions. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input horse emotion data into AI, and the AI ​​can analyze the data to determine management priorities.

[0122] The management department can select the optimal management method when managing horses, taking into account the horses' geographical location. For example, the management department can select an appropriate management method by considering the climatic conditions of the horses' current location. For example, the management department can select an appropriate management method by considering the breeding environment of the horses' current location. For example, the management department can select an appropriate management method by considering competitions or events in the horses' current location. In this way, the management department can select the optimal management method by taking into account the horses' geographical location. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input the horses' geographical location information into AI, and the AI ​​can analyze the data to select the optimal management method.

[0123] The management department can analyze the horses' social media activity during management and propose management measures. For example, the management department can propose management measures for stress reduction based on the horses' social media activity. For example, the management department can propose management measures for maintaining health based on the horses' social media activity. For example, the management department can propose management measures for improving performance based on the horses' social media activity. In this way, the management department can propose appropriate management measures by analyzing the horses' social media activity. Some or all of the above processing in the management department may be performed using AI, for example, or without AI. For example, the management department can input the horses' social media data into AI, and the AI ​​can analyze the data and propose management measures.

[0124] The data collection unit can estimate the horse's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the horse's emotions are unstable, the data collection unit will prioritize data collection for stress reduction. For example, if the horse's emotions are relaxed, the data collection unit will continue with normal data collection. For example, if the horse's emotions are stressed, the data collection unit will collect data to identify the stressors. This allows the data collection unit to collect appropriate data by adjusting the timing of data collection based on the horse's emotions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input horse emotion data into AI, which can analyze the data and adjust the timing of data collection.

[0125] The data collection unit can analyze the horse's past data collection history and select the optimal data collection method. For example, the data collection unit can select a data collection method for stress reduction based on the horse's past data collection history. For example, the data collection unit can select a data collection method for maintaining health based on the horse's past data collection history. For example, the data collection unit can select a data collection method for improving performance based on the horse's past data collection history. In this way, the data collection unit can select the optimal data collection method by analyzing the horse's past data collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the horse's past data collection history into AI, and the AI ​​can analyze the data and select the optimal data collection method.

[0126] The data collection unit can estimate the horse's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the horse's emotions are unstable, the data collection unit will prioritize collecting data for stress reduction. For example, if the horse's emotions are relaxed, the data collection unit will collect normal data. For example, if the horse's emotions are stressed, the data collection unit will prioritize collecting data to identify stressors. In this way, the data collection unit can prioritize the collection of important data by determining the priority of data to collect based on the horse's emotions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input horse emotion data into AI, and the AI ​​can analyze the data and determine the priority of data to collect.

[0127] The data collection unit can prioritize the collection of highly relevant data by considering the horse's geographical location during data collection. For example, the data collection unit can prioritize the collection of relevant data by considering the climatic conditions of the horse's current location. For example, the data collection unit can prioritize the collection of relevant data by considering the horse's breeding environment at its current location. For example, the data collection unit can prioritize the collection of relevant data by considering competitions and events at the horse's current location. In this way, the data collection unit can prioritize the collection of highly relevant data by considering the horse's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the horse's geographical location information into AI, and the AI ​​can analyze the data and prioritize the collection of highly relevant data.

[0128] The monitoring unit can estimate the horse's emotions and adjust the monitoring method based on the estimated emotions. For example, if the horse's emotions are unstable, the monitoring unit will prioritize monitoring methods to reduce stress. For example, if the horse's emotions are relaxed, the monitoring unit will continue with the normal monitoring method. For example, if the horse's emotions are stressed, the monitoring unit will implement monitoring methods to identify stressors. This allows the monitoring unit to perform appropriate monitoring by adjusting the monitoring method based on the horse's emotions. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input horse emotion data into AI, which can then analyze the data and adjust the monitoring method.

[0129] The monitoring unit can analyze the horse's past stress levels during monitoring and select the optimal monitoring method. For example, the monitoring unit can select a monitoring method for stress reduction based on the horse's past stress levels. For example, the monitoring unit can select a monitoring method for maintaining health based on the horse's past stress levels. For example, the monitoring unit can select a monitoring method for improving performance based on the horse's past stress levels. In this way, the monitoring unit can select the optimal monitoring method by analyzing the horse's past stress levels. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the horse's past stress level data into AI, and the AI ​​can analyze the data and select the optimal monitoring method.

[0130] The monitoring unit can estimate the horse's emotions and determine monitoring priorities based on the estimated emotions. For example, if the horse's emotions are unstable, the monitoring unit will prioritize monitoring to reduce stress. For example, if the horse's emotions are relaxed, the monitoring unit will continue normal monitoring. For example, if the horse's emotions are stressed, the monitoring unit will prioritize monitoring to identify stressors. In this way, the monitoring unit can prioritize important monitoring by determining monitoring priorities based on the horse's emotions. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input horse emotion data into AI, and the AI ​​can analyze the data to determine monitoring priorities.

[0131] The monitoring unit can select the optimal monitoring method while considering the horse's geographical location information. For example, the monitoring unit can select an appropriate monitoring method by considering the climatic conditions of the horse's current location. For example, the monitoring unit can select an appropriate monitoring method by considering the horse's living environment at its current location. For example, the monitoring unit can select an appropriate monitoring method by considering competitions or events at the horse's current location. In this way, the monitoring unit can select the optimal monitoring method by considering the horse's geographical location information. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the horse's geographical location information into AI, and the AI ​​can analyze the data to select the optimal monitoring method.

[0132] The service provider can estimate the horse's emotions and adjust the method of providing the nutrition plan based on the estimated emotions. For example, if the horse's emotions are unstable, the service provider will prioritize providing a stress-reducing nutrition plan. For example, if the horse's emotions are relaxed, the service provider will provide a normal nutrition plan. For example, if the horse's emotions are stressed, the service provider will identify the stressors and provide an appropriate nutrition plan. In this way, the service provider can provide an appropriate nutrition plan by adjusting the method of providing the nutrition plan based on the horse's emotions. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input horse emotion data into AI, and the AI ​​can analyze the data and adjust the method of providing the nutrition plan.

[0133] The service provider can select the optimal nutrition plan by analyzing the horse's past health data when providing a nutrition plan. For example, the service provider can select a nutrition plan for maintaining health based on the horse's past health data. For example, the service provider can select a nutrition plan for improving performance based on the horse's past health data. For example, the service provider can select a nutrition plan for reducing stress based on the horse's past health data. In this way, the service provider can select the optimal nutrition plan by analyzing the horse's past health data. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the horse's past health data into AI, and the AI ​​can analyze the data to select the optimal nutrition plan.

[0134] The service provider can estimate the horse's emotions and determine the priority of nutritional plans based on the estimated emotions. For example, if the horse's emotions are unstable, the service provider will prioritize providing a stress-reducing nutritional plan. For example, if the horse's emotions are relaxed, the service provider will provide a normal nutritional plan. For example, if the horse's emotions are stressed, the service provider will identify the stressors and provide an appropriate nutritional plan. In this way, the service provider can prioritize important nutritional plans by determining the priority of nutritional plans based on the horse's emotions. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input horse emotion data into AI, and the AI ​​can analyze the data to determine the priority of nutritional plans.

[0135] The service provider can provide an optimal nutrition plan by considering the horse's geographical location when providing a nutrition plan. For example, the service provider can provide an appropriate nutrition plan by considering the climatic conditions of the horse's current location. For example, the service provider can provide an appropriate nutrition plan by considering the breeding environment of the horse's current location. For example, the service provider can provide an appropriate nutrition plan by considering competitions or events in the horse's current location. In this way, the service provider can provide an optimal nutrition plan by considering the horse's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input the horse's geographical location information into AI, and the AI ​​can analyze the data to provide an optimal nutrition plan.

[0136] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0137] The analysis unit can estimate the horse's emotions and determine the priority of analysis based on the estimated emotions. For example, if the horse's emotions are unstable, it will prioritize analyzing data related to stress reduction. If the horse's emotions are relaxed, it will analyze normal health data. If the horse's emotions are stressed, it will prioritize analyzing data to identify stressors. In this way, the analysis unit can prioritize the analysis of important data by determining the priority of analysis based on the horse's emotions. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input horse emotion data into an AI, which can then analyze the data and determine the priority of analysis.

[0138] The Ministry of Education can estimate the emotions of horse owners and prioritize educational content based on those emotions. For example, if an owner is stressed, important content will be prioritized to allow for quick learning. If an owner is relaxed, detailed content will be provided to allow for thorough learning. If an owner is excited, engaging content will be prioritized to increase motivation to learn. In this way, the Ministry of Education can enable horse owners to learn efficiently by prioritizing educational content based on their emotions. Some or all of the above processes by the Ministry of Education may be performed using AI or not. For example, the Ministry of Education can input horse owner emotion data into an AI, which can then analyze the data to determine the priority of educational content.

[0139] The management department can estimate the horse's emotions and adjust management methods based on the estimated emotions. For example, if a horse's emotions are unstable, management methods to reduce stress will be prioritized. If the horse's emotions are relaxed, normal management methods will continue. If the horse's emotions are stressed, the stressors will be identified and appropriate measures will be taken. In this way, the management department can reduce the horse's stress by adjusting management methods based on its emotions. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input horse emotion data into an AI, which can then analyze the data and adjust management methods.

[0140] The data collection unit can estimate the horse's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the horse's emotions are unstable, data collection for stress reduction will be prioritized. If the horse's emotions are relaxed, normal data collection will continue. If the horse's emotions are stressed, data collection will be conducted to identify the stressors. This allows the data collection unit to collect appropriate data by adjusting the timing of data collection based on the horse's emotions. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input horse emotion data into an AI, which can then analyze the data and adjust the timing of data collection.

[0141] The service provider can estimate the horse's emotions and adjust the method of providing the nutrition plan based on the estimated emotions. For example, if the horse's emotions are unstable, a stress-reducing nutrition plan will be prioritized. If the horse's emotions are relaxed, a normal nutrition plan will be provided. If the horse's emotions are stressed, the stressors will be identified and an appropriate nutrition plan will be provided. In this way, the service provider can provide an appropriate nutrition plan by adjusting the method of providing the nutrition plan based on the horse's emotions. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input horse emotion data into AI, and the AI ​​can analyze the data and adjust the method of providing the nutrition plan.

[0142] The analysis unit can improve the accuracy of its analysis by referring to the horse's past health data when analyzing the horse's health data. For example, it can more accurately assess the horse's current health status based on its past health data. It can analyze fluctuations in health status based on the horse's past health data and create a long-term health management plan. It can also detect specific health problems early based on the horse's past health data. In this way, the analysis unit can improve the accuracy of its analysis by referring to the horse's past health data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the horse's past health data into an AI, which can then analyze the data to improve the accuracy of the analysis.

[0143] The Ministry of Education can analyze a horse owner's past learning history and select the most suitable educational method. For example, it can provide educational methods that allow for review and application based on what the horse owner has learned in the past. It can also provide educational methods that focus on topics the horse owner has struggled with in the past. Furthermore, it can provide educational methods that engage the horse owner's interest based on topics they have enjoyed learning in the past. This allows the Ministry of Education to select the most suitable educational method based on the horse owner's past learning history, thereby enabling effective education. Some or all of the above processes by the Ministry of Education may be performed using AI, or not. For example, the Ministry of Education can input the horse owner's learning history data into an AI, which can then analyze the data and select the most suitable educational method.

[0144] The management department can analyze a horse's past behavioral data to select the optimal management method. For example, it can select a management method to reduce stress based on the horse's past behavioral data. It can also select a management method to maintain health based on the horse's past behavioral data. In this way, the management department can select the optimal management method by analyzing the horse's past behavioral data. Some or all of the above processes in the management department may be performed using AI, or not. For example, the management department can input the horse's past behavioral data into an AI, which can then analyze the data and select the optimal management method.

[0145] The data collection unit can analyze the horse's past data collection history and select the optimal data collection method. For example, it can select a data collection method for stress reduction based on the horse's past data collection history. It can also select a data collection method for maintaining health based on the horse's past data collection history. In this way, the data collection unit can select the optimal data collection method by analyzing the horse's past data collection history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the horse's past data collection history into an AI, which can then analyze the data and select the optimal data collection method.

[0146] The monitoring unit can analyze a horse's past stress levels and select the optimal monitoring method. For example, it can select a monitoring method to reduce stress based on the horse's past stress levels. It can also select a monitoring method to maintain health based on the horse's past stress levels. It can also select a monitoring method to improve performance based on the horse's past stress levels. In this way, the monitoring unit can select the optimal monitoring method by analyzing the horse's past stress levels. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input the horse's past stress level data into AI, and the AI ​​can analyze the data and select the optimal monitoring method.

[0147] The following briefly describes the processing flow for example form 2.

[0148] Step 1: The matching unit automatically matches horses with potential adopters. For example, it considers the horse's characteristics and the adopter's requirements to make the best possible match. Specifically, it suggests suitable adopters based on information such as the horse's age, health condition, and temperament. Step 2: The analysis unit performs multidimensional analysis of the horses matched by the matching unit. For example, it analyzes the horses' behavioral data, health data, environmental data, etc., to comprehensively evaluate the horses' condition. Specifically, it analyzes the horses' exercise levels, diet, stress levels, etc., to understand the horses' health status. Step 3: The Education Department provides education and support to horse owners based on the results analyzed by the Analysis Department. For example, it provides information on horse management and training to educate and support horse owners. Specifically, it provides information on horse health management methods, nutrition management methods, stress management methods, etc. Step 4: The management department performs behavioral prediction, nutritional management, and stress level management based on the information provided by the education department. For example, they predict behavior based on the horses' past data and real-time information to help manage stress and maintain health. Specifically, they analyze the horses' behavioral patterns and take appropriate measures before stress levels rise. They also provide optimal nutritional plans based on each horse's individual health condition and activity level to support their health.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements described above, including the matching unit, analysis unit, education unit, management unit, collection unit, monitoring unit, and provision unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the matching unit is implemented by the control unit 46A of the smart device 14 and performs optimal matching considering the horse's characteristics and the conditions of the adoptive home. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the horse's behavioral data, health data, environmental data, etc., to comprehensively evaluate the horse's condition. The education unit is implemented by, for example, the control unit 46A of the smart device 14 and provides information on horse management and training to educate horse owners. The management unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and predicts the horse's behavior based on past data and real-time information to help with stress management and health maintenance. The collection unit collects the horse's behavioral data and health data using, for example, the camera 42 and sensors of the smart device 14. The monitoring unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and monitors the stress level of the horses. The provisioning unit is implemented, for example, by the control unit 46A of the smart device 14, and provides an optimal nutrition plan based on the health status and activity level of each individual horse. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0153] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] Each of the multiple elements described above, including the matching unit, analysis unit, education unit, management unit, collection unit, monitoring unit, and provision unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the matching unit is implemented by the control unit 46A of the smart glasses 214 and performs optimal matching considering the horse's characteristics and the conditions of the prospective adopter. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the horse's behavioral data, health data, environmental data, etc., to comprehensively evaluate the horse's condition. The education unit is implemented by, for example, the control unit 46A of the smart glasses 214 and provides information on horse management and training to educate horse owners. The management unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and predicts the horse's behavior based on past data and real-time information to help with stress management and health maintenance. The collection unit collects the horse's behavioral data and health data using, for example, the camera 42 and sensors of the smart glasses 214. The monitoring unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and monitors the horse's stress level. The provisioning unit is implemented, for example, by the control unit 46A of the smart glasses 214, and provides an optimal nutrition plan based on the individual horse's health status and activity level. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0169] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.).

[0181] 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.

[0182] 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.

[0183] 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.

[0184] Each of the multiple elements described above, including the matching unit, analysis unit, education unit, management unit, collection unit, monitoring unit, and provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the matching unit is implemented by the control unit 46A of the headset terminal 314 and performs optimal matching considering the horse's characteristics and the conditions of the prospective adopter. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the horse's behavioral data, health data, environmental data, etc., to comprehensively evaluate the horse's condition. The education unit is implemented by, for example, the control unit 46A of the headset terminal 314 and provides information on horse management and training to support the education of horse owners. The management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and predicts the horse's behavior based on past data and real-time information to help with stress management and health maintenance. The collection unit collects the horse's behavioral data and health data using, for example, the camera 42 and sensors of the headset terminal 314. The monitoring unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and monitors the stress level of the horses. The provisioning unit is implemented, for example, by the control unit 46A of the headset terminal 314, and provides an optimal nutrition plan based on the health status and activity level of each individual horse. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0185] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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).

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.).

[0198] 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.

[0199] 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.

[0200] 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.

[0201] Each of the multiple elements described above, including the matching unit, analysis unit, education unit, management unit, collection unit, monitoring unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the matching unit is implemented by the control unit 46A of the robot 414 and performs optimal matching considering the horse's characteristics and the conditions of the prospective adopter. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the horse's behavioral data, health data, environmental data, etc., to comprehensively evaluate the horse's condition. The education unit is implemented by, for example, the control unit 46A of the robot 414 and provides information on horse management and training to educate horse owners. The management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and predicts the horse's behavior based on past data and real-time information to help with stress management and health maintenance. The collection unit collects the horse's behavioral data and health data using, for example, the camera 42 and sensors of the robot 414. The monitoring unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and monitors the stress level of the horses. The supply unit is implemented, for example, by the control unit 46A of the robot 414, and provides an optimal nutrition plan based on the health status and activity level of each individual horse. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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."

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] (Note 1) A matching unit that automatically matches prospective adopters with horses, An analysis unit that performs multidimensional analysis of horses matched by the matching unit, Based on the results of the analysis performed by the aforementioned analysis unit, the Education Unit provides education and support to horse owners, The system includes a management department that performs behavioral prediction, nutritional management, and stress level management based on information provided by the aforementioned education department. A system characterized by the following features. (Note 2) It includes a data collection unit for collecting horse behavior data. The system described in Appendix 1, characterized by the features described herein. (Note 3) It is equipped with a data collection unit for collecting horse health data. The system described in Appendix 1, characterized by the features described herein. (Note 4) It is equipped with a monitoring unit to monitor the stress levels of horses. The system described in Appendix 1, characterized by the features described herein. (Note 5) It has a service department that provides nutritional plans for horses. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is Collect data on the horses' exercise levels, diet, and stress levels. The system described in Appendix 2, characterized by the features described herein. (Note 7) The aforementioned analysis unit, The collected data is analyzed to assess the horse's health. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned management department, Analyze the horse's behavioral patterns and take appropriate measures before stress levels rise. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned supply unit is, We provide an optimal nutritional plan based on the individual health condition and activity level of each horse. The system described in Appendix 5, characterized by the features described herein. (Note 10) The matching unit is We estimate the horse's emotions and adjust the selection criteria for adoptive homes based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The matching unit is We analyze the past acceptance history of the receiving facilities and select the most suitable matching method. The system described in Appendix 1, characterized by the features described herein. (Note 12) The matching unit is We evaluate the horse's suitability based on the environment and conditions of the receiving location, thereby improving the accuracy of the matching process. The system described in Appendix 1, characterized by the features described herein. (Note 13) The matching unit is The system estimates the horse's emotions and determines the priority of potential adopters based on those estimates. The system described in Appendix 1, characterized by the features described herein. (Note 14) The matching unit is We will prioritize selecting the most relevant pickup locations, taking into account their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 15) The matching unit is Analyze the social media activity of potential adopters and select the most relevant adopters. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, We estimate the horse's emotions and adjust the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the horse's health condition. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the horse's category. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, The system estimates the horse's emotions and adjusts the length of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the horse data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on relevant horse data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned Ministry of Education, The system estimates the owner's emotions and adjusts the way the training content is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned Ministry of Education, During training, the level of detail in the training content is adjusted based on the horse's health condition. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned Ministry of Education, During training, the optimal training method is selected based on the horse owner's past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned Ministry of Education, The system estimates the owner's feelings and prioritizes the training content based on those estimated feelings. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned Ministry of Education, During training, we provide highly relevant educational content while taking into account the geographical location of the horse owner. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned Ministry of Education, During training, we analyze the social media activity of horse owners and provide relevant educational content. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned management department, The system estimates the horse's emotions and adjusts management methods based on those estimates. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned management department, During management, the optimal management method is selected by analyzing the horse's past behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned management department, During management, customize management methods based on the horse's current health condition. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned management department, The system estimates the horse's emotions and determines management priorities based on those estimates. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned management department, During management, the optimal management method is selected considering the horse's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned management department, During management, we analyze the horses' social media activity and propose management strategies. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned collection unit is The system estimates the horse's emotions and adjusts the timing of data collection based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned collection unit is Analyze the horse's past data collection history to select the optimal data collection method. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned collection unit is The system estimates the horse's emotions and prioritizes the data to collect based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data, taking into account the horses' geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 38) The aforementioned monitoring unit, The system estimates the horse's emotions and adjusts the monitoring method based on the estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 39) The aforementioned monitoring unit, During monitoring, the horse's past stress levels are analyzed to select the optimal monitoring method. The system described in Appendix 4, characterized by the features described herein. (Note 40) The aforementioned monitoring unit, The system estimates the horse's emotions and determines monitoring priorities based on the estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 41) The aforementioned monitoring unit, During monitoring, the optimal monitoring method is selected considering the horse's geographical location. The system described in Appendix 4, characterized by the features described herein. (Note 42) The aforementioned supply unit is, The system estimates the horse's emotions and adjusts the delivery method of the nutrition plan based on the estimated emotions. The system described in Appendix 5, characterized by the features described herein. (Note 43) The aforementioned supply unit is, When providing a nutrition plan, analyze the horse's past health data to select an optimal nutrition plan The system according to appended note 5, characterized in that (Appended note 44) The providing unit Estimates the horse's emotion and determines the priority order of the nutrition plan based on the estimated horse's emotion The system according to appended note 5, characterized in that (Appended note 45) The providing unit When providing a nutrition plan, provides an optimal nutrition plan in consideration of the horse's geographical location information The system according to appended note 5, characterized in that

Explanation of reference numerals

[0221] 10, 210, 310, 410 Data processing system 12 Data processing device 14 Smart device 214 Smart glasses 314 Headset-type terminal 414 Robot

Claims

1. A matching unit that automatically matches prospective adopters with horses, An analysis unit that performs multidimensional analysis of horses matched by the matching unit, Based on the results of the analysis performed by the aforementioned analysis unit, the Education Unit provides education and support to horse owners, The system includes a management department that performs behavioral prediction, nutritional management, and stress level management based on information provided by the aforementioned education department. A system characterized by the following features.

2. It includes a data collection unit for collecting horse behavior data. The system according to feature 1.

3. It is equipped with a data collection unit for collecting horse health data. The system according to feature 1.

4. It is equipped with a monitoring unit to monitor the stress levels of horses. The system according to feature 1.

5. It has a service department that provides nutritional plans for horses. The system according to feature 1.

6. The aforementioned collection unit is Collect data on the horses' exercise levels, diet, and stress levels. The system according to feature 2.

7. The aforementioned analysis unit, The collected data is analyzed to assess the horse's health. The system according to feature 1.

8. The aforementioned management department, Analyze the horse's behavioral patterns and take appropriate measures before stress levels rise. The system according to feature 1.

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