System

Abstract

An object of a system according to an embodiment is to comprehensively manage a health condition of a pet and provide appropriate advice and service.SOLUTION: A system includes a reception part, an analysis part, a provision part, an introduction part, a selection part, and a consultation part. The reception unit inputs information on a health condition of a pet. The analysis part analyzes the information input by the reception part to grasp the health condition. The providing unit provides advice based on the health condition grasped by the analysis unit. The introduction unit introduces appropriate pet food based on the advice provided by the providing unit. The selection unit selects the nearest animal hospital based on the advice provided by the providing unit. The consultation unit proposes online consultation with the veterinarian based on the advice provided by the providing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have had the problem of not being able to adequately manage pet health conditions comprehensively and provide appropriate advice and services.

[0005] The system according to the embodiment aims to comprehensively manage the health condition of pets and provide appropriate advice and services. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a provision unit, an introduction unit, a selection unit, and a consultation unit. The reception unit inputs information regarding the pet's health condition. The analysis unit analyzes the information input by the reception unit to determine the health condition. The provision unit provides advice based on the health condition determined by the analysis unit. The introduction unit recommends appropriate pet food based on the advice provided by the provision unit. The selection unit selects the nearest veterinary clinic based on the advice provided by the provision unit. The consultation unit suggests consulting a veterinarian online based on the advice provided by the provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can comprehensively manage the health condition of pets and provide appropriate advice and services. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple 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), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) An interactive consultation system according to an embodiment of the present invention efficiently assesses a pet's health condition and provides appropriate advice and support. In this system, pet owners input information about their pet's health, and AI analyzes that information and provides advice. For example, if a pet is not feeling well, the owner inputs its symptoms, and AI provides appropriate advice based on that information. Owners can also receive advice on choosing pet food and selecting a veterinary clinic. For example, by inputting information such as the pet's weight, food intake, and exercise, the AI ​​evaluates the pet's health condition. Next, when the owner inputs concerns or questions about the pet's health, the AI ​​analyzes that information and provides appropriate advice. For example, if a pet is not feeling well, the AI ​​suggests appropriate measures based on the pet's symptoms. Furthermore, when the owner inputs information about the pet's diet, the AI ​​analyzes that information and recommends the optimal pet food. For example, the AI ​​may recommend an appropriate pet food based on the pet's age, weight, allergies, and other factors. Next, when the owner inputs information about the pet's health condition, the AI ​​analyzes that information and recommends the nearest veterinary clinic. For example, based on the pet's symptoms, the AI ​​may suggest the nearest veterinary clinic where the pet can be taken immediately in case of an emergency. Finally, when owners input information about their pet's health, the AI ​​analyzes that information and suggests online veterinarian consultations. For example, if an online consultation is appropriate based on the pet's symptoms, the AI ​​will suggest an online consultation with a veterinarian. This allows the interactive consultation system to fully grasp the pet's health condition and provide appropriate advice. Owners can also receive support in selecting pet food and veterinary clinics, making pet health management more efficient.

[0029] The interactive consultation system according to the embodiment includes a reception unit, an analysis unit, a provision unit, an introduction unit, a selection unit, and a consultation unit. The reception unit inputs information about the pet's health condition. The information about the pet's health condition includes, but is not limited to, for example, weight, amount of food eaten, amount of exercise, excretion status, and body temperature. The reception unit inputs, for example, the pet's weight. The reception unit can also input the amount of food eaten by the pet. The reception unit can also input the amount of exercise done by the pet. The analysis unit analyzes the information input by the reception unit to determine the health condition. The analysis unit evaluates the health condition based on information such as the pet's weight, amount of food eaten, and amount of exercise. The analysis unit can also evaluate the health condition based on information such as the pet's body temperature and excretion status. The analysis unit can also evaluate the pet's health condition using AI. The provision unit provides advice based on the health condition determined by the analysis unit. For example, if the pet is not feeling well, the provision unit suggests an appropriate solution based on the pet's symptoms. The providing unit can also provide advice such as dietary changes and exercise recommendations based on the pet's health condition. The providing unit can also use AI to provide advice based on the pet's health condition. The introduction unit recommends optimal pet food based on the advice provided by the providing unit. The introduction unit can recommend appropriate pet food, for example, taking into account the pet's age, weight, allergy information, etc. The introduction unit can also recommend appropriate pet food, for example, taking into account the pet's dietary preferences, etc. The introduction unit can also use AI to recommend optimal pet food based on the pet's health condition. The selection unit selects the nearest veterinary clinic based on the advice provided by the providing unit. The selection unit can recommend the nearest veterinary clinic, for example, based on the pet's symptoms and urgency. The selection unit can also recommend the nearest veterinary clinic based on the pet's health condition. The selection unit can also use AI to select the nearest veterinary clinic based on the pet's health condition. The consultation unit suggests online consultation with a veterinarian based on the advice provided by the providing unit.For example, the consultation unit suggests online consultation with a veterinarian if online consultation is appropriate based on the pet's symptoms. The consultation unit can also suggest online consultation based on the pet's health condition. The consultation unit can also use AI to suggest online consultation with a veterinarian based on the pet's health condition. This allows the interactive consultation system according to the embodiment to efficiently understand the pet's health condition and provide appropriate advice and support.

[0030] The receiving unit can input information on the pet's weight, amount of food eaten, amount of exercise, excretion status, and body temperature. The weight information includes, for example, inputting the pet's weight in kilograms. The receiving unit, for example, measures the pet's weight and inputs the value. The food amount information includes, for example, inputting the amount of food the pet consumes per day in grams. The receiving unit, for example, measures the pet's amount of food eaten and inputs the value. The exercise amount information includes, for example, inputting the amount of exercise the pet does per day in hours. The receiving unit, for example, measures the pet's exercise time and inputs the value. The excretion status information includes, for example, inputting the number of times the pet excretes and the status. The receiving unit, for example, records the number of times the pet excretes and inputs the value. The body temperature information includes, for example, inputting the pet's body temperature in degrees Celsius. The receiving unit, for example, measures the pet's body temperature and inputs the value. This allows for more accurate analysis by inputting detailed health information about the pet.

[0031] The analysis unit can evaluate the health condition of the pet based on the information input by the reception unit. The evaluation of the health condition includes, for example, evaluation using a scoring system based on information such as the pet's weight, amount of food, and amount of exercise. The analysis unit inputs information such as the pet's weight, amount of food, and amount of exercise, and evaluates the health condition using a scoring system. The evaluation of the health condition also includes evaluation using a diagnostic algorithm based on information such as the pet's body temperature and excretion status. The analysis unit inputs information such as the pet's body temperature and excretion status, and evaluates the health condition using a diagnostic algorithm. The evaluation of the health condition also includes evaluation of the pet's health condition using AI. The analysis unit inputs information such as the pet's weight, amount of food, and amount of exercise into AI, and the AI ​​evaluates the health condition. This allows the pet's health condition to be accurately evaluated based on the input information.

[0032] When a pet is in poor health, the providing unit can suggest an appropriate countermeasure based on the symptoms. Specific symptoms of poor health include, for example, loss of appetite, vomiting, diarrhea, etc. The providing unit can suggest an appropriate countermeasure based on, for example, the pet's loss of appetite. The providing unit can also suggest an appropriate countermeasure based on the pet's vomiting symptom. The providing unit can also suggest an appropriate countermeasure based on the pet's diarrhea symptom. Appropriate countermeasures include, for example, first aid, administration of medicine, and visiting a hospital. For example, the providing unit can suggest hydration as first aid for a pet with loss of appetite. The providing unit can also suggest administration of medicine for a pet vomiting. The providing unit can also suggest visiting a hospital for a pet with diarrhea. This makes it possible to respond quickly by suggesting an appropriate countermeasure when a pet is in poor health.

[0033] The introduction unit can recommend appropriate pet food by taking into consideration the pet's age, weight, allergy information, and dietary preferences. Age information includes, for example, inputting the pet's age in months. The introduction unit, for example, inputs the pet's age and recommends appropriate pet food based on that information. Weight information includes, for example, inputting the pet's weight in kilograms. The introduction unit, for example, inputs the pet's weight and recommends appropriate pet food based on that information. Allergy information includes, for example, inputting information on ingredients to which the pet is allergic. The introduction unit, for example, inputs the pet's allergy information and recommends appropriate pet food based on that information. Dietary preferences include, for example, inputting information on the ingredients and flavors that the pet prefers. The introduction unit, for example, inputs the pet's dietary preferences and recommends appropriate pet food based on that information. This makes it possible to recommend optimal pet food according to the individual needs of the pet.

[0034] The selection unit can suggest the nearest veterinary hospital based on the pet's symptoms and urgency. Symptom information includes, for example, the pet's fever, cough, diarrhea, etc. The selection unit can suggest the nearest veterinary hospital based on, for example, the pet's fever symptom. The selection unit can also suggest the nearest veterinary hospital based on the pet's cough symptom. The selection unit can also suggest the nearest veterinary hospital based on the pet's diarrhea symptom. Urgency information includes, for example, the severity of the symptoms and the time since the onset of the symptoms. For example, if the pet's symptoms are severe, the selection unit can suggest a veterinary hospital that can handle emergency care. If the pet's symptoms are mild, the selection unit can also suggest a veterinary hospital that can provide regular medical care. If the pet's symptoms are unknown, the selection unit can also suggest a veterinary hospital that can provide a detailed diagnosis. This makes it possible to suggest the most appropriate veterinary hospital based on the pet's symptoms and urgency.

[0035] The consultation unit can suggest an online consultation with a veterinarian when online consultation is appropriate. Online consultations include, for example, methods such as video calls, chats, and emails. For example, the consultation unit can suggest an online consultation via video calls based on the pet's symptoms. The consultation unit can also suggest an online consultation via chat based on the pet's health condition. The consultation unit can also suggest an online consultation via email based on the pet's symptoms. This makes it possible to quickly suggest a consultation with a veterinarian when online consultation is appropriate.

[0036] The reception unit can analyze the owner's past input history and select the optimal input method. For example, the reception unit can automatically display information that the owner has frequently input in the past as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the owner has used in the past. The reception unit can also predict and suggest information that will be used during a specific time period based on the owner's past input history. This enables efficient information input by selecting the optimal input method based on the past input history. The analysis of the past input history is performed based on data such as input frequency and input content. For example, the reception unit inputs the owner's past input data into the generation AI, which then selects the optimal input method. The reception unit can also allow the generation AI to suggest input methods based on the owner's past input history.

[0037] The reception unit can customize input items based on the type and age of the pet when inputting information. For example, if the pet is a puppy, the reception unit can prioritize input of information about the pet's growth (weight, amount of food, etc.). Furthermore, if the pet is elderly, the reception unit can also input detailed information about the pet's health (body temperature, excretion status, etc.). The reception unit can also input information about specific health risks depending on the pet's type. This allows more appropriate information to be input by customizing input items according to the pet's type and age. Information about the pet's type and age is based on, for example, classifications such as dog, cat, and bird, or age categories such as puppy, adult dog, and senior dog. For example, the reception unit inputs data about the pet's type and age into the generation AI, and the generation AI customizes the input items. Furthermore, the reception unit can also allow the generation AI to suggest input items based on the pet's type and age.

[0038] The reception unit can select the optimal input means according to the owner's input method when inputting information. For example, if the owner prefers voice input, the reception unit can prioritize support for voice input. Also, if the owner prefers text input, the reception unit can also prioritize support for text input. Also, if the owner prefers image input, the reception unit can allow the owner to enter information by uploading a photo of the pet. This enables efficient information input by selecting the optimal input means according to the owner's input method. The selection of the owner's input method is based on the type, such as voice input, text input, or image input. For example, the reception unit inputs data on the owner's input method into the generation AI, which then selects the optimal input means. Also, the reception unit can allow the generation AI to suggest an input means based on the owner's input method.

[0039] When inputting information, the reception unit can prioritize inputting highly relevant information by taking into account the owner's geographical location information. For example, if the owner lives in an urban area, the reception unit can prioritize inputting information about the pet's exercise amount. Furthermore, if the owner lives in a rural area, the reception unit can prioritize inputting information about the pet's safety when going out. Furthermore, if the owner lives in a specific region, the reception unit can prioritize inputting information about health risks specific to that region. In this way, highly relevant information can be prioritized by taking the owner's geographical location information into consideration. The owner's geographical location information can be acquired based on, for example, GPS data or address information. For example, the reception unit inputs the owner's geographical location data to the generation AI, which then suggests highly relevant information. Furthermore, the reception unit can also allow the generation AI to customize input items based on the owner's geographical location information.

[0040] When inputting information, the reception unit can analyze the owner's social media activity and input relevant information. For example, if the owner frequently posts photos of their pet on social media, the reception unit can extract information about the pet's health from the photos. Furthermore, if the owner posts about their pet's diet on social media, the reception unit can input the amount and type of food based on that information. Furthermore, if the owner posts about their pet's exercise on social media, the reception unit can input the amount of exercise based on that information. This allows relevant information to be input efficiently by analyzing the owner's social media activity. Social media activity is analyzed based on data such as the content of posts and the number of followers. For example, the reception unit inputs the owner's social media data into the generation AI, which then extracts relevant information. Furthermore, the reception unit can allow the generation AI to suggest input items based on the owner's social media activity.

[0041] The reception unit can customize the input method by reflecting the owner's past feedback when inputting information. For example, if the owner has provided feedback on the input method in the past, the reception unit can improve the input method based on that feedback. Furthermore, if the owner has previously reported a problem with input, the reception unit can suggest an input method to resolve the problem. Furthermore, if the owner has previously submitted a request regarding input, the reception unit can provide an input method that reflects that request. In this way, by reflecting the owner's past feedback, a more appropriate input method can be provided. Analysis of past feedback is performed based on data such as satisfaction ratings and areas for improvement. For example, the reception unit inputs the owner's past feedback data into the generation AI, which then customizes the input method. Furthermore, the reception unit can also allow the generation AI to suggest an input method based on the owner's past feedback.

[0042] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the pet's health condition. For example, if the pet's health condition is good, the analysis unit provides basic analysis results. Furthermore, if the pet's health condition is deteriorating, the analysis unit can provide detailed analysis results and suggest specific measures. Furthermore, if the pet's health condition is unknown, the analysis unit can collect detailed information and provide analysis results. In this way, by adjusting the level of detail of the analysis according to the importance of the pet's health condition, appropriate analysis results can be provided. The importance of the health condition is evaluated based on, for example, the severity and urgency of the symptoms. For example, the analysis unit inputs the pet's symptom data into the generation AI, and the generation AI evaluates the importance of the health condition. Furthermore, the analysis unit can allow the generation AI to adjust the level of detail of the analysis based on the pet's health condition data.

[0043] During analysis, the analysis unit can apply different analysis algorithms depending on the type and age of the pet. For example, in the case of a puppy, the analysis unit can apply an analysis algorithm related to growth to evaluate the health condition. In addition, in the case of an elderly pet, the analysis unit can apply an analysis algorithm related to aging to evaluate health risks. In addition, in the case of a specific type of pet, the analysis unit can apply an analysis algorithm related to health risks specific to that type. In this way, by applying an analysis algorithm according to the type and age of the pet, more accurate analysis results can be provided. The application of the analysis algorithm is based on, for example, a machine learning algorithm or a statistical analysis algorithm. For example, the analysis unit inputs data on the type and age of the pet into the generation AI, and the generation AI applies an appropriate analysis algorithm. In addition, the analysis unit can allow the generation AI to select an analysis algorithm based on the type and age of the pet.

[0044] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the owner's past analysis results. For example, the analysis unit adjusts the analysis algorithm based on health information provided by the owner in the past, thereby improving accuracy. The analysis unit can also improve the analysis algorithm based on the results of advice the owner received in the past. The analysis unit can also optimize the analysis algorithm based on feedback provided by the owner in the past. In this way, the accuracy of the analysis can be improved by referring to the owner's past analysis results. Past analysis results are used based on data such as diagnosis history and treatment history. For example, the analysis unit inputs the owner's past analysis data into the generation AI, which then adjusts the analysis algorithm. The analysis unit can also allow the generation AI to improve the accuracy of the analysis based on the owner's past analysis results.

[0045] During analysis, the analysis unit can determine the priority of analysis based on the timing of submission of the pet's health status. For example, in the case of a highly urgent health condition, the analysis unit prioritizes analysis and provides results quickly. The analysis unit can also perform analysis with normal priority in the case of a regular health check. The analysis unit can also adjust the priority of analysis based on health information submitted by the owner at a specific time. This enables a prompt response by determining the priority of analysis based on the timing of submission of the pet's health status. The evaluation of the submission timing is based on, for example, the submission date or submission time. For example, the analysis unit inputs data on the submission timing of the pet's health information into the generation AI, and the generation AI determines the priority of analysis. The analysis unit can also allow the generation AI to adjust the order of analysis based on the submission timing.

[0046] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the pet's health condition. For example, if the pet's health condition is deteriorating, the analysis unit prioritizes analysis of relevant information. Furthermore, if the pet's health condition is good, the analysis unit can also prioritize analysis of basic information. Furthermore, if the pet's health condition is unknown, the analysis unit can also prioritize analysis of detailed information. In this way, by adjusting the order of analysis based on the relevance of the pet's health condition, important information can be prioritized for analysis. The evaluation of the relevance of health conditions is based on, for example, commonalities in symptoms and medical history. For example, the analysis unit inputs data on the pet's health condition into the generation AI, and the generation AI prioritizes analysis of highly relevant information. Furthermore, the analysis unit can also adjust the order of analysis by the generation AI based on the relevance of health conditions.

[0047] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the owner's level of expertise. For example, if the owner has technical expertise, the analysis unit can provide the analysis results using detailed technical terminology. Alternatively, if the owner does not have technical expertise, the analysis unit can provide the analysis results in simpler terms. The analysis unit can also adjust the way the analysis results are presented according to the owner's level of expertise. This makes it possible to provide analysis results that are easier to understand by adjusting the use of technical terminology in the analysis according to the owner's level of expertise. The expertise level is evaluated based on classifications such as beginner, intermediate, and advanced. For example, the analysis unit inputs data on the owner's level of expertise into the generation AI, which then selects appropriate technical terminology. The analysis unit can also adjust the way the generation AI presents the analysis results based on the owner's level of expertise.

[0048] When providing advice, the providing unit can adjust the level of detail of the advice based on the importance of the pet's health condition. For example, if the pet's health condition is good, the providing unit can provide basic advice. Furthermore, if the pet's health condition is deteriorating, the providing unit can provide detailed advice and suggest specific measures. Furthermore, if the pet's health condition is unknown, the providing unit can collect detailed information and provide advice. In this way, appropriate advice can be provided by adjusting the level of detail of the advice according to the importance of the pet's health condition. The importance of the health condition is evaluated based on, for example, the severity and urgency of the symptoms. For example, the providing unit inputs data on the pet's symptoms into the generating AI, and the generating AI evaluates the importance of the health condition. Furthermore, the providing unit can cause the generating AI to adjust the level of detail of the advice based on the data on the pet's health condition.

[0049] When providing advice, the providing unit can apply different advice algorithms depending on the type and age of the pet. For example, in the case of a puppy, the providing unit can apply an advice algorithm related to growth to evaluate the health condition. In addition, in the case of an elderly pet, the providing unit can apply an advice algorithm related to aging to evaluate health risks. In addition, in the case of a specific type of pet, the providing unit can apply an advice algorithm related to health risks specific to that type. In this way, more appropriate advice can be provided by applying an advice algorithm according to the type and age of the pet. The application of the advice algorithm is based on, for example, a machine learning algorithm or a rule-based algorithm. For example, the providing unit inputs data on the type and age of the pet into the generating AI, and the generating AI applies an appropriate advice algorithm. In addition, the providing unit can cause the generating AI to select an advice algorithm based on the type and age of the pet.

[0050] When providing advice, the providing unit can improve the accuracy of the advice by referring to the owner's past advice results. For example, the providing unit can adjust the advice algorithm based on the results of advice the owner received in the past, thereby improving accuracy. The providing unit can also improve the advice algorithm based on feedback provided by the owner in the past. The providing unit can also evaluate the effectiveness of advice the owner received in the past and optimize the advice algorithm. In this way, the accuracy of the advice can be improved by referring to the owner's past advice results. The use of past advice results is based on data such as diagnosis history and treatment history. For example, the providing unit inputs the owner's past advice data into the generating AI, which then adjusts the advice algorithm. The providing unit can also cause the generating AI to improve the accuracy of the advice based on the owner's past advice results.

[0051] When providing advice, the providing unit can determine the priority of the advice based on the time of submission of the pet's health condition. For example, the providing unit provides advice with priority in the case of a highly urgent health condition. The providing unit can also provide advice with normal priority in the case of a regular health check. The providing unit can also adjust the priority of the advice based on the health information submitted by the owner at a specific time. This enables a prompt response by determining the priority of advice based on the time of submission of the pet's health condition. The evaluation of the submission time is based on, for example, the submission date or submission time. For example, the providing unit inputs data on the time of submission of the pet's health information into the generating AI, and the generating AI determines the priority of the advice. The providing unit can also allow the generating AI to adjust the order of advice based on the submission time.

[0052] When providing advice, the providing unit can adjust the order of advice based on the relevance of the pet's health condition. For example, if the pet's health condition is deteriorating, the providing unit can prioritize advice on relevant information. Furthermore, if the pet's health condition is good, the providing unit can prioritize advice on basic information. Furthermore, if the pet's health condition is unknown, the providing unit can prioritize advice on detailed information. In this way, by adjusting the order of advice based on the relevance of the pet's health condition, it is possible to prioritize advice on important information. The evaluation of the relevance of the health condition is performed based on, for example, commonalities in symptoms and medical history. For example, the providing unit inputs data on the pet's health condition into the generation AI, and the generation AI prioritizes advice on highly relevant information. Furthermore, the providing unit can cause the generation AI to adjust the order of advice based on the relevance of the health condition.

[0053] When providing advice, the providing unit can adjust the use of technical terms in the advice depending on the owner's level of expertise. For example, if the owner has technical expertise, the providing unit can provide the advice using detailed technical terms. Also, if the owner does not have technical expertise, the providing unit can provide the advice in simple language. The providing unit can also adjust the way the advice is expressed depending on the owner's level of expertise. In this way, by adjusting the use of technical terms in the advice depending on the owner's level of expertise, it is possible to provide advice that is easier to understand. The expertise level is evaluated based on classifications such as beginner, intermediate, and advanced. For example, the providing unit inputs data on the owner's level of expertise into the generating AI, which then selects appropriate technical terms. The providing unit can also cause the generating AI to adjust the way the advice is expressed based on the owner's level of expertise.

[0054] When introducing pet food, the introduction unit can select the most appropriate pet food by taking into consideration the pet's health condition and allergy information. For example, if the pet has allergies, the introduction unit can prioritize introducing allergy-friendly pet food. Furthermore, if the pet's health condition is deteriorating, the introduction unit can also introduce pet food that supports health. Furthermore, if the pet's health condition is good, the introduction unit can also introduce balanced pet food. This allows for the selection of a more appropriate pet food by taking the pet's health condition and allergy information into consideration. Health condition evaluation is based on, for example, body temperature, appetite, activity level, etc. For example, the introduction unit inputs data on the pet's health condition into the generation AI, which then selects the most appropriate pet food. Allergy information evaluation is based on, for example, the type of allergen and symptoms, etc. For example, the introduction unit can input the pet's allergy data into the generation AI, which then selects allergy-friendly pet food.

[0055] When introducing pet food, the introduction unit can customize the pet food to be introduced based on the pet's age and weight. For example, for a puppy, the introduction unit can introduce pet food that supports growth. For an elderly pet, the introduction unit can also introduce pet food that supports aging. The introduction unit can also introduce pet food with an appropriate calorie content based on the pet's weight. This allows for customizing pet food based on the pet's age and weight, making it possible to introduce more appropriate pet food. Age evaluation is performed based on classifications such as puppy, adult dog, and senior dog. For example, the introduction unit inputs the pet's age data into the generation AI, which then selects appropriate pet food. Weight evaluation is performed based on units such as kilograms and pounds. For example, the introduction unit can input the pet's weight data into the generation AI, which then selects pet food with an appropriate calorie content.

[0056] The introduction unit can improve the introduction method by reflecting the owner's past feedback when introducing pet food. For example, the introduction unit improves the pet food introduction method based on feedback provided by the owner in the past. The introduction unit can also introduce the most suitable pet food based on the owner's evaluations of pet foods purchased in the past. The introduction unit can also customize the pet food introduction method by reflecting requests provided by the owner in the past. In this way, by reflecting the owner's past feedback, a more appropriate pet food introduction method can be provided. The analysis of past feedback is performed based on data such as satisfaction ratings and areas for improvement. For example, the introduction unit inputs the owner's past feedback data into the generation AI, which then improves the introduction method. The introduction unit can also customize the introduction method by the generation AI based on the owner's past feedback.

[0057] When introducing pet food, the introduction unit can select the most appropriate pet food by taking into account the pet's geographical location information. For example, if the owner lives in an urban area, the introduction unit can introduce pet food that is easily available for purchase. Furthermore, if the owner lives in a rural area, the introduction unit can also introduce pet food that can be purchased online. Furthermore, if the owner lives in a specific region, the introduction unit can also introduce pet food that is popular in that region. This makes it possible to select a more appropriate pet food by taking into account the pet's geographical location information. Geographical location information is acquired based on, for example, GPS data or address information. For example, the introduction unit inputs the owner's geographical location data into the generation AI, which then selects the most appropriate pet food. Furthermore, the introduction unit can allow the generation AI to suggest pet food based on the geographical location information.

[0058] When recommending pet food, the recommendation unit can analyze the pet's social media activity and recommend related pet foods. For example, if the owner posts about their pet's diet on social media, the recommendation unit can recommend pet foods based on that information. Also, if the owner posts about their pet's health on social media, the recommendation unit can recommend pet foods that support health based on that information. Also, if the owner posts about their pet's preferences on social media, the recommendation unit can recommend pet foods based on that information. In this way, by analyzing the pet's social media activity, more appropriate pet foods can be recommended. Social media activity analysis is performed based on data such as the content of posts and the number of followers. For example, the recommendation unit inputs the owner's social media data into the generation AI, which selects related pet foods. Also, the introduction unit can have the generation AI suggest pet foods based on the social media activity.

[0059] The introduction unit can customize the introduction method by reflecting the pet's past feedback when introducing pet food. For example, the introduction unit improves the pet food introduction method based on feedback provided by the owner in the past. The introduction unit can also introduce the most suitable pet food based on the owner's evaluation of pet food purchased in the past. The introduction unit can also customize the pet food introduction method by reflecting requests provided by the owner in the past. In this way, by reflecting the pet's past feedback, a more appropriate pet food introduction method can be provided. Analysis of past feedback is performed based on data such as satisfaction ratings and areas for improvement. For example, the introduction unit inputs the owner's past feedback data into the generation AI, which then improves the introduction method. The introduction unit can also have the generation AI customize the introduction method based on the owner's past feedback.

[0060] When selecting a veterinary clinic, the selection unit can select the most appropriate veterinary clinic by taking into consideration the pet's health condition and urgency. For example, if the pet's health condition is deteriorating, the selection unit can prioritize selecting a veterinary clinic that can provide emergency care. Furthermore, if the pet's health condition is good, the selection unit can also select a veterinary clinic that can provide regular health checks. Furthermore, if the pet's health condition is unknown, the selection unit can also select a veterinary clinic that can provide detailed diagnoses. This allows for the selection of a more appropriate veterinary clinic by taking the pet's health condition and urgency into consideration. The health condition can be evaluated based on, for example, body temperature, appetite, activity level, etc. For example, the selection unit inputs data on the pet's health condition into the generation AI, which then selects the most appropriate veterinary clinic. The urgency can be evaluated based on, for example, the severity of symptoms and the time since onset. For example, the selection unit can input data on the pet's urgency into the generation AI, which then selects a veterinary clinic that can provide emergency care.

[0061] When selecting a veterinary clinic, the selection unit can customize the veterinary clinic to be selected based on the type and age of the pet. For example, in the case of a puppy, the selection unit selects a veterinary clinic that is skilled in diagnosing growth. In addition, in the case of an elderly pet, the selection unit can select a veterinary clinic that is skilled in diagnosing aging. In addition, in the case of a specific type of pet, the selection unit can select a veterinary clinic that is skilled in diagnosing health risks specific to that type. In this way, by customizing the veterinary clinic based on the type and age of the pet, a more appropriate veterinary clinic can be selected. The evaluation of the type and age of the pet is performed based on, for example, classifications such as dog, cat, and bird, or age categories such as puppy, adult dog, and senior dog. For example, the selection unit inputs data on the type and age of the pet into the generation AI, and the generation AI selects an appropriate veterinary clinic. In addition, the selection unit can allow the generation AI to customize the veterinary clinic based on the type and age of the pet.

[0062] The selection unit can improve the selection method by reflecting the owner's past feedback when selecting a veterinary clinic. For example, the selection unit improves the veterinary clinic selection method based on feedback provided by the owner in the past. The selection unit can also select the most suitable veterinary clinic based on the owner's evaluations of veterinary clinics that the owner has visited in the past. The selection unit can also customize the veterinary clinic selection method by reflecting requests provided by the owner in the past. In this way, by reflecting the owner's past feedback, a more appropriate veterinary clinic selection method can be provided. The analysis of past feedback is performed based on data such as satisfaction ratings and areas for improvement. For example, the selection unit inputs the owner's past feedback data into the generation AI, which then improves the selection method. The selection unit can also customize the selection method by the generation AI based on the owner's past feedback.

[0063] When selecting a veterinary clinic, the selection unit can select the most appropriate veterinary clinic by taking into account the pet's geographical location information. For example, if the owner lives in an urban area, the selection unit can select a veterinary clinic that is easily accessible. Furthermore, if the owner lives in a rural area, the selection unit can select a veterinary clinic that offers online consultations. Furthermore, if the owner lives in a specific area, the selection unit can select a veterinary clinic that has a good reputation in that area. This allows for a more appropriate veterinary clinic to be selected by taking the pet's geographical location information into consideration. Geographical location information can be acquired based on, for example, GPS data or address information. For example, the selection unit inputs the owner's geographical location data into the generation AI, which then selects the most appropriate veterinary clinic. Furthermore, the selection unit can allow the generation AI to suggest veterinary clinics based on the geographical location information.

[0064] When selecting a veterinary clinic, the selection unit can analyze the social media activity of the pet and select a relevant veterinary clinic. For example, if the owner posts about the pet's health on social media, the selection unit can select a veterinary clinic based on that information. Furthermore, if the owner posts about the pet's symptoms on social media, the selection unit can select the most suitable veterinary clinic based on that information. Furthermore, if the owner posts about the pet's preferences on social media, the selection unit can select a veterinary clinic based on that information. In this way, by analyzing the pet's social media activity, a more appropriate veterinary clinic can be selected. The analysis of social media activity is performed based on data such as the content of posts and the number of followers. For example, the selection unit inputs the owner's social media data into the generation AI, which then selects a relevant veterinary clinic. Furthermore, the selection unit can have the generation AI suggest a veterinary clinic based on the social media activity.

[0065] The selection unit can customize the selection method by reflecting the pet's past feedback when selecting a veterinary clinic. For example, the selection unit improves the veterinary clinic selection method based on feedback provided by the owner in the past. The selection unit can also select the most suitable veterinary clinic based on the owner's evaluations of veterinary clinics that the owner has visited in the past. The selection unit can also customize the veterinary clinic selection method by reflecting requests provided by the owner in the past. In this way, by reflecting the pet's past feedback, a more appropriate veterinary clinic selection method can be provided. The analysis of past feedback is performed based on data such as satisfaction ratings and areas for improvement. For example, the selection unit inputs the owner's past feedback data into the generation AI, which then improves the selection method. The selection unit can also customize the selection method by the generation AI based on the owner's past feedback.

[0066] When proposing an online consultation, the consultation unit can select the optimal consultation method taking into consideration the pet's health condition and urgency. For example, if the pet's health condition is deteriorating, the consultation unit can suggest an online consultation that allows for emergency response. Furthermore, if the pet's health condition is good, the consultation unit can suggest an online consultation that allows for regular health checks. Furthermore, if the pet's health condition is unknown, the consultation unit can suggest an online consultation that allows for a detailed diagnosis. This allows for a more appropriate consultation method to be selected by taking the pet's health condition and urgency into consideration. Health condition evaluation is based on, for example, body temperature, appetite, activity level, etc. For example, the consultation unit inputs data on the pet's health condition into the generation AI, which then selects the optimal consultation method. Urgency evaluation is based on, for example, the severity of symptoms and the time since onset. For example, the consultation unit can input data on the pet's urgency into the generation AI, which then selects a consultation method that allows for emergency response.

[0067] When proposing an online consultation, the consultation unit can customize the consultation method to be proposed based on the type and age of the pet. For example, for a puppy, the consultation unit can propose an online consultation that specializes in advice about growth. Furthermore, for an elderly pet, the consultation unit can propose an online consultation that specializes in advice about aging. Furthermore, for a specific type of pet, the consultation unit can propose an online consultation that specializes in advice about health risks specific to that type. In this way, by customizing the consultation method based on the type and age of the pet, a more appropriate consultation method can be provided. The evaluation of the pet's type and age is performed based on, for example, classifications such as dog, cat, and bird, or age categories such as puppy, adult dog, and senior dog. For example, the consultation unit inputs data on the pet's type and age into the generation AI, which then selects an appropriate consultation method. The consultation unit can also have the generation AI customize the consultation method based on the pet's type and age.

[0068] When proposing an online consultation, the consultation unit can improve the proposal method by reflecting the owner's past feedback. For example, the consultation unit improves the online consultation proposal method based on feedback provided by the owner in the past. The consultation unit can also propose an optimal online consultation based on evaluations of online consultations received by the owner in the past. The consultation unit can also customize the online consultation proposal method by reflecting requests provided by the owner in the past. In this way, by reflecting the owner's past feedback, a more appropriate online consultation proposal method can be provided. Analysis of past feedback is performed based on data such as satisfaction ratings and areas for improvement. For example, the consultation unit inputs the owner's past feedback data into the generation AI, which then improves the proposal method. The consultation unit can also have the generation AI customize the proposal method based on the owner's past feedback.

[0069] When proposing an online consultation, the consultation unit can select the optimal consultation method by taking into account the pet's geographical location information. For example, if the owner lives in an urban area, the consultation unit can suggest an easily accessible online consultation. Furthermore, if the owner lives in a rural area, the consultation unit can also suggest a method where online consultation is available. Furthermore, if the owner lives in a specific area, the consultation unit can also suggest an online consultation that has a good reputation in that area. In this way, by taking the pet's geographical location information into consideration, a more appropriate online consultation method can be selected. Geographical location information is acquired based on, for example, GPS data or address information. For example, the consultation unit inputs the owner's geographical location data into the generation AI, which then selects the optimal consultation method. Furthermore, the consultation unit can have the generation AI suggest a consultation method based on the geographical location information.

[0070] When suggesting online consultations, the consultation unit can analyze the pet's social media activity and suggest relevant consultation methods. For example, if the owner posts about the pet's health on social media, the consultation unit can suggest online consultations based on that information. In addition, if the owner posts about the pet's symptoms on social media, the consultation unit can suggest the most appropriate online consultation based on that information. In addition, if the owner posts about the pet's preferences on social media, the consultation unit can suggest online consultations based on that information. In this way, by analyzing the pet's social media activity, it is possible to suggest more appropriate online consultation methods. Social media activity analysis is performed based on data such as the content of posts and the number of followers. For example, the consultation unit inputs the owner's social media data into a generation AI, which then selects a relevant consultation method. In addition, the consultation unit can have the generation AI suggest a consultation method based on the social media activity.

[0071] When proposing an online consultation, the consultation unit can customize the proposal method by reflecting the pet's past feedback. For example, the consultation unit improves the online consultation proposal method based on feedback provided by the owner in the past. The consultation unit can also propose the optimal online consultation based on evaluations of online consultations received by the owner in the past. The consultation unit can also customize the online consultation proposal method by reflecting requests provided by the owner in the past. In this way, by reflecting the owner's past feedback, a more appropriate online consultation proposal method can be provided. Analysis of past feedback is performed based on data such as satisfaction ratings and areas for improvement. For example, the consultation unit inputs the owner's past feedback data into the generation AI, which then improves the proposal method. The consultation unit can also have the generation AI customize the proposal method based on the owner's past feedback.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The analysis unit can also refer to the pet's past health data when assessing the pet's health condition. For example, the current health condition can be more accurately assessed based on the pet's past weight fluctuations, food intake, and exercise history. The analysis unit can also take the pet's past medical history and treatment history into account when assessing the pet's health condition. Furthermore, the analysis unit can also use the pet's past health data to predict future health risks. This makes it possible to assess the pet's health condition more accurately by utilizing the pet's past health data.

[0074] The providing unit can also provide preventive advice to the owner based on the pet's health condition. For example, if the pet's weight is increasing, the providing unit can advise the owner to reduce the amount of food. Also, if the pet is not getting enough exercise, the providing unit can advise the owner to exercise more. Furthermore, even if the pet's health condition is good, the providing unit can recommend regular health checks. This makes it possible to provide preventive advice to maintain the pet's health condition.

[0075] The referral department can also suggest health supplements to pet owners based on the pet's health condition. For example, if the pet's joint health is a concern, the referral department can suggest joint support supplements. Also, if the pet's coat is in poor condition, the referral department can suggest supplements to improve coat condition. Furthermore, the referral department can suggest supplements to boost the pet's immunity. In this way, by suggesting supplements according to the pet's health condition, it is possible to support the maintenance of health.

[0076] The selection unit can also introduce the owner to a specific specialist based on the pet's health condition. For example, if the pet has a heart problem, the selection unit can introduce a veterinarian specializing in heart disease. If the pet has a skin problem, the selection unit can introduce a veterinarian specializing in skin disease. Furthermore, if the pet has a behavioral problem, the selection unit can introduce a veterinarian specializing in behavior. This allows the pet owner to receive appropriate treatment by being introduced to a specialist according to the pet's health condition.

[0077] The consultation department can also propose health management plans to pet owners based on the pet's health condition. For example, it can propose a diet plan to manage the pet's weight. It can also propose an exercise plan to address the pet's lack of exercise. It can also propose a plan for regular health checks to maintain the pet's health. This allows pet owners to manage their pet's health in a planned manner.

[0078] The processing flow of the first embodiment will be briefly explained below.

[0079] Step 1: The reception unit inputs information about the pet's health condition. Information about the pet's health condition includes, for example, weight, amount of food eaten, amount of exercise, excretion status, body temperature, etc. By inputting this information, the reception unit provides basic data for understanding the pet's health condition. Step 2: The analysis unit analyzes the information entered by the reception unit to determine the pet's health condition. The analysis unit evaluates the pet's health condition based on information such as weight, amount of food eaten, amount of exercise, body temperature, and excretion status. It can also use AI to evaluate the pet's health condition. Step 3: The provider provides advice based on the health status identified by the analysis unit. If the pet is not feeling well, the provider suggests appropriate measures based on the symptoms. It can also provide advice such as dietary changes and recommended exercise. It is also possible to provide advice using AI. Step 4: The introduction department recommends the best pet food based on the advice provided by the provision department. The introduction department considers the pet's age, weight, allergy information, dietary preferences, etc. to recommend the best pet food. It can also use AI to recommend the best pet food based on the pet's health condition. Step 5: The selection unit selects the nearest veterinary clinic based on the advice provided by the provider. The selection unit suggests the nearest veterinary clinic based on the pet's symptoms, urgency, and health condition. It can also use AI to select the nearest veterinary clinic based on the pet's health condition. Step 6: The consultation unit suggests online consultation with a veterinarian based on the advice provided by the provider. The consultation unit suggests online consultation with a veterinarian if online consultation is appropriate based on the pet's symptoms and health condition. The consultation unit can also use AI to suggest online consultation with a veterinarian based on the pet's health condition.

[0080] (Example 2) An interactive consultation system according to an embodiment of the present invention efficiently assesses a pet's health condition and provides appropriate advice and support. In this system, pet owners input information about their pet's health, and AI analyzes that information and provides advice. For example, if a pet is not feeling well, the owner inputs its symptoms, and AI provides appropriate advice based on that information. Owners can also receive advice on choosing pet food and selecting a veterinary clinic. For example, by inputting information such as the pet's weight, food intake, and exercise, the AI ​​evaluates the pet's health condition. Next, when the owner inputs concerns or questions about the pet's health, the AI ​​analyzes that information and provides appropriate advice. For example, if a pet is not feeling well, the AI ​​suggests appropriate measures based on the pet's symptoms. Furthermore, when the owner inputs information about the pet's diet, the AI ​​analyzes that information and recommends the optimal pet food. For example, the AI ​​may recommend an appropriate pet food based on the pet's age, weight, allergies, and other factors. Next, when the owner inputs information about the pet's health condition, the AI ​​analyzes that information and recommends the nearest veterinary clinic. For example, based on the pet's symptoms, the AI ​​may suggest the nearest veterinary clinic where the pet can be taken immediately in case of an emergency. Finally, when owners input information about their pet's health, the AI ​​analyzes that information and suggests online veterinarian consultations. For example, if an online consultation is appropriate based on the pet's symptoms, the AI ​​will suggest an online consultation with a veterinarian. This allows the interactive consultation system to fully grasp the pet's health condition and provide appropriate advice. Owners can also receive support in selecting pet food and veterinary clinics, making pet health management more efficient.

[0081] The interactive consultation system according to the embodiment includes a reception unit, an analysis unit, a provision unit, an introduction unit, a selection unit, and a consultation unit. The reception unit inputs information about the pet's health condition. The information about the pet's health condition includes, but is not limited to, for example, weight, amount of food eaten, amount of exercise, excretion status, and body temperature. The reception unit inputs, for example, the pet's weight. The reception unit can also input the amount of food eaten by the pet. The reception unit can also input the amount of exercise done by the pet. The analysis unit analyzes the information input by the reception unit to determine the health condition. The analysis unit evaluates the health condition based on information such as the pet's weight, amount of food eaten, and amount of exercise. The analysis unit can also evaluate the health condition based on information such as the pet's body temperature and excretion status. The analysis unit can also evaluate the pet's health condition using AI. The provision unit provides advice based on the health condition determined by the analysis unit. For example, if the pet is not feeling well, the provision unit suggests an appropriate solution based on the pet's symptoms. The providing unit can also provide advice such as dietary changes and exercise recommendations based on the pet's health condition. The providing unit can also use AI to provide advice based on the pet's health condition. The introduction unit recommends optimal pet food based on the advice provided by the providing unit. The introduction unit can recommend appropriate pet food, for example, taking into account the pet's age, weight, allergy information, etc. The introduction unit can also recommend appropriate pet food, for example, taking into account the pet's dietary preferences, etc. The introduction unit can also use AI to recommend optimal pet food based on the pet's health condition. The selection unit selects the nearest veterinary clinic based on the advice provided by the providing unit. The selection unit can recommend the nearest veterinary clinic, for example, based on the pet's symptoms and urgency. The selection unit can also recommend the nearest veterinary clinic based on the pet's health condition. The selection unit can also use AI to select the nearest veterinary clinic based on the pet's health condition. The consultation unit suggests online consultation with a veterinarian based on the advice provided by the providing unit.For example, the consultation unit suggests online consultation with a veterinarian if online consultation is appropriate based on the pet's symptoms. The consultation unit can also suggest online consultation based on the pet's health condition. The consultation unit can also use AI to suggest online consultation with a veterinarian based on the pet's health condition. This allows the interactive consultation system according to the embodiment to efficiently understand the pet's health condition and provide appropriate advice and support.

[0082] The receiving unit can input information on the pet's weight, amount of food eaten, amount of exercise, excretion status, and body temperature. The weight information includes, for example, inputting the pet's weight in kilograms. The receiving unit, for example, measures the pet's weight and inputs the value. The food amount information includes, for example, inputting the amount of food the pet consumes per day in grams. The receiving unit, for example, measures the pet's amount of food eaten and inputs the value. The exercise amount information includes, for example, inputting the amount of exercise the pet does per day in hours. The receiving unit, for example, measures the pet's exercise time and inputs the value. The excretion status information includes, for example, inputting the number of times the pet excretes and the status. The receiving unit, for example, records the number of times the pet excretes and inputs the value. The body temperature information includes, for example, inputting the pet's body temperature in degrees Celsius. The receiving unit, for example, measures the pet's body temperature and inputs the value. This allows for more accurate analysis by inputting detailed health information about the pet.

[0083] The analysis unit can evaluate the health condition of the pet based on the information input by the reception unit. The evaluation of the health condition includes, for example, evaluation using a scoring system based on information such as the pet's weight, amount of food, and amount of exercise. The analysis unit inputs information such as the pet's weight, amount of food, and amount of exercise, and evaluates the health condition using a scoring system. The evaluation of the health condition also includes evaluation using a diagnostic algorithm based on information such as the pet's body temperature and excretion status. The analysis unit inputs information such as the pet's body temperature and excretion status, and evaluates the health condition using a diagnostic algorithm. The evaluation of the health condition also includes evaluation of the pet's health condition using AI. The analysis unit inputs information such as the pet's weight, amount of food, and amount of exercise into AI, and the AI ​​evaluates the health condition. This allows the pet's health condition to be accurately evaluated based on the input information.

[0084] When a pet is in poor health, the providing unit can suggest an appropriate countermeasure based on the symptoms. Specific symptoms of poor health include, for example, loss of appetite, vomiting, diarrhea, etc. The providing unit can suggest an appropriate countermeasure based on, for example, the pet's loss of appetite. The providing unit can also suggest an appropriate countermeasure based on the pet's vomiting symptom. The providing unit can also suggest an appropriate countermeasure based on the pet's diarrhea symptom. Appropriate countermeasures include, for example, first aid, administration of medicine, and visiting a hospital. For example, the providing unit can suggest hydration as first aid for a pet with loss of appetite. The providing unit can also suggest administration of medicine for a pet vomiting. The providing unit can also suggest visiting a hospital for a pet with diarrhea. This makes it possible to respond quickly by suggesting an appropriate countermeasure when a pet is in poor health.

[0085] The introduction unit can recommend appropriate pet food by taking into consideration the pet's age, weight, allergy information, and dietary preferences. Age information includes, for example, inputting the pet's age in months. The introduction unit, for example, inputs the pet's age and recommends appropriate pet food based on that information. Weight information includes, for example, inputting the pet's weight in kilograms. The introduction unit, for example, inputs the pet's weight and recommends appropriate pet food based on that information. Allergy information includes, for example, inputting information on ingredients to which the pet is allergic. The introduction unit, for example, inputs the pet's allergy information and recommends appropriate pet food based on that information. Dietary preferences include, for example, inputting information on the ingredients and flavors that the pet prefers. The introduction unit, for example, inputs the pet's dietary preferences and recommends appropriate pet food based on that information. This makes it possible to recommend optimal pet food according to the individual needs of the pet.

[0086] The selection unit can suggest the nearest veterinary hospital based on the pet's symptoms and urgency. Symptom information includes, for example, the pet's fever, cough, diarrhea, etc. The selection unit can suggest the nearest veterinary hospital based on, for example, the pet's fever symptom. The selection unit can also suggest the nearest veterinary hospital based on the pet's cough symptom. The selection unit can also suggest the nearest veterinary hospital based on the pet's diarrhea symptom. Urgency information includes, for example, the severity of the symptoms and the time since the onset of the symptoms. For example, if the pet's symptoms are severe, the selection unit can suggest a veterinary hospital that can handle emergency care. If the pet's symptoms are mild, the selection unit can also suggest a veterinary hospital that can provide regular medical care. If the pet's symptoms are unknown, the selection unit can also suggest a veterinary hospital that can provide a detailed diagnosis. This makes it possible to suggest the most appropriate veterinary hospital based on the pet's symptoms and urgency.

[0087] The consultation unit can suggest an online consultation with a veterinarian when online consultation is appropriate. Online consultations include, for example, methods such as video calls, chats, and emails. For example, the consultation unit can suggest an online consultation via video calls based on the pet's symptoms. The consultation unit can also suggest an online consultation via chat based on the pet's health condition. The consultation unit can also suggest an online consultation via email based on the pet's symptoms. This makes it possible to quickly suggest a consultation with a veterinarian when online consultation is appropriate.

[0088] The reception unit can estimate the owner's emotions and adjust the timing of information input based on the estimated owner's emotions. For example, if the owner is feeling stressed, the reception unit can delay the timing at which the AI ​​prompts the owner to input information, allowing the owner to input in a relaxed state. Furthermore, if the owner is relaxed, the reception unit can actively prompt the owner to input information, guiding the owner to enter more detailed information. Furthermore, if the owner is in a hurry, the reception unit can prompt the owner to input information quickly, allowing the owner to enter the minimum necessary information. This allows for more appropriate information input by adjusting the timing of information input according to the owner's emotions. The owner's emotions can be estimated using technologies such as facial expression recognition and voice analysis. For example, the reception unit can input the owner's facial expression data into the generation AI, which then estimates the owner's emotions. Furthermore, the reception unit can input the owner's voice data into the generation AI, which then estimates the owner's emotions.

[0089] The reception unit can analyze the owner's past input history and select the optimal input method. For example, the reception unit can automatically display information that the owner has frequently input in the past as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the owner has used in the past. The reception unit can also predict and suggest information that will be used during a specific time period based on the owner's past input history. This enables efficient information input by selecting the optimal input method based on the past input history. The analysis of the past input history is performed based on data such as input frequency and input content. For example, the reception unit inputs the owner's past input data into the generation AI, which then selects the optimal input method. The reception unit can also allow the generation AI to suggest input methods based on the owner's past input history.

[0090] The reception unit can customize input items based on the type and age of the pet when inputting information. For example, if the pet is a puppy, the reception unit can prioritize input of information about the pet's growth (weight, amount of food, etc.). Furthermore, if the pet is elderly, the reception unit can also input detailed information about the pet's health (body temperature, excretion status, etc.). The reception unit can also input information about specific health risks depending on the pet's type. This allows more appropriate information to be input by customizing input items according to the pet's type and age. Information about the pet's type and age is based on, for example, classifications such as dog, cat, and bird, or age categories such as puppy, adult dog, and senior dog. For example, the reception unit inputs data about the pet's type and age into the generation AI, and the generation AI customizes the input items. Furthermore, the reception unit can also allow the generation AI to suggest input items based on the pet's type and age.

[0091] The reception unit can select the optimal input means according to the owner's input method when inputting information. For example, if the owner prefers voice input, the reception unit can prioritize support for voice input. Also, if the owner prefers text input, the reception unit can also prioritize support for text input. Also, if the owner prefers image input, the reception unit can allow the owner to enter information by uploading a photo of the pet. This enables efficient information input by selecting the optimal input means according to the owner's input method. The selection of the owner's input method is based on the type, such as voice input, text input, or image input. For example, the reception unit inputs data on the owner's input method into the generation AI, which then selects the optimal input means. Also, the reception unit can allow the generation AI to suggest an input means based on the owner's input method.

[0092] The reception unit can estimate the owner's emotions and determine the priority of information to be input based on the estimated owner's emotions. For example, if the owner is feeling anxious, the reception unit can prioritize input of important health information (body temperature, excretion status, etc.). Furthermore, if the owner is relaxed, the reception unit can also input detailed information (amount of food eaten, amount of exercise, etc.). Furthermore, if the owner is in a hurry, the reception unit can also input the minimum necessary information (weight, body temperature, etc.). In this way, by determining the priority of information to be input according to the owner's emotions, important information can be input preferentially. The owner's emotions can be estimated using technologies such as facial expression recognition and voice analysis. For example, the reception unit can input the owner's facial expression data into the generation AI, which then estimates the owner's emotions. Furthermore, the reception unit can input the owner's voice data into the generation AI, which then estimates the owner's emotions.

[0093] When inputting information, the reception unit can prioritize inputting highly relevant information by taking into account the owner's geographical location information. For example, if the owner lives in an urban area, the reception unit can prioritize inputting information about the pet's exercise amount. Furthermore, if the owner lives in a rural area, the reception unit can prioritize inputting information about the pet's safety when going out. Furthermore, if the owner lives in a specific region, the reception unit can prioritize inputting information about health risks specific to that region. In this way, highly relevant information can be prioritized by taking the owner's geographical location information into consideration. The owner's geographical location information can be acquired based on, for example, GPS data or address information. For example, the reception unit inputs the owner's geographical location data to the generation AI, which then suggests highly relevant information. Furthermore, the reception unit can also allow the generation AI to customize input items based on the owner's geographical location information.

[0094] When inputting information, the reception unit can analyze the owner's social media activity and input relevant information. For example, if the owner frequently posts photos of their pet on social media, the reception unit can extract information about the pet's health from the photos. Furthermore, if the owner posts about their pet's diet on social media, the reception unit can input the amount and type of food based on that information. Furthermore, if the owner posts about their pet's exercise on social media, the reception unit can input the amount of exercise based on that information. This allows relevant information to be input efficiently by analyzing the owner's social media activity. Social media activity is analyzed based on data such as the content of posts and the number of followers. For example, the reception unit inputs the owner's social media data into the generation AI, which then extracts relevant information. Furthermore, the reception unit can allow the generation AI to suggest input items based on the owner's social media activity.

[0095] The reception unit can customize the input method by reflecting the owner's past feedback when inputting information. For example, if the owner has provided feedback on the input method in the past, the reception unit can improve the input method based on that feedback. Furthermore, if the owner has previously reported a problem with input, the reception unit can suggest an input method to resolve the problem. Furthermore, if the owner has previously submitted a request regarding input, the reception unit can provide an input method that reflects that request. In this way, by reflecting the owner's past feedback, a more appropriate input method can be provided. Analysis of past feedback is performed based on data such as satisfaction ratings and areas for improvement. For example, the reception unit inputs the owner's past feedback data into the generation AI, which then customizes the input method. Furthermore, the reception unit can also allow the generation AI to suggest an input method based on the owner's past feedback.

[0096] The analysis unit can estimate the owner's emotions and adjust the way the analysis is presented based on the estimated owner's emotions. For example, if the owner is feeling anxious, the analysis unit can provide the analysis results in a simple, easy-to-understand format. If the owner is relaxed, the analysis unit can also provide detailed analysis results to promote deeper understanding. If the owner is in a hurry, the analysis unit can also provide concise analysis results that focus on the main points. This makes it possible to provide analysis results that are easier to understand by adjusting the way the analysis is presented according to the owner's emotions. The owner's emotions can be estimated using technologies such as facial expression recognition and voice analysis. For example, the analysis unit inputs the owner's facial expression data into the generation AI, which then estimates the owner's emotions. The analysis unit can also input the owner's voice data into the generation AI, which then estimates the owner's emotions.

[0097] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the pet's health condition. For example, if the pet's health condition is good, the analysis unit provides basic analysis results. Furthermore, if the pet's health condition is deteriorating, the analysis unit can provide detailed analysis results and suggest specific measures. Furthermore, if the pet's health condition is unknown, the analysis unit can collect detailed information and provide analysis results. In this way, by adjusting the level of detail of the analysis according to the importance of the pet's health condition, appropriate analysis results can be provided. The importance of the health condition is evaluated based on, for example, the severity and urgency of the symptoms. For example, the analysis unit inputs the pet's symptom data into the generation AI, and the generation AI evaluates the importance of the health condition. Furthermore, the analysis unit can allow the generation AI to adjust the level of detail of the analysis based on the pet's health condition data.

[0098] During analysis, the analysis unit can apply different analysis algorithms depending on the type and age of the pet. For example, in the case of a puppy, the analysis unit can apply an analysis algorithm related to growth to evaluate the health condition. In addition, in the case of an elderly pet, the analysis unit can apply an analysis algorithm related to aging to evaluate health risks. In addition, in the case of a specific type of pet, the analysis unit can apply an analysis algorithm related to health risks specific to that type. In this way, by applying an analysis algorithm according to the type and age of the pet, more accurate analysis results can be provided. The application of the analysis algorithm is based on, for example, a machine learning algorithm or a statistical analysis algorithm. For example, the analysis unit inputs data on the type and age of the pet into the generation AI, and the generation AI applies an appropriate analysis algorithm. In addition, the analysis unit can allow the generation AI to select an analysis algorithm based on the type and age of the pet.

[0099] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the owner's past analysis results. For example, the analysis unit adjusts the analysis algorithm based on health information provided by the owner in the past, thereby improving accuracy. The analysis unit can also improve the analysis algorithm based on the results of advice the owner received in the past. The analysis unit can also optimize the analysis algorithm based on feedback provided by the owner in the past. In this way, the accuracy of the analysis can be improved by referring to the owner's past analysis results. Past analysis results are used based on data such as diagnosis history and treatment history. For example, the analysis unit inputs the owner's past analysis data into the generation AI, which then adjusts the analysis algorithm. The analysis unit can also allow the generation AI to improve the accuracy of the analysis based on the owner's past analysis results.

[0100] The analysis unit can estimate the owner's emotions and adjust the length of the analysis based on the estimated owner's emotions. For example, if the owner is feeling anxious, the analysis unit can provide a short and concise analysis result. If the owner is relaxed, the analysis unit can also provide a detailed analysis result to promote deeper understanding. If the owner is in a hurry, the analysis unit can also provide a concise and quick analysis result. This allows for adjusting the length of the analysis according to the owner's emotions to provide more appropriate analysis results. The owner's emotions can be estimated using technologies such as facial expression recognition and voice analysis. For example, the analysis unit can input the owner's facial expression data into the generation AI, which then estimates the owner's emotions. The analysis unit can also input the owner's voice data into the generation AI, which then estimates the owner's emotions.

[0101] During analysis, the analysis unit can determine the priority of analysis based on the timing of submission of the pet's health status. For example, in the case of a highly urgent health condition, the analysis unit prioritizes analysis and provides results quickly. The analysis unit can also perform analysis with normal priority in the case of a regular health check. The analysis unit can also adjust the priority of analysis based on health information submitted by the owner at a specific time. This enables a prompt response by determining the priority of analysis based on the timing of submission of the pet's health status. The evaluation of the submission timing is based on, for example, the submission date or submission time. For example, the analysis unit inputs data on the submission timing of the pet's health information into the generation AI, and the generation AI determines the priority of analysis. The analysis unit can also allow the generation AI to adjust the order of analysis based on the submission timing.

[0102] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the pet's health condition. For example, if the pet's health condition is deteriorating, the analysis unit prioritizes analysis of relevant information. Furthermore, if the pet's health condition is good, the analysis unit can also prioritize analysis of basic information. Furthermore, if the pet's health condition is unknown, the analysis unit can also prioritize analysis of detailed information. In this way, by adjusting the order of analysis based on the relevance of the pet's health condition, important information can be prioritized for analysis. The evaluation of the relevance of health conditions is based on, for example, commonalities in symptoms and medical history. For example, the analysis unit inputs data on the pet's health condition into the generation AI, and the generation AI prioritizes analysis of highly relevant information. Furthermore, the analysis unit can also adjust the order of analysis by the generation AI based on the relevance of health conditions.

[0103] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the owner's level of expertise. For example, if the owner has technical expertise, the analysis unit can provide the analysis results using detailed technical terminology. Alternatively, if the owner does not have technical expertise, the analysis unit can provide the analysis results in simpler terms. The analysis unit can also adjust the way the analysis results are presented according to the owner's level of expertise. This makes it possible to provide analysis results that are easier to understand by adjusting the use of technical terminology in the analysis according to the owner's level of expertise. The expertise level is evaluated based on classifications such as beginner, intermediate, and advanced. For example, the analysis unit inputs data on the owner's level of expertise into the generation AI, which then selects appropriate technical terminology. The analysis unit can also adjust the way the generation AI presents the analysis results based on the owner's level of expertise.

[0104] The providing unit can estimate the owner's emotions and adjust the way the advice is expressed based on the estimated owner's emotions. For example, if the owner is feeling anxious, the providing unit can provide simple and easy-to-understand advice. If the owner is relaxed, the providing unit can also provide detailed advice to encourage deeper understanding. If the owner is in a hurry, the providing unit can also provide concise advice that focuses on the main points. This makes it possible to provide advice that is easier to understand by adjusting the way the advice is expressed according to the owner's emotions. The owner's emotions are estimated using technologies such as facial expression recognition and voice analysis. For example, the providing unit inputs the owner's facial expression data into the generation AI, which then estimates the owner's emotions. The providing unit can also input the owner's voice data into the generation AI, which then estimates the owner's emotions.

[0105] When providing advice, the providing unit can adjust the level of detail of the advice based on the importance of the pet's health condition. For example, if the pet's health condition is good, the providing unit can provide basic advice. Furthermore, if the pet's health condition is deteriorating, the providing unit can provide detailed advice and suggest specific measures. Furthermore, if the pet's health condition is unknown, the providing unit can collect detailed information and provide advice. In this way, appropriate advice can be provided by adjusting the level of detail of the advice according to the importance of the pet's health condition. The importance of the health condition is evaluated based on, for example, the severity and urgency of the symptoms. For example, the providing unit inputs data on the pet's symptoms into the generating AI, and the generating AI evaluates the importance of the health condition. Furthermore, the providing unit can cause the generating AI to adjust the level of detail of the advice based on the data on the pet's health condition.

[0106] When providing advice, the providing unit can apply different advice algorithms depending on the type and age of the pet. For example, in the case of a puppy, the providing unit can apply an advice algorithm related to growth to evaluate the health condition. In addition, in the case of an elderly pet, the providing unit can apply an advice algorithm related to aging to evaluate health risks. In addition, in the case of a specific type of pet, the providing unit can apply an advice algorithm related to health risks specific to that type. In this way, more appropriate advice can be provided by applying an advice algorithm according to the type and age of the pet. The application of the advice algorithm is based on, for example, a machine learning algorithm or a rule-based algorithm. For example, the providing unit inputs data on the type and age of the pet into the generating AI, and the generating AI applies an appropriate advice algorithm. In addition, the providing unit can cause the generating AI to select an advice algorithm based on the type and age of the pet.

[0107] When providing advice, the providing unit can improve the accuracy of the advice by referring to the owner's past advice results. For example, the providing unit can adjust the advice algorithm based on the results of advice the owner received in the past, thereby improving accuracy. The providing unit can also improve the advice algorithm based on feedback provided by the owner in the past. The providing unit can also evaluate the effectiveness of advice the owner received in the past and optimize the advice algorithm. In this way, the accuracy of the advice can be improved by referring to the owner's past advice results. The use of past advice results is based on data such as diagnosis history and treatment history. For example, the providing unit inputs the owner's past advice data into the generating AI, which then adjusts the advice algorithm. The providing unit can also cause the generating AI to improve the accuracy of the advice based on the owner's past advice results.

[0108] The providing unit can estimate the owner's emotions and adjust the length of the advice based on the estimated owner's emotions. For example, if the owner is feeling anxious, the providing unit can provide short, to-the-point advice. If the owner is relaxed, the providing unit can also provide detailed advice to encourage deeper understanding. If the owner is in a hurry, the providing unit can also provide concise, quick advice. This allows the length of advice to be adjusted according to the owner's emotions, making it possible to provide more appropriate advice. The owner's emotions are estimated using technologies such as facial expression recognition and voice analysis. For example, the providing unit inputs the owner's facial expression data into the generation AI, which then estimates the owner's emotions. The providing unit can also input the owner's voice data into the generation AI, which then estimates the owner's emotions.

[0109] When providing advice, the providing unit can determine the priority of the advice based on the time of submission of the pet's health condition. For example, the providing unit provides advice with priority in the case of a highly urgent health condition. The providing unit can also provide advice with normal priority in the case of a regular health check. The providing unit can also adjust the priority of the advice based on the health information submitted by the owner at a specific time. This enables a prompt response by determining the priority of advice based on the time of submission of the pet's health condition. The evaluation of the submission time is based on, for example, the submission date or submission time. For example, the providing unit inputs data on the time of submission of the pet's health information into the generating AI, and the generating AI determines the priority of the advice. The providing unit can also allow the generating AI to adjust the order of advice based on the submission time.

[0110] When providing advice, the providing unit can adjust the order of advice based on the relevance of the pet's health condition. For example, if the pet's health condition is deteriorating, the providing unit can prioritize advice on relevant information. Furthermore, if the pet's health condition is good, the providing unit can prioritize advice on basic information. Furthermore, if the pet's health condition is unknown, the providing unit can prioritize advice on detailed information. In this way, by adjusting the order of advice based on the relevance of the pet's health condition, it is possible to prioritize advice on important information. The evaluation of the relevance of the health condition is performed based on, for example, commonalities in symptoms and medical history. For example, the providing unit inputs data on the pet's health condition into the generation AI, and the generation AI prioritizes advice on highly relevant information. Furthermore, the providing unit can cause the generation AI to adjust the order of advice based on the relevance of the health condition.

[0111] When providing advice, the providing unit can adjust the use of technical terms in the advice depending on the owner's level of expertise. For example, if the owner has technical expertise, the providing unit can provide the advice using detailed technical terms. Also, if the owner does not have technical expertise, the providing unit can provide the advice in simple language. The providing unit can also adjust the way the advice is expressed depending on the owner's level of expertise. In this way, by adjusting the use of technical terms in the advice depending on the owner's level of expertise, it is possible to provide advice that is easier to understand. The expertise level is evaluated based on classifications such as beginner, intermediate, and advanced. For example, the providing unit inputs data on the owner's level of expertise into the generating AI, which then selects appropriate technical terms. The providing unit can also cause the generating AI to adjust the way the advice is expressed based on the owner's level of expertise.

[0112] The introduction unit can estimate the owner's emotions and determine the priority of pet foods to introduce based on the estimated owner's emotions. For example, if the owner is feeling anxious, the introduction unit can prioritize highly reliable pet foods. Furthermore, if the owner is relaxed, the introduction unit can also introduce a wide variety of pet foods. Furthermore, if the owner is in a hurry, the introduction unit can prioritize easily purchased pet foods. In this way, by determining the priority of pet foods to introduce according to the owner's emotions, more appropriate pet foods can be introduced. The owner's emotions are estimated using technologies such as facial expression recognition and voice analysis. For example, the introduction unit inputs the owner's facial expression data into the generation AI, which then estimates the owner's emotions. Furthermore, the introduction unit can input the owner's voice data into the generation AI, which then estimates the owner's emotions.

[0113] When introducing pet food, the introduction unit can select the most appropriate pet food by taking into consideration the pet's health condition and allergy information. For example, if the pet has allergies, the introduction unit can prioritize introducing allergy-friendly pet food. Furthermore, if the pet's health condition is deteriorating, the introduction unit can also introduce pet food that supports health. Furthermore, if the pet's health condition is good, the introduction unit can also introduce balanced pet food. This allows for the selection of a more appropriate pet food by taking the pet's health condition and allergy information into consideration. Health condition evaluation is based on, for example, body temperature, appetite, activity level, etc. For example, the introduction unit inputs data on the pet's health condition into the generation AI, which then selects the most appropriate pet food. Allergy information evaluation is based on, for example, the type of allergen and symptoms, etc. For example, the introduction unit can input the pet's allergy data into the generation AI, which then selects allergy-friendly pet food.

[0114] When introducing pet food, the introduction unit can customize the pet food to be introduced based on the pet's age and weight. For example, for a puppy, the introduction unit can introduce pet food that supports growth. For an elderly pet, the introduction unit can also introduce pet food that supports aging. The introduction unit can also introduce pet food with an appropriate calorie content based on the pet's weight. This allows for customizing pet food based on the pet's age and weight, making it possible to introduce more appropriate pet food. Age evaluation is performed based on classifications such as puppy, adult dog, and senior dog. For example, the introduction unit inputs the pet's age data into the generation AI, which then selects appropriate pet food. Weight evaluation is performed based on units such as kilograms and pounds. For example, the introduction unit can input the pet's weight data into the generation AI, which then selects pet food with an appropriate calorie content.

[0115] The introduction unit can improve the introduction method by reflecting the owner's past feedback when introducing pet food. For example, the introduction unit improves the pet food introduction method based on feedback provided by the owner in the past. The introduction unit can also introduce the most suitable pet food based on the owner's evaluations of pet foods purchased in the past. The introduction unit can also customize the pet food introduction method by reflecting requests provided by the owner in the past. In this way, by reflecting the owner's past feedback, a more appropriate pet food introduction method can be provided. The analysis of past feedback is performed based on data such as satisfaction ratings and areas for improvement. For example, the introduction unit inputs the owner's past feedback data into the generation AI, which then improves the introduction method. The introduction unit can also customize the introduction method by the generation AI based on the owner's past feedback.

[0116] The introduction unit can estimate the owner's emotions and adjust the display method of the introduced pet food based on the estimated owner's emotions. For example, if the owner is feeling anxious, the introduction unit can provide a simple and easy-to-understand display method. If the owner is relaxed, the introduction unit can also provide a display method including detailed information. If the owner is in a hurry, the introduction unit can also provide a concise display method that focuses on the main points. This makes it possible to provide more easily understandable information by adjusting the display method of the pet food according to the owner's emotions. The owner's emotions can be estimated using technologies such as facial expression recognition and voice analysis. For example, the introduction unit inputs the owner's facial expression data into the generation AI, which then estimates the owner's emotions. The introduction unit can also input the owner's voice data into the generation AI, which then estimates the owner's emotions.

[0117] When introducing pet food, the introduction unit can select the most appropriate pet food by taking into account the pet's geographical location information. For example, if the owner lives in an urban area, the introduction unit can introduce pet food that is easily available for purchase. Furthermore, if the owner lives in a rural area, the introduction unit can also introduce pet food that can be purchased online. Furthermore, if the owner lives in a specific region, the introduction unit can also introduce pet food that is popular in that region. This makes it possible to select a more appropriate pet food by taking into account the pet's geographical location information. Geographical location information is acquired based on, for example, GPS data or address information. For example, the introduction unit inputs the owner's geographical location data into the generation AI, which then selects the most appropriate pet food. Furthermore, the introduction unit can allow the generation AI to suggest pet food based on the geographical location information.

[0118] When recommending pet food, the recommendation unit can analyze the pet's social media activity and recommend related pet foods. For example, if the owner posts about their pet's diet on social media, the recommendation unit can recommend pet foods based on that information. Also, if the owner posts about their pet's health on social media, the recommendation unit can recommend pet foods that support health based on that information. Also, if the owner posts about their pet's preferences on social media, the recommendation unit can recommend pet foods based on that information. In this way, by analyzing the pet's social media activity, more appropriate pet foods can be recommended. Social media activity analysis is performed based on data such as the content of posts and the number of followers. For example, the recommendation unit inputs the owner's social media data into the generation AI, which selects related pet foods. Also, the introduction unit can have the generation AI suggest pet foods based on the social media activity.

[0119] The introduction unit can customize the introduction method by reflecting the pet's past feedback when introducing pet food. For example, the introduction unit improves the pet food introduction method based on feedback provided by the owner in the past. The introduction unit can also introduce the most suitable pet food based on the owner's evaluation of pet food purchased in the past. The introduction unit can also customize the pet food introduction method by reflecting requests provided by the owner in the past. In this way, by reflecting the pet's past feedback, a more appropriate pet food introduction method can be provided. Analysis of past feedback is performed based on data such as satisfaction ratings and areas for improvement. For example, the introduction unit inputs the owner's past feedback data into the generation AI, which then improves the introduction method. The introduction unit can also have the generation AI customize the introduction method based on the owner's past feedback.

[0120] The selection unit can estimate the owner's emotions and adjust the selection criteria for a veterinary clinic based on the estimated owner's emotions. For example, if the owner is feeling anxious, the selection unit can prioritize selecting a reliable veterinary clinic. Furthermore, if the owner is relaxed, the selection unit can select a veterinary clinic with a wide variety of options. Furthermore, if the owner is in a hurry, the selection unit can prioritize selecting a veterinary clinic that is easily accessible. This allows the selection of a more appropriate veterinary clinic by adjusting the selection criteria for a veterinary clinic according to the owner's emotions. The owner's emotions can be estimated using technologies such as facial expression recognition and voice analysis. For example, the selection unit inputs the owner's facial expression data into the generation AI, which then estimates the owner's emotions. The selection unit can also input the owner's voice data into the generation AI, which then estimates the owner's emotions.

[0121] When selecting a veterinary clinic, the selection unit can select the most appropriate veterinary clinic by taking into consideration the pet's health condition and urgency. For example, if the pet's health condition is deteriorating, the selection unit can prioritize selecting a veterinary clinic that can provide emergency care. Furthermore, if the pet's health condition is good, the selection unit can also select a veterinary clinic that can provide regular health checks. Furthermore, if the pet's health condition is unknown, the selection unit can also select a veterinary clinic that can provide detailed diagnoses. This allows for the selection of a more appropriate veterinary clinic by taking the pet's health condition and urgency into consideration. The health condition can be evaluated based on, for example, body temperature, appetite, activity level, etc. For example, the selection unit inputs data on the pet's health condition into the generation AI, which then selects the most appropriate veterinary clinic. The urgency can be evaluated based on, for example, the severity of symptoms and the time since onset. For example, the selection unit can input data on the pet's urgency into the generation AI, which then selects a veterinary clinic that can provide emergency care.

[0122] When selecting a veterinary clinic, the selection unit can customize the veterinary clinic to be selected based on the type and age of the pet. For example, in the case of a puppy, the selection unit selects a veterinary clinic that is skilled in diagnosing growth. In addition, in the case of an elderly pet, the selection unit can select a veterinary clinic that is skilled in diagnosing aging. In addition, in the case of a specific type of pet, the selection unit can select a veterinary clinic that is skilled in diagnosing health risks specific to that type. In this way, by customizing the veterinary clinic based on the type and age of the pet, a more appropriate veterinary clinic can be selected. The evaluation of the type and age of the pet is performed based on, for example, classifications such as dog, cat, and bird, or age categories such as puppy, adult dog, and senior dog. For example, the selection unit inputs data on the type and age of the pet into the generation AI, and the generation AI selects an appropriate veterinary clinic. In addition, the selection unit can allow the generation AI to customize the veterinary clinic based on the type and age of the pet.

[0123] The selection unit can improve the selection method by reflecting the owner's past feedback when selecting a veterinary clinic. For example, the selection unit improves the veterinary clinic selection method based on feedback provided by the owner in the past. The selection unit can also select the most suitable veterinary clinic based on the owner's evaluations of veterinary clinics that the owner has visited in the past. The selection unit can also customize the veterinary clinic selection method by reflecting requests provided by the owner in the past. In this way, by reflecting the owner's past feedback, a more appropriate veterinary clinic selection method can be provided. The analysis of past feedback is performed based on data such as satisfaction ratings and areas for improvement. For example, the selection unit inputs the owner's past feedback data into the generation AI, which then improves the selection method. The selection unit can also customize the selection method by the generation AI based on the owner's past feedback.

[0124] The selection unit can estimate the owner's emotions and adjust the order in which the veterinary clinic selection results are displayed based on the estimated owner's emotions. For example, if the owner is feeling anxious, the selection unit can prioritize displaying reliable veterinary clinics. Furthermore, if the owner is relaxed, the selection unit can also display a wide variety of veterinary clinics. Furthermore, if the owner is in a hurry, the selection unit can prioritize displaying easily accessible veterinary clinics. This allows for more appropriate information to be provided by adjusting the order in which the veterinary clinic selection results are displayed according to the owner's emotions. The owner's emotions can be estimated using technologies such as facial expression recognition and voice analysis. For example, the selection unit inputs the owner's facial expression data into a generation AI, which then estimates the owner's emotions. Furthermore, the selection unit can input the owner's voice data into a generation AI, which then estimates the owner's emotions.

[0125] When selecting a veterinary clinic, the selection unit can select the most appropriate veterinary clinic by taking into account the pet's geographical location information. For example, if the owner lives in an urban area, the selection unit can select a veterinary clinic that is easily accessible. Furthermore, if the owner lives in a rural area, the selection unit can select a veterinary clinic that offers online consultations. Furthermore, if the owner lives in a specific area, the selection unit can select a veterinary clinic that has a good reputation in that area. This allows for a more appropriate veterinary clinic to be selected by taking the pet's geographical location information into consideration. Geographical location information can be acquired based on, for example, GPS data or address information. For example, the selection unit inputs the owner's geographical location data into the generation AI, which then selects the most appropriate veterinary clinic. Furthermore, the selection unit can allow the generation AI to suggest veterinary clinics based on the geographical location information.

[0126] When selecting a veterinary clinic, the selection unit can analyze the social media activity of the pet and select a relevant veterinary clinic. For example, if the owner posts about the pet's health on social media, the selection unit can select a veterinary clinic based on that information. Furthermore, if the owner posts about the pet's symptoms on social media, the selection unit can select the most suitable veterinary clinic based on that information. Furthermore, if the owner posts about the pet's preferences on social media, the selection unit can select a veterinary clinic based on that information. In this way, by analyzing the pet's social media activity, a more appropriate veterinary clinic can be selected. The analysis of social media activity is performed based on data such as the content of posts and the number of followers. For example, the selection unit inputs the owner's social media data into the generation AI, which then selects a relevant veterinary clinic. Furthermore, the selection unit can have the generation AI suggest a veterinary clinic based on the social media activity.

[0127] The selection unit can customize the selection method by reflecting the pet's past feedback when selecting a veterinary clinic. For example, the selection unit improves the veterinary clinic selection method based on feedback provided by the owner in the past. The selection unit can also select the most suitable veterinary clinic based on the owner's evaluations of veterinary clinics that the owner has visited in the past. The selection unit can also customize the veterinary clinic selection method by reflecting requests provided by the owner in the past. In this way, by reflecting the pet's past feedback, a more appropriate veterinary clinic selection method can be provided. The analysis of past feedback is performed based on data such as satisfaction ratings and areas for improvement. For example, the selection unit inputs the owner's past feedback data into the generation AI, which then improves the selection method. The selection unit can also customize the selection method by the generation AI based on the owner's past feedback.

[0128] The consultation unit can estimate the owner's emotions and adjust the method of proposing online consultation based on the estimated owner's emotions. For example, if the owner is feeling anxious, the consultation unit can suggest a quick and concise online consultation. Furthermore, if the owner is relaxed, the consultation unit can suggest a detailed online consultation. Furthermore, if the owner is in a hurry, the consultation unit can suggest a concise online consultation that focuses on the main points. In this way, by adjusting the method of proposing online consultation according to the owner's emotions, more appropriate suggestions can be provided. The owner's emotions are estimated using technologies such as facial expression recognition and voice analysis. For example, the consultation unit inputs the owner's facial expression data into the generation AI, which then estimates the owner's emotions. Furthermore, the consultation unit can input the owner's voice data into the generation AI, which then estimates the owner's emotions.

[0129] When proposing an online consultation, the consultation unit can select the optimal consultation method taking into consideration the pet's health condition and urgency. For example, if the pet's health condition is deteriorating, the consultation unit can suggest an online consultation that allows for emergency response. Furthermore, if the pet's health condition is good, the consultation unit can suggest an online consultation that allows for regular health checks. Furthermore, if the pet's health condition is unknown, the consultation unit can suggest an online consultation that allows for a detailed diagnosis. This allows for a more appropriate consultation method to be selected by taking the pet's health condition and urgency into consideration. Health condition evaluation is based on, for example, body temperature, appetite, activity level, etc. For example, the consultation unit inputs data on the pet's health condition into the generation AI, which then selects the optimal consultation method. Urgency evaluation is based on, for example, the severity of symptoms and the time since onset. For example, the consultation unit can input data on the pet's urgency into the generation AI, which then selects a consultation method that allows for emergency response.

[0130] When proposing an online consultation, the consultation unit can customize the consultation method to be proposed based on the type and age of the pet. For example, for a puppy, the consultation unit can propose an online consultation that specializes in advice about growth. Furthermore, for an elderly pet, the consultation unit can propose an online consultation that specializes in advice about aging. Furthermore, for a specific type of pet, the consultation unit can propose an online consultation that specializes in advice about health risks specific to that type. In this way, by customizing the consultation method based on the type and age of the pet, a more appropriate consultation method can be provided. The evaluation of the pet's type and age is performed based on, for example, classifications such as dog, cat, and bird, or age categories such as puppy, adult dog, and senior dog. For example, the consultation unit inputs data on the pet's type and age into the generation AI, which then selects an appropriate consultation method. The consultation unit can also have the generation AI customize the consultation method based on the pet's type and age.

[0131] When proposing an online consultation, the consultation unit can improve the proposal method by reflecting the owner's past feedback. For example, the consultation unit improves the online consultation proposal method based on feedback provided by the owner in the past. The consultation unit can also propose an optimal online consultation based on evaluations of online consultations received by the owner in the past. The consultation unit can also customize the online consultation proposal method by reflecting requests provided by the owner in the past. In this way, by reflecting the owner's past feedback, a more appropriate online consultation proposal method can be provided. Analysis of past feedback is performed based on data such as satisfaction ratings and areas for improvement. For example, the consultation unit inputs the owner's past feedback data into the generation AI, which then improves the proposal method. The consultation unit can also have the generation AI customize the proposal method based on the owner's past feedback.

[0132] The consultation unit can estimate the owner's emotions and determine the priority of online consultations based on the estimated owner's emotions. For example, if the owner is feeling anxious, the consultation unit can prioritize and suggest online consultations with a high level of urgency. The consultation unit can also suggest detailed online consultations if the owner is relaxed. The consultation unit can also suggest concise online consultations that focus on the main points if the owner is in a hurry. This enables more appropriate responses by determining the priority of online consultations according to the owner's emotions. The owner's emotions can be estimated using technologies such as facial expression recognition and voice analysis. For example, the consultation unit inputs the owner's facial expression data into the generation AI, which then estimates the owner's emotions. The consultation unit can also input the owner's voice data into the generation AI, which then estimates the owner's emotions.

[0133] When proposing an online consultation, the consultation unit can select the optimal consultation method by taking into account the pet's geographical location information. For example, if the owner lives in an urban area, the consultation unit can suggest an easily accessible online consultation. Furthermore, if the owner lives in a rural area, the consultation unit can also suggest a method where online consultation is available. Furthermore, if the owner lives in a specific area, the consultation unit can also suggest an online consultation that has a good reputation in that area. In this way, by taking the pet's geographical location information into consideration, a more appropriate online consultation method can be selected. Geographical location information is acquired based on, for example, GPS data or address information. For example, the consultation unit inputs the owner's geographical location data into the generation AI, which then selects the optimal consultation method. Furthermore, the consultation unit can have the generation AI suggest a consultation method based on the geographical location information.

[0134] When suggesting online consultations, the consultation unit can analyze the pet's social media activity and suggest relevant consultation methods. For example, if the owner posts about the pet's health on social media, the consultation unit can suggest online consultations based on that information. In addition, if the owner posts about the pet's symptoms on social media, the consultation unit can suggest the most appropriate online consultation based on that information. In addition, if the owner posts about the pet's preferences on social media, the consultation unit can suggest online consultations based on that information. In this way, by analyzing the pet's social media activity, it is possible to suggest more appropriate online consultation methods. Social media activity analysis is performed based on data such as the content of posts and the number of followers. For example, the consultation unit inputs the owner's social media data into a generation AI, which then selects a relevant consultation method. In addition, the consultation unit can have the generation AI suggest a consultation method based on the social media activity.

[0135] When proposing an online consultation, the consultation unit can customize the proposal method by reflecting the pet's past feedback. For example, the consultation unit improves the online consultation proposal method based on feedback provided by the owner in the past. The consultation unit can also propose the optimal online consultation based on evaluations of online consultations received by the owner in the past. The consultation unit can also customize the online consultation proposal method by reflecting requests provided by the owner in the past. In this way, by reflecting the owner's past feedback, a more appropriate online consultation proposal method can be provided. Analysis of past feedback is performed based on data such as satisfaction ratings and areas for improvement. For example, the consultation unit inputs the owner's past feedback data into the generation AI, which then improves the proposal method. The consultation unit can also have the generation AI customize the proposal method based on the owner's past feedback. === Hard Collateral 1-1 === For example, each of the multiple elements including the reception unit, analysis unit, provision unit, introduction unit, selection unit, and consultation unit is realized by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14, and the owner inputs information about the pet's health condition. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the input information to determine the pet's health condition. The provision unit is realized by the specific processing unit 290 of the data processing device 12, and provides advice based on the analysis results. The introduction unit is realized by the specific processing unit 290 of the data processing device 12, and recommends the optimal pet food. The selection unit is realized by the specific processing unit 290 of the data processing device 12, and selects the nearest veterinary clinic. The consultation unit is realized by the control unit 46A of the smart device 14, and suggests consulting a veterinarian online. === Hard Collateral 1-2 === For example, each of the multiple elements including the reception unit, analysis unit, provision unit, introduction unit, selection unit, and consultation unit is realized by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214, and the owner inputs information about the pet's health condition. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the input information to understand the health condition. The provision unit is realized by the specific processing unit 290 of the data processing device 12, and provides advice based on the analysis results. The introduction unit is realized by the specific processing unit 290 of the data processing device 12, and recommends the optimal pet food. The selection unit is realized by the specific processing unit 290 of the data processing device 12, and selects the nearest veterinary clinic. The consultation unit is realized by the control unit 46A of the smart glasses 214, and suggests consulting a veterinarian online. === Hard Collateral 1-3 === For example, each of the multiple elements including the reception unit, analysis unit, provision unit, introduction unit, selection unit, and consultation unit is realized by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314, and the owner inputs information about the pet's health condition. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the input information to determine the pet's health condition. The provision unit is realized by the specific processing unit 290 of the data processing device 12, and provides advice based on the analysis results. The introduction unit is realized by the specific processing unit 290 of the data processing device 12, and recommends the optimal pet food. The selection unit is realized by the specific processing unit 290 of the data processing device 12, and selects the nearest veterinary clinic. The consultation unit is realized by the control unit 46A of the headset-type terminal 314, and suggests consulting a veterinarian online. === Hard Collateral 1-4 === For example, each of the multiple elements including the reception unit, analysis unit, provision unit, introduction unit, selection unit, and consultation unit is realized by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and the owner inputs information about the pet's health condition. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the input information to determine the pet's health condition. The provision unit is realized by the specific processing unit 290 of the data processing device 12, and provides advice based on the analysis results. The introduction unit is realized by the specific processing unit 290 of the data processing device 12, and recommends the optimal pet food. The selection unit is realized by the specific processing unit 290 of the data processing device 12, and selects the nearest veterinary clinic. The consultation unit is realized by the control unit 46A of the robot 414, and suggests consulting a veterinarian online.

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

[0137] The analysis unit can also refer to the pet's past health data when assessing the pet's health condition. For example, the current health condition can be more accurately assessed based on the pet's past weight fluctuations, food intake, and exercise history. The analysis unit can also take the pet's past medical history and treatment history into account when assessing the pet's health condition. Furthermore, the analysis unit can also use the pet's past health data to predict future health risks. This makes it possible to assess the pet's health condition more accurately by utilizing the pet's past health data.

[0138] The providing unit can also provide preventive advice to the owner based on the pet's health condition. For example, if the pet's weight is increasing, the providing unit can advise the owner to reduce the amount of food. Also, if the pet is not getting enough exercise, the providing unit can advise the owner to exercise more. Furthermore, even if the pet's health condition is good, the providing unit can recommend regular health checks. This makes it possible to provide preventive advice to maintain the pet's health condition.

[0139] The referral department can also suggest health supplements to pet owners based on the pet's health condition. For example, if the pet's joint health is a concern, the referral department can suggest joint support supplements. Also, if the pet's coat is in poor condition, the referral department can suggest supplements to improve coat condition. Furthermore, the referral department can suggest supplements to boost the pet's immunity. In this way, by suggesting supplements according to the pet's health condition, it is possible to support the maintenance of health.

[0140] The selection unit can also introduce the owner to a specific specialist based on the pet's health condition. For example, if the pet has a heart problem, the selection unit can introduce a veterinarian specializing in heart disease. If the pet has a skin problem, the selection unit can introduce a veterinarian specializing in skin disease. Furthermore, if the pet has a behavioral problem, the selection unit can introduce a veterinarian specializing in behavior. This allows the pet owner to receive appropriate treatment by being introduced to a specialist according to the pet's health condition.

[0141] The consultation department can also propose health management plans to pet owners based on the pet's health condition. For example, it can propose a diet plan to manage the pet's weight. It can also propose an exercise plan to address the pet's lack of exercise. It can also propose a plan for regular health checks to maintain the pet's health. This allows pet owners to manage their pet's health in a planned manner.

[0142] The reception unit can estimate the owner's emotions and determine the priority of information to be input based on the estimated owner's emotions. For example, if the owner is feeling anxious, important health information (body temperature, excretion status, etc.) can be input first. If the owner is relaxed, detailed information (amount of food eaten, amount of exercise, etc.) can be input. If the owner is in a hurry, the minimum necessary information (weight, body temperature, etc.) can be input. In this way, by determining the priority of information to be input according to the owner's emotions, important information can be input first. The owner's emotions can be estimated using technologies such as facial expression recognition and voice analysis. For example, the reception unit inputs the owner's facial expression data into the generation AI, which then estimates the owner's emotions. The reception unit can also input the owner's voice data into the generation AI, which then estimates the owner's emotions.

[0143] The analysis unit can estimate the owner's emotions and adjust the way the analysis is presented based on the estimated owner's emotions. For example, if the owner is feeling anxious, the analysis results can be provided in a simple, easy-to-understand format. If the owner is relaxed, detailed analysis results can be provided to encourage deeper understanding. If the owner is in a hurry, concise analysis results that focus on the main points can be provided. By adjusting the way the analysis is presented according to the owner's emotions, it is possible to provide analysis results that are easier to understand. The owner's emotions can be estimated using technologies such as facial expression recognition and voice analysis. For example, the analysis unit inputs the owner's facial expression data into the generation AI, which then estimates the owner's emotions. The analysis unit can also input the owner's voice data into the generation AI, which then estimates the owner's emotions.

[0144] The providing unit can estimate the owner's emotions and adjust the way the advice is expressed based on the estimated owner's emotions. For example, if the owner is feeling anxious, it can provide simple, easy-to-understand advice. If the owner is relaxed, it can provide detailed advice to encourage deeper understanding. If the owner is in a hurry, it can provide concise advice that focuses on the main points. This makes it possible to provide advice that is easier to understand by adjusting the way the advice is expressed according to the owner's emotions. The owner's emotions are estimated using technologies such as facial expression recognition and voice analysis. For example, the providing unit inputs the owner's facial expression data into the generation AI, which then estimates the owner's emotions. The providing unit can also input the owner's voice data into the generation AI, which then estimates the owner's emotions.

[0145] The introduction unit can estimate the owner's emotions and prioritize the pet foods to introduce based on the estimated owner's emotions. For example, if the owner is feeling anxious, it can prioritize highly reliable pet foods. If the owner is relaxed, it can also prioritize a wide variety of pet foods. If the owner is in a hurry, it can also prioritize easily purchased pet foods. This allows the system to prioritize the pet foods to introduce based on the owner's emotions, thereby introducing more appropriate pet foods. The owner's emotions are estimated using technologies such as facial expression recognition and voice analysis. For example, the introduction unit inputs the owner's facial expression data into the generation AI, which then estimates the owner's emotions. The introduction unit can also input the owner's voice data into the generation AI, which then estimates the owner's emotions.

[0146] The selection unit can estimate the owner's emotions and adjust the selection criteria for a veterinary clinic based on the estimated owner's emotions. For example, if the owner is feeling anxious, it can prioritize selecting a reliable veterinary clinic. If the owner is relaxed, it can select a veterinary clinic with a wide variety of options. If the owner is in a hurry, it can prioritize selecting a veterinary clinic that is easily accessible. By adjusting the selection criteria for a veterinary clinic according to the owner's emotions, it is possible to select a more appropriate veterinary clinic. The owner's emotions can be estimated using technologies such as facial expression recognition and voice analysis. For example, the selection unit inputs the owner's facial expression data into the generation AI, which then estimates the owner's emotions. The selection unit can also input the owner's voice data into the generation AI, which then estimates the owner's emotions.

[0147] The processing flow of the second embodiment will be briefly explained below.

[0148] Step 1: The reception unit inputs information about the pet's health condition. Information about the pet's health condition includes, for example, weight, amount of food eaten, amount of exercise, excretion status, body temperature, etc. By inputting this information, the reception unit provides basic data for understanding the pet's health condition. Step 2: The analysis unit analyzes the information entered by the reception unit to determine the pet's health condition. The analysis unit evaluates the pet's health condition based on information such as weight, amount of food eaten, amount of exercise, body temperature, and excretion status. It can also use AI to evaluate the pet's health condition. Step 3: The provider provides advice based on the health status identified by the analysis unit. If the pet is not feeling well, the provider suggests appropriate measures based on the symptoms. It can also provide advice such as dietary changes and recommended exercise. It is also possible to provide advice using AI. Step 4: The introduction department recommends the best pet food based on the advice provided by the provision department. The introduction department considers the pet's age, weight, allergy information, dietary preferences, etc. to recommend the best pet food. It can also use AI to recommend the best pet food based on the pet's health condition. Step 5: The selection unit selects the nearest veterinary clinic based on the advice provided by the provider. The selection unit suggests the nearest veterinary clinic based on the pet's symptoms, urgency, and health condition. It can also use AI to select the nearest veterinary clinic based on the pet's health condition. Step 6: The consultation unit suggests online consultation with a veterinarian based on the advice provided by the provider. The consultation unit suggests online consultation with a veterinarian if online consultation is appropriate based on the pet's symptoms and health condition. The consultation unit can also use AI to suggest online consultation with a veterinarian based on the pet's health condition.

[0149] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0154] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0155] The data processing device 12 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0160] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0161] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0162] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0163] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0164] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0165] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0166] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0167] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0170] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0171] The data processing device 12 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0172] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0173] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0174] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0175] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0176] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0177] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0178] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0179] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0180] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0181] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0182] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0183] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0184] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0186] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0187] The data processing device 12 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0188] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0189] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0190] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0191] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0192] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0193] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0194] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0195] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0196] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0197] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0198] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0199] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0200] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0201] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0202] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0203] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0204] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0205] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0206] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0207] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0208] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0209] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0210] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0211] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0212] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0213] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0214] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0215] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0216] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0217] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0218] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0219] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0220] [Explanation of symbols]

[0221] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception desk where information about the pet's health condition is entered; an analysis unit that analyzes the information input by the reception unit to grasp the health condition; a providing unit that provides advice based on the health condition grasped by the analyzing unit; an introduction unit that introduces appropriate pet food based on the advice provided by the provision unit; a selection unit that selects the nearest veterinary hospital based on the advice provided by the provision unit; a consultation unit that suggests consulting a veterinarian online based on the advice provided by the provision unit; A system characterized by:

2. The reception unit Enter your pet's weight, food intake, exercise, excretion status, and body temperature information. The system of claim 1 .

3. The analysis unit Evaluating the health condition of the pet based on the information input by the reception unit The system of claim 1 .

4. The providing unit If your pet is unwell, we will suggest appropriate treatment based on the symptoms. The system of claim 1 .

5. The introduction unit Recommend appropriate pet food based on your pet's age, weight, allergies, and dietary preferences The system of claim 1 .

6. The selection unit Suggests the nearest veterinary clinic based on your pet's condition and urgency The system of claim 1 .

7. The consultation department: Offer an online consultation with your veterinarian when this is appropriate The system of claim 1 .

8. The reception unit Estimate the owner's emotions and adjust the timing of information input based on the estimated emotions of the owner. The system of claim 1 .

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