system

Abstract

The system according to this embodiment aims to help pet owners understand their pet's symptoms and daily behavior, and to assist them in deciding when to take their pet to a veterinarian at the appropriate time. [Solution] The system according to this embodiment comprises a reception unit, an analysis unit, a determination unit, and a provision unit. The reception unit receives input from the pet owner regarding the pet's symptoms and daily condition. The analysis unit analyzes the data entered by the reception unit. The determination unit determines the necessity of a veterinary visit based on the data analyzed by the analysis unit. The provision unit provides information on seasonal symptoms and prevalent diseases based on the results obtained by the determination unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, 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 character of the chatbot, 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 for a pet owner to grasp the symptoms and daily conditions of a pet and make a judgment to visit a veterinarian at an appropriate timing.

[0005] The system according to the embodiment aims to assist a pet owner in grasping the symptoms and daily conditions of a pet and making a judgment to visit a veterinarian at an appropriate timing.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a determination unit, and a provision unit. The reception unit receives input from the pet owner regarding the pet's symptoms and daily condition. The analysis unit analyzes the data entered by the reception unit. The determination unit determines the necessity of a veterinary visit based on the data analyzed by the analysis unit. The provision unit provides information on seasonal symptoms and prevalent diseases based on the results obtained by the determination unit. [Effects of the Invention]

[0007] The system according to this embodiment can help pet owners understand their pet's symptoms and daily behavior, and make decisions about when to take their pet to a veterinarian. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The pet health management system according to an embodiment of the present invention is a system designed to solve the problem of busy pet owners who find it difficult to take their pets to the veterinarian. This pet health management system allows pet owners to post information about their pet's symptoms and daily life into an app, where an AI analyzes the posted data to determine the need for a veterinary visit. Furthermore, the pet health management system continuously learns from the data collected from the app and provides information on seasonal symptoms and prevalent diseases. This makes it easier for pet owners to understand their pet's health and take them to the veterinarian at the appropriate time. For example, a pet owner posts information about their pet's symptoms and daily life into the app. For instance, if a pet has a poor appetite, the owner enters this symptom into the app. This information is then input into the AI. Next, the AI ​​analyzes the input information and determines the need for a veterinary visit. The AI ​​analyzes past data and similar cases to determine the necessity of a visit. For example, if the loss of appetite persists, the AI ​​recommends a veterinary visit. In addition, the AI ​​continuously learns from the data collected from the app and provides information on seasonal symptoms and prevalent diseases. For example, it provides information on seasonal allergies and prevalent infectious diseases, enabling pet owners to take preventative measures. This system makes it easier for pet owners to understand their pet's health and take them to the vet at the appropriate time. Furthermore, even when owners are busy, the AI ​​provides support, allowing them to manage their pet's health with peace of mind. For example, even when an owner is busy with work, the AI ​​can monitor their pet's health and notify them of any abnormalities, enabling early intervention. In this way, an AI system that supports pet health management reduces the burden on owners and is an effective means of protecting pets' health. Thus, a pet health management system makes it easier for owners to understand their pet's health and take them to the vet at the appropriate time.

[0029] The pet health management system according to this embodiment comprises a reception unit, an analysis unit, a judgment unit, and a provision unit. The reception unit allows pet owners to input their pet's symptoms and daily condition. By allowing pet owners to input their pet's symptoms and daily condition, the AI ​​can more easily collect data. For example, the reception unit allows pet owners to input their pet's symptoms and daily condition into an app. The app is designed to be user-friendly for pet owners by designing the input screen and data storage methods. The analysis unit analyzes the data entered by the reception unit. The analysis unit performs analysis based on past data and similar cases. For example, the analysis unit performs analysis using statistical analysis of data and machine learning algorithms. The judgment unit determines the need for a veterinary visit based on the data analyzed by the analysis unit. The judgment unit makes a judgment based on criteria such as the severity and duration of symptoms. For example, the judgment unit recommends a veterinary visit if loss of appetite persists. The provision unit provides information on seasonal symptoms and prevalent diseases based on the results obtained by the judgment unit. The provision unit provides information for pet owners to take preventive measures. For example, the information provider can provide information on seasonal allergies and prevalent infectious diseases. This makes it easier for pet owners to understand their pet's health status and take them to the veterinarian at the appropriate time.

[0030] The reception system allows pet owners to input information about their pets' symptoms and daily routines. This input makes it easier for the AI ​​to collect data. For example, the reception system uses an app where owners input information about their pets' symptoms and daily routines. The app is designed to be user-friendly for pet owners through features such as a well-designed input screen and data storage methods. Specifically, the app's input screen has an intuitive and easy-to-understand interface, allowing owners to easily input pet information. For instance, questions about the pet's health and behavior might be displayed, allowing users to answer simply by tapping options. Voice input functionality could also be incorporated, enabling owners to input information by simply speaking. Furthermore, the entered data is stored in the cloud, allowing owners to access it anytime, anywhere. This allows owners to continuously record their pets' health and provide information quickly when needed. The reception system not only collects data but also includes features to maintain data integrity and consistency. For example, if there are inconsistencies in the entered data or important information is missing, the app automatically displays a warning and prompts the owner to correct it. Additionally, regular reminders are sent to help pet owners remember to enter their data. This allows the reception department to collect accurate and reliable data, improving the overall accuracy of the system.

[0031] The analysis unit analyzes data entered by the reception unit. The analysis unit performs analysis based on past data and similar cases. For example, the analysis unit uses statistical analysis and machine learning algorithms to perform analysis. Specifically, statistical analysis analyzes patterns of pet symptoms and behavior to detect signs of abnormalities. For example, it statistically analyzes data such as loss of appetite, weight loss, and abnormal behavior to clarify the frequency and correlation of abnormalities. By using machine learning algorithms, it can learn from past data and perform analysis with high accuracy on new data. For example, it can use deep learning-based image analysis technology to evaluate the health status of pets from photos and videos. Furthermore, it can use natural language processing technology to analyze text data entered by pet owners to understand the details of symptoms and behaviors. By combining these technologies, the analysis unit can comprehensively evaluate the health status of pets and use this to help in the early detection and prevention of abnormalities. In addition, the analysis unit has a data visualization function, displaying analysis results in graphs and charts so that pet owners can understand their pet's health status at a glance. This allows the analysis unit to provide pet owners with easy-to-understand and useful information, supporting them in managing their pets' health.

[0032] The assessment unit determines the need for a veterinary visit based on data analyzed by the analysis unit. The assessment unit makes its decision based on criteria such as the severity and duration of symptoms. For example, the assessment unit recommends a veterinary visit if loss of appetite persists. Specifically, the assessment unit evaluates the pet's health condition based on data provided by the analysis unit and determines whether there are any signs of abnormality. For example, it analyzes the pet's biological data such as body temperature, heart rate, and respiratory rate, and determines that there may be an abnormality if it falls outside the normal range. It also considers the pet's behavioral data and the duration of symptoms to evaluate the severity of the abnormality. For example, it recommends a veterinary visit if loss of appetite persists for several days or if abnormal behavior is frequently observed. Based on these criteria, the assessment unit comprehensively evaluates the pet's health condition and determines the appropriate course of action. The assessment unit also provides specific advice and instructions to the owner. For example, if the pet's symptoms are mild, it advises on home care methods and precautions, and if the symptoms are severe, it instructs the owner to take the pet to a veterinarian immediately. This allows the diagnostic unit to support pet owners in managing their pets' health at the appropriate time. Furthermore, the diagnostic unit can analyze trends in pet health based on past data and similar cases, and predict future risks. This enables pet owners to manage their pets' health over the long term and take preventative measures.

[0033] The information provider will provide information on seasonal symptoms and prevalent diseases based on the results obtained by the assessment unit. The information provider will also provide information to help pet owners take preventative measures. For example, the information provider will provide information on seasonal allergies and prevalent infectious diseases. Specifically, the information provider will provide information on diseases and symptoms that pets are prone to depending on seasonal changes in temperature and humidity. For example, it will provide information on diseases and symptoms to be aware of each season, such as hay fever and allergies in spring, heatstroke and parasites in summer, skin diseases and digestive problems in autumn, and colds and arthritis in winter. It will also provide the latest information on prevalent infectious diseases and advise on preventative measures and treatment methods. For example, it will provide information on symptoms, preventative measures, and treatments for infectious diseases such as influenza, norovirus, and parvovirus. The information provider will utilize in-app notifications, email, and social media to provide this information to pet owners in an easy-to-understand manner. For example, when there is a change of season or signs of an outbreak are observed, information will be provided via in-app push notifications and email so that pet owners can respond quickly. Furthermore, the service provider could consider distributing regular newsletters and blog posts to continuously provide pet owners with information useful for managing their pets' health. This would allow the service provider to support pet owners in staying informed about their pets' health and taking appropriate preventative measures. In addition, the service provider could collect feedback from pet owners and continuously improve the quality and content of the information they provide. This would enable the service provider to always provide pet owners with the latest and most useful information and support them in managing their pets' health.

[0034] The information provider can provide information on seasonal allergies and prevalent infectious diseases. For example, the information provider can clarify the specific types and symptoms of seasonal allergies. For example, the information provider can provide information on hay fever and dust mite allergies. The information provider can also clarify the specific types and symptoms of prevalent infectious diseases. For example, the information provider can provide information on influenza and norovirus. By providing information on seasonal allergies and prevalent infectious diseases, pet owners can take preventive measures. Some or all of the processing described above in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input information on seasonal allergies and prevalent infectious diseases into a generating AI, which can then analyze and provide the information.

[0035] The analysis unit can perform analysis based on past data and similar cases. For example, the analysis unit can perform analysis based on past medical records and health check data. For example, the analysis unit can retrieve past medical records from a database and use them for analysis. The analysis unit can also perform analysis based on data from other pets with the same symptoms. For example, the analysis unit can search for similar cases from a database and use them for analysis. This improves the accuracy of the analysis by performing analysis based on past data and similar cases. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past data and similar cases into a generating AI, which can then analyze the data and output the results.

[0036] The assessment unit can recommend a veterinary visit if loss of appetite persists. The assessment unit makes its judgment based, for example, on changes in food intake and appetite. For example, the assessment unit recommends a veterinary visit if the pet's food intake is less than usual. The assessment unit can also recommend a veterinary visit if the pet's appetite changes rapidly. For example, the assessment unit recommends a veterinary visit if the pet's appetite decreases rapidly. This allows for early and appropriate action by recommending a veterinary visit when loss of appetite persists. Some or all of the above processing in the assessment unit may be performed using AI, for example, or without AI. For example, the assessment unit can input the pet's food intake and changes in appetite into a generating AI, which can then determine the need for a veterinary visit.

[0037] The reception unit allows pet owners to input information about their pets' symptoms and daily routines into the app. For example, the reception unit allows pet owners to input information about their pets' symptoms and daily routines through the app's input screen. For example, the reception unit displays fields on the app's input screen for pet owners to input information about their pets' symptoms and daily routines. The reception unit can also save the entered data and send it to the analysis unit. For example, the reception unit saves the entered data to a database and sends it to the analysis unit. This makes it easier for the AI ​​to collect data as pet owners input information about their pets' symptoms and daily routines into the app. Some or all of the above-described processes in the reception unit may be performed using AI, or not using AI. For example, the reception unit can send the data entered on the app's input screen to a generating AI, which can then analyze the data.

[0038] The information provider can provide owners with information to help them take preventative measures. For example, the information provider can provide information on vaccinations and improvements to the living environment. For example, the information provider can provide a vaccination schedule to maintain the pet's health. The information provider can also provide advice on improving the pet's living environment. For example, the information provider can provide methods for keeping the pet's living environment clean. This improves pet health management by providing owners with information to help them take preventative measures. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input information on preventative measures into a generating AI, which can then analyze and provide the information.

[0039] The reception desk can analyze the owner's past input history and suggest the optimal input method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the owner has frequently used in the past. For example, if the reception desk has frequently used voice input in the past, it will prioritize suggesting voice input. The reception desk can also analyze the patterns of symptoms the owner has entered in the past and customize the input fields. For example, the reception desk can analyze the patterns of symptoms the owner has entered in the past and customize the input fields. The reception desk can also suggest the optimal input timing by considering the time of day the owner has entered in the past. For example, the reception desk can suggest the optimal input timing by considering the time of day the owner has entered in the past. In this way, by analyzing the owner's past input history, the reception desk can suggest the optimal input method. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the owner's past input history into a generating AI, and the generating AI can suggest the optimal input method.

[0040] The reception system can customize input fields based on the pet's breed and age when symptoms are entered. For example, if the pet is elderly, the reception system can add specific symptom input fields appropriate for its age. The reception system can also display input fields related to specific diseases and symptoms depending on the pet's breed. For example, the reception system can display input fields related to diseases and symptoms specific to certain breeds such as dogs and cats. The reception system can also adjust the priority of input fields considering the pet's age and breed. This allows for the input of more appropriate information by customizing input fields based on the pet's breed and age. Some or all of the above processing in the reception system may be performed using AI, or not. For example, the reception system can input data about the pet's breed and age into a generating AI, which can then customize the input fields.

[0041] The reception system can prioritize the input of highly relevant symptoms by considering the owner's geographical location when symptoms are entered. For example, the reception system may prompt the owner to prioritize the input of symptoms related to diseases prevalent in their area. The reception system can also prompt the owner to prioritize the input of relevant symptoms based on the climate conditions of their area. The reception system can also prompt the owner to prioritize the input of relevant symptoms based on environmental factors in their area (e.g., pollen, allergens). This allows the system to prioritize the input of highly relevant symptoms by considering the owner's geographical location. Some or all of the above processing in the reception system may be performed using AI, for example, or without AI. For example, the reception desk can input the owner's geographical location information into a generating AI, which can then prioritize inputting symptoms that are highly relevant.

[0042] The reception desk can input relevant symptoms by analyzing the owner's social media activity when symptoms are entered. For example, the reception desk can analyze photos and videos of pets shared by the owner on social media and input relevant symptoms. The reception desk can also input relevant symptoms based on the pet's health status mentioned by the owner on social media. For example, the reception desk can input relevant symptoms based on information from veterinarians and pet-related accounts that the owner follows on social media. For example, the reception desk can input relevant symptoms based on information from veterinarians and pet-related accounts that the owner follows on social media. This allows the reception desk to input relevant symptoms by analyzing the owner's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the owner's social media activity data into a generating AI, which can then input relevant symptoms.

[0043] The analysis unit can optimize its analysis algorithm by referring to the pet's past health data during analysis. For example, the analysis unit prioritizes analyzing data related to the pet's current symptoms based on the pet's past health data. The analysis unit can also adjust the parameters of the analysis algorithm by referring to the pet's past health data. The analysis unit can also improve the accuracy of analysis for specific diseases or symptoms based on the pet's past health data. This allows the analysis algorithm to be optimized by referring to the pet's past health data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the pet's past health data into a generating AI, which can then optimize the analysis algorithm.

[0044] 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 apply analysis methods for specific diseases and symptoms depending on the type of pet. For example, the analysis unit can apply analysis methods for diseases and symptoms specific to certain breeds such as dogs and cats. The analysis unit can also apply analysis methods for age-specific diseases and symptoms depending on the age of the pet. For example, the analysis unit can apply analysis methods for age-specific diseases and symptoms depending on the age of the pet. The analysis unit can also select the optimal analysis method considering the type and age of the pet. For example, the analysis unit selects the optimal analysis method considering the type and age of the pet. This improves the accuracy of the analysis by applying different analysis methods depending on the type and age of the pet. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the type and age of the pet into a generating AI, and the generating AI can apply different analysis methods.

[0045] The analysis unit can perform analysis while considering the geographical distribution of pets. For example, the analysis unit can perform analysis while considering the climatic conditions of the pets' residential areas. The analysis unit can also perform analysis while considering diseases prevalent in the pets' residential areas. The analysis unit can also perform analysis while considering environmental factors (e.g., pollen, allergens) in the pets' residential areas. By considering the geographical distribution of pets, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the geographical distribution of pets into a generating AI, and the generating AI can perform the analysis.

[0046] The analysis unit can improve the accuracy of its analysis by referring to relevant pet literature during the analysis. For example, the analysis unit can improve the accuracy of its analysis by referring to the latest research papers related to the symptoms of pets. The analysis unit can also improve the accuracy of its analysis by referring to specialized books on pet diseases. The analysis unit can also improve the accuracy of its analysis by referring to reliable websites on pet health. In this way, the accuracy of the analysis is improved by referring to relevant pet literature. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant pet literature into a generating AI, which can then improve the accuracy of the analysis.

[0047] The judgment unit can optimize its judgment algorithm by referring to the pet's past medical history during the judgment process. For example, the judgment unit prioritizes determining data related to the current symptoms based on the pet's past medical history. The judgment unit can also adjust the parameters of the judgment algorithm by referring to the pet's past medical history. The judgment unit can also improve the accuracy of its judgment for specific diseases or symptoms based on the pet's past medical history. This allows the judgment algorithm to be optimized by referring to the pet's past medical history. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input the pet's past medical history into a generating AI, which can then optimize the judgment algorithm.

[0048] The judgment unit can apply different judgment criteria depending on the type and age of the pet during the judgment process. For example, the judgment unit can apply judgment criteria for specific diseases and symptoms depending on the type of pet. For example, the judgment unit can apply judgment criteria for diseases and symptoms specific to certain breeds such as dogs and cats. The judgment unit can also apply judgment criteria for age-specific diseases and symptoms depending on the age of the pet. For example, the judgment unit can apply judgment criteria for age-specific diseases and symptoms depending on the age of the pet. The judgment unit can also select the optimal judgment criteria considering the type and age of the pet. For example, the judgment unit selects the optimal judgment criteria considering the type and age of the pet. This improves the accuracy of the judgment by applying different judgment criteria depending on the type and age of the pet. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without using AI. For example, the judgment unit can input data on the type and age of the pet into a generating AI, and the generating AI can apply different judgment criteria.

[0049] The determination unit can make a determination by considering the geographical distribution of pets. For example, the determination unit can make a determination by considering the climatic conditions of the pet's residential area. The determination unit can also make a determination by considering diseases prevalent in the pet's residential area. The determination unit can also make a determination by considering environmental factors (e.g., pollen, allergens) in the pet's residential area. By considering the geographical distribution of pets, a more appropriate determination result can be provided. Some or all of the above processing in the determination unit may be performed using AI, for example, or without AI. For example, the determination unit can input data on the geographical distribution of pets into a generating AI, and the generating AI can make a determination.

[0050] The judgment unit can improve the accuracy of its judgment by referring to relevant literature on pets during the judgment process. For example, the judgment unit can improve the accuracy of its judgment by referring to the latest research papers related to the pet's symptoms. The judgment unit can also improve the accuracy of its judgment by referring to specialized books on pet diseases. The judgment unit can also improve the accuracy of its judgment by referring to reliable websites on pet health. In this way, the accuracy of the judgment is improved by referring to relevant literature on pets. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input relevant literature on pets into a generating AI, which can then improve the accuracy of its judgment.

[0051] The information provider can customize the information provided by referring to the pet's past health data at the time of provision. For example, the information provider can provide information related to the pet's current symptoms based on the pet's past health data. The information provider can also provide information on preventive measures by referring to the pet's past health data. The information provider can also provide information on specific diseases or symptoms based on the pet's past health data. This allows the information provided to be customized by referring to the pet's past health data. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the pet's past health data into a generating AI, which can then customize the information provided.

[0052] The information provider can apply different information provision methods depending on the type and age of the pet at the time of provision. For example, the provider can provide information on diseases and symptoms specific to the type of pet. For example, the provider can provide information on diseases and symptoms specific to certain breeds such as dogs and cats. The provider can also provide information on age-specific diseases and symptoms depending on the age of the pet. For example, the provider can provide information on age-specific diseases and symptoms depending on the age of the pet. The provider can also select the optimal information provision method considering the type and age of the pet. For example, the provider selects the optimal information provision method considering the type and age of the pet. This allows for the provision of more appropriate information by applying different information provision methods depending on the type and age of the pet. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can input data on the type and age of the pet into a generating AI, and the generating AI can apply different information provision methods.

[0053] The information provider can provide information while considering the geographical distribution of pets. For example, the information provider can provide information while considering the climatic conditions of the pet's residential area. The information provider can also provide information about diseases prevalent in the pet's residential area. The information provider can also provide information while considering environmental factors (e.g., pollen, allergens) in the pet's residential area. By considering the geographical distribution of pets, more appropriate information can be provided. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input data on the geographical distribution of pets into a generating AI, and the generating AI can provide the information.

[0054] The information provider can improve the accuracy of the information provided by referring to relevant pet literature at the time of provision. For example, the information provider can improve the accuracy of the information by referring to the latest research papers related to pet symptoms. The information provider can also improve the accuracy of the information by referring to specialized books on pet diseases. The information provider can also improve the accuracy of the information by referring to reliable websites on pet health. In this way, the accuracy of the information provided is improved by referring to relevant pet literature. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input relevant pet literature into a generating AI, which can then improve the accuracy of the information.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The analysis unit can consider the pet's exercise data when analyzing its health status. For example, data obtained from an activity tracker attached to the pet can be transmitted to the analysis unit, and the pet's exercise level and activity patterns can be used in the analysis. The analysis unit can also combine the pet's exercise data with symptom data for analysis. For example, if the pet's exercise level suddenly decreases, that data can be compared with symptom data for analysis. This allows for a more accurate analysis of the pet's health status by considering the pet's exercise data.

[0057] The assessment unit can consider the pet's dietary data when determining the pet's health status. For example, the owner can input data on the pet's diet and intake into the app and send it to the assessment unit for use in the assessment. The assessment unit can also combine the pet's dietary data with symptom data to make a determination. For example, if the pet's food intake has decreased, this data can be compared with the symptom data to make a determination. This allows for a more accurate assessment of the pet's health status by considering the pet's dietary data.

[0058] The service provider can provide customized information by referring to the pet's past health data when providing pet health information. For example, it can provide information related to the pet's current symptoms based on the pet's past health data. The service provider can also provide information on preventative measures by referring to the pet's past health data. For example, it can provide information on specific diseases or symptoms based on the pet's past health data. This allows for the customization of the information provided by referring to the pet's past health data.

[0059] The reception system can display input fields tailored to the pet's breed and age when pet owners enter their pet's symptoms. For example, if the pet is elderly, specific symptom input fields corresponding to its age can be added. The reception system can also display input fields related to specific diseases and symptoms depending on the pet's breed. For example, it can display input fields related to diseases and symptoms specific to certain breeds such as dogs and cats. This allows for more accurate information to be entered by customizing the input fields based on the pet's breed and age.

[0060] The reception desk can analyze the owner's past input history when they enter their pet's symptoms and suggest the most suitable input method. For example, it can prioritize suggesting input methods that the owner has frequently used in the past (voice, text, etc.). It can also analyze patterns of symptoms the owner has entered in the past and customize the input fields. In this way, by analyzing the owner's past input history, it can suggest the most suitable input method.

[0061] The service provider can provide pet health information while considering the geographical distribution of pets. For example, it can provide information while considering the climatic conditions of the pet's residential area. It can also provide information on diseases prevalent in the pet's residential area. By considering the geographical distribution of pets, it is possible to provide more appropriate information.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The reception desk allows pet owners to input their pet's symptoms and daily habits. For example, pet owners use an app to input their pet's symptoms and daily habits. The app is designed to be user-friendly for pet owners through features such as a well-designed input screen and data storage methods. Step 2: The analysis unit analyzes the data entered by the reception unit. The analysis unit performs the analysis based on past data and similar cases. For example, it may use statistical analysis of the data or machine learning algorithms. Step 3: The assessment unit determines the need for a veterinary visit based on the data analyzed by the analysis unit. The assessment unit makes its decision based on criteria such as the severity and duration of symptoms. For example, it recommends a veterinary visit if loss of appetite persists. Step 4: The providing unit provides information on seasonal symptoms and prevalent diseases based on the results obtained by the assessment unit. The providing unit provides information for pet owners to take preventive measures. For example, it provides information on seasonal allergies and prevalent infectious diseases.

[0064] (Example of form 2) The pet health management system according to an embodiment of the present invention is a system designed to solve the problem of busy pet owners who find it difficult to take their pets to the veterinarian. This pet health management system allows pet owners to post information about their pet's symptoms and daily life into an app, where an AI analyzes the posted data to determine the need for a veterinary visit. Furthermore, the pet health management system continuously learns from the data collected from the app and provides information on seasonal symptoms and prevalent diseases. This makes it easier for pet owners to understand their pet's health and take them to the veterinarian at the appropriate time. For example, a pet owner posts information about their pet's symptoms and daily life into the app. For instance, if a pet has a poor appetite, the owner enters this symptom into the app. This information is then input into the AI. Next, the AI ​​analyzes the input information and determines the need for a veterinary visit. The AI ​​analyzes past data and similar cases to determine the necessity of a visit. For example, if the loss of appetite persists, the AI ​​recommends a veterinary visit. In addition, the AI ​​continuously learns from the data collected from the app and provides information on seasonal symptoms and prevalent diseases. For example, it provides information on seasonal allergies and prevalent infectious diseases, enabling pet owners to take preventative measures. This system makes it easier for pet owners to understand their pet's health and take them to the vet at the appropriate time. Furthermore, even when owners are busy, the AI ​​provides support, allowing them to manage their pet's health with peace of mind. For example, even when an owner is busy with work, the AI ​​can monitor their pet's health and notify them of any abnormalities, enabling early intervention. In this way, an AI system that supports pet health management reduces the burden on owners and is an effective means of protecting pets' health. Thus, a pet health management system makes it easier for owners to understand their pet's health and take them to the vet at the appropriate time.

[0065] The pet health management system according to this embodiment comprises a reception unit, an analysis unit, a judgment unit, and a provision unit. The reception unit allows pet owners to input their pet's symptoms and daily condition. By allowing pet owners to input their pet's symptoms and daily condition, the AI ​​can more easily collect data. For example, the reception unit allows pet owners to input their pet's symptoms and daily condition into an app. The app is designed to be user-friendly for pet owners by designing the input screen and data storage methods. The analysis unit analyzes the data entered by the reception unit. The analysis unit performs analysis based on past data and similar cases. For example, the analysis unit performs analysis using statistical analysis of data and machine learning algorithms. The judgment unit determines the need for a veterinary visit based on the data analyzed by the analysis unit. The judgment unit makes a judgment based on criteria such as the severity and duration of symptoms. For example, the judgment unit recommends a veterinary visit if loss of appetite persists. The provision unit provides information on seasonal symptoms and prevalent diseases based on the results obtained by the judgment unit. The provision unit provides information for pet owners to take preventive measures. For example, the information provider can provide information on seasonal allergies and prevalent infectious diseases. This makes it easier for pet owners to understand their pet's health status and take them to the veterinarian at the appropriate time.

[0066] The reception system allows pet owners to input information about their pets' symptoms and daily routines. This input makes it easier for the AI ​​to collect data. For example, the reception system uses an app where owners input information about their pets' symptoms and daily routines. The app is designed to be user-friendly for pet owners through features such as a well-designed input screen and data storage methods. Specifically, the app's input screen has an intuitive and easy-to-understand interface, allowing owners to easily input pet information. For instance, questions about the pet's health and behavior might be displayed, allowing users to answer simply by tapping options. Voice input functionality could also be incorporated, enabling owners to input information by simply speaking. Furthermore, the entered data is stored in the cloud, allowing owners to access it anytime, anywhere. This allows owners to continuously record their pets' health and provide information quickly when needed. The reception system not only collects data but also includes features to maintain data integrity and consistency. For example, if there are inconsistencies in the entered data or important information is missing, the app automatically displays a warning and prompts the owner to correct it. Additionally, regular reminders are sent to help pet owners remember to enter their data. This allows the reception department to collect accurate and reliable data, improving the overall accuracy of the system.

[0067] The analysis unit analyzes data entered by the reception unit. The analysis unit performs analysis based on past data and similar cases. For example, the analysis unit uses statistical analysis and machine learning algorithms to perform analysis. Specifically, statistical analysis analyzes patterns of pet symptoms and behavior to detect signs of abnormalities. For example, it statistically analyzes data such as loss of appetite, weight loss, and abnormal behavior to clarify the frequency and correlation of abnormalities. By using machine learning algorithms, it can learn from past data and perform analysis with high accuracy on new data. For example, it can use deep learning-based image analysis technology to evaluate the health status of pets from photos and videos. Furthermore, it can use natural language processing technology to analyze text data entered by pet owners to understand the details of symptoms and behaviors. By combining these technologies, the analysis unit can comprehensively evaluate the health status of pets and use this to help in the early detection and prevention of abnormalities. In addition, the analysis unit has a data visualization function, displaying analysis results in graphs and charts so that pet owners can understand their pet's health status at a glance. This allows the analysis unit to provide pet owners with easy-to-understand and useful information, supporting them in managing their pets' health.

[0068] The assessment unit determines the need for a veterinary visit based on data analyzed by the analysis unit. The assessment unit makes its decision based on criteria such as the severity and duration of symptoms. For example, the assessment unit recommends a veterinary visit if loss of appetite persists. Specifically, the assessment unit evaluates the pet's health condition based on data provided by the analysis unit and determines whether there are any signs of abnormality. For example, it analyzes the pet's biological data such as body temperature, heart rate, and respiratory rate, and determines that there may be an abnormality if it falls outside the normal range. It also considers the pet's behavioral data and the duration of symptoms to evaluate the severity of the abnormality. For example, it recommends a veterinary visit if loss of appetite persists for several days or if abnormal behavior is frequently observed. Based on these criteria, the assessment unit comprehensively evaluates the pet's health condition and determines the appropriate course of action. The assessment unit also provides specific advice and instructions to the owner. For example, if the pet's symptoms are mild, it advises on home care methods and precautions, and if the symptoms are severe, it instructs the owner to take the pet to a veterinarian immediately. This allows the diagnostic unit to support pet owners in managing their pets' health at the appropriate time. Furthermore, the diagnostic unit can analyze trends in pet health based on past data and similar cases, and predict future risks. This enables pet owners to manage their pets' health over the long term and take preventative measures.

[0069] The information provider will provide information on seasonal symptoms and prevalent diseases based on the results obtained by the assessment unit. The information provider will also provide information to help pet owners take preventative measures. For example, the information provider will provide information on seasonal allergies and prevalent infectious diseases. Specifically, the information provider will provide information on diseases and symptoms that pets are prone to depending on seasonal changes in temperature and humidity. For example, it will provide information on diseases and symptoms to be aware of each season, such as hay fever and allergies in spring, heatstroke and parasites in summer, skin diseases and digestive problems in autumn, and colds and arthritis in winter. It will also provide the latest information on prevalent infectious diseases and advise on preventative measures and treatment methods. For example, it will provide information on symptoms, preventative measures, and treatments for infectious diseases such as influenza, norovirus, and parvovirus. The information provider will utilize in-app notifications, email, and social media to provide this information to pet owners in an easy-to-understand manner. For example, when there is a change of season or signs of an outbreak are observed, information will be provided via in-app push notifications and email so that pet owners can respond quickly. Furthermore, the service provider could consider distributing regular newsletters and blog posts to continuously provide pet owners with information useful for managing their pets' health. This would allow the service provider to support pet owners in staying informed about their pets' health and taking appropriate preventative measures. In addition, the service provider could collect feedback from pet owners and continuously improve the quality and content of the information they provide. This would enable the service provider to always provide pet owners with the latest and most useful information and support them in managing their pets' health.

[0070] The information provider can provide information on seasonal allergies and prevalent infectious diseases. For example, the information provider can clarify the specific types and symptoms of seasonal allergies. For example, the information provider can provide information on hay fever and dust mite allergies. The information provider can also clarify the specific types and symptoms of prevalent infectious diseases. For example, the information provider can provide information on influenza and norovirus. By providing information on seasonal allergies and prevalent infectious diseases, pet owners can take preventive measures. Some or all of the processing described above in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input information on seasonal allergies and prevalent infectious diseases into a generating AI, which can then analyze and provide the information.

[0071] The analysis unit can perform analysis based on past data and similar cases. For example, the analysis unit can perform analysis based on past medical records and health check data. For example, the analysis unit can retrieve past medical records from a database and use them for analysis. The analysis unit can also perform analysis based on data from other pets with the same symptoms. For example, the analysis unit can search for similar cases from a database and use them for analysis. This improves the accuracy of the analysis by performing analysis based on past data and similar cases. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past data and similar cases into a generating AI, which can then analyze the data and output the results.

[0072] The assessment unit can recommend a veterinary visit if loss of appetite persists. The assessment unit makes its judgment based, for example, on changes in food intake and appetite. For example, the assessment unit recommends a veterinary visit if the pet's food intake is less than usual. The assessment unit can also recommend a veterinary visit if the pet's appetite changes rapidly. For example, the assessment unit recommends a veterinary visit if the pet's appetite decreases rapidly. This allows for early and appropriate action by recommending a veterinary visit when loss of appetite persists. Some or all of the above processing in the assessment unit may be performed using AI, for example, or without AI. For example, the assessment unit can input the pet's food intake and changes in appetite into a generating AI, which can then determine the need for a veterinary visit.

[0073] The reception unit allows pet owners to input information about their pets' symptoms and daily routines into the app. For example, the reception unit allows pet owners to input information about their pets' symptoms and daily routines through the app's input screen. For example, the reception unit displays fields on the app's input screen for pet owners to input information about their pets' symptoms and daily routines. The reception unit can also save the entered data and send it to the analysis unit. For example, the reception unit saves the entered data to a database and sends it to the analysis unit. This makes it easier for the AI ​​to collect data as pet owners input information about their pets' symptoms and daily routines into the app. Some or all of the above-described processes in the reception unit may be performed using AI, or not using AI. For example, the reception unit can send the data entered on the app's input screen to a generating AI, which can then analyze the data.

[0074] The information provider can provide owners with information to help them take preventative measures. For example, the information provider can provide information on vaccinations and improvements to the living environment. For example, the information provider can provide a vaccination schedule to maintain the pet's health. The information provider can also provide advice on improving the pet's living environment. For example, the information provider can provide methods for keeping the pet's living environment clean. This improves pet health management by providing owners with information to help them take preventative measures. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input information on preventative measures into a generating AI, which can then analyze and provide the information.

[0075] The reception system can estimate the owner's emotions and adjust the timing of symptom input based on the estimated emotions. For example, if the owner is feeling anxious, the reception system can immediately send a notification prompting them to input symptoms. The reception system can also send a notification if the owner is relaxed, informing them that they can postpone symptom input. The reception system can also set a reminder for the owner to input symptoms later if they are busy. This allows for timely input by adjusting the timing of symptom input according to the owner's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes at the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the owner's emotional data into a generating AI, which can then estimate the emotions and adjust the timing of symptom input.

[0076] The reception desk can analyze the owner's past input history and suggest the optimal input method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the owner has frequently used in the past. For example, if the reception desk has frequently used voice input in the past, it will prioritize suggesting voice input. The reception desk can also analyze the patterns of symptoms the owner has entered in the past and customize the input fields. For example, the reception desk can analyze the patterns of symptoms the owner has entered in the past and customize the input fields. The reception desk can also suggest the optimal input timing by considering the time of day the owner has entered in the past. For example, the reception desk can suggest the optimal input timing by considering the time of day the owner has entered in the past. In this way, by analyzing the owner's past input history, the reception desk can suggest the optimal input method. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the owner's past input history into a generating AI, and the generating AI can suggest the optimal input method.

[0077] The reception system can customize input fields based on the pet's breed and age when symptoms are entered. For example, if the pet is elderly, the reception system can add specific symptom input fields appropriate for its age. The reception system can also display input fields related to specific diseases and symptoms depending on the pet's breed. For example, the reception system can display input fields related to diseases and symptoms specific to certain breeds such as dogs and cats. The reception system can also adjust the priority of input fields considering the pet's age and breed. This allows for the input of more appropriate information by customizing input fields based on the pet's breed and age. Some or all of the above processing in the reception system may be performed using AI, or not. For example, the reception system can input data about the pet's breed and age into a generating AI, which can then customize the input fields.

[0078] The reception system can estimate the owner's emotions and determine the priority of symptoms to be entered based on the estimated emotions. For example, if the owner is feeling anxious, the reception system will prompt them to prioritize entering serious symptoms. The reception system can also prompt the owner to enter minor symptoms first if they are relaxed. The reception system can also prompt the owner to enter only important symptoms first if they are busy. This allows for the prioritization of important symptoms by determining the priority of symptoms to be entered according to the owner's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generating AI may be a text generating AI (e.g., LLM) or a multimodal generating AI, but is not limited to such examples. Some or all of the processing described above in the reception area may be performed using AI, or not using AI. For example, the reception area can input the owner's emotional data into a generating AI, which can then estimate the emotions and determine the priority of the symptoms.

[0079] The reception system can prioritize the input of highly relevant symptoms by considering the owner's geographical location when symptoms are entered. For example, the reception system may prompt the owner to prioritize the input of symptoms related to diseases prevalent in their area. The reception system can also prompt the owner to prioritize the input of relevant symptoms based on the climate conditions of their area. The reception system can also prompt the owner to prioritize the input of relevant symptoms based on environmental factors in their area (e.g., pollen, allergens). This allows the system to prioritize the input of highly relevant symptoms by considering the owner's geographical location. Some or all of the above processing in the reception system may be performed using AI, for example, or without AI. For example, the reception desk can input the owner's geographical location information into a generating AI, which can then prioritize inputting symptoms that are highly relevant.

[0080] The reception desk can input relevant symptoms by analyzing the owner's social media activity when symptoms are entered. For example, the reception desk can analyze photos and videos of pets shared by the owner on social media and input relevant symptoms. The reception desk can also input relevant symptoms based on the pet's health status mentioned by the owner on social media. For example, the reception desk can input relevant symptoms based on information from veterinarians and pet-related accounts that the owner follows on social media. For example, the reception desk can input relevant symptoms based on information from veterinarians and pet-related accounts that the owner follows on social media. This allows the reception desk to input relevant symptoms by analyzing the owner's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the owner's social media activity data into a generating AI, which can then input relevant symptoms.

[0081] The analysis unit can estimate the owner's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the owner is feeling anxious, the analysis unit can increase the accuracy of the analysis to provide more detailed results. The analysis unit can also maintain a normal level of accuracy when the owner is relaxed. For example, if the owner is relaxed, the analysis unit can maintain a normal level of accuracy. The analysis unit can also adjust the accuracy of the analysis to provide faster results when the owner is busy. For example, if the owner is busy, the analysis unit can adjust the accuracy of the analysis to provide faster results. This allows for more appropriate analysis results by adjusting the accuracy of the analysis according to the owner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the owner's emotional data into a generating AI, which can then estimate the emotions and adjust the accuracy of the analysis.

[0082] The analysis unit can optimize its analysis algorithm by referring to the pet's past health data during analysis. For example, the analysis unit prioritizes analyzing data related to the pet's current symptoms based on the pet's past health data. The analysis unit can also adjust the parameters of the analysis algorithm by referring to the pet's past health data. The analysis unit can also improve the accuracy of analysis for specific diseases or symptoms based on the pet's past health data. This allows the analysis algorithm to be optimized by referring to the pet's past health data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the pet's past health data into a generating AI, which can then optimize the analysis algorithm.

[0083] 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 apply analysis methods for specific diseases and symptoms depending on the type of pet. For example, the analysis unit can apply analysis methods for diseases and symptoms specific to certain breeds such as dogs and cats. The analysis unit can also apply analysis methods for age-specific diseases and symptoms depending on the age of the pet. For example, the analysis unit can apply analysis methods for age-specific diseases and symptoms depending on the age of the pet. The analysis unit can also select the optimal analysis method considering the type and age of the pet. For example, the analysis unit selects the optimal analysis method considering the type and age of the pet. This improves the accuracy of the analysis by applying different analysis methods depending on the type and age of the pet. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the type and age of the pet into a generating AI, and the generating AI can apply different analysis methods.

[0084] The analysis unit can estimate the owner's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the owner is feeling anxious, the analysis unit will display detailed analysis results. For example, if the owner is feeling anxious, the analysis unit will display detailed analysis results. The analysis unit can also display concise analysis results if the owner is relaxed. For example, if the owner is relaxed, the analysis unit will display concise analysis results. The analysis unit can also display concise analysis results if the owner is busy. For example, if the owner is busy, the analysis unit will display concise analysis results. By adjusting the display method of the analysis results according to the owner's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the owner's emotional data into a generating AI, which can then estimate the emotions and adjust the display method of the analysis results.

[0085] The analysis unit can perform analysis while considering the geographical distribution of pets. For example, the analysis unit can perform analysis while considering the climatic conditions of the pets' residential areas. The analysis unit can also perform analysis while considering diseases prevalent in the pets' residential areas. The analysis unit can also perform analysis while considering environmental factors (e.g., pollen, allergens) in the pets' residential areas. By considering the geographical distribution of pets, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the geographical distribution of pets into a generating AI, and the generating AI can perform the analysis.

[0086] The analysis unit can improve the accuracy of its analysis by referring to relevant pet literature during the analysis. For example, the analysis unit can improve the accuracy of its analysis by referring to the latest research papers related to the symptoms of pets. The analysis unit can also improve the accuracy of its analysis by referring to specialized books on pet diseases. The analysis unit can also improve the accuracy of its analysis by referring to reliable websites on pet health. In this way, the accuracy of the analysis is improved by referring to relevant pet literature. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant pet literature into a generating AI, which can then improve the accuracy of the analysis.

[0087] The judgment unit can estimate the owner's emotions and determine the need for a medical examination based on the estimated emotions. For example, if the owner is feeling anxious, the judgment unit will determine a higher need for a medical examination. For example, if the owner is feeling anxious, the judgment unit will determine a higher need for a medical examination. The judgment unit can also determine the need for a medical examination at a normal level if the owner is relaxed. For example, if the owner is relaxed, the judgment unit will determine the need for a medical examination at a normal level. The judgment unit can also quickly determine the need for a medical examination if the owner is busy. For example, if the judgment unit quickly determines the need for a medical examination if the owner is busy. This allows for more appropriate decisions regarding medical examinations by determining the need for a medical examination in accordance with the owner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input the owner's emotional data into a generating AI, which can then estimate the emotions and determine the need for a medical examination.

[0088] The judgment unit can optimize its judgment algorithm by referring to the pet's past medical history during the judgment process. For example, the judgment unit prioritizes determining data related to the current symptoms based on the pet's past medical history. The judgment unit can also adjust the parameters of the judgment algorithm by referring to the pet's past medical history. The judgment unit can also improve the accuracy of its judgment for specific diseases or symptoms based on the pet's past medical history. This allows the judgment algorithm to be optimized by referring to the pet's past medical history. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input the pet's past medical history into a generating AI, which can then optimize the judgment algorithm.

[0089] The judgment unit can apply different judgment criteria depending on the type and age of the pet during the judgment process. For example, the judgment unit can apply judgment criteria for specific diseases and symptoms depending on the type of pet. For example, the judgment unit can apply judgment criteria for diseases and symptoms specific to certain breeds such as dogs and cats. The judgment unit can also apply judgment criteria for age-specific diseases and symptoms depending on the age of the pet. For example, the judgment unit can apply judgment criteria for age-specific diseases and symptoms depending on the age of the pet. The judgment unit can also select the optimal judgment criteria considering the type and age of the pet. For example, the judgment unit selects the optimal judgment criteria considering the type and age of the pet. This improves the accuracy of the judgment by applying different judgment criteria depending on the type and age of the pet. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without using AI. For example, the judgment unit can input data on the type and age of the pet into a generating AI, and the generating AI can apply different judgment criteria.

[0090] The judgment unit can estimate the owner's emotions and adjust the display method of the judgment result based on the estimated owner's emotions. For example, if the owner is feeling anxious, the judgment unit will display a detailed judgment result. For example, if the owner is feeling anxious, the judgment unit will display a detailed judgment result. The judgment unit can also display a concise judgment result if the owner is relaxed. For example, if the owner is relaxed, the judgment unit will display a concise judgment result. The judgment unit can also display a summary judgment result if the owner is busy. For example, if the owner is busy, the judgment unit will display a summary judgment result. In this way, by adjusting the display method of the judgment result according to the owner's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input the owner's emotional data into a generating AI, which can then estimate the emotions and adjust the display method of the judgment result.

[0091] The determination unit can make a determination by considering the geographical distribution of pets. For example, the determination unit can make a determination by considering the climatic conditions of the pet's residential area. The determination unit can also make a determination by considering diseases prevalent in the pet's residential area. The determination unit can also make a determination by considering environmental factors (e.g., pollen, allergens) in the pet's residential area. By considering the geographical distribution of pets, a more appropriate determination result can be provided. Some or all of the above processing in the determination unit may be performed using AI, for example, or without AI. For example, the determination unit can input data on the geographical distribution of pets into a generating AI, and the generating AI can make a determination.

[0092] The judgment unit can improve the accuracy of its judgment by referring to relevant literature on pets during the judgment process. For example, the judgment unit can improve the accuracy of its judgment by referring to the latest research papers related to the pet's symptoms. The judgment unit can also improve the accuracy of its judgment by referring to specialized books on pet diseases. The judgment unit can also improve the accuracy of its judgment by referring to reliable websites on pet health. In this way, the accuracy of the judgment is improved by referring to relevant literature on pets. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input relevant literature on pets into a generating AI, which can then improve the accuracy of its judgment.

[0093] The information provider can estimate the owner's emotions and determine the priority of the information to provide based on the estimated emotions. For example, if the owner is feeling anxious, the information provider will prioritize providing important information. For example, if the owner is feeling anxious, the information provider will prioritize providing important information. The information provider can also provide general information if the owner is relaxed. For example, if the owner is relaxed, the information provider will provide general information. The information provider can also provide concise information if the owner is busy. For example, if the owner is busy, the information provider will provide concise information. In this way, by determining the priority of the information to provide according to the owner's emotions, important information can be provided preferentially. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the service provider can input the owner's emotional data into a generating AI, which can then estimate the emotions and determine the priority of the information.

[0094] The information provider can customize the information provided by referring to the pet's past health data at the time of provision. For example, the information provider can provide information related to the pet's current symptoms based on the pet's past health data. The information provider can also provide information on preventive measures by referring to the pet's past health data. The information provider can also provide information on specific diseases or symptoms based on the pet's past health data. This allows the information provided to be customized by referring to the pet's past health data. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the pet's past health data into a generating AI, which can then customize the information provided.

[0095] The information provider can apply different information provision methods depending on the type and age of the pet at the time of provision. For example, the provider can provide information on diseases and symptoms specific to the type of pet. For example, the provider can provide information on diseases and symptoms specific to certain breeds such as dogs and cats. The provider can also provide information on age-specific diseases and symptoms depending on the age of the pet. For example, the provider can provide information on age-specific diseases and symptoms depending on the age of the pet. The provider can also select the optimal information provision method considering the type and age of the pet. For example, the provider selects the optimal information provision method considering the type and age of the pet. This allows for the provision of more appropriate information by applying different information provision methods depending on the type and age of the pet. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can input data on the type and age of the pet into a generating AI, and the generating AI can apply different information provision methods.

[0096] The information provider can estimate the owner's emotions and adjust how the information is displayed based on the estimated emotions. For example, if the owner is feeling anxious, the information provider can display detailed information. For example, if the owner is feeling anxious, the information provider can display detailed information. The information provider can also display concise information if the owner is relaxed. For example, if the owner is relaxed, the information provider can display concise information. The information provider can also display concise information if the owner is busy. For example, if the owner is busy, the information provider can display concise information. By adjusting how the information is displayed according to the owner's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the service provider can input the owner's emotional data into a generating AI, which can then estimate the emotions and adjust how the information is displayed.

[0097] The information provider can provide information while considering the geographical distribution of pets. For example, the information provider can provide information while considering the climatic conditions of the pet's residential area. The information provider can also provide information about diseases prevalent in the pet's residential area. The information provider can also provide information while considering environmental factors (e.g., pollen, allergens) in the pet's residential area. By considering the geographical distribution of pets, more appropriate information can be provided. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input data on the geographical distribution of pets into a generating AI, and the generating AI can provide the information.

[0098] The information provider can improve the accuracy of the information provided by referring to relevant pet literature at the time of provision. For example, the information provider can improve the accuracy of the information by referring to the latest research papers related to pet symptoms. The information provider can also improve the accuracy of the information by referring to specialized books on pet diseases. The information provider can also improve the accuracy of the information by referring to reliable websites on pet health. In this way, the accuracy of the information provided is improved by referring to relevant pet literature. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input relevant pet literature into a generating AI, which can then improve the accuracy of the information.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The reception unit can provide a voice input function when pet owners enter information about their pet's symptoms and daily life. For example, when a pet owner describes their pet's symptoms verbally into the app, the reception unit converts the voice data into text and sends it to the analysis unit. The reception unit can also analyze the tone and speed of the pet owner's voice during voice input to estimate the pet owner's emotions. For example, if the pet owner's voice is unstable, the reception unit will estimate their emotions and send it to the analysis unit. This allows pet owners to report their pet's symptoms more easily by using voice input, and the emotion estimation function can be used to improve the accuracy of the analysis.

[0101] The analysis unit can consider the pet's exercise data when analyzing its health status. For example, data obtained from an activity tracker attached to the pet can be transmitted to the analysis unit, and the pet's exercise level and activity patterns can be used in the analysis. The analysis unit can also combine the pet's exercise data with symptom data for analysis. For example, if the pet's exercise level suddenly decreases, that data can be compared with symptom data for analysis. This allows for a more accurate analysis of the pet's health status by considering the pet's exercise data.

[0102] The assessment unit can consider the pet's dietary data when determining the pet's health status. For example, the owner can input data on the pet's diet and intake into the app and send it to the assessment unit for use in the assessment. The assessment unit can also combine the pet's dietary data with symptom data to make a determination. For example, if the pet's food intake has decreased, this data can be compared with the symptom data to make a determination. This allows for a more accurate assessment of the pet's health status by considering the pet's dietary data.

[0103] The service provider can provide customized information by referring to the pet's past health data when providing pet health information. For example, it can provide information related to the pet's current symptoms based on the pet's past health data. The service provider can also provide information on preventative measures by referring to the pet's past health data. For example, it can provide information on specific diseases or symptoms based on the pet's past health data. This allows for the customization of the information provided by referring to the pet's past health data.

[0104] The reception system can display input fields tailored to the pet's breed and age when pet owners enter their pet's symptoms. For example, if the pet is elderly, specific symptom input fields corresponding to its age can be added. The reception system can also display input fields related to specific diseases and symptoms depending on the pet's breed. For example, it can display input fields related to diseases and symptoms specific to certain breeds such as dogs and cats. This allows for more accurate information to be entered by customizing the input fields based on the pet's breed and age.

[0105] The analysis unit can estimate the owner's emotions when analyzing the pet's health status and adjust the accuracy of the analysis based on the estimated emotions. For example, if the owner is feeling anxious, the accuracy of the analysis can be increased to provide more detailed results. Conversely, if the owner is relaxed, the accuracy of the analysis can be maintained at a normal level. In this way, by adjusting the accuracy of the analysis according to the owner's emotions, more appropriate analysis results can be provided.

[0106] The assessment unit can estimate the owner's emotions when determining the pet's health condition and determine the need for a veterinary visit based on those emotions. For example, if the owner is feeling anxious, the need for a visit will be judged as higher. Conversely, if the owner is relaxed, the need for a visit can be judged as normal. This allows for more appropriate decisions regarding the need for a visit by judging it in accordance with the owner's emotions.

[0107] The information provider can estimate the owner's emotions when providing pet health information and prioritize the information to be provided based on those emotions. For example, if the owner is feeling anxious, important information will be provided first. Conversely, if the owner is relaxed, general information can be provided. This allows the system to prioritize important information by determining the priority of information provided according to the owner's emotions.

[0108] The reception desk can analyze the owner's past input history when they enter their pet's symptoms and suggest the most suitable input method. For example, it can prioritize suggesting input methods that the owner has frequently used in the past (voice, text, etc.). It can also analyze patterns of symptoms the owner has entered in the past and customize the input fields. In this way, by analyzing the owner's past input history, it can suggest the most suitable input method.

[0109] The service provider can provide pet health information while considering the geographical distribution of pets. For example, it can provide information while considering the climatic conditions of the pet's residential area. It can also provide information on diseases prevalent in the pet's residential area. By considering the geographical distribution of pets, it is possible to provide more appropriate information.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The reception desk allows pet owners to input their pet's symptoms and daily habits. For example, pet owners use an app to input their pet's symptoms and daily habits. The app is designed to be user-friendly for pet owners through features such as a well-designed input screen and data storage methods. Step 2: The analysis unit analyzes the data entered by the reception unit. The analysis unit performs the analysis based on past data and similar cases. For example, it may use statistical analysis of the data or machine learning algorithms. Step 3: The assessment unit determines the need for a veterinary visit based on the data analyzed by the analysis unit. The assessment unit makes its decision based on criteria such as the severity and duration of symptoms. For example, it recommends a veterinary visit if loss of appetite persists. Step 4: The providing unit provides information on seasonal symptoms and prevalent diseases based on the results obtained by the assessment unit. The providing unit provides information for pet owners to take preventive measures. For example, it provides information on seasonal allergies and prevalent infectious diseases.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the reception unit, analysis unit, determination unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, where the owner inputs the pet's symptoms and daily condition into the app. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, where it analyzes the data input by the reception unit. The determination unit is implemented by the identification processing unit 290 of the data processing unit 12, where it determines the need for a veterinary visit based on the analyzed data. The provision unit is implemented by the control unit 46A of the smart device 14, where it provides information on seasonal symptoms and prevalent diseases based on the results obtained by the determination unit. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the reception unit, analysis unit, judgment unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, where the owner inputs the pet's symptoms and daily condition into the app. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, where it analyzes the data input by the reception unit. The judgment unit is implemented by the identification processing unit 290 of the data processing unit 12, where it determines the need for a veterinary visit based on the analyzed data. The provision unit is implemented by the control unit 46A of the smart glasses 214, where it provides information on seasonal symptoms and prevalent diseases based on the results obtained by the judgment unit. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] Each of the multiple elements described above, including the reception unit, analysis unit, judgment unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, where the owner inputs the pet's symptoms and daily condition into the app. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, where it analyzes the data input by the reception unit. The judgment unit is implemented by the identification processing unit 290 of the data processing unit 12, where it determines the need for a veterinary visit based on the analyzed data. The provision unit is implemented by the control unit 46A of the headset terminal 314, where it provides information on seasonal symptoms and prevalent diseases based on the results obtained by the judgment unit. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the reception unit, analysis unit, judgment unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, where the owner inputs the pet's symptoms and daily condition into the app. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, where it analyzes the data input by the reception unit. The judgment unit is implemented by the identification processing unit 290 of the data processing unit 12, where it determines the need for a veterinary visit based on the analyzed data. The provision unit is implemented by the control unit 46A of the robot 414, where it provides information on seasonal symptoms and prevalent diseases based on the results obtained by the judgment unit. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] (Note 1) The reception area is where pet owners enter information about their pet's symptoms and daily life, An analysis unit analyzes the data input by the reception unit, A determination unit that determines the necessity of a veterinary visit based on the data analyzed by the aforementioned analysis unit, The system includes a providing unit that provides information on seasonal symptoms and epidemic diseases based on the results obtained by the determination unit. A system characterized by the following features. (Note 2) The aforementioned supply unit is, Provides information on seasonal allergies and prevalent infectious diseases. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The analysis is based on past data and similar cases. The system described in Appendix 1, characterized by the features described herein. (Note 4) The determination unit, If loss of appetite persists, we recommend consulting a veterinarian. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is The pet owner enters information about their pet's symptoms and daily life into the app. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Provides information to help pet owners take preventative measures. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the owner's emotions and adjusts the timing of symptom input based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is We analyze the owner's past input history and suggest the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering symptoms, the input fields are customized based on the pet's breed and age. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates the owner's emotions and determines the priority of the symptoms to input based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering symptoms, the system prioritizes the input of symptoms that are most relevant, taking into account the owner's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When entering symptoms, the system analyzes the owner's social media activity and inputs relevant symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the owner's emotions and adjusts the accuracy of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by referring to 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, The system estimates the owner's emotions and adjusts the display method of the analysis results based on the estimated emotions of the owner. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the geographical distribution of pets will be taken into consideration. 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 determination unit, The system estimates the owner's emotions and determines the need for a medical examination based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The determination unit, During the assessment process, the assessment algorithm is optimized by referring to the pet's past medical history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The determination unit, When making a determination, different criteria will be applied depending on the type and age of the pet. The system described in Appendix 1, characterized by the features described herein. (Note 22) The determination unit, The system estimates the owner's emotions and adjusts the display method of the judgment results based on the estimated emotions of the owner. The system described in Appendix 1, characterized by the features described herein. (Note 23) The determination unit, When making a determination, the geographical distribution of pets is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The determination unit, When making a diagnosis, we refer to relevant literature on pets to improve the accuracy of the diagnosis. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, The system estimates the owner's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing information, the system customizes the information provided by referencing the pet's past health data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing information, different information delivery methods will be applied depending on the type and age of the pet. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, The system estimates the owner's emotions and adjusts how the information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing information, consider the geographical distribution of pets. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing information, we will refer to relevant pet-related literature to improve the accuracy of the information provided. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0184] 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 reception area is where pet owners enter information about their pet's symptoms and daily life, An analysis unit analyzes the data input by the reception unit, A determination unit that determines the necessity of a veterinary visit based on the data analyzed by the aforementioned analysis unit, The system includes a providing unit that provides information on seasonal symptoms and epidemic diseases based on the results obtained by the determination unit. A system characterized by the following features.

2. The aforementioned supply unit is, Provides information on seasonal allergies and prevalent infectious diseases. The system according to feature 1.

3. The aforementioned analysis unit, The analysis is based on past data and similar cases. The system according to feature 1.

4. The determination unit, If loss of appetite persists, we recommend consulting a veterinarian. The system according to feature 1.

5. The aforementioned reception unit is The pet owner enters information about their pet's symptoms and daily life into the app. The system according to feature 1.

6. The aforementioned supply unit is, Provides information to help pet owners take preventative measures. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the owner's emotions and adjusts the timing of symptom input based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is We analyze the owner's past input history and suggest the optimal input method. The system according to feature 1.

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