system

Abstract

The system according to this embodiment aims to monitor a pet's behavior and health in real time and detect abnormalities early. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a notification unit, a generation unit, and a community support unit. The collection unit collects pet behavior and health data. The analysis unit analyzes the data collected by the collection unit. The notification unit detects abnormalities based on the data analyzed by the analysis unit and notifies the owner. The generation unit understands the pet's behavior and emotions based on the data analyzed by the analysis unit and transmits that information to the owner. The community support unit supports information sharing among owners.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to grasp the behavior and health status of a pet in real time and detect abnormalities at an early stage.

[0005] The system according to the embodiment aims to grasp the behavior and health status of a pet in real time and detect abnormalities at an early stage.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a notification unit, a generation unit, and a community support unit. The data collection unit collects pet behavior and health data. The analysis unit analyzes the data collected by the data collection unit. The notification unit detects abnormalities based on the data analyzed by the analysis unit and notifies the owner. The generation unit understands the pet's behavior and emotions based on the data analyzed by the analysis unit and transmits that information to the owner. The community support unit supports information sharing among owners. [Effects of the Invention]

[0007] The system according to this embodiment can monitor a pet's behavior and health in real time and detect abnormalities early. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The pet management system according to an embodiment of the present invention is a groundbreaking system in which AI learns pet behavior and health data to perform anomaly detection and prediction. This pet management system uses a generating AI to understand pet behavior and emotions and communicate that information to the owner in human language. This allows for a deeper understanding of the pet's needs and enables quicker responses. In addition, the AI ​​supports the community through information sharing links among owners, facilitating dialogue and consultation among users and increasing knowledge about pet health. By providing subscription services and access to the community, it creates an environment where owners can deepen their bond with their pets and manage their health with peace of mind. First, the pet management system uses AI to collect and learn pet behavior and health data. For example, it collects data such as pet diet, exercise, sleep, and weight, and the AI ​​analyzes this data. This allows the AI ​​to understand the pet's health status and notify the owner if an abnormality is detected. Next, the generating AI understands the pet's behavior and emotions. For example, if a pet is feeling anxious or repeating a specific behavior, the generating AI analyzes that behavior and emotion and communicates it to the owner in human language. This allows the owner to understand their pet's needs more deeply and respond quickly. Furthermore, the AI-powered community support link for information sharing among owners. For example, users can ask questions and seek advice about their pets' health within the community, and the AI ​​provides appropriate information. This promotes dialogue and consultation among users, increasing their knowledge about pet health. Finally, the service provides access to a subscription service and the community. Owners can regularly check their pets' health and obtain necessary information. They can also share information with other owners through community access, deepening their bond with their pets. This creates an environment where pet health management can be done with greater peace of mind, and owners can deepen their bond with their pets. For example, by regularly checking their pets' health and responding quickly if any abnormalities are detected, owners can maintain their pets' health. In addition, through information sharing within the community, owners can receive advice and information from other owners, which can be used to help manage their pets' health.This allows pet management systems to more effectively manage pet health.

[0029] The pet management system according to this embodiment comprises a data collection unit, an analysis unit, a notification unit, a generation unit, and a community support unit. The data collection unit collects pet behavior and health data. For example, the data collection unit collects data such as pet diet, exercise, sleep, and weight. The data collection unit can automatically collect this data using AI. For example, the data collection unit can use a sensor to record the type and amount of food the pet eats. The data collection unit can also use an activity tracker attached to the pet to measure the pet's activity level. Furthermore, the data collection unit can use a sensor installed in the pet's bed to monitor the pet's sleep patterns. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can use AI to analyze the collected data and understand the pet's health status. For example, the analysis unit can analyze fluctuations in the pet's weight and detect changes in its health status. The analysis unit can also analyze changes in the pet's activity level and detect insufficient or excessive exercise. Furthermore, the analysis unit can analyze the pet's sleep patterns and detect insufficient or excessive sleep. The notification unit detects anomalies based on data analyzed by the analysis unit and notifies the owner. Using AI, the notification unit can quickly notify the owner when an anomaly is detected. For example, the notification unit can notify the owner if the pet's weight increases rapidly. It can also notify the owner if the pet's activity level decreases rapidly. Furthermore, it can notify the owner if the pet's sleep pattern is abnormal. The generation unit understands the pet's behavior and emotions based on data analyzed by the analysis unit and communicates this information to the owner. Using generation AI, the generation unit can analyze the pet's behavior and emotions and communicate them to the owner in human language. For example, if the pet is feeling anxious, the generation unit can explain the reason to the owner. It can also explain the meaning of a behavior if the pet is repeating a particular action. Furthermore, the generation unit can analyze the pet's emotions and communicate them to the owner. The community support unit supports information sharing among owners.The community support department can use AI to support information sharing among owners and provide knowledge about pet health. For example, the community support department allows users to ask questions and seek advice about their pets' health within the community. Furthermore, the community support department can use AI to provide appropriate information. In addition, the community support department can facilitate dialogue and consultation among users and increase their knowledge about pet health. As a result, the pet management system according to this embodiment can collect, analyze, notify, generate, and share pet behavior and health data.

[0030] The data collection unit collects behavioral and health data from pets. For example, it collects data on pets' diet, exercise, sleep, and weight. The unit can use AI to automatically collect this data. Specifically, it can use sensors to record the type and amount of food the pet eats. These sensors are attached to the pet's food bowl and automatically record data during meals. The unit can also use activity trackers attached to the pet to measure its activity level. These activity trackers are attached to the pet's collar or harness and monitor the pet's movements in real time. Furthermore, the unit can use sensors placed in the pet's bedding to monitor its sleep patterns. These sensors are placed under the pet's bedding and detect the pet's movements and body temperature to assess sleep quality. The collected data is transmitted wirelessly to a central database and updated in real time. This allows the unit to gain a detailed understanding of the pet's behavior and health and quickly provide necessary information. Additionally, the unit can adjust the frequency and accuracy of data collection to provide flexible responses to specific situations and conditions. For example, if a pet's health deteriorates, the frequency of data collection can be increased for more detailed monitoring. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the data collection unit. Using AI, the analysis unit can analyze the collected data and understand the health status of pets. Specifically, it can analyze fluctuations in a pet's weight and detect changes in their health. For example, if a pet's weight increases rapidly, it may indicate overeating or lack of exercise, prompting the owner to take action. The analysis unit can also analyze changes in a pet's activity level and detect insufficient or excessive exercise. If activity levels decrease, it may indicate that the pet is ill or stressed, allowing for early intervention. Furthermore, the analysis unit can analyze a pet's sleep patterns and detect sleep deprivation or excessive sleep. Abnormal sleep patterns can affect a pet's health, requiring appropriate action. The analysis unit comprehensively analyzes this data to understand the pet's health status in real time. In addition, the analysis unit can utilize historical data and statistical information to perform long-term health management and trend analysis. For example, based on historical data, it can predict pet health risks under specific seasons or environmental conditions and propose preventive measures. This allows the analysis unit to contribute not only to real-time health management but also to long-term health maintenance, providing comprehensive support for pet health.

[0032] The notification unit detects anomalies based on data analyzed by the analysis unit and notifies the owner. Using AI, the notification unit can quickly notify the owner when an anomaly is detected. Specifically, it can notify the owner if the pet's weight increases rapidly. Notifications are sent via smartphone apps, email, SMS, etc., allowing the owner to respond immediately. The notification unit can also notify the owner if the pet's activity level decreases rapidly. This allows the owner to notice changes in their pet's health early and take appropriate action. Furthermore, the notification unit can notify the owner if the pet's sleep pattern is abnormal. For example, if the pet frequently wakes up at night, it may indicate stress or a health problem, prompting the owner to take notice. These notifications can be customized, allowing the notification method and frequency to be adjusted according to the owner's preferences. This enables the notification unit to provide owners with quick and appropriate information, supporting pet health management. Additionally, the notification unit can collect feedback from owners and continuously improve the accuracy and effectiveness of its notifications. This allows the notification unit to provide owners with optimal information and more effectively support pet health management.

[0033] The generation unit understands the pet's behavior and emotions based on the data analyzed by the analysis unit and communicates this information to the owner. Using generation AI, the generation unit can analyze the pet's behavior and emotions and communicate them to the owner in human language. Specifically, if the pet is feeling anxious, it can explain the reason to the owner. For example, if the pet is feeling anxious at a specific time, it can analyze what is happening during that time and communicate it to the owner. The generation unit can also explain the meaning of a pet's repeated behavior to the owner. For example, if the pet barks frequently, it can analyze the cause and communicate it to the owner. Furthermore, the generation unit can analyze the pet's emotions and communicate them to the owner. For example, if the pet is happy, it can explain the reason to the owner. The generation unit uses natural language generation technology to convey this information to the owner in an easy-to-understand manner. This allows the owner to understand their pet's behavior and emotions more deeply and respond appropriately. In addition, the generation unit can continuously improve the accuracy and effectiveness of the generated content based on feedback from the owner. This allows the generating unit to provide owners with optimal information and facilitate smoother communication with their pets.

[0034] The Community Support Department supports information sharing among pet owners. Using AI, the Community Support Department can facilitate information sharing among owners and provide knowledge about pet health. Specifically, owners can ask questions and seek advice about their pet's health within the community. For example, if an owner posts a question about their pet's diet or exercise, they can receive answers from other owners and experts. The Community Support Department can also use AI to provide appropriate information. For example, it can provide information about specific symptoms in pets and advice for health management. Furthermore, the Community Support Department can promote dialogue and consultation among users, increasing their knowledge about pet health. For example, it can regularly hold online seminars and workshops, providing a platform for owners to share information. This allows the Community Support Department to strengthen collaboration among owners and provide comprehensive support for pet health management. Additionally, based on feedback from owners, the Community Support Department can continuously improve the quality of the information and services it provides. This enables the Community Support Department to provide owners with optimal information and more effectively support pet health management.

[0035] The data collection unit can collect data on pets' diet, exercise, sleep, weight, and other information. For example, the data collection unit can use a sensor to record the type and amount of food the pet eats. For example, the data collection unit can use an activity tracker attached to the pet to measure the pet's activity level. For example, the data collection unit can use a sensor placed in the pet's bedding to monitor the pet's sleep patterns. This allows the data collection unit to collect diverse behavioral and health data from pets. Some or all of the above-described processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data from a sensor recording the type and amount of food the pet eats into a generating AI, which then analyzes and collects the data.

[0036] The analysis unit can analyze the collected data and understand the pet's health status. For example, the analysis unit can analyze changes in the pet's weight and detect changes in its health status. For example, the analysis unit can analyze changes in the pet's exercise level and detect insufficient or excessive exercise. For example, the analysis unit can analyze the pet's sleep patterns and detect insufficient or excessive sleep. In this way, the analysis unit can accurately understand the pet's health status. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the collected data into a generating AI, which can then analyze the data to understand the health status.

[0037] The notification unit can notify the owner when an abnormality is detected. For example, the notification unit can notify the owner if the pet's weight increases rapidly. For example, the notification unit can notify the owner if the pet's activity level decreases rapidly. For example, the notification unit can notify the owner if the pet's sleep pattern is abnormal. This allows the notification unit to quickly notify the owner when an abnormality is detected. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the data on the detected abnormality into a generating AI, which can then analyze the abnormality and notify the owner.

[0038] The generation unit can analyze the pet's behavior and emotions and communicate them to the owner in human language. For example, if the pet is feeling anxious, the generation unit can explain the reason to the owner. For example, if the pet is repeating a particular behavior, the generation unit can explain the meaning of that behavior to the owner. For example, the generation unit can analyze the pet's emotions and communicate them to the owner. In this way, the generation unit can communicate the pet's behavior and emotions to the owner in an easy-to-understand manner. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input data on the pet's behavior and emotions into a generation AI, which can then analyze the data and communicate it to the owner.

[0039] The Community Support Department can support information sharing among owners and provide knowledge about pet health. For example, the Community Support Department allows users to ask questions and seek advice about pet health within the community. The Community Support Department can use AI to provide appropriate information. For example, the Community Support Department can facilitate dialogue and consultation among users and increase knowledge about pet health. This allows the Community Support Department to promote information sharing among owners and provide knowledge about pet health. Some or all of the above processes in the Community Support Department may be performed using AI or not. For example, the Community Support Department can input data on questions and consultations about pet health into a generating AI, which can then provide appropriate information.

[0040] The data collection unit can estimate the pet's emotions and adjust the frequency of data collection based on the estimated emotions. For example, if the pet is stressed, the data collection unit can reduce the frequency of data collection to alleviate the pet's burden. For example, if the pet is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. For example, if the pet is excited, the data collection unit can temporarily increase the frequency of data collection to record changes in behavior in detail. This allows the data collection unit to adjust the frequency of data collection according to the pet's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 data collection unit may be performed using AI or not. For example, the data collection unit can input pet emotion data into the generative AI, which can then adjust the frequency of data collection.

[0041] The data collection unit can analyze past behavioral data of pets and select the optimal data collection method. For example, if a pet was active during a specific time period in the past, the data collection unit can concentrate data collection during that time period. For example, if a pet exhibited abnormal behavior in a specific location in the past, the data collection unit can intensify data collection at that location. For example, the data collection unit can analyze past behavioral patterns of pets and create the most efficient data collection schedule. This allows the data collection unit to select the optimal data collection method based on the pet's past behavioral data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input past behavioral data of pets into a generating AI, which can then select the optimal data collection method.

[0042] The data collection unit can filter data based on the pet's current health status and activity level during data collection. For example, if the pet is in good health, the data collection unit can perform normal data collection. If the pet is in poor health, for example, the data collection unit can limit data collection and collect only the necessary data. If the pet is at a high activity level, for example, the data collection unit can collect detailed behavioral data. This allows the data collection unit to filter data collection based on the pet's current health status and activity level. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on the pet's health status and activity level into a generating AI, which can then filter the data collection.

[0043] The data collection unit can estimate the pet's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the pet is anxious, the data collection unit can prioritize collecting stress-related data. For example, if the pet is relaxed, the data collection unit can prioritize collecting data related to its health status. For example, if the pet is excited, the data collection unit can prioritize collecting data related to its behavioral patterns. This allows the data collection unit to determine the priority of data to collect based on the pet's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit can input pet emotion data into a generative AI and determine the priority of data to be collected by the generative AI.

[0044] The data collection unit can prioritize the collection of highly relevant data by considering the pet's living environment information during data collection. For example, if the pet is indoors, the data collection unit can prioritize the collection of data related to the indoor environment. For example, if the pet is outdoors, the data collection unit can prioritize the collection of data related to the external environment. For example, if the pet is in a specific room, the data collection unit can prioritize the collection of environmental data for that room. In this way, the data collection unit can prioritize the collection of highly relevant data by considering the pet's living environment information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the pet's living environment information into a generating AI, and the generating AI can prioritize the collection of highly relevant data.

[0045] The data collection unit can analyze the social media activity of pet owners and collect relevant data during data collection. For example, if an owner posts about their pet's health, the data collection unit can collect data based on the content of that post. For example, if an owner posts about their pet's behavior, the data collection unit can collect data related to that behavior. For example, if an owner posts about their pet's diet, the data collection unit can collect data related to that diet. In this way, the data collection unit can analyze the social media activity of pet owners and collect relevant data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on the owner's social media activity into a generating AI, and the generating AI can collect relevant data.

[0046] The analysis unit can estimate the pet's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the pet is anxious, the analysis unit can enhance stress-related data analysis. For example, if the pet is relaxed, the analysis unit can enhance health-related data analysis. For example, if the pet is excited, the analysis unit can enhance behavioral pattern data analysis. This allows the analysis unit to adjust the analysis algorithm based on the pet's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input pet emotion data into a generative AI, which can then adjust the analysis algorithm.

[0047] The analysis unit can improve the accuracy of its analysis by referring to the pet's past health data during the analysis. For example, the analysis unit can analyze the pet's current health status by referring to the pet's past health check results. For example, the analysis unit can detect abnormalities early by referring to the pet's past medical history. For example, the analysis unit can analyze the pet's current weight data by referring to the pet's past weight fluctuations. In this way, the analysis unit can improve the accuracy of its analysis by referring to the pet's past health data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the pet's past health data into a generating AI, which can then improve the accuracy of the analysis.

[0048] The analysis unit can apply different analysis methods depending on the type and age of the pet during analysis. For example, the analysis unit can perform growth-related data analysis on young pets. For example, the analysis unit can perform health maintenance-related data analysis on elderly pets. For example, the analysis unit can perform health problems specific to a particular type of pet. This allows the analysis unit to apply an appropriate analysis method according to the type and age of the pet. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input data on the type and age of the pet into a generating AI, which can then apply an appropriate analysis method.

[0049] The analysis unit can estimate the pet's emotions and adjust the display method of the analysis results based on the estimated emotions of the pet. For example, if the pet is feeling anxious, the analysis unit can highlight stress-related data. For example, if the pet is relaxed, the analysis unit can display detailed data on its health status. For example, if the pet is excited, the analysis unit can visually display data on its behavioral patterns. This allows the analysis unit to adjust the display method of the analysis results based on the pet's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input pet emotion data into a generative AI, which can then adjust the display method of the analysis results.

[0050] The analysis unit can perform analysis while taking into account the pet's living environment data. For example, if the pet spends a lot of time indoors, the analysis unit can perform analysis while taking into account indoor environment data. For example, if the pet spends a lot of time outdoors, the analysis unit can perform analysis while taking into account external environment data. For example, if the pet spends a lot of time in a particular room, the analysis unit can perform analysis while taking into account the environment data of that room. In this way, the analysis unit can perform analysis while taking into account the pet's living environment data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the pet's living environment data into a generating AI, and the generating AI can perform the analysis.

[0051] The analysis unit can improve the accuracy of its analysis by referring to relevant pet literature during the analysis process. For example, the analysis unit can perform analysis by referring to the latest research papers on pet health. For example, the analysis unit can perform analysis by referring to past research data on pet behavior. For example, the analysis unit can perform analysis by referring to literature on health problems specific to pet breeds. In this way, the analysis unit can improve the accuracy of its analysis by referring to relevant pet literature. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input data from relevant pet literature into a generating AI, which can then improve the accuracy of the analysis.

[0052] 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 feeling anxious, the notification unit can quickly notify the owner so that they can respond immediately. For example, if the pet is relaxed, the notification unit can delay the timing of the notification. For example, if the pet is excited, the notification unit can immediately notify the owner to draw their attention. In this way, the notification unit can adjust the timing of notifications based on the pet's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input pet emotion data into a generative AI, which can then adjust the timing of the notification.

[0053] The notification unit can adjust the level of detail of a notification based on the importance of the pet's health condition. For example, if the pet's health condition is deteriorating, the notification unit can provide a detailed notification. For example, if the pet's health condition is good, the notification unit can provide a concise notification. For example, if the pet's health condition is fluctuating, the notification unit can provide a notification with an appropriate level of detail. In this way, the notification unit can adjust the level of detail of a notification based on the importance of the pet's health condition. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input pet health condition data into a generating AI, and the generating AI can adjust the level of detail of the notification.

[0054] The notification unit can select the optimal notification method by referring to the pet owner's past response history when sending a notification. For example, the notification unit can prioritize using notification methods that the owner has responded to quickly in the past. For example, the notification unit can avoid notification methods that the owner has ignored in the past. For example, the notification unit can analyze the owner's past response history and select the most effective notification method. This allows the notification unit to select the optimal notification method based on the pet owner's past response history. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input data on the owner's past response history into a generating AI, which can then select the optimal notification method.

[0055] The notification unit can estimate the pet's emotions and determine the priority of notifications based on the estimated emotions. For example, if the pet is feeling anxious, the notification unit can set a high priority for notifications. For example, if the pet is relaxed, the notification unit can set a low priority for notifications. For example, if the pet is excited, the notification unit can set a medium priority for notifications. In this way, the notification unit can determine the priority of notifications based on the pet's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input pet emotion data into a generative AI, and the generative AI can determine the priority of notifications.

[0056] The notification unit can select the optimal notification method when sending a notification, taking into account the geographical location information of the pet owner. For example, the notification unit can send a detailed notification if the owner is at home. For example, the notification unit can send a concise notification if the owner is away from home. For example, the notification unit can select a notification method appropriate for a specific location if the owner is in that location. This allows the notification unit to select the optimal notification method based on the geographical location information of the pet owner. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the owner's geographical location data into a generating AI, which can then select the optimal notification method.

[0057] The notification unit can analyze the pet owner's social media activity at the time of notification and send relevant notifications. For example, if the owner posts about their pet's health, the notification unit can send a notification based on the content of that post. For example, if the owner posts about their pet's behavior, the notification unit can send a notification related to that behavior. For example, if the owner posts about their pet's food, the notification unit can send a notification related to that food. In this way, the notification unit can send relevant notifications based on the pet owner's social media activity. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input data on the owner's social media activity into a generating AI, and the generating AI can send relevant notifications.

[0058] The generation unit can estimate the pet's emotions and adjust the way the generated information is presented based on the estimated emotions. For example, if the pet is feeling anxious, the generation unit can present the information in gentle language. For example, if the pet is relaxed, the generation unit can provide detailed information. For example, if the pet is excited, the generation unit can provide concise and clear information. This allows the generation unit to adjust the way the generated information is presented based on the pet's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using the generation AI or not. For example, the generation unit can input pet emotion data into the generation AI, which can then adjust the way the information is presented.

[0059] The generation unit can adjust the level of detail of the information it generates based on the importance of the pet's behavior during generation. For example, the generation unit can provide detailed information about important behaviors. For example, it can provide concise information about general behaviors. For example, it can provide information about specific behaviors with a moderate level of detail. In this way, the generation unit can adjust the level of detail of the information it generates based on the importance of the pet's behavior. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input pet behavior data into a generation AI, and the generation AI can adjust the level of detail of the information.

[0060] The generation unit can apply different generation algorithms depending on the category of the pet's behavior during generation. For example, the generation unit can apply a food-related generation algorithm to behaviors related to eating. For example, the generation unit can apply an exercise-related generation algorithm to behaviors related to exercise. For example, the generation unit can apply a sleep-related generation algorithm to behaviors related to sleeping. In this way, the generation unit can apply an appropriate generation algorithm depending on the category of the pet's behavior. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input data on the pet's behavior category into a generation AI, and the generation AI can apply an appropriate generation algorithm.

[0061] The generation unit can estimate the pet's emotions and adjust the length of the information it generates based on the estimated emotions. For example, if the pet is anxious, the generation unit can provide short, concise information. For example, if the pet is relaxed, the generation unit can provide detailed information. For example, if the pet is excited, the generation unit can provide concise and clear information. This allows the generation unit to adjust the length of the information it generates based on the pet's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using the generation AI or not. For example, the generation unit can input pet emotion data into the generation AI, which can then adjust the length of the information.

[0062] The generation unit can determine the priority of the information to be generated based on the timing of the pet's behavior. For example, the generation unit can prioritize providing information about recent behavior. For example, the generation unit can postpone providing information about past behavior. For example, the generation unit can prioritize providing information about behavior that occurred during a specific period. This allows the generation unit to determine the priority of the information to be generated based on the timing of the pet's behavior. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input data on the timing of the pet's behavior into a generation AI, and the generation AI can determine the priority of the information.

[0063] The generation unit can adjust the order of the information it generates based on the relevance of the pet's behaviors during generation. For example, the generation unit can provide information about highly relevant behaviors first. For example, the generation unit can postpone providing information about less relevant behaviors. For example, the generation unit can provide information related to a specific behavior all at once. This allows the generation unit to adjust the order of the information it generates based on the relevance of the pet's behaviors. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input data on the relevance of the pet's behaviors into a generation AI, which can then adjust the order of the information.

[0064] The community support unit can estimate a pet's emotions and adjust how information is provided within the community based on the estimated emotions. For example, if a pet is feeling anxious, the community support unit can provide reassuring information. For example, if a pet is relaxed, the community support unit can provide detailed information. For example, if a pet is excited, the community support unit can provide concise and clear information. This allows the community support unit to adjust how information is provided within the community 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 above processing in the community support unit may be performed using AI or not. For example, the community support unit can input pet emotion data into a generative AI, which can then adjust how information is provided.

[0065] The community support department can provide optimal information by referring to the pet owner's past question history during community support. For example, the community support department can provide relevant information based on the questions the owner has asked in the past. For example, the community support department can analyze the owner's past question history and provide the most appropriate information. For example, the community support department can provide additional information based on the answers the owner has received in the past. This allows the community support department to provide optimal information based on the pet owner's past question history. Some or all of the above processing in the community support department may be performed using AI or not. For example, the community support department can input data from the owner's past question history into a generating AI, which can then provide optimal information.

[0066] The Community Support Department can apply different support methods depending on the pet's health condition during community support. For example, the Community Support Department can provide general health information to pets in good health. For example, the Community Support Department can provide specialized health information to pets whose health is deteriorating. For example, the Community Support Department can provide appropriate support information to pets whose health condition is fluctuating. This allows the Community Support Department to apply appropriate support methods according to the pet's health condition. Some or all of the above processing in the Community Support Department may be performed using AI or not. For example, the Community Support Department can input data on the pet's health condition into a generating AI, which can then apply an appropriate support method.

[0067] The community support unit can estimate a pet's emotions and prioritize information within the community based on the estimated emotions. For example, if a pet is anxious, the community support unit can prioritize providing reassuring information. For example, if a pet is relaxed, the community support unit can prioritize providing detailed information. For example, if a pet is excited, the community support unit can prioritize providing concise and clear information. This allows the community support unit to prioritize information within the community 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 above processing in the community support unit may be performed using AI or not. For example, the community support unit can input pet emotion data into a generative AI, which can then determine the priority of the information.

[0068] The Community Support Department can provide optimal information during community support by considering the geographical location of the pet owner. For example, if the owner is at home, the Community Support Department can provide detailed information. For example, if the owner is out, the Community Support Department can provide concise information. For example, if the owner is in a specific location, the Community Support Department can provide information appropriate to that location. In this way, the Community Support Department can provide optimal information based on the geographical location of the pet owner. Some or all of the above processing in the Community Support Department may be performed using AI or not. For example, the Community Support Department can input the owner's geographical location data into a generating AI, and the generating AI can provide optimal information.

[0069] The Community Support Department can analyze the social media activity of pet owners and provide relevant information when providing community support. For example, if an owner posts about their pet's health, the Community Support Department can provide information based on that post. For example, if an owner posts about their pet's behavior, the Community Support Department can provide information related to that behavior. For example, if an owner posts about their pet's diet, the Community Support Department can provide information related to that diet. In this way, the Community Support Department can provide relevant information based on the social media activity of pet owners. Some or all of the above processing in the Community Support Department may be performed using AI or not. For example, the Community Support Department can input data on the owner's social media activity into a generating AI, and the generating AI can provide relevant information.

[0070] The community support department can provide optimal information by referring to the pet owner's past question history during community support. For example, the community support department can provide relevant information based on the questions the owner has asked in the past. For example, the community support department can analyze the owner's past question history and provide the most appropriate information. For example, the community support department can provide additional information based on the answers the owner has received in the past. This allows the community support department to provide optimal information based on the pet owner's past question history. Some or all of the above processing in the community support department may be performed using AI or not. For example, the community support department can input data from the owner's past question history into a generating AI, which can then provide optimal information.

[0071] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0072] The pet management system can also incorporate preventative health management functions based on pet behavior data. For example, the data collection unit can collect pet behavior data over a long period, and the analysis unit can analyze this data to predict potential health risks in the future. This allows owners to understand their pet's health risks in advance and take preventative measures. For instance, if a pet exhibits a specific behavioral pattern and the analysis unit determines that this behavior may cause health problems in the future, the notification unit can notify the owner of the risk and suggest appropriate countermeasures. Furthermore, the generation unit can suggest specific preventative measures to owners based on pet behavior data. For example, if a pet is not getting enough exercise, the generation unit can suggest an exercise plan to the owner and provide specific actions to maintain the pet's health. In addition, the community support unit can share information with other owners and spread knowledge about preventative health management. In this way, the pet management system can maintain pet health by predicting health risks in advance and taking preventative measures.

[0073] The pet management system can further provide customized training programs based on pet behavior data. For example, the data collection unit can collect pet behavior data, and the analysis unit can analyze that data to identify the pet's training needs. This allows owners to receive customized training programs to address specific behavioral problems with their pets. For instance, if a pet repeatedly exhibits a particular behavior, the analysis unit can identify the cause of that behavior, and the generation unit can suggest specific training methods to the owner. The notification unit can also notify the owner of the training progress and adjust the training program as needed. Furthermore, the community support unit can share information with other owners and provide advice and support regarding training. In this way, the pet management system can provide customized training programs based on pet behavior data and effectively solve pet behavioral problems.

[0074] The pet management system can also incorporate nutritional management functions based on pet behavioral data. For example, the data collection unit can collect pet dietary data, and the analysis unit can analyze this data to understand the pet's nutritional status. This allows owners to receive specific advice on how to properly manage their pet's nutrition. For instance, if a pet is deficient in a particular nutrient, the analysis unit can identify the deficiency, and the generation unit can propose an appropriate diet plan to the owner. The notification unit can also notify owners of important information regarding their pet's nutritional status and adjust the diet plan as needed. Furthermore, the community support unit can share information with other owners and spread knowledge about nutritional management. In this way, the pet management system can provide specific advice on how to properly manage pet nutrition and maintain their health.

[0075] The pet management system can also incorporate stress management functions based on pet behavior data. For example, a data collection unit can collect pet behavior data, and an analysis unit can analyze that data to understand the pet's stress level. This allows owners to receive specific advice on how to reduce their pet's stress. For instance, if a pet exhibits a particular behavior and the analysis unit determines that this behavior is the cause of stress, the generation unit can suggest specific methods for stress reduction to the owner. Furthermore, a notification unit can inform owners of important information regarding their pet's stress level and adjust the stress management plan as needed. Additionally, a community support unit can share information with other owners and spread knowledge about stress management. This enables the pet management system to properly manage pet stress levels and provide specific advice for maintaining their health.

[0076] The pet management system can also provide rehabilitation programs based on pet behavioral data. For example, the data collection unit can collect pet behavioral data, and the analysis unit can analyze that data to identify the pet's rehabilitation needs. This allows owners to receive customized rehabilitation programs to address their pet's specific health problems. For instance, if a pet experiences pain when performing a particular movement, the analysis unit can identify the cause, and the generation unit can suggest specific rehabilitation methods to the owner. The notification unit can also notify owners of the rehabilitation progress and adjust the rehabilitation program as needed. Furthermore, the community support unit can share information with other owners and provide advice and support regarding rehabilitation. In this way, the pet management system can provide customized rehabilitation programs based on pet behavioral data and effectively resolve pet health problems.

[0077] The following briefly describes the processing flow for example form 1.

[0078] Step 1: The data collection unit collects pet behavior and health data. The data collection unit collects data such as pet diet, exercise, sleep, and weight. The data collection unit can use AI to automatically collect this data. For example, the data collection unit can use sensors to record the type and amount of food the pet eats. The data collection unit can also use activity trackers attached to the pet to measure the pet's activity level. Furthermore, the data collection unit can use sensors placed in the pet's bedding to monitor the pet's sleep patterns. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses AI to analyze the collected data and understand the pet's health status. For example, the analysis unit can analyze changes in the pet's weight and detect changes in its health status. It can also analyze changes in the pet's activity level and detect insufficient or excessive exercise. Furthermore, the analysis unit can analyze the pet's sleep patterns and detect insufficient or excessive sleep. Step 3: The notification unit detects anomalies based on the data analyzed by the analysis unit and notifies the owner. The notification unit uses AI to quickly notify the owner when an anomaly is detected. For example, the notification unit can notify the owner if the pet's weight increases rapidly. It can also notify the owner if the pet's activity level decreases rapidly. Furthermore, it can notify the owner if the pet's sleep pattern is abnormal. Step 4: The generation unit understands the pet's behavior and emotions based on the data analyzed by the analysis unit and communicates this information to the owner. The generation unit can analyze the pet's behavior and emotions using generation AI and communicate it to the owner in human language. For example, if the pet is feeling anxious, the generation unit can explain the reason to the owner. Also, if the pet is repeating a particular behavior, the generation unit can explain the meaning of that behavior to the owner. Furthermore, the generation unit can analyze the pet's emotions and communicate them to the owner. Step 5: The Community Support Department will support information sharing among owners. The Community Support Department can use AI to support information sharing among owners and provide knowledge about pet health. For example, the Community Support Department will allow users to ask questions and seek advice about pet health within the community. The Community Support Department can also use AI to provide appropriate information. Furthermore, the Community Support Department can facilitate dialogue and consultation among users and increase their knowledge about pet health.

[0079] (Example of form 2) The pet management system according to an embodiment of the present invention is a groundbreaking system in which AI learns pet behavior and health data to perform anomaly detection and prediction. This pet management system uses a generating AI to understand pet behavior and emotions and communicate that information to the owner in human language. This allows for a deeper understanding of the pet's needs and enables quicker responses. In addition, the AI ​​supports the community through information sharing links among owners, facilitating dialogue and consultation among users and increasing knowledge about pet health. By providing subscription services and access to the community, it creates an environment where owners can deepen their bond with their pets and manage their health with peace of mind. First, the pet management system uses AI to collect and learn pet behavior and health data. For example, it collects data such as pet diet, exercise, sleep, and weight, and the AI ​​analyzes this data. This allows the AI ​​to understand the pet's health status and notify the owner if an abnormality is detected. Next, the generating AI understands the pet's behavior and emotions. For example, if a pet is feeling anxious or repeating a specific behavior, the generating AI analyzes that behavior and emotion and communicates it to the owner in human language. This allows the owner to understand their pet's needs more deeply and respond quickly. Furthermore, the AI-powered community support link for information sharing among owners. For example, users can ask questions and seek advice about their pets' health within the community, and the AI ​​provides appropriate information. This promotes dialogue and consultation among users, increasing their knowledge about pet health. Finally, the service provides access to a subscription service and the community. Owners can regularly check their pets' health and obtain necessary information. They can also share information with other owners through community access, deepening their bond with their pets. This creates an environment where pet health management can be done with greater peace of mind, and owners can deepen their bond with their pets. For example, by regularly checking their pets' health and responding quickly if any abnormalities are detected, owners can maintain their pets' health. In addition, through information sharing within the community, owners can receive advice and information from other owners, which can be used to help manage their pets' health.This allows pet management systems to more effectively manage pet health.

[0080] The pet management system according to this embodiment comprises a data collection unit, an analysis unit, a notification unit, a generation unit, and a community support unit. The data collection unit collects pet behavior and health data. For example, the data collection unit collects data such as pet diet, exercise, sleep, and weight. The data collection unit can automatically collect this data using AI. For example, the data collection unit can use a sensor to record the type and amount of food the pet eats. The data collection unit can also use an activity tracker attached to the pet to measure the pet's activity level. Furthermore, the data collection unit can use a sensor installed in the pet's bed to monitor the pet's sleep patterns. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can use AI to analyze the collected data and understand the pet's health status. For example, the analysis unit can analyze fluctuations in the pet's weight and detect changes in its health status. The analysis unit can also analyze changes in the pet's activity level and detect insufficient or excessive exercise. Furthermore, the analysis unit can analyze the pet's sleep patterns and detect insufficient or excessive sleep. The notification unit detects anomalies based on data analyzed by the analysis unit and notifies the owner. Using AI, the notification unit can quickly notify the owner when an anomaly is detected. For example, the notification unit can notify the owner if the pet's weight increases rapidly. It can also notify the owner if the pet's activity level decreases rapidly. Furthermore, it can notify the owner if the pet's sleep pattern is abnormal. The generation unit understands the pet's behavior and emotions based on data analyzed by the analysis unit and communicates this information to the owner. Using generation AI, the generation unit can analyze the pet's behavior and emotions and communicate them to the owner in human language. For example, if the pet is feeling anxious, the generation unit can explain the reason to the owner. It can also explain the meaning of a behavior if the pet is repeating a particular action. Furthermore, the generation unit can analyze the pet's emotions and communicate them to the owner. The community support unit supports information sharing among owners.The community support department can use AI to support information sharing among owners and provide knowledge about pet health. For example, the community support department allows users to ask questions and seek advice about their pets' health within the community. Furthermore, the community support department can use AI to provide appropriate information. In addition, the community support department can facilitate dialogue and consultation among users and increase their knowledge about pet health. As a result, the pet management system according to this embodiment can collect, analyze, notify, generate, and share pet behavior and health data.

[0081] The data collection unit collects behavioral and health data from pets. For example, it collects data on pets' diet, exercise, sleep, and weight. The unit can use AI to automatically collect this data. Specifically, it can use sensors to record the type and amount of food the pet eats. These sensors are attached to the pet's food bowl and automatically record data during meals. The unit can also use activity trackers attached to the pet to measure its activity level. These activity trackers are attached to the pet's collar or harness and monitor the pet's movements in real time. Furthermore, the unit can use sensors placed in the pet's bedding to monitor its sleep patterns. These sensors are placed under the pet's bedding and detect the pet's movements and body temperature to assess sleep quality. The collected data is transmitted wirelessly to a central database and updated in real time. This allows the unit to gain a detailed understanding of the pet's behavior and health and quickly provide necessary information. Additionally, the unit can adjust the frequency and accuracy of data collection to provide flexible responses to specific situations and conditions. For example, if a pet's health deteriorates, the frequency of data collection can be increased for more detailed monitoring. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0082] The analysis unit analyzes the data collected by the data collection unit. Using AI, the analysis unit can analyze the collected data and understand the health status of pets. Specifically, it can analyze fluctuations in a pet's weight and detect changes in their health. For example, if a pet's weight increases rapidly, it may indicate overeating or lack of exercise, prompting the owner to take action. The analysis unit can also analyze changes in a pet's activity level and detect insufficient or excessive exercise. If activity levels decrease, it may indicate that the pet is ill or stressed, allowing for early intervention. Furthermore, the analysis unit can analyze a pet's sleep patterns and detect sleep deprivation or excessive sleep. Abnormal sleep patterns can affect a pet's health, requiring appropriate action. The analysis unit comprehensively analyzes this data to understand the pet's health status in real time. In addition, the analysis unit can utilize historical data and statistical information to perform long-term health management and trend analysis. For example, based on historical data, it can predict pet health risks under specific seasons or environmental conditions and propose preventive measures. This allows the analysis unit to contribute not only to real-time health management but also to long-term health maintenance, providing comprehensive support for pet health.

[0083] The notification unit detects anomalies based on data analyzed by the analysis unit and notifies the owner. Using AI, the notification unit can quickly notify the owner when an anomaly is detected. Specifically, it can notify the owner if the pet's weight increases rapidly. Notifications are sent via smartphone apps, email, SMS, etc., allowing the owner to respond immediately. The notification unit can also notify the owner if the pet's activity level decreases rapidly. This allows the owner to notice changes in their pet's health early and take appropriate action. Furthermore, the notification unit can notify the owner if the pet's sleep pattern is abnormal. For example, if the pet frequently wakes up at night, it may indicate stress or a health problem, prompting the owner to take notice. These notifications can be customized, allowing the notification method and frequency to be adjusted according to the owner's preferences. This enables the notification unit to provide owners with quick and appropriate information, supporting pet health management. Additionally, the notification unit can collect feedback from owners and continuously improve the accuracy and effectiveness of its notifications. This allows the notification unit to provide owners with optimal information and more effectively support pet health management.

[0084] The generation unit understands the pet's behavior and emotions based on the data analyzed by the analysis unit and communicates this information to the owner. Using generation AI, the generation unit can analyze the pet's behavior and emotions and communicate them to the owner in human language. Specifically, if the pet is feeling anxious, it can explain the reason to the owner. For example, if the pet is feeling anxious at a specific time, it can analyze what is happening during that time and communicate it to the owner. The generation unit can also explain the meaning of a pet's repeated behavior to the owner. For example, if the pet barks frequently, it can analyze the cause and communicate it to the owner. Furthermore, the generation unit can analyze the pet's emotions and communicate them to the owner. For example, if the pet is happy, it can explain the reason to the owner. The generation unit uses natural language generation technology to convey this information to the owner in an easy-to-understand manner. This allows the owner to understand their pet's behavior and emotions more deeply and respond appropriately. In addition, the generation unit can continuously improve the accuracy and effectiveness of the generated content based on feedback from the owner. This allows the generating unit to provide owners with optimal information and facilitate smoother communication with their pets.

[0085] The Community Support Department supports information sharing among pet owners. Using AI, the Community Support Department can facilitate information sharing among owners and provide knowledge about pet health. Specifically, owners can ask questions and seek advice about their pet's health within the community. For example, if an owner posts a question about their pet's diet or exercise, they can receive answers from other owners and experts. The Community Support Department can also use AI to provide appropriate information. For example, it can provide information about specific symptoms in pets and advice for health management. Furthermore, the Community Support Department can promote dialogue and consultation among users, increasing their knowledge about pet health. For example, it can regularly hold online seminars and workshops, providing a platform for owners to share information. This allows the Community Support Department to strengthen collaboration among owners and provide comprehensive support for pet health management. Additionally, based on feedback from owners, the Community Support Department can continuously improve the quality of the information and services it provides. This enables the Community Support Department to provide owners with optimal information and more effectively support pet health management.

[0086] The data collection unit can collect data on pets' diet, exercise, sleep, weight, and other information. For example, the data collection unit can use a sensor to record the type and amount of food the pet eats. For example, the data collection unit can use an activity tracker attached to the pet to measure the pet's activity level. For example, the data collection unit can use a sensor placed in the pet's bedding to monitor the pet's sleep patterns. This allows the data collection unit to collect diverse behavioral and health data from pets. Some or all of the above-described processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data from a sensor recording the type and amount of food the pet eats into a generating AI, which then analyzes and collects the data.

[0087] The analysis unit can analyze the collected data and understand the pet's health status. For example, the analysis unit can analyze changes in the pet's weight and detect changes in its health status. For example, the analysis unit can analyze changes in the pet's exercise level and detect insufficient or excessive exercise. For example, the analysis unit can analyze the pet's sleep patterns and detect insufficient or excessive sleep. In this way, the analysis unit can accurately understand the pet's health status. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the collected data into a generating AI, which can then analyze the data to understand the health status.

[0088] The notification unit can notify the owner when an abnormality is detected. For example, the notification unit can notify the owner if the pet's weight increases rapidly. For example, the notification unit can notify the owner if the pet's activity level decreases rapidly. For example, the notification unit can notify the owner if the pet's sleep pattern is abnormal. This allows the notification unit to quickly notify the owner when an abnormality is detected. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the data on the detected abnormality into a generating AI, which can then analyze the abnormality and notify the owner.

[0089] The generation unit can analyze the pet's behavior and emotions and communicate them to the owner in human language. For example, if the pet is feeling anxious, the generation unit can explain the reason to the owner. For example, if the pet is repeating a particular behavior, the generation unit can explain the meaning of that behavior to the owner. For example, the generation unit can analyze the pet's emotions and communicate them to the owner. In this way, the generation unit can communicate the pet's behavior and emotions to the owner in an easy-to-understand manner. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input data on the pet's behavior and emotions into a generation AI, which can then analyze the data and communicate it to the owner.

[0090] The Community Support Department can support information sharing among owners and provide knowledge about pet health. For example, the Community Support Department allows users to ask questions and seek advice about pet health within the community. The Community Support Department can use AI to provide appropriate information. For example, the Community Support Department can facilitate dialogue and consultation among users and increase knowledge about pet health. This allows the Community Support Department to promote information sharing among owners and provide knowledge about pet health. Some or all of the above processes in the Community Support Department may be performed using AI or not. For example, the Community Support Department can input data on questions and consultations about pet health into a generating AI, which can then provide appropriate information.

[0091] The data collection unit can estimate the pet's emotions and adjust the frequency of data collection based on the estimated emotions. For example, if the pet is stressed, the data collection unit can reduce the frequency of data collection to alleviate the pet's burden. For example, if the pet is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. For example, if the pet is excited, the data collection unit can temporarily increase the frequency of data collection to record changes in behavior in detail. This allows the data collection unit to adjust the frequency of data collection according to the pet's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 data collection unit may be performed using AI or not. For example, the data collection unit can input pet emotion data into the generative AI, which can then adjust the frequency of data collection.

[0092] The data collection unit can analyze past behavioral data of pets and select the optimal data collection method. For example, if a pet was active during a specific time period in the past, the data collection unit can concentrate data collection during that time period. For example, if a pet exhibited abnormal behavior in a specific location in the past, the data collection unit can intensify data collection at that location. For example, the data collection unit can analyze past behavioral patterns of pets and create the most efficient data collection schedule. This allows the data collection unit to select the optimal data collection method based on the pet's past behavioral data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input past behavioral data of pets into a generating AI, which can then select the optimal data collection method.

[0093] The data collection unit can filter data based on the pet's current health status and activity level during data collection. For example, if the pet is in good health, the data collection unit can perform normal data collection. If the pet is in poor health, for example, the data collection unit can limit data collection and collect only the necessary data. If the pet is at a high activity level, for example, the data collection unit can collect detailed behavioral data. This allows the data collection unit to filter data collection based on the pet's current health status and activity level. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on the pet's health status and activity level into a generating AI, which can then filter the data collection.

[0094] The data collection unit can estimate the pet's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the pet is anxious, the data collection unit can prioritize collecting stress-related data. For example, if the pet is relaxed, the data collection unit can prioritize collecting data related to its health status. For example, if the pet is excited, the data collection unit can prioritize collecting data related to its behavioral patterns. This allows the data collection unit to determine the priority of data to collect based on the pet's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit can input pet emotion data into a generative AI and determine the priority of data to be collected by the generative AI.

[0095] The data collection unit can prioritize the collection of highly relevant data by considering the pet's living environment information during data collection. For example, if the pet is indoors, the data collection unit can prioritize the collection of data related to the indoor environment. For example, if the pet is outdoors, the data collection unit can prioritize the collection of data related to the external environment. For example, if the pet is in a specific room, the data collection unit can prioritize the collection of environmental data for that room. In this way, the data collection unit can prioritize the collection of highly relevant data by considering the pet's living environment information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the pet's living environment information into a generating AI, and the generating AI can prioritize the collection of highly relevant data.

[0096] The data collection unit can analyze the social media activity of pet owners and collect relevant data during data collection. For example, if an owner posts about their pet's health, the data collection unit can collect data based on the content of that post. For example, if an owner posts about their pet's behavior, the data collection unit can collect data related to that behavior. For example, if an owner posts about their pet's diet, the data collection unit can collect data related to that diet. In this way, the data collection unit can analyze the social media activity of pet owners and collect relevant data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on the owner's social media activity into a generating AI, and the generating AI can collect relevant data.

[0097] The analysis unit can estimate the pet's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the pet is anxious, the analysis unit can enhance stress-related data analysis. For example, if the pet is relaxed, the analysis unit can enhance health-related data analysis. For example, if the pet is excited, the analysis unit can enhance behavioral pattern data analysis. This allows the analysis unit to adjust the analysis algorithm based on the pet's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input pet emotion data into a generative AI, which can then adjust the analysis algorithm.

[0098] The analysis unit can improve the accuracy of its analysis by referring to the pet's past health data during the analysis. For example, the analysis unit can analyze the pet's current health status by referring to the pet's past health check results. For example, the analysis unit can detect abnormalities early by referring to the pet's past medical history. For example, the analysis unit can analyze the pet's current weight data by referring to the pet's past weight fluctuations. In this way, the analysis unit can improve the accuracy of its analysis by referring to the pet's past health data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the pet's past health data into a generating AI, which can then improve the accuracy of the analysis.

[0099] The analysis unit can apply different analysis methods depending on the type and age of the pet during analysis. For example, the analysis unit can perform growth-related data analysis on young pets. For example, the analysis unit can perform health maintenance-related data analysis on elderly pets. For example, the analysis unit can perform health problems specific to a particular type of pet. This allows the analysis unit to apply an appropriate analysis method according to the type and age of the pet. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input data on the type and age of the pet into a generating AI, which can then apply an appropriate analysis method.

[0100] The analysis unit can estimate the pet's emotions and adjust the display method of the analysis results based on the estimated emotions of the pet. For example, if the pet is feeling anxious, the analysis unit can highlight stress-related data. For example, if the pet is relaxed, the analysis unit can display detailed data on its health status. For example, if the pet is excited, the analysis unit can visually display data on its behavioral patterns. This allows the analysis unit to adjust the display method of the analysis results based on the pet's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input pet emotion data into a generative AI, which can then adjust the display method of the analysis results.

[0101] The analysis unit can perform analysis while taking into account the pet's living environment data. For example, if the pet spends a lot of time indoors, the analysis unit can perform analysis while taking into account indoor environment data. For example, if the pet spends a lot of time outdoors, the analysis unit can perform analysis while taking into account external environment data. For example, if the pet spends a lot of time in a particular room, the analysis unit can perform analysis while taking into account the environment data of that room. In this way, the analysis unit can perform analysis while taking into account the pet's living environment data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the pet's living environment data into a generating AI, and the generating AI can perform the analysis.

[0102] The analysis unit can improve the accuracy of its analysis by referring to relevant pet literature during the analysis process. For example, the analysis unit can perform analysis by referring to the latest research papers on pet health. For example, the analysis unit can perform analysis by referring to past research data on pet behavior. For example, the analysis unit can perform analysis by referring to literature on health problems specific to pet breeds. In this way, the analysis unit can improve the accuracy of its analysis by referring to relevant pet literature. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input data from relevant pet literature into a generating AI, which can then improve the accuracy of the analysis.

[0103] 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 feeling anxious, the notification unit can quickly notify the owner so that they can respond immediately. For example, if the pet is relaxed, the notification unit can delay the timing of the notification. For example, if the pet is excited, the notification unit can immediately notify the owner to draw their attention. In this way, the notification unit can adjust the timing of notifications based on the pet's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input pet emotion data into a generative AI, which can then adjust the timing of the notification.

[0104] The notification unit can adjust the level of detail of a notification based on the importance of the pet's health condition. For example, if the pet's health condition is deteriorating, the notification unit can provide a detailed notification. For example, if the pet's health condition is good, the notification unit can provide a concise notification. For example, if the pet's health condition is fluctuating, the notification unit can provide a notification with an appropriate level of detail. In this way, the notification unit can adjust the level of detail of a notification based on the importance of the pet's health condition. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input pet health condition data into a generating AI, and the generating AI can adjust the level of detail of the notification.

[0105] The notification unit can select the optimal notification method by referring to the pet owner's past response history when sending a notification. For example, the notification unit can prioritize using notification methods that the owner has responded to quickly in the past. For example, the notification unit can avoid notification methods that the owner has ignored in the past. For example, the notification unit can analyze the owner's past response history and select the most effective notification method. This allows the notification unit to select the optimal notification method based on the pet owner's past response history. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input data on the owner's past response history into a generating AI, which can then select the optimal notification method.

[0106] The notification unit can estimate the pet's emotions and determine the priority of notifications based on the estimated emotions. For example, if the pet is feeling anxious, the notification unit can set a high priority for notifications. For example, if the pet is relaxed, the notification unit can set a low priority for notifications. For example, if the pet is excited, the notification unit can set a medium priority for notifications. In this way, the notification unit can determine the priority of notifications based on the pet's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input pet emotion data into a generative AI, and the generative AI can determine the priority of notifications.

[0107] The notification unit can select the optimal notification method when sending a notification, taking into account the geographical location information of the pet owner. For example, the notification unit can send a detailed notification if the owner is at home. For example, the notification unit can send a concise notification if the owner is away from home. For example, the notification unit can select a notification method appropriate for a specific location if the owner is in that location. This allows the notification unit to select the optimal notification method based on the geographical location information of the pet owner. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the owner's geographical location data into a generating AI, which can then select the optimal notification method.

[0108] The notification unit can analyze the pet owner's social media activity at the time of notification and send relevant notifications. For example, if the owner posts about their pet's health, the notification unit can send a notification based on the content of that post. For example, if the owner posts about their pet's behavior, the notification unit can send a notification related to that behavior. For example, if the owner posts about their pet's food, the notification unit can send a notification related to that food. In this way, the notification unit can send relevant notifications based on the pet owner's social media activity. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input data on the owner's social media activity into a generating AI, and the generating AI can send relevant notifications.

[0109] The generation unit can estimate the pet's emotions and adjust the way the generated information is presented based on the estimated emotions. For example, if the pet is feeling anxious, the generation unit can present the information in gentle language. For example, if the pet is relaxed, the generation unit can provide detailed information. For example, if the pet is excited, the generation unit can provide concise and clear information. This allows the generation unit to adjust the way the generated information is presented based on the pet's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using the generation AI or not. For example, the generation unit can input pet emotion data into the generation AI, which can then adjust the way the information is presented.

[0110] The generation unit can adjust the level of detail of the information it generates based on the importance of the pet's behavior during generation. For example, the generation unit can provide detailed information about important behaviors. For example, it can provide concise information about general behaviors. For example, it can provide information about specific behaviors with a moderate level of detail. In this way, the generation unit can adjust the level of detail of the information it generates based on the importance of the pet's behavior. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input pet behavior data into a generation AI, and the generation AI can adjust the level of detail of the information.

[0111] The generation unit can apply different generation algorithms depending on the category of the pet's behavior during generation. For example, the generation unit can apply a food-related generation algorithm to behaviors related to eating. For example, the generation unit can apply an exercise-related generation algorithm to behaviors related to exercise. For example, the generation unit can apply a sleep-related generation algorithm to behaviors related to sleeping. In this way, the generation unit can apply an appropriate generation algorithm depending on the category of the pet's behavior. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input data on the pet's behavior category into a generation AI, and the generation AI can apply an appropriate generation algorithm.

[0112] The generation unit can estimate the pet's emotions and adjust the length of the information it generates based on the estimated emotions. For example, if the pet is anxious, the generation unit can provide short, concise information. For example, if the pet is relaxed, the generation unit can provide detailed information. For example, if the pet is excited, the generation unit can provide concise and clear information. This allows the generation unit to adjust the length of the information it generates based on the pet's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using the generation AI or not. For example, the generation unit can input pet emotion data into the generation AI, which can then adjust the length of the information.

[0113] The generation unit can determine the priority of the information to be generated based on the timing of the pet's behavior. For example, the generation unit can prioritize providing information about recent behavior. For example, the generation unit can postpone providing information about past behavior. For example, the generation unit can prioritize providing information about behavior that occurred during a specific period. This allows the generation unit to determine the priority of the information to be generated based on the timing of the pet's behavior. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input data on the timing of the pet's behavior into a generation AI, and the generation AI can determine the priority of the information.

[0114] The generation unit can adjust the order of the information it generates based on the relevance of the pet's behaviors during generation. For example, the generation unit can provide information about highly relevant behaviors first. For example, the generation unit can postpone providing information about less relevant behaviors. For example, the generation unit can provide information related to a specific behavior all at once. This allows the generation unit to adjust the order of the information it generates based on the relevance of the pet's behaviors. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input data on the relevance of the pet's behaviors into a generation AI, which can then adjust the order of the information.

[0115] The community support unit can estimate a pet's emotions and adjust how information is provided within the community based on the estimated emotions. For example, if a pet is feeling anxious, the community support unit can provide reassuring information. For example, if a pet is relaxed, the community support unit can provide detailed information. For example, if a pet is excited, the community support unit can provide concise and clear information. This allows the community support unit to adjust how information is provided within the community 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 above processing in the community support unit may be performed using AI or not. For example, the community support unit can input pet emotion data into a generative AI, which can then adjust how information is provided.

[0116] The community support department can provide optimal information by referring to the pet owner's past question history during community support. For example, the community support department can provide relevant information based on the questions the owner has asked in the past. For example, the community support department can analyze the owner's past question history and provide the most appropriate information. For example, the community support department can provide additional information based on the answers the owner has received in the past. This allows the community support department to provide optimal information based on the pet owner's past question history. Some or all of the above processing in the community support department may be performed using AI or not. For example, the community support department can input data from the owner's past question history into a generating AI, which can then provide optimal information.

[0117] The Community Support Department can apply different support methods depending on the pet's health condition during community support. For example, the Community Support Department can provide general health information to pets in good health. For example, the Community Support Department can provide specialized health information to pets whose health is deteriorating. For example, the Community Support Department can provide appropriate support information to pets whose health condition is fluctuating. This allows the Community Support Department to apply appropriate support methods according to the pet's health condition. Some or all of the above processing in the Community Support Department may be performed using AI or not. For example, the Community Support Department can input data on the pet's health condition into a generating AI, which can then apply an appropriate support method.

[0118] The community support unit can estimate a pet's emotions and prioritize information within the community based on the estimated emotions. For example, if a pet is anxious, the community support unit can prioritize providing reassuring information. For example, if a pet is relaxed, the community support unit can prioritize providing detailed information. For example, if a pet is excited, the community support unit can prioritize providing concise and clear information. This allows the community support unit to prioritize information within the community 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 above processing in the community support unit may be performed using AI or not. For example, the community support unit can input pet emotion data into a generative AI, which can then determine the priority of the information.

[0119] The Community Support Department can provide optimal information during community support by considering the geographical location of the pet owner. For example, if the owner is at home, the Community Support Department can provide detailed information. For example, if the owner is out, the Community Support Department can provide concise information. For example, if the owner is in a specific location, the Community Support Department can provide information appropriate to that location. In this way, the Community Support Department can provide optimal information based on the geographical location of the pet owner. Some or all of the above processing in the Community Support Department may be performed using AI or not. For example, the Community Support Department can input the owner's geographical location data into a generating AI, and the generating AI can provide optimal information.

[0120] The Community Support Department can analyze the social media activity of pet owners and provide relevant information when providing community support. For example, if an owner posts about their pet's health, the Community Support Department can provide information based on that post. For example, if an owner posts about their pet's behavior, the Community Support Department can provide information related to that behavior. For example, if an owner posts about their pet's diet, the Community Support Department can provide information related to that diet. In this way, the Community Support Department can provide relevant information based on the social media activity of pet owners. Some or all of the above processing in the Community Support Department may be performed using AI or not. For example, the Community Support Department can input data on the owner's social media activity into a generating AI, and the generating AI can provide relevant information.

[0121] The community support department can provide optimal information by referring to the pet owner's past question history during community support. For example, the community support department can provide relevant information based on the questions the owner has asked in the past. For example, the community support department can analyze the owner's past question history and provide the most appropriate information. For example, the community support department can provide additional information based on the answers the owner has received in the past. This allows the community support department to provide optimal information based on the pet owner's past question history. Some or all of the above processing in the community support department may be performed using AI or not. For example, the community support department can input data from the owner's past question history into a generating AI, which can then provide optimal information.

[0122] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0123] The pet management system can also incorporate preventative health management functions based on pet behavior data. For example, the data collection unit can collect pet behavior data over a long period, and the analysis unit can analyze this data to predict potential health risks in the future. This allows owners to understand their pet's health risks in advance and take preventative measures. For instance, if a pet exhibits a specific behavioral pattern and the analysis unit determines that this behavior may cause health problems in the future, the notification unit can notify the owner of the risk and suggest appropriate countermeasures. Furthermore, the generation unit can suggest specific preventative measures to owners based on pet behavior data. For example, if a pet is not getting enough exercise, the generation unit can suggest an exercise plan to the owner and provide specific actions to maintain the pet's health. In addition, the community support unit can share information with other owners and spread knowledge about preventative health management. In this way, the pet management system can maintain pet health by predicting health risks in advance and taking preventative measures.

[0124] The pet management system can further provide customized training programs based on pet behavior data. For example, the data collection unit can collect pet behavior data, and the analysis unit can analyze that data to identify the pet's training needs. This allows owners to receive customized training programs to address specific behavioral problems with their pets. For instance, if a pet repeatedly exhibits a particular behavior, the analysis unit can identify the cause of that behavior, and the generation unit can suggest specific training methods to the owner. The notification unit can also notify the owner of the training progress and adjust the training program as needed. Furthermore, the community support unit can share information with other owners and provide advice and support regarding training. In this way, the pet management system can provide customized training programs based on pet behavior data and effectively solve pet behavioral problems.

[0125] The pet management system can also incorporate nutritional management functions based on pet behavioral data. For example, the data collection unit can collect pet dietary data, and the analysis unit can analyze this data to understand the pet's nutritional status. This allows owners to receive specific advice on how to properly manage their pet's nutrition. For instance, if a pet is deficient in a particular nutrient, the analysis unit can identify the deficiency, and the generation unit can propose an appropriate diet plan to the owner. The notification unit can also notify owners of important information regarding their pet's nutritional status and adjust the diet plan as needed. Furthermore, the community support unit can share information with other owners and spread knowledge about nutritional management. In this way, the pet management system can provide specific advice on how to properly manage pet nutrition and maintain their health.

[0126] The pet management system can also incorporate stress management functions based on pet behavior data. For example, a data collection unit can collect pet behavior data, and an analysis unit can analyze that data to understand the pet's stress level. This allows owners to receive specific advice on how to reduce their pet's stress. For instance, if a pet exhibits a particular behavior and the analysis unit determines that this behavior is the cause of stress, the generation unit can suggest specific methods for stress reduction to the owner. Furthermore, a notification unit can inform owners of important information regarding their pet's stress level and adjust the stress management plan as needed. Additionally, a community support unit can share information with other owners and spread knowledge about stress management. This enables the pet management system to properly manage pet stress levels and provide specific advice for maintaining their health.

[0127] The pet management system can also provide rehabilitation programs based on pet behavioral data. For example, the data collection unit can collect pet behavioral data, and the analysis unit can analyze that data to identify the pet's rehabilitation needs. This allows owners to receive customized rehabilitation programs to address their pet's specific health problems. For instance, if a pet experiences pain when performing a particular movement, the analysis unit can identify the cause, and the generation unit can suggest specific rehabilitation methods to the owner. The notification unit can also notify owners of the rehabilitation progress and adjust the rehabilitation program as needed. Furthermore, the community support unit can share information with other owners and provide advice and support regarding rehabilitation. In this way, the pet management system can provide customized rehabilitation programs based on pet behavioral data and effectively resolve pet health problems.

[0128] The pet management system can further provide interactive games based on the pet's emotions by utilizing an emotion estimation function based on the pet's behavioral data. For example, the data collection unit collects the pet's behavioral data, and the analysis unit analyzes this data to estimate the pet's emotions. This allows the generation unit to generate interactive games based on the pet's emotions and provide them to the owner. For instance, if the pet is feeling anxious, the generation unit can suggest a relaxing game. Similarly, if the pet is excited, the generation unit can suggest an active game to help the pet release energy. Furthermore, the notification unit can notify the owner of the game's progress and adjust the game content as needed. This enables the pet management system to provide interactive games based on the pet's emotions, reducing stress and maintaining the pet's health.

[0129] The pet management system can further provide music tailored to the pet's emotions by using an emotion estimation function based on the pet's behavioral data. For example, the data collection unit collects pet behavioral data, and the analysis unit analyzes this data to estimate the pet's emotions. This allows the generation unit to generate appropriate music based on the pet's emotions and provide it to the owner. For instance, if the pet is feeling anxious, the generation unit can suggest relaxing music. If the pet is excited, the generation unit can suggest upbeat music to help release energy. Furthermore, the notification unit can inform the owner of the music's effect and adjust the music content as needed. This allows the pet management system to provide music tailored to the pet's emotions, reducing stress and maintaining their health.

[0130] The pet management system can further provide feedback based on the pet's emotions by using an emotion estimation function based on the pet's behavioral data. For example, the collection unit collects the pet's behavioral data, and the analysis unit analyzes that data to estimate the pet's emotions. This allows the generation unit to generate appropriate feedback based on the pet's emotions and provide it to the owner. For example, if the pet is feeling anxious, the generation unit can suggest reassuring feedback. If the pet is excited, the generation unit can suggest feedback to help the pet release its energy. Furthermore, the notification unit can notify the owner of the effect of the feedback and adjust the content of the feedback as needed. In this way, the pet management system can provide feedback based on the pet's emotions, reduce the pet's stress, and maintain its health.

[0131] The pet management system can further provide a meal plan based on the pet's emotions by using an emotion estimation function based on the pet's behavioral data. For example, the collection unit collects the pet's behavioral data, and the analysis unit analyzes that data to estimate the pet's emotions. This allows the generation unit to generate an appropriate meal plan based on the pet's emotions and provide it to the owner. For instance, if the pet is feeling anxious, the generation unit can suggest a meal plan with a relaxing effect. Similarly, if the pet is excited, the generation unit can suggest a meal plan to help the pet expend energy. Furthermore, the notification unit can inform the owner of the effectiveness of the meal plan and adjust its contents as needed. This allows the pet management system to provide a meal plan based on the pet's emotions, reducing stress and maintaining the pet's health.

[0132] The pet management system can further provide exercise plans based on the pet's emotions by using an emotion estimation function based on the pet's behavioral data. For example, the collection unit collects the pet's behavioral data, and the analysis unit analyzes that data to estimate the pet's emotions. This allows the generation unit to generate an appropriate exercise plan based on the pet's emotions and provide it to the owner. For instance, if the pet is feeling anxious, the generation unit can suggest an exercise plan with a relaxing effect. Similarly, if the pet is excited, the generation unit can suggest an exercise plan to help the pet release energy. Furthermore, the notification unit can inform the owner of the effectiveness of the exercise plan and adjust its content as needed. This allows the pet management system to provide exercise plans based on the pet's emotions, reducing stress and maintaining the pet's health.

[0133] The following briefly describes the processing flow for example form 2.

[0134] Step 1: The data collection unit collects pet behavior and health data. The data collection unit collects data such as pet diet, exercise, sleep, and weight. The data collection unit can use AI to automatically collect this data. For example, the data collection unit can use sensors to record the type and amount of food the pet eats. The data collection unit can also use activity trackers attached to the pet to measure the pet's activity level. Furthermore, the data collection unit can use sensors placed in the pet's bedding to monitor the pet's sleep patterns. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses AI to analyze the collected data and understand the pet's health status. For example, the analysis unit can analyze changes in the pet's weight and detect changes in its health status. It can also analyze changes in the pet's activity level and detect insufficient or excessive exercise. Furthermore, the analysis unit can analyze the pet's sleep patterns and detect insufficient or excessive sleep. Step 3: The notification unit detects anomalies based on the data analyzed by the analysis unit and notifies the owner. The notification unit uses AI to quickly notify the owner when an anomaly is detected. For example, the notification unit can notify the owner if the pet's weight increases rapidly. It can also notify the owner if the pet's activity level decreases rapidly. Furthermore, it can notify the owner if the pet's sleep pattern is abnormal. Step 4: The generation unit understands the pet's behavior and emotions based on the data analyzed by the analysis unit and communicates this information to the owner. The generation unit can analyze the pet's behavior and emotions using generation AI and communicate it to the owner in human language. For example, if the pet is feeling anxious, the generation unit can explain the reason to the owner. Also, if the pet is repeating a particular behavior, the generation unit can explain the meaning of that behavior to the owner. Furthermore, the generation unit can analyze the pet's emotions and communicate them to the owner. Step 5: The Community Support Department will support information sharing among owners. The Community Support Department can use AI to support information sharing among owners and provide knowledge about pet health. For example, the Community Support Department will allow users to ask questions and seek advice about pet health within the community. The Community Support Department can also use AI to provide appropriate information. Furthermore, the Community Support Department can facilitate dialogue and consultation among users and increase their knowledge about pet health.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] Each of the multiple elements described above, including the collection unit, analysis unit, notification unit, generation unit, and community support unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects pet behavior and health data using the sensors and activity tracker of the smart device 14. The analysis unit analyzes the collected data using the identification processing unit 290 of the data processing unit 12 to understand the pet's health status. The notification unit detects abnormalities using the identification processing unit 290 of the data processing unit 12 and notifies the owner through the smart device 14. The generation unit analyzes the pet's behavior and emotions using the identification processing unit 290 of the data processing unit 12 and communicates this to the owner through the smart device 14. The community support unit supports information sharing among owners using the identification processing unit 290 of the data processing unit 12 and provides appropriate information through the smart device 14. 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.

[0139] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] Each of the multiple elements described above, including the collection unit, analysis unit, notification unit, generation unit, and community support unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects pet behavior and health data using the sensors and activity tracker of the smart glasses 214. The analysis unit analyzes the collected data using the identification processing unit 290 of the data processing unit 12 to understand the pet's health status. The notification unit detects abnormalities using the identification processing unit 290 of the data processing unit 12 and notifies the owner through the smart glasses 214. The generation unit analyzes the pet's behavior and emotions using the identification processing unit 290 of the data processing unit 12 and communicates this to the owner through the smart glasses 214. The community support unit supports information sharing among owners using the identification processing unit 290 of the data processing unit 12 and provides appropriate information through the smart glasses 214. 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.

[0155] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] Each of the multiple elements described above, including the collection unit, analysis unit, notification unit, generation unit, and community support unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the collection unit collects pet behavior and health data using the sensors and activity tracker of the headset terminal 314. The analysis unit analyzes the collected data using the identification processing unit 290 of the data processing unit 12 to understand the pet's health status. The notification unit detects abnormalities using the identification processing unit 290 of the data processing unit 12 and notifies the owner via the headset terminal 314. The generation unit analyzes the pet's behavior and emotions using the identification processing unit 290 of the data processing unit 12 and communicates this to the owner via the headset terminal 314. The community support unit supports information sharing among owners using the identification processing unit 290 of the data processing unit 12 and provides appropriate information via the headset terminal 314. 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.

[0171] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0176] 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).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.).

[0184] 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.

[0185] 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.

[0186] 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.

[0187] Each of the multiple elements described above, including the collection unit, analysis unit, notification unit, generation unit, and community support unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects pet behavior and health data using the robot 414's sensors and activity tracker. The analysis unit analyzes the collected data using the identification processing unit 290 of the data processing unit 12 to understand the pet's health status. The notification unit detects abnormalities using the identification processing unit 290 of the data processing unit 12 and notifies the owner through the robot 414. The generation unit analyzes the pet's behavior and emotions using the identification processing unit 290 of the data processing unit 12 and communicates this to the owner through the robot 414. The community support unit supports information sharing among owners using the identification processing unit 290 of the data processing unit 12 and provides appropriate information through the robot 414. 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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."

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] (Note 1) A data collection unit that collects pet behavior and health data, An analysis unit analyzes the data collected by the aforementioned collection unit, A notification unit detects an anomaly based on the data analyzed by the aforementioned analysis unit and notifies the owner, Based on the data analyzed by the aforementioned analysis unit, a generation unit understands the pet's behavior and emotions and transmits that information to the owner. It includes a community support department that supports information sharing among owners. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect data on pets' diet, exercise, sleep, weight, etc. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed to understand the health status of the pet. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned notification unit, Notify the owner if an abnormality is detected. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is It analyzes the pet's behavior and emotions and communicates them to the owner in human language. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned Community Support Department We support information sharing among pet owners and provide knowledge about pet health. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the pet's emotions and adjust the frequency of data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze past pet behavior data to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed based on the pet's current health status and activity level. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is We estimate the pet's emotions and prioritize the data to collect based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, prioritize the collection of highly relevant data, taking into account information about the pet's living environment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, we analyze the social media activity of pet owners and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the pet's emotions and adjusts the analysis algorithm based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the accuracy of the analysis is improved by referencing the pet's past health data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analytical methods are applied depending on the type and age of the pet. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the pet's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the analysis will take into account data about the pet's living environment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, we refer to relevant pet-related literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) 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 20) The aforementioned notification unit, When a notification is sent, the level of detail in the notification will be adjusted based on the importance of the pet's health condition. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned notification unit, When sending a notification, the system will refer to the pet owner's past response history to select the most suitable notification method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned notification unit, It estimates the pet's emotions and prioritizes notifications based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned notification unit, When sending notifications, the system will select the most suitable notification method, taking into account the pet owner's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned notification unit, When sending notifications, the system analyzes the pet owner's social media activity and sends relevant notifications. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is We estimate the pet's emotions and adjust how the information generated based on those estimated emotions is represented. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is During generation, adjust the level of detail of the information generated based on the importance of the pet's behavior. The system described in Appendix 1, characterized by the features described herein. (Note 27) The generating unit is During generation, different generation algorithms are applied depending on the category of the pet's behavior. The system described in Appendix 1, characterized by the features described herein. (Note 28) The generating unit is It estimates the pet's emotions and adjusts the length of the information generated based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The generating unit is During generation, the priority of the information to be generated is determined based on when the pet's behavior occurred. The system described in Appendix 1, characterized by the features described herein. (Note 30) The generating unit is During generation, the order of the generated information is adjusted based on the relevance of the pet's behavior. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned Community Support Department We estimate the emotions of pets and adjust how information is provided within the community based on the estimated emotions of the pets. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned Community Support Department When providing community support, we refer to the pet owner's past question history to provide the most relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned Community Support Department When providing community support, different support methods are applied depending on the pet's health condition. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned Community Support Department It estimates the emotions of pets and prioritizes information within the community based on the estimated emotions of the pets. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned Community Support Department When providing community support, we take into account the pet owner's geographical location to provide the most relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned Community Support Department During community support, we analyze pet owners' social media activity and provide relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned Community Support Department When providing community support, we refer to the pet owner's past question history to provide the most relevant information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0207] 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 collection department collects pet behavior and health data, An analysis unit analyzes the data collected by the aforementioned collection unit, A notification unit detects an anomaly based on the data analyzed by the aforementioned analysis unit and notifies the owner, Based on the data analyzed by the aforementioned analysis unit, a generation unit understands the pet's behavior and emotions and transmits that information to the owner. It includes a community support department that supports information sharing among owners. A system characterized by the following features.

2. The aforementioned collection unit is Collect data on pets' diet, exercise, sleep, weight, etc. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed to understand the health status of the pet. The system according to feature 1.

4. The aforementioned notification unit, Notify the owner if an abnormality is detected. The system according to feature 1.

5. The generating unit is It analyzes the pet's behavior and emotions and communicates them to the owner in human language. The system according to feature 1.

6. The aforementioned Community Support Department We support information sharing among pet owners and provide knowledge about pet health. The system according to feature 1.

7. The aforementioned collection unit is We estimate the pet's emotions and adjust the frequency of data collection based on the estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze past pet behavior data to select the optimal data collection method. The system according to feature 1.

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