system

Abstract

The system according to this embodiment aims to monitor the health status of pets in real time and to respond quickly when an abnormality occurs. [Solution] The system according to the embodiment comprises a monitoring unit, a notification unit, a cooperation unit, and a proposal unit. The monitoring unit monitors the pet's health data in real time. The notification unit notifies of abnormalities based on the health data monitored by the monitoring unit. The cooperation unit cooperates with a veterinarian based on the abnormalities notified by the notification unit. The proposal unit learns the pet's behavior patterns and proposes an optimal training program.

Description

Technical Field

[0005]

[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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to monitor the health status of a pet in real time and respond promptly when an abnormality occurs.

[0005] The system according to the embodiment aims to monitor the health status of a pet in real time and respond promptly when an abnormality occurs.

Means for Solving the Problems

[0006] The system according to the embodiment comprises a monitoring unit, a notification unit, a collaboration unit, and a proposal unit. The monitoring unit monitors the pet's health data in real time. The notification unit notifies of abnormalities based on the health data monitored by the monitoring unit. The collaboration unit collaborates with a veterinarian based on the abnormalities notified by the notification unit. The proposal unit learns the pet's behavior patterns and proposes an optimal training program. [Effects of the Invention]

[0007] The system according to this embodiment can monitor the health status of pets in real time and respond quickly when an abnormality occurs. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The pet care agent system according to an embodiment of the present invention is a system that supports pet health management and training. The pet care agent system utilizes AI to monitor pet health data in real time and notifies the user if an abnormality is detected. This enables early detection of pet health problems and prompt response. The pet care agent system also collaborates with veterinarians as needed to share information about the pet's health status. Furthermore, the pet care agent system learns the pet's behavior patterns and proposes an optimal training program. This improves the pet's behavioral problems and facilitates smooth communication with the owner. For example, the pet care agent system constantly monitors the pet's health data through a real-time health monitoring system. For example, it collects data such as the pet's body temperature, heart rate, and activity level, and notifies the user if an abnormality is detected. Next, the pet care agent system provides individualized training based on behavior pattern analysis. The AI ​​learns the pet's behavior patterns and proposes an optimal training program. For example, if a pet repeatedly performs a specific behavior, the system proposes a training method to improve that behavior. Furthermore, the pet care agent system shares information about the pet's health status with veterinarians through a data sharing function, facilitating appropriate responses. The Pet Care Agent System simplifies pet health management and enables pet training even for those with limited time. It also facilitates early detection and rapid response to pet health problems. This is expected to extend the average lifespan of pets, reduce veterinary costs, and improve the success rate of resolving pet behavioral issues. The Pet Care Agent System incorporates technological innovations such as AI-powered real-time health monitoring, machine learning-based behavioral pattern analysis, and a simplified user interface. The target audience is people aged 20-60 who consider their pets family members and are highly concerned with their health and well-being. The Pet Care Agent System addresses the issue of delayed response to pet health problems, monitoring pet health in real time, providing necessary information, and facilitating appropriate action.This enables early detection and rapid response to pet health problems. Generative AI is used to analyze pet behavior patterns and monitor their health. If an abnormality is detected, it will notify and suggest solutions. The pet care market, particularly the health management and training services market, is estimated at 2 trillion yen, and based on market penetration and projected customer numbers in the first year, a market size of 20 billion yen is expected. The growing awareness of pet health and advancements in AI technology present an excellent time to enter the market. The Pet Care Agent System aims to improve the quality of life for pets and their owners, and to raise social awareness of pet health issues. Now is the time to change the future of pets and their families with the Pet Care Agent System. This allows the Pet Care Agent System to support pet health management and training.

[0029] The pet care agent system according to this embodiment comprises a monitoring unit, a notification unit, a coordination unit, and a suggestion unit. The monitoring unit monitors the pet's health data in real time. The monitoring unit collects and monitors health data such as the pet's body temperature, heart rate, and activity level in real time. For example, the monitoring unit measures the pet's body temperature with a sensor and collects the data in real time. The monitoring unit can also monitor the heart rate and notify if an abnormality is detected. Furthermore, the monitoring unit can measure the pet's activity level and monitor the data in real time. For example, the monitoring unit detects the pet's movement with a sensor and measures the activity level. The notification unit notifies of abnormalities based on the health data monitored by the monitoring unit. For example, the notification unit notifies if the pet's body temperature is abnormally high or its heart rate is abnormally fast. For example, the notification unit issues an alert if the pet's body temperature exceeds a certain range. The notification unit can also issue a warning if the heart rate is abnormally fast. Furthermore, the notification unit can also notify if the pet's activity level is abnormally low. For example, the notification unit issues an alert if the pet's activity level falls below a certain standard. The coordination unit coordinates with the veterinarian based on the abnormality notified by the notification unit. The coordination unit, for example, shares the pet's health data with the veterinarian to encourage appropriate action. The coordination unit contacts the veterinarian if the pet's body temperature is abnormally high. The coordination unit can also notify the veterinarian if the heart rate is abnormally fast. Furthermore, the coordination unit can coordinate with the veterinarian if the pet's activity level is abnormally low. For example, the coordination unit sends the pet's health data to the veterinarian to encourage appropriate action. The suggestion unit learns the pet's behavior patterns and proposes an optimal training program. For example, if the pet repeats a particular behavior, the suggestion unit proposes training methods to improve that behavior. For example, if the pet barks, the suggestion unit proposes training methods to improve that behavior. The suggestion unit can also propose training methods to improve the pet biting behavior. Furthermore, the suggestion unit can also propose training methods to improve the pet jumping behavior.For example, the proposed method suggests a training method to prevent excessive barking in pets. This allows the pet care agent system according to the embodiment to support pet health management and training.

[0030] The monitoring unit monitors pet health data in real time. For example, it collects and monitors health data such as the pet's body temperature, heart rate, and activity level. Specifically, to measure the pet's body temperature, it uses sensors attached to the pet's collar or harness. These sensors continuously measure the pet's body temperature and transmit the data wirelessly to a central database. For heart rate monitoring, a heart rate sensor attached to the pet's chest is used. This sensor measures the pet's heart rate in real time and can immediately notify if an abnormality is detected. Furthermore, accelerometers and gyroscopes are used to measure the pet's activity level. These sensors can detect the pet's movements in detail and accurately measure its activity level. For example, it collects data such as how far the pet walked, how far it ran, and how long it rested. This allows the monitoring unit to comprehensively monitor the pet's health and respond quickly if an abnormality occurs. Additionally, the monitoring unit stores the collected data on a cloud server and can refer to past data as needed. This allows for understanding long-term changes in the pet's health and enabling appropriate health management.

[0031] The notification unit alerts owners of abnormalities based on health data monitored by the monitoring unit. For example, the notification unit will notify owners if their pet's body temperature is abnormally high or its heart rate is abnormally fast. Specifically, it will issue an alert if the pet's body temperature exceeds a certain range. For example, if the pet's body temperature exceeds 39 degrees Celsius, it will notify the owner via a smartphone app. Similarly, if the heart rate is abnormally fast, it will issue a warning if it exceeds a certain range. For example, if the pet's heart rate exceeds 160 beats per minute, it will warn the owner. Furthermore, the notification unit can also notify owners if their pet's activity level is abnormally low. For example, it will issue an alert if the pet's activity level falls below a certain standard. This allows owners to respond quickly if an abnormality occurs in their pet's health. When an abnormality is detected, the notification unit can also suggest specific actions to take to the owner. For example, if the pet's body temperature is high, it will suggest cooling methods and hydration methods. If the heart rate is fast, it will suggest keeping the pet calm or consulting a veterinarian. This allows the notification unit to provide support to pet owners so that they can take appropriate action if an abnormality occurs in their pet's health.

[0032] The liaison unit collaborates with veterinarians based on abnormalities notified by the notification unit. For example, the liaison unit shares pet health data with veterinarians to encourage appropriate action. Specifically, it contacts veterinarians if a pet's body temperature is abnormally high. For example, if a pet's body temperature exceeds 39 degrees Celsius, the liaison unit automatically notifies the veterinarian and shares the pet's health data. It also notifies veterinarians if the heart rate is abnormally fast and exceeds a certain range. For example, if a pet's heart rate exceeds 160 beats per minute, the liaison unit notifies the veterinarian and shares the pet's health data. Furthermore, the liaison unit can collaborate with veterinarians if a pet's activity level is abnormally low. For example, if a pet's activity level falls below a certain standard, the liaison unit notifies the veterinarian and shares the pet's health data. This allows veterinarians to understand the pet's health status and take appropriate action. Through collaboration with veterinarians, the liaison unit can support pet health management. For example, it can provide support to owners by conveying advice and instructions from veterinarians to owners so that they can take appropriate action. Furthermore, the Liaison Department can support the long-term management of pets' health through collaboration with veterinarians. This allows the Liaison Department to comprehensively support pet health management and play a crucial role in maintaining pets' health.

[0033] The proposal function learns the pet's behavior patterns and proposes an optimal training program. For example, if a pet repeatedly engages in a particular behavior, the proposal function will suggest training methods to improve that behavior. Specifically, if a pet barks, it will suggest training methods to improve that behavior. For example, if a pet barks unnecessarily, the proposal function will suggest training methods to prevent excessive barking. For example, it will suggest giving the pet a treat each time it barks to suppress the barking behavior. It can also suggest training methods to improve biting behavior. For example, it will suggest giving the pet a toy each time it bites to suppress the biting behavior. Furthermore, it can suggest training methods to improve jumping behavior. For example, it will suggest ignoring the pet each time it jumps to suppress the jumping behavior. By learning the pet's behavior patterns and proposing an optimal training program, the proposal function can improve the pet's behavior and maintain a good relationship with its owner. In addition, the proposal function can utilize AI to learn the pet's behavior patterns. The AI ​​can analyze the pet's behavior data and propose the optimal training method. This allows the proposal department to provide an effective training program to improve pet behavior.

[0034] The monitoring unit can monitor the pet's health data, such as body temperature, heart rate, and activity level, in real time. For example, the monitoring unit can measure the pet's body temperature with a sensor and collect the data in real time. The monitoring unit can also monitor the heart rate and notify if an abnormality is detected. Furthermore, the monitoring unit can measure the pet's activity level and monitor the data in real time. For example, the monitoring unit can detect the pet's movement with a sensor and measure its activity level. This allows for real-time monitoring of the pet's health data, such as body temperature, heart rate, and activity level, ensuring that the pet's health status is always known. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the pet's body temperature data into a generating AI and have the generating AI detect abnormalities.

[0035] The notification unit can detect and notify of abnormalities based on health data monitored by the monitoring unit. For example, the notification unit will notify if the pet's body temperature is abnormally high or its heart rate is abnormally fast. For example, the notification unit will issue an alert if the pet's body temperature exceeds a certain range. The notification unit can also issue a warning if the heart rate is abnormally fast. Furthermore, the notification unit can also notify if the pet's activity level is abnormally low. For example, the notification unit will issue an alert if the pet's activity level falls below a certain standard. This allows for early detection of pet health problems and prompt response by detecting and notifying of abnormalities based on monitored health data. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input health data monitored by the monitoring unit into a generating AI and have the generating AI perform abnormality detection and notification.

[0036] The collaboration unit can share data and collaborate with veterinarians based on anomalies notified by the notification unit. For example, the collaboration unit can share pet health data with veterinarians to prompt appropriate action. For example, the collaboration unit can contact veterinarians if a pet's body temperature is abnormally high. The collaboration unit can also notify veterinarians if a pet's heart rate is abnormally fast. Furthermore, the collaboration unit can collaborate with veterinarians if a pet's activity level is abnormally low. For example, the collaboration unit can send pet health data to veterinarians to prompt appropriate action. In this way, appropriate action can be prompted by sharing data and collaborating with veterinarians based on notified anomalies. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input anomaly data notified by the notification unit into a generating AI and have the generating AI perform the collaboration with veterinarians.

[0037] The suggestion unit can learn the pet's behavior patterns and propose an optimal training program. For example, if the pet repeats a particular behavior, the suggestion unit can propose a training method to improve that behavior. For example, if the pet barks, the suggestion unit can propose a training method to improve that behavior. The suggestion unit can also propose a training method to improve the pet's biting behavior. Furthermore, the suggestion unit can propose a training method to improve the pet's jumping behavior. For example, if the pet barks, the suggestion unit can propose a training method to prevent excessive barking. In this way, by learning the pet's behavior patterns and proposing an optimal training program, the system improves the pet's behavioral problems and facilitates smoother communication with the owner. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input pet behavior data into a generating AI and have the generating AI execute the proposal of an optimal training program.

[0038] The suggestion unit can propose training methods to improve a pet's repetitive behavior. For example, it can propose training methods to improve a pet's barking behavior. For example, it can propose training methods to improve a pet's biting behavior. Furthermore, it can propose training methods to improve a pet's jumping behavior. For example, it can propose training methods to prevent excessive barking in the case of a pet barking. In this way, by proposing training methods to improve a pet's repetitive behavior, the pet's behavioral problems can be effectively improved. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input pet behavior data into a generating AI and have the generating AI execute suggestions for training methods to improve the behavior.

[0039] The monitoring unit can analyze the pet's past health data and optimize a monitoring algorithm that enables early detection of abnormalities. For example, the monitoring unit can detect signs of abnormalities from past health data and issue early warnings. For example, the monitoring unit can analyze patterns in health data and build an abnormality prediction model. Furthermore, the monitoring unit can also analyze the frequency of abnormalities and adjust the monitoring algorithm. This allows for early detection of pet health problems by analyzing the pet's past health data and optimizing the monitoring algorithm that enables early detection of abnormalities. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the pet's past health data into a generating AI and have the generating AI perform optimization of the monitoring algorithm that enables early detection of abnormalities.

[0040] The monitoring unit can detect abnormalities in health data based on the pet's living environment and diet during monitoring. For example, the monitoring unit can detect abnormalities in health data in response to changes in the pet's living environment. The monitoring unit can also detect abnormalities in health data based on changes in the pet's diet. Furthermore, the monitoring unit can detect abnormalities in health data by considering both the pet's living environment and diet. This allows for a more accurate understanding of the pet's health status by detecting abnormalities in health data based on the pet's living environment and diet. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input pet living environment data and diet data into a generating AI and have the generating AI perform the detection of abnormalities in health data.

[0041] The monitoring unit can prioritize monitoring health data based on environmental factors, taking into account the pet's geographical location during monitoring. For example, if the pet is in a hot and humid area, the monitoring unit will prioritize monitoring body temperature. If the pet is in a cold region, the monitoring unit will prioritize monitoring body temperature and activity level. Furthermore, if the pet is in an urban area, the monitoring unit can also prioritize monitoring respiratory rate, taking into account the effects of air quality. This allows for more appropriate monitoring of the pet's health status by prioritizing the monitoring of health data based on environmental factors, taking into account the pet's geographical location. 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 pet's geographical location information into a generating AI and have the generating AI perform priority monitoring of health data based on environmental factors.

[0042] The monitoring unit can adjust the timing of health data monitoring, taking into account the pet owner's lifestyle. For example, the monitoring unit can intensify monitoring of the pet's activity level while the owner is at work. For example, it can intensify monitoring of the pet's heart rate and body temperature after the owner has returned home. Furthermore, the monitoring unit can reduce the frequency of monitoring the pet's health data while the owner is sleeping. This allows for more appropriate monitoring of the pet's health by adjusting the timing of health data monitoring, taking into account the pet owner's lifestyle. 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 owner's lifestyle data into a generating AI and have the generating AI adjust the timing of health data monitoring.

[0043] The notification unit can adjust the urgency of the notification based on the severity of the anomaly when it sends a notification. For example, if the anomaly is serious, the notification unit will immediately send an emergency notification. For example, if the anomaly is minor, the notification unit will use the normal notification method. Furthermore, the notification unit can also adjust the urgency of the notification in stages according to the severity of the anomaly. This allows for appropriate notifications according to the pet's health condition by adjusting the urgency of the notification based on the severity of the anomaly. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input anomaly severity data into a generating AI and have the generating AI perform the adjustment of the notification urgency.

[0044] The notification unit can, at the time of notification, refer to the pet's health data history to identify the cause of an abnormality and reflect it in the notification content. For example, the notification unit can identify the cause of an abnormality from the health data history and include details in the notification content. For example, if the cause of an abnormality cannot be identified, the notification unit will state that fact in the notification content. Furthermore, if there are multiple possible causes of the abnormality, the notification unit can also include the most likely cause in the notification content. In this way, by referring to the pet's health data history to identify the cause of an abnormality and reflecting it in the notification content, detailed information about the pet's health status can be provided. Some or all of the above processing in the notification unit may be performed using AI, for example, or without using AI. For example, the notification unit can input the pet's health data history into a generating AI and have the generating AI perform the task of identifying the cause of the abnormality and reflecting it in the notification content.

[0045] The notification unit can select the optimal notification method when sending a notification, taking into account the pet owner's current activity status. For example, if the owner is at work, the notification unit may send a notification via email. If the owner is driving, for example, the notification unit may use an audio notification. Furthermore, the notification unit can delay the notification if the owner is sleeping. This allows pet owners to receive notifications in the most optimal way by selecting the optimal notification method based on their current activity status. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input data on the owner's current activity status into a generating AI and have the generating AI select the optimal notification method.

[0046] The notification unit can select the optimal notification method when sending a notification, taking into account the pet owner's current activity status. For example, if the owner is at work, the notification unit may send a notification via email. If the owner is driving, for example, the notification unit may use an audio notification. Furthermore, the notification unit can delay the notification if the owner is sleeping. This allows pet owners to receive notifications in the most optimal way by selecting the optimal notification method based on their current activity status. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input data on the owner's current activity status into a generating AI and have the generating AI select the optimal notification method.

[0047] The notification unit can supplement notification content by referring to external data related to the pet's health data (e.g., weather information) when sending a notification. For example, the notification unit can refer to weather information and include it in the notification content if it may affect the pet's health data. For example, the notification unit can refer to external data and add information related to the pet's health data to the notification content. Furthermore, the notification unit can also combine weather information and health data to supplement the notification content. This allows for the provision of more detailed information by supplementing the notification content by referring to external data related to the pet's health data. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input weather information data into a generating AI and have the generating AI perform the supplementation of the notification content.

[0048] The integration unit can optimize the information provided to veterinarians by referring to the pet's health data history during integration. For example, the integration unit can extract important information from the health data history and provide it to the veterinarian. For example, the integration unit can analyze the health data history, identify the cause of abnormalities, and provide this information to the veterinarian. Furthermore, the integration unit can optimize and provide the information necessary for the veterinarian based on the health data history. This allows for the efficient provision of necessary information to veterinarians by optimizing the information provided to veterinarians by referring to the pet's health data history. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the pet's health data history into a generating AI and have the generating AI perform the optimization of the information provided.

[0049] The collaboration unit can adjust the frequency of collaboration with the veterinarian according to the pet's health condition during collaboration. For example, if the pet's health condition is deteriorating, the collaboration unit will increase the collaboration frequency. For example, if the pet's health condition is stable, the collaboration unit will decrease the collaboration frequency. Furthermore, the collaboration unit can also adjust the collaboration frequency in stages according to the pet's health condition. This allows for appropriate collaboration according to the pet's health condition by adjusting the collaboration frequency with the veterinarian. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input pet health condition data into a generating AI and have the generating AI perform the adjustment of the collaboration frequency.

[0050] The collaboration unit can select a method of collaboration with a veterinarian, taking into consideration the pet owner's wishes, when collaborating. For example, if the owner requests emergency treatment, the collaboration unit will immediately contact the veterinarian. For example, if the owner requests routine treatment, the collaboration unit will use the standard collaboration method. Furthermore, the collaboration unit can adjust the collaboration method according to the owner's wishes. This allows for appropriate collaboration that meets the owner's needs by selecting a collaboration method with a veterinarian that takes the pet owner's wishes into consideration. Some or all of the above-described processes in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input owner's preference data into a generating AI and have the generating AI select the collaboration method.

[0051] The integration unit can supplement the integration content by referring to external data related to pet health data (e.g., local infectious disease information) during integration. For example, the integration unit can refer to local infectious disease information to supplement the information provided to veterinarians. For example, the integration unit can refer to external data to provide veterinarians with information related to pet health data. Furthermore, the integration unit can also combine local infectious disease information and health data to supplement the integration content. This allows for the provision of more detailed information by supplementing the integration content by referring to external data related to pet health data. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input local infectious disease information data into a generating AI and have the generating AI perform the supplementation of the integration content.

[0052] The proposal unit can analyze the pet's past behavioral data to select the optimal training method when making a proposal. For example, the proposal unit can select training methods that the pet prefers based on past behavioral data. For example, the proposal unit can analyze past behavioral data and avoid training methods that the pet dislikes. Furthermore, the proposal unit can also propose the optimal training method for the pet based on past behavioral data. This makes it possible to provide the best possible training for the pet by analyzing the pet's past behavioral data and selecting the optimal training method. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the pet's past behavioral data into a generating AI and have the generating AI select the optimal training method.

[0053] The proposal unit can customize training programs based on the pet's living environment and the owner's lifestyle when making a proposal. For example, the proposal unit can propose an indoor training program according to the pet's living environment. For example, the proposal unit can propose a short and effective training program tailored to the owner's lifestyle. Furthermore, the proposal unit can also customize the training program considering both the pet's living environment and the owner's lifestyle. This allows for optimal training for the pet by customizing the training program based on the pet's living environment and the owner's lifestyle. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input pet living environment data and owner lifestyle data into a generating AI and have the generating AI perform the customization of the training program.

[0054] The proposal unit can select a training method appropriate to the environment by considering the pet's geographical location information when making a proposal. For example, if the pet lives in an urban area, the proposal unit will select an indoor training method. For example, if the pet lives in a suburban area, the proposal unit will select an outdoor training method. Furthermore, the proposal unit can also select the optimal training method based on the pet's geographical location information. This makes it possible to provide the best possible training for the pet by selecting a training method appropriate to the environment by considering the pet's geographical location information. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI. For example, the proposal unit can input the pet's geographical location information into a generating AI and have the generating AI select a training method appropriate to the environment.

[0055] The suggestion unit can improve the training program by incorporating feedback from pet owners during the suggestion process. For example, the suggestion unit improves the training program based on owner feedback. For example, the suggestion unit customizes the training program by incorporating owner opinions. Furthermore, the suggestion unit can collect owner feedback and evaluate the effectiveness of the training program. This allows for optimal training for pets by improving the training program based on owner feedback. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input owner feedback data into a generating AI and have the generating AI perform improvements to the training program.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The monitoring unit not only monitors the pet's health data in real time but also collects behavioral data and can detect abnormal behavior. For example, if a pet exhibits an unusual behavioral pattern, it can detect the abnormal behavior and notify the notification unit. Furthermore, the monitoring unit can analyze the pet's behavioral data and identify the cause of the abnormal behavior. For example, if a pet suddenly exhibits aggressive behavior, it can identify the cause and notify the owner. This allows for real-time monitoring of not only the pet's health but also its behavior, enabling early detection of abnormal behavior.

[0058] The monitoring unit not only monitors pet health data in real time, but can also detect abnormalities in health data based on the pet's living environment and diet. For example, it can detect abnormalities in health data in response to changes in the pet's living environment. Furthermore, it can also detect abnormalities in health data based on changes in the pet's diet. This allows for a more accurate understanding of the pet's health status by detecting abnormalities in health data based on the pet's living environment and diet.

[0059] The notification unit not only notifies of abnormalities based on health data monitored by the monitoring unit, but can also adjust the urgency of the notification based on the severity of the abnormality. For example, if the abnormality is serious, an emergency notification can be sent immediately. Furthermore, if the abnormality is minor, the normal notification method can be used. This allows for appropriate notifications tailored to the pet's health condition by adjusting the urgency of the notification based on the severity of the abnormality.

[0060] The collaboration unit not only collaborates with veterinarians based on abnormalities notified by the notification unit, but can also optimize the information provided to veterinarians by referring to the pet's health data history. For example, it can extract important information from the health data history and provide it to the veterinarian. Furthermore, it can analyze the health data history, identify the cause of the abnormality, and provide it to the veterinarian. By optimizing the information provided to veterinarians by referring to the pet's health data history, the system can efficiently provide veterinarians with the information they need.

[0061] The proposal function can not only learn a pet's behavioral patterns and propose an optimal training program, but also analyze the pet's past behavioral data to select the most suitable training method. For example, it can select training methods that the pet prefers based on past behavioral data. Furthermore, it can analyze past behavioral data to avoid training methods that the pet dislikes. In this way, by analyzing the pet's past behavioral data and selecting the optimal training method, it becomes possible to provide the best possible training for the pet.

[0062] The proposal function not only learns the pet's behavioral patterns and proposes the optimal training program, but can also customize the training program based on the pet's living environment and the owner's lifestyle. For example, it can propose an indoor training program according to the pet's living environment. Furthermore, it can propose a short and effective training program tailored to the owner's lifestyle. In this way, by customizing the training program based on the pet's living environment and the owner's lifestyle, it becomes possible to provide the best possible training for the pet.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The monitoring unit monitors the pet's health data in real time. The monitoring unit collects and monitors health data such as the pet's body temperature, heart rate, and activity level in real time. The monitoring unit measures the pet's body temperature with a sensor and collects the data in real time. It can also monitor the heart rate and send notifications if an abnormality is detected. Furthermore, it can detect the pet's movement with a sensor and measure its activity level. Step 2: The notification unit notifies of abnormalities based on health data monitored by the monitoring unit. For example, the notification unit will notify if the pet's body temperature is abnormally high or its heart rate is abnormally fast. The notification unit can also issue an alert if the pet's body temperature exceeds a certain range and a warning if its heart rate is abnormally fast. Furthermore, it can also notify if the pet's activity level is abnormally low. Step 3: The liaison unit collaborates with veterinarians based on abnormalities notified by the notification unit. For example, the liaison unit shares pet health data with veterinarians to encourage appropriate action. It contacts veterinarians if the pet's body temperature is abnormally high or its heart rate is abnormally fast. It can also collaborate with veterinarians if the pet's activity level is abnormally low. Step 4: The proposal team learns the pet's behavioral patterns and proposes an optimal training program. For example, if the pet repeatedly engages in a particular behavior, the proposal team will suggest training methods to improve that behavior. For example, if the pet barks, bites, or jumps, the proposal team will suggest training methods to improve each specific behavior.

[0065] (Example of form 2) The pet care agent system according to an embodiment of the present invention is a system that supports pet health management and training. The pet care agent system utilizes AI to monitor pet health data in real time and notifies the user if an abnormality is detected. This enables early detection of pet health problems and prompt response. The pet care agent system also collaborates with veterinarians as needed to share information about the pet's health status. Furthermore, the pet care agent system learns the pet's behavior patterns and proposes an optimal training program. This improves the pet's behavioral problems and facilitates smooth communication with the owner. For example, the pet care agent system constantly monitors the pet's health data through a real-time health monitoring system. For example, it collects data such as the pet's body temperature, heart rate, and activity level, and notifies the user if an abnormality is detected. Next, the pet care agent system provides individualized training based on behavior pattern analysis. The AI ​​learns the pet's behavior patterns and proposes an optimal training program. For example, if a pet repeatedly performs a specific behavior, the system proposes a training method to improve that behavior. Furthermore, the pet care agent system shares information about the pet's health status with veterinarians through a data sharing function, facilitating appropriate responses. The Pet Care Agent System simplifies pet health management and enables pet training even for those with limited time. It also facilitates early detection and rapid response to pet health problems. This is expected to extend the average lifespan of pets, reduce veterinary costs, and improve the success rate of resolving pet behavioral issues. The Pet Care Agent System incorporates technological innovations such as AI-powered real-time health monitoring, machine learning-based behavioral pattern analysis, and a simplified user interface. The target audience is people aged 20-60 who consider their pets family members and are highly concerned with their health and well-being. The Pet Care Agent System addresses the issue of delayed response to pet health problems, monitoring pet health in real time, providing necessary information, and facilitating appropriate action.This enables early detection and rapid response to pet health problems. Generative AI is used to analyze pet behavior patterns and monitor their health. If an abnormality is detected, it will notify and suggest solutions. The pet care market, particularly the health management and training services market, is estimated at 2 trillion yen, and based on market penetration and projected customer numbers in the first year, a market size of 20 billion yen is expected. The growing awareness of pet health and advancements in AI technology present an excellent time to enter the market. The Pet Care Agent System aims to improve the quality of life for pets and their owners, and to raise social awareness of pet health issues. Now is the time to change the future of pets and their families with the Pet Care Agent System. This allows the Pet Care Agent System to support pet health management and training.

[0066] The pet care agent system according to this embodiment comprises a monitoring unit, a notification unit, a coordination unit, and a suggestion unit. The monitoring unit monitors the pet's health data in real time. The monitoring unit collects and monitors health data such as the pet's body temperature, heart rate, and activity level in real time. For example, the monitoring unit measures the pet's body temperature with a sensor and collects the data in real time. The monitoring unit can also monitor the heart rate and notify if an abnormality is detected. Furthermore, the monitoring unit can measure the pet's activity level and monitor the data in real time. For example, the monitoring unit detects the pet's movement with a sensor and measures the activity level. The notification unit notifies of abnormalities based on the health data monitored by the monitoring unit. For example, the notification unit notifies if the pet's body temperature is abnormally high or its heart rate is abnormally fast. For example, the notification unit issues an alert if the pet's body temperature exceeds a certain range. The notification unit can also issue a warning if the heart rate is abnormally fast. Furthermore, the notification unit can also notify if the pet's activity level is abnormally low. For example, the notification unit issues an alert if the pet's activity level falls below a certain standard. The coordination unit coordinates with the veterinarian based on the abnormality notified by the notification unit. The coordination unit, for example, shares the pet's health data with the veterinarian to encourage appropriate action. The coordination unit contacts the veterinarian if the pet's body temperature is abnormally high. The coordination unit can also notify the veterinarian if the heart rate is abnormally fast. Furthermore, the coordination unit can coordinate with the veterinarian if the pet's activity level is abnormally low. For example, the coordination unit sends the pet's health data to the veterinarian to encourage appropriate action. The suggestion unit learns the pet's behavior patterns and proposes an optimal training program. For example, if the pet repeats a particular behavior, the suggestion unit proposes training methods to improve that behavior. For example, if the pet barks, the suggestion unit proposes training methods to improve that behavior. The suggestion unit can also propose training methods to improve the pet biting behavior. Furthermore, the suggestion unit can also propose training methods to improve the pet jumping behavior.For example, the proposed method suggests a training method to prevent excessive barking in pets. This allows the pet care agent system according to the embodiment to support pet health management and training.

[0067] The monitoring unit monitors pet health data in real time. For example, it collects and monitors health data such as the pet's body temperature, heart rate, and activity level. Specifically, to measure the pet's body temperature, it uses sensors attached to the pet's collar or harness. These sensors continuously measure the pet's body temperature and transmit the data wirelessly to a central database. For heart rate monitoring, a heart rate sensor attached to the pet's chest is used. This sensor measures the pet's heart rate in real time and can immediately notify if an abnormality is detected. Furthermore, accelerometers and gyroscopes are used to measure the pet's activity level. These sensors can detect the pet's movements in detail and accurately measure its activity level. For example, it collects data such as how far the pet walked, how far it ran, and how long it rested. This allows the monitoring unit to comprehensively monitor the pet's health and respond quickly if an abnormality occurs. Additionally, the monitoring unit stores the collected data on a cloud server and can refer to past data as needed. This allows for understanding long-term changes in the pet's health and enabling appropriate health management.

[0068] The notification unit alerts owners of abnormalities based on health data monitored by the monitoring unit. For example, the notification unit will notify owners if their pet's body temperature is abnormally high or its heart rate is abnormally fast. Specifically, it will issue an alert if the pet's body temperature exceeds a certain range. For example, if the pet's body temperature exceeds 39 degrees Celsius, it will notify the owner via a smartphone app. Similarly, if the heart rate is abnormally fast, it will issue a warning if it exceeds a certain range. For example, if the pet's heart rate exceeds 160 beats per minute, it will warn the owner. Furthermore, the notification unit can also notify owners if their pet's activity level is abnormally low. For example, it will issue an alert if the pet's activity level falls below a certain standard. This allows owners to respond quickly if an abnormality occurs in their pet's health. When an abnormality is detected, the notification unit can also suggest specific actions to take to the owner. For example, if the pet's body temperature is high, it will suggest cooling methods and hydration methods. If the heart rate is fast, it will suggest keeping the pet calm or consulting a veterinarian. This allows the notification unit to provide support to pet owners so that they can take appropriate action if an abnormality occurs in their pet's health.

[0069] The liaison unit collaborates with veterinarians based on abnormalities notified by the notification unit. For example, the liaison unit shares pet health data with veterinarians to encourage appropriate action. Specifically, it contacts veterinarians if a pet's body temperature is abnormally high. For example, if a pet's body temperature exceeds 39 degrees Celsius, the liaison unit automatically notifies the veterinarian and shares the pet's health data. It also notifies veterinarians if the heart rate is abnormally fast and exceeds a certain range. For example, if a pet's heart rate exceeds 160 beats per minute, the liaison unit notifies the veterinarian and shares the pet's health data. Furthermore, the liaison unit can collaborate with veterinarians if a pet's activity level is abnormally low. For example, if a pet's activity level falls below a certain standard, the liaison unit notifies the veterinarian and shares the pet's health data. This allows veterinarians to understand the pet's health status and take appropriate action. Through collaboration with veterinarians, the liaison unit can support pet health management. For example, it can provide support to owners by conveying advice and instructions from veterinarians to owners so that they can take appropriate action. Furthermore, the Liaison Department can support the long-term management of pets' health through collaboration with veterinarians. This allows the Liaison Department to comprehensively support pet health management and play a crucial role in maintaining pets' health.

[0070] The proposal function learns the pet's behavior patterns and proposes an optimal training program. For example, if a pet repeatedly engages in a particular behavior, the proposal function will suggest training methods to improve that behavior. Specifically, if a pet barks, it will suggest training methods to improve that behavior. For example, if a pet barks unnecessarily, the proposal function will suggest training methods to prevent excessive barking. For example, it will suggest giving the pet a treat each time it barks to suppress the barking behavior. It can also suggest training methods to improve biting behavior. For example, it will suggest giving the pet a toy each time it bites to suppress the biting behavior. Furthermore, it can suggest training methods to improve jumping behavior. For example, it will suggest ignoring the pet each time it jumps to suppress the jumping behavior. By learning the pet's behavior patterns and proposing an optimal training program, the proposal function can improve the pet's behavior and maintain a good relationship with its owner. In addition, the proposal function can utilize AI to learn the pet's behavior patterns. The AI ​​can analyze the pet's behavior data and propose the optimal training method. This allows the proposal department to provide an effective training program to improve pet behavior.

[0071] The monitoring unit can monitor the pet's health data, such as body temperature, heart rate, and activity level, in real time. For example, the monitoring unit can measure the pet's body temperature with a sensor and collect the data in real time. The monitoring unit can also monitor the heart rate and notify if an abnormality is detected. Furthermore, the monitoring unit can measure the pet's activity level and monitor the data in real time. For example, the monitoring unit can detect the pet's movement with a sensor and measure its activity level. This allows for real-time monitoring of the pet's health data, such as body temperature, heart rate, and activity level, ensuring that the pet's health status is always known. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the pet's body temperature data into a generating AI and have the generating AI detect abnormalities.

[0072] The notification unit can detect and notify of abnormalities based on health data monitored by the monitoring unit. For example, the notification unit will notify if the pet's body temperature is abnormally high or its heart rate is abnormally fast. For example, the notification unit will issue an alert if the pet's body temperature exceeds a certain range. The notification unit can also issue a warning if the heart rate is abnormally fast. Furthermore, the notification unit can also notify if the pet's activity level is abnormally low. For example, the notification unit will issue an alert if the pet's activity level falls below a certain standard. This allows for early detection of pet health problems and prompt response by detecting and notifying of abnormalities based on monitored health data. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input health data monitored by the monitoring unit into a generating AI and have the generating AI perform abnormality detection and notification.

[0073] The collaboration unit can share data and collaborate with veterinarians based on anomalies notified by the notification unit. For example, the collaboration unit can share pet health data with veterinarians to prompt appropriate action. For example, the collaboration unit can contact veterinarians if a pet's body temperature is abnormally high. The collaboration unit can also notify veterinarians if a pet's heart rate is abnormally fast. Furthermore, the collaboration unit can collaborate with veterinarians if a pet's activity level is abnormally low. For example, the collaboration unit can send pet health data to veterinarians to prompt appropriate action. In this way, appropriate action can be prompted by sharing data and collaborating with veterinarians based on notified anomalies. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input anomaly data notified by the notification unit into a generating AI and have the generating AI perform the collaboration with veterinarians.

[0074] The suggestion unit can learn the pet's behavior patterns and propose an optimal training program. For example, if the pet repeats a particular behavior, the suggestion unit can propose a training method to improve that behavior. For example, if the pet barks, the suggestion unit can propose a training method to improve that behavior. The suggestion unit can also propose a training method to improve the pet's biting behavior. Furthermore, the suggestion unit can propose a training method to improve the pet's jumping behavior. For example, if the pet barks, the suggestion unit can propose a training method to prevent excessive barking. In this way, by learning the pet's behavior patterns and proposing an optimal training program, the system improves the pet's behavioral problems and facilitates smoother communication with the owner. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input pet behavior data into a generating AI and have the generating AI execute the proposal of an optimal training program.

[0075] The suggestion unit can propose training methods to improve a pet's repetitive behavior. For example, it can propose training methods to improve a pet's barking behavior. For example, it can propose training methods to improve a pet's biting behavior. Furthermore, it can propose training methods to improve a pet's jumping behavior. For example, it can propose training methods to prevent excessive barking in the case of a pet barking. In this way, by proposing training methods to improve a pet's repetitive behavior, the pet's behavioral problems can be effectively improved. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input pet behavior data into a generating AI and have the generating AI execute suggestions for training methods to improve the behavior.

[0076] The monitoring unit can estimate the pet's emotions and adjust the frequency of monitoring health data based on the estimated emotions. For example, if the pet is stressed, the monitoring unit can increase the frequency of monitoring heart rate and body temperature. For example, if the pet is relaxed, the monitoring unit can decrease the frequency of monitoring activity level. Furthermore, if the pet is excited, the monitoring unit can also increase the frequency of monitoring heart rate and body temperature. This allows for more appropriate monitoring of the pet's health by adjusting the frequency of monitoring health data based on the pet's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI or not using AI. For example, the monitoring unit can input pet emotion data into a generative AI and have the generative AI adjust the frequency of monitoring health data.

[0077] The monitoring unit can analyze the pet's past health data and optimize a monitoring algorithm that enables early detection of abnormalities. For example, the monitoring unit can detect signs of abnormalities from past health data and issue early warnings. For example, the monitoring unit can analyze patterns in health data and build an abnormality prediction model. Furthermore, the monitoring unit can also analyze the frequency of abnormalities and adjust the monitoring algorithm. This allows for early detection of pet health problems by analyzing the pet's past health data and optimizing the monitoring algorithm that enables early detection of abnormalities. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the pet's past health data into a generating AI and have the generating AI perform optimization of the monitoring algorithm that enables early detection of abnormalities.

[0078] The monitoring unit can detect abnormalities in health data based on the pet's living environment and diet during monitoring. For example, the monitoring unit can detect abnormalities in health data in response to changes in the pet's living environment. The monitoring unit can also detect abnormalities in health data based on changes in the pet's diet. Furthermore, the monitoring unit can detect abnormalities in health data by considering both the pet's living environment and diet. This allows for a more accurate understanding of the pet's health status by detecting abnormalities in health data based on the pet's living environment and diet. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input pet living environment data and diet data into a generating AI and have the generating AI perform the detection of abnormalities in health data.

[0079] The monitoring unit can estimate the pet's emotions and determine the priority of health data to monitor based on the estimated emotions. For example, if the pet is stressed, the monitoring unit will prioritize monitoring heart rate and body temperature. If the pet is relaxed, the monitoring unit will prioritize monitoring activity level. Furthermore, if the pet is excited, the monitoring unit may also prioritize monitoring heart rate and body temperature. This allows for more appropriate monitoring of the pet's health by prioritizing health data based on the pet's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI or not using AI. For example, the monitoring unit can input pet emotion data into a generative AI and have the generative AI determine the priority of health data.

[0080] The monitoring unit can prioritize monitoring health data based on environmental factors, taking into account the pet's geographical location during monitoring. For example, if the pet is in a hot and humid area, the monitoring unit will prioritize monitoring body temperature. If the pet is in a cold region, the monitoring unit will prioritize monitoring body temperature and activity level. Furthermore, if the pet is in an urban area, the monitoring unit can also prioritize monitoring respiratory rate, taking into account the effects of air quality. This allows for more appropriate monitoring of the pet's health status by prioritizing the monitoring of health data based on environmental factors, taking into account the pet's geographical location. 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 pet's geographical location information into a generating AI and have the generating AI perform priority monitoring of health data based on environmental factors.

[0081] The monitoring unit can adjust the timing of health data monitoring, taking into account the pet owner's lifestyle. For example, the monitoring unit can intensify monitoring of the pet's activity level while the owner is at work. For example, it can intensify monitoring of the pet's heart rate and body temperature after the owner has returned home. Furthermore, the monitoring unit can reduce the frequency of monitoring the pet's health data while the owner is sleeping. This allows for more appropriate monitoring of the pet's health by adjusting the timing of health data monitoring, taking into account the pet owner's lifestyle. 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 owner's lifestyle data into a generating AI and have the generating AI adjust the timing of health data monitoring.

[0082] The notification unit can estimate the pet's emotions and adjust the method of abnormal notification based on the estimated emotions. For example, if the pet is stressed, the notification unit will send an emergency notification to the owner. For example, if the pet is relaxed, the notification unit will use the normal notification method. Furthermore, if the pet is excited, the notification unit can increase the frequency of notifications. This allows for appropriate notifications according to the pet's health condition by adjusting the method of abnormal notification based on the pet's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not using AI. For example, the notification unit can input pet emotion data into the generative AI and have the generative AI adjust the method of abnormal notification.

[0083] The notification unit can adjust the urgency of the notification based on the severity of the anomaly when it sends a notification. For example, if the anomaly is serious, the notification unit will immediately send an emergency notification. For example, if the anomaly is minor, the notification unit will use the normal notification method. Furthermore, the notification unit can also adjust the urgency of the notification in stages according to the severity of the anomaly. This allows for appropriate notifications according to the pet's health condition by adjusting the urgency of the notification based on the severity of the anomaly. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input anomaly severity data into a generating AI and have the generating AI perform the adjustment of the notification urgency.

[0084] The notification unit can, at the time of notification, refer to the pet's health data history to identify the cause of an abnormality and reflect it in the notification content. For example, the notification unit can identify the cause of an abnormality from the health data history and include details in the notification content. For example, if the cause of an abnormality cannot be identified, the notification unit will state that fact in the notification content. Furthermore, if there are multiple possible causes of the abnormality, the notification unit can also include the most likely cause in the notification content. In this way, by referring to the pet's health data history to identify the cause of an abnormality and reflecting it in the notification content, detailed information about the pet's health status can be provided. Some or all of the above processing in the notification unit may be performed using AI, for example, or without using AI. For example, the notification unit can input the pet's health data history into a generating AI and have the generating AI perform the task of identifying the cause of the abnormality and reflecting it in the notification content.

[0085] The notification unit can estimate the pet's emotions and adjust the timing of notifications based on the estimated emotions. For example, if the pet is stressed, the notification unit will send a notification immediately. For example, if the pet is relaxed, the notification unit will delay the timing of the notification. Furthermore, if the pet is excited, the notification unit can also speed up the timing of the notification. By adjusting the timing of notifications based on the pet's emotions, it becomes possible to provide appropriate notifications according to the pet's health condition. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input pet emotion data into the generative AI and have the generative AI adjust the timing of notifications.

[0086] The notification unit can select the optimal notification method when sending a notification, taking into account the pet owner's current activity status. For example, if the owner is at work, the notification unit may send a notification via email. If the owner is driving, for example, the notification unit may use an audio notification. Furthermore, the notification unit can delay the notification if the owner is sleeping. This allows pet owners to receive notifications in the most optimal way by selecting the optimal notification method based on their current activity status. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input data on the owner's current activity status into a generating AI and have the generating AI select the optimal notification method.

[0087] The notification unit can select the optimal notification method when sending a notification, taking into account the pet owner's current activity status. For example, if the owner is at work, the notification unit may send a notification via email. If the owner is driving, for example, the notification unit may use an audio notification. Furthermore, the notification unit can delay the notification if the owner is sleeping. This allows pet owners to receive notifications in the most optimal way by selecting the optimal notification method based on their current activity status. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input data on the owner's current activity status into a generating AI and have the generating AI select the optimal notification method.

[0088] The notification unit can supplement notification content by referring to external data related to the pet's health data (e.g., weather information) when sending a notification. For example, the notification unit can refer to weather information and include it in the notification content if it may affect the pet's health data. For example, the notification unit can refer to external data and add information related to the pet's health data to the notification content. Furthermore, the notification unit can also combine weather information and health data to supplement the notification content. This allows for the provision of more detailed information by supplementing the notification content by referring to external data related to the pet's health data. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input weather information data into a generating AI and have the generating AI perform the supplementation of the notification content.

[0089] The coordination unit can estimate the pet's emotions and adjust the coordination method with the veterinarian based on the estimated emotions. For example, if the pet is stressed, the coordination unit will send an emergency contact to the veterinarian. For example, if the pet is relaxed, the coordination unit will use the normal coordination method. Furthermore, if the pet is excited, the coordination unit can increase the coordination frequency. This allows for appropriate coordination according to the pet's health condition by adjusting the coordination method with the veterinarian based on the pet's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the coordination unit may be performed using AI or not using AI. For example, the coordination unit can input pet emotion data into the generative AI and have the generative AI adjust the coordination method with the veterinarian.

[0090] The integration unit can optimize the information provided to veterinarians by referring to the pet's health data history during integration. For example, the integration unit can extract important information from the health data history and provide it to the veterinarian. For example, the integration unit can analyze the health data history, identify the cause of abnormalities, and provide this information to the veterinarian. Furthermore, the integration unit can optimize and provide the information necessary for the veterinarian based on the health data history. This allows for the efficient provision of necessary information to veterinarians by optimizing the information provided to veterinarians by referring to the pet's health data history. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the pet's health data history into a generating AI and have the generating AI perform the optimization of the information provided.

[0091] The collaboration unit can adjust the frequency of collaboration with the veterinarian according to the pet's health condition during collaboration. For example, if the pet's health condition is deteriorating, the collaboration unit will increase the collaboration frequency. For example, if the pet's health condition is stable, the collaboration unit will decrease the collaboration frequency. Furthermore, the collaboration unit can also adjust the collaboration frequency in stages according to the pet's health condition. This allows for appropriate collaboration according to the pet's health condition by adjusting the collaboration frequency with the veterinarian. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input pet health condition data into a generating AI and have the generating AI perform the adjustment of the collaboration frequency.

[0092] The communication unit can estimate the pet's emotions and adjust the timing of contact with the veterinarian based on the estimated emotions. For example, if the pet is stressed, the communication unit will immediately contact the veterinarian. For example, if the pet is relaxed, the communication unit will delay the contact. Furthermore, if the pet is excited, the communication unit can also speed up the contact. By adjusting the timing of contact with the veterinarian based on the pet's emotions, appropriate contact can be made according to the pet's health condition. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input pet emotion data into the generative AI and have the generative AI perform the adjustment of the contact timing.

[0093] The collaboration unit can select a method of collaboration with a veterinarian, taking into consideration the pet owner's wishes, when collaborating. For example, if the owner requests emergency treatment, the collaboration unit will immediately contact the veterinarian. For example, if the owner requests routine treatment, the collaboration unit will use the standard collaboration method. Furthermore, the collaboration unit can adjust the collaboration method according to the owner's wishes. This allows for appropriate collaboration that meets the owner's needs by selecting a collaboration method with a veterinarian that takes the pet owner's wishes into consideration. Some or all of the above-described processes in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input owner's preference data into a generating AI and have the generating AI select the collaboration method.

[0094] The integration unit can supplement the integration content by referring to external data related to pet health data (e.g., local infectious disease information) during integration. For example, the integration unit can refer to local infectious disease information to supplement the information provided to veterinarians. For example, the integration unit can refer to external data to provide veterinarians with information related to pet health data. Furthermore, the integration unit can also combine local infectious disease information and health data to supplement the integration content. This allows for the provision of more detailed information by supplementing the integration content by referring to external data related to pet health data. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input local infectious disease information data into a generating AI and have the generating AI perform the supplementation of the integration content.

[0095] The suggestion unit can estimate the pet's emotions and adjust the training program content based on the estimated emotions. For example, if the pet is stressed, the suggestion unit can suggest a relaxing training program. If the pet is relaxed, the suggestion unit can suggest an active training program. Furthermore, if the pet is excited, the suggestion unit can also suggest a calming training program. By adjusting the training program content based on the pet's emotions, appropriate training tailored to the pet's health condition becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input pet emotion data into a generative AI and have the generative AI adjust the training program content.

[0096] The proposal unit can analyze the pet's past behavioral data to select the optimal training method when making a proposal. For example, the proposal unit can select training methods that the pet prefers based on past behavioral data. For example, the proposal unit can analyze past behavioral data and avoid training methods that the pet dislikes. Furthermore, the proposal unit can also propose the optimal training method for the pet based on past behavioral data. This makes it possible to provide the best possible training for the pet by analyzing the pet's past behavioral data and selecting the optimal training method. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the pet's past behavioral data into a generating AI and have the generating AI select the optimal training method.

[0097] The proposal unit can customize training programs based on the pet's living environment and the owner's lifestyle when making a proposal. For example, the proposal unit can propose an indoor training program according to the pet's living environment. For example, the proposal unit can propose a short and effective training program tailored to the owner's lifestyle. Furthermore, the proposal unit can also customize the training program considering both the pet's living environment and the owner's lifestyle. This allows for optimal training for the pet by customizing the training program based on the pet's living environment and the owner's lifestyle. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input pet living environment data and owner lifestyle data into a generating AI and have the generating AI perform the customization of the training program.

[0098] The proposed system can estimate a pet's emotions and prioritize training programs based on the estimated emotions. For example, if the pet is stressed, the system will prioritize relaxing training programs. If the pet is relaxed, the system will prioritize active training programs. Furthermore, if the pet is excited, the system can also prioritize calming training programs. This allows for optimal training for the pet by prioritizing training programs based on the pet's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the proposed system may be performed using AI or not. For example, the proposed system can input pet emotion data into a generative AI and have the generative AI determine the priority of training programs.

[0099] The proposal unit can select a training method appropriate to the environment by considering the pet's geographical location information when making a proposal. For example, if the pet lives in an urban area, the proposal unit will select an indoor training method. For example, if the pet lives in a suburban area, the proposal unit will select an outdoor training method. Furthermore, the proposal unit can also select the optimal training method based on the pet's geographical location information. This makes it possible to provide the best possible training for the pet by selecting a training method appropriate to the environment by considering the pet's geographical location information. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI. For example, the proposal unit can input the pet's geographical location information into a generating AI and have the generating AI select a training method appropriate to the environment.

[0100] The suggestion unit can improve the training program by incorporating feedback from pet owners during the suggestion process. For example, the suggestion unit improves the training program based on owner feedback. For example, the suggestion unit customizes the training program by incorporating owner opinions. Furthermore, the suggestion unit can collect owner feedback and evaluate the effectiveness of the training program. This allows for optimal training for pets by improving the training program based on owner feedback. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input owner feedback data into a generating AI and have the generating AI perform improvements to the training program.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] The monitoring unit not only monitors the pet's health data in real time but also collects behavioral data and can detect abnormal behavior. For example, if a pet exhibits an unusual behavioral pattern, it can detect the abnormal behavior and notify the notification unit. Furthermore, the monitoring unit can analyze the pet's behavioral data and identify the cause of the abnormal behavior. For example, if a pet suddenly exhibits aggressive behavior, it can identify the cause and notify the owner. This allows for real-time monitoring of not only the pet's health but also its behavior, enabling early detection of abnormal behavior.

[0103] The monitoring unit not only monitors pet health data in real time, but can also estimate the pet's emotions and detect abnormalities in health data based on those emotions. For example, if a pet is stressed, the frequency of detecting abnormalities in heart rate or body temperature can be increased. Furthermore, if a pet is relaxed, the monitoring unit can decrease the frequency of detecting abnormalities in activity levels. This allows for more appropriate monitoring of the pet's health status by detecting abnormalities in health data based on the pet's emotions.

[0104] The notification unit not only notifies of abnormalities based on health data monitored by the monitoring unit, but can also estimate the pet's emotions and adjust the notification content based on those emotions. For example, if the pet is stressed, it can send an emergency notification to the owner. Furthermore, if the pet is relaxed, the normal notification method can be used. This allows for appropriate notifications tailored to the pet's health condition by adjusting the notification content based on the pet's emotions.

[0105] The communication unit not only coordinates with veterinarians based on abnormalities notified by the notification unit, but can also estimate the pet's emotions and adjust the method of communication with the veterinarian based on those emotions. For example, if the pet is stressed, an emergency contact can be sent to the veterinarian. Furthermore, if the pet is relaxed, the normal communication method can be used. By adjusting the method of communication with the veterinarian based on the pet's emotions, appropriate communication can be provided according to the pet's health condition.

[0106] The suggestion function not only learns the pet's behavioral patterns and proposes the optimal training program, but can also estimate the pet's emotions and adjust the content of the training program based on those emotions. For example, if the pet is stressed, it can suggest a training program that promotes relaxation. Furthermore, if the pet is relaxed, it can suggest an active training program. By adjusting the content of the training program based on the pet's emotions, it becomes possible to provide appropriate training tailored to the pet's health condition.

[0107] The monitoring unit not only monitors pet health data in real time, but can also detect abnormalities in health data based on the pet's living environment and diet. For example, it can detect abnormalities in health data in response to changes in the pet's living environment. Furthermore, it can also detect abnormalities in health data based on changes in the pet's diet. This allows for a more accurate understanding of the pet's health status by detecting abnormalities in health data based on the pet's living environment and diet.

[0108] The notification unit not only notifies of abnormalities based on health data monitored by the monitoring unit, but can also adjust the urgency of the notification based on the severity of the abnormality. For example, if the abnormality is serious, an emergency notification can be sent immediately. Furthermore, if the abnormality is minor, the normal notification method can be used. This allows for appropriate notifications tailored to the pet's health condition by adjusting the urgency of the notification based on the severity of the abnormality.

[0109] The collaboration unit not only collaborates with veterinarians based on abnormalities notified by the notification unit, but can also optimize the information provided to veterinarians by referring to the pet's health data history. For example, it can extract important information from the health data history and provide it to the veterinarian. Furthermore, it can analyze the health data history, identify the cause of the abnormality, and provide it to the veterinarian. By optimizing the information provided to veterinarians by referring to the pet's health data history, the system can efficiently provide veterinarians with the information they need.

[0110] The proposal function can not only learn a pet's behavioral patterns and propose an optimal training program, but also analyze the pet's past behavioral data to select the most suitable training method. For example, it can select training methods that the pet prefers based on past behavioral data. Furthermore, it can analyze past behavioral data to avoid training methods that the pet dislikes. In this way, by analyzing the pet's past behavioral data and selecting the optimal training method, it becomes possible to provide the best possible training for the pet.

[0111] The proposal function not only learns the pet's behavioral patterns and proposes the optimal training program, but can also customize the training program based on the pet's living environment and the owner's lifestyle. For example, it can propose an indoor training program according to the pet's living environment. Furthermore, it can propose a short and effective training program tailored to the owner's lifestyle. In this way, by customizing the training program based on the pet's living environment and the owner's lifestyle, it becomes possible to provide the best possible training for the pet.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The monitoring unit monitors the pet's health data in real time. The monitoring unit collects and monitors health data such as the pet's body temperature, heart rate, and activity level in real time. The monitoring unit measures the pet's body temperature with a sensor and collects the data in real time. It can also monitor the heart rate and send notifications if an abnormality is detected. Furthermore, it can detect the pet's movement with a sensor and measure its activity level. Step 2: The notification unit notifies of abnormalities based on health data monitored by the monitoring unit. For example, the notification unit will notify if the pet's body temperature is abnormally high or its heart rate is abnormally fast. The notification unit can also issue an alert if the pet's body temperature exceeds a certain range and a warning if its heart rate is abnormally fast. Furthermore, it can also notify if the pet's activity level is abnormally low. Step 3: The liaison unit collaborates with veterinarians based on abnormalities notified by the notification unit. For example, the liaison unit shares pet health data with veterinarians to encourage appropriate action. It contacts veterinarians if the pet's body temperature is abnormally high or its heart rate is abnormally fast. It can also collaborate with veterinarians if the pet's activity level is abnormally low. Step 4: The proposal team learns the pet's behavioral patterns and proposes an optimal training program. For example, if the pet repeatedly engages in a particular behavior, the proposal team will suggest training methods to improve that behavior. For example, if the pet barks, bites, or jumps, the proposal team will suggest training methods to improve each specific behavior.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] Each of the multiple elements described above, including the monitoring unit, notification unit, collaboration unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the monitoring unit uses the sensors of the smart device 14 to monitor the pet's body temperature, heart rate, and activity level in real time. The notification unit detects abnormalities using the control unit 46A of the smart device 14 and issues an alert. The collaboration unit collaborates with a veterinarian using the specific processing unit 290 of the data processing unit 12 to share health data. The proposal unit learns the pet's behavior patterns using the specific processing unit 290 of the data processing unit 12 and proposes an optimal training program. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] Each of the multiple elements described above, including the monitoring unit, notification unit, collaboration unit, and proposal unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the monitoring unit uses the sensors of the smart glasses 214 to monitor the pet's body temperature, heart rate, and activity level in real time. The notification unit detects abnormalities using the control unit 46A of the smart glasses 214 and issues an alert. The collaboration unit collaborates with a veterinarian using the specific processing unit 290 of the data processing unit 12 to share health data. The proposal unit learns the pet's behavior patterns using the specific processing unit 290 of the data processing unit 12 and proposes an optimal training program. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] Each of the multiple elements described above, including the monitoring unit, notification unit, collaboration unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the monitoring unit uses the sensors of the headset terminal 314 to monitor the pet's body temperature, heart rate, and activity level in real time. The notification unit detects abnormalities using the control unit 46A of the headset terminal 314 and issues an alert. The collaboration unit collaborates with a veterinarian using the specific processing unit 290 of the data processing unit 12 to share health data. The proposal unit learns the pet's behavior patterns using the specific processing unit 290 of the data processing unit 12 and proposes an optimal training program. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] Each of the multiple elements described above, including the monitoring unit, notification unit, collaboration unit, and proposal unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the monitoring unit uses the robot 414's sensors to monitor the pet's body temperature, heart rate, and activity level in real time. The notification unit detects abnormalities using the robot 414's control unit 46A and issues an alert. The collaboration unit collaborates with a veterinarian using the data processing unit 12's specific processing unit 290 to share health data. The proposal unit learns the pet's behavior patterns using the data processing unit 12's specific processing unit 290 and proposes an optimal training program. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] (Note 1) The monitoring department monitors pet health data in real time, A notification unit that notifies of an abnormality based on health data monitored by the aforementioned monitoring unit, A coordination unit that collaborates with a veterinarian based on an abnormality notified by the aforementioned notification unit, It includes a proposal unit that learns the pet's behavioral patterns and suggests the optimal training program. A system characterized by the following features. (Note 2) The aforementioned monitoring unit, Monitor your pet's health data in real time, including body temperature, heart rate, and activity level. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned notification unit, Based on the health data monitored by the aforementioned monitoring unit, abnormalities are detected and notified. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned linkage unit is, Based on the abnormality notified by the aforementioned notification unit, data is shared and coordinated with the veterinarian. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Learn your pet's behavioral patterns and propose the optimal training program. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, If your pet repeatedly engages in a particular behavior, we can suggest training methods to improve that behavior. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned monitoring unit, It estimates the pet's emotions and adjusts the frequency of monitoring health data based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned monitoring unit, We analyze your pet's past health data and optimize monitoring algorithms to enable early detection of abnormalities. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned monitoring unit, During monitoring, abnormalities in health data are detected based on the pet's living environment and diet. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned monitoring unit, It estimates the pet's emotions and prioritizes health data to monitor based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned monitoring unit, During monitoring, the system prioritizes monitoring health data based on environmental factors, taking into account the pet's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned monitoring unit, During monitoring, the timing of health data monitoring is adjusted to take into account the pet owner's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned notification unit, The system estimates the pet's emotions and adjusts the method of anomaly notifications based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned notification unit, When a notification is sent, the urgency of the notification will be adjusted based on the severity of the anomaly. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned notification unit, When sending a notification, the system will refer to the pet's health data history to identify the cause of the abnormality and reflect this in the notification content. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned notification unit, It estimates the pet's emotions and adjusts the timing of notifications based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned notification unit, When sending a notification, the system will select the most appropriate notification method, taking into account the pet owner's current activity status. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned notification unit, When sending a notification, the system will select the most appropriate notification method, taking into account the pet owner's current activity status. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned notification unit, When sending notifications, the system supplements the notification content by referencing external data related to the pet's health data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned linkage unit is, We estimate the pet's emotions and adjust our collaboration with veterinarians based on those estimates. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned linkage unit is, During integration, the system optimizes the information provided to veterinarians by referencing the pet's health data history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned linkage unit is, During the collaboration process, the frequency of communication with the veterinarian will be adjusted according to the pet's health condition. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned linkage unit is, We estimate the pet's emotions and adjust the timing of contacting the veterinarian based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned linkage unit is, When collaborating, the method of collaboration with veterinarians will be selected taking into consideration the wishes of the pet owners. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned linkage unit is, During integration, external data related to pet health data is referenced to supplement the integrated data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, The system estimates the pet's emotions and adjusts the training program based on those estimates. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When making a proposal, we analyze the pet's past behavioral data to select the optimal training method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, When making a proposal, we customize the training program based on the pet's living environment and the owner's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, It estimates the pet's emotions and prioritizes training programs based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, When making a proposal, we will select a training method that is appropriate for the environment, taking into account the pet's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned proposal section is, When making proposals, we improve training programs by incorporating feedback from pet owners. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The monitoring department monitors pet health data in real time, A notification unit that notifies of an abnormality based on health data monitored by the aforementioned monitoring unit, A coordination unit that collaborates with a veterinarian based on an abnormality notified by the aforementioned notification unit, It includes a proposal unit that learns the pet's behavioral patterns and suggests the optimal training program. A system characterized by the following features.

2. The aforementioned monitoring unit, Monitor your pet's health data in real time, including body temperature, heart rate, and activity level. The system according to feature 1.

3. The aforementioned notification unit, Based on the health data monitored by the aforementioned monitoring unit, abnormalities are detected and notified. The system according to feature 1.

4. The aforementioned linkage unit is, Based on the abnormality notified by the aforementioned notification unit, data is shared and coordinated with the veterinarian. The system according to feature 1.

5. The aforementioned proposal section is, Learn your pet's behavioral patterns and propose the optimal training program. The system according to feature 1.

6. The aforementioned proposal section is, If your pet repeatedly engages in a particular behavior, we can suggest training methods to improve that behavior. The system according to feature 1.

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

8. The aforementioned monitoring unit, We analyze your pet's past health data and optimize monitoring algorithms to enable early detection of abnormalities. The system according to feature 1.

9. The aforementioned monitoring unit, During monitoring, abnormalities in health data are detected based on the pet's living environment and diet. The system according to feature 1.

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