system

Abstract

The system according to this embodiment aims to reduce the burden on pet owners through online diagnosis of their pets. [Solution] The system according to the embodiment comprises a reception unit, a medical interview unit, an analysis unit, and a diagnostic unit. The reception unit is where the owner inputs basic information and symptoms of their pet. The medical interview unit conducts a medical interview based on the information input by the reception unit. The analysis unit analyzes images and videos of the pet based on the information obtained by the medical interview unit. The diagnostic unit makes a final diagnosis based on the analysis results obtained by the analysis unit.

Description

Technical Field

[0004] ,

[0006] , , , ,

[0005] , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

[0007] The system according to this embodiment can reduce the burden on pet owners through online diagnosis of their pets. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The online pet diagnostic system according to an embodiment of the present invention is a system that provides online pet diagnostics and prescriptions for medication to HELPO subscribers who own dogs and cats. The online pet diagnostic system uses AI to perform image diagnosis and interviews with pets, aiming to enable online diagnoses with fewer veterinarians. This will reduce the burden on pet owners and the burden of hospital visits for pets. First, the owner inputs basic information and symptoms of their pet. For example, they input information such as "female, 5 years old, spayed, has been lethargic since yesterday." This information is input into the AI. Next, the AI ​​conducts an interview based on the input information. For example, it asks questions such as "Has the pet vomited?" and "Have there been any changes in urine?", to which the owner answers. The AI ​​analyzes these answers and identifies the pet's symptoms. Furthermore, the AI ​​analyzes images and videos of the pet. For example, it analyzes information such as whether the pet's eyes are vacant or whether its breathing is normal, and determines key points. For example, it may determine that there is a 70% chance of ureteral stones and a 20% chance of cystitis. A veterinarian confirms the AI's judgment and makes a final diagnosis. For example, the system might make a diagnosis such as, "There is a possibility of ureteral stones, so we will remotely prescribe XX. If it does not improve, an X-ray will be necessary, so please come to the clinic." This system makes it possible to reduce the burden on pet owners and the burden of vet visits for pets. For example, even if the pet owner has a job, they can reduce the number of vet visits by using online diagnosis. It also reduces the burden on the pet itself. Furthermore, the AI ​​also supports veterinarians in their diagnoses. For example, it analyzes X-ray images and test results to help make a final decision based on multiple cases. This improves the accuracy of veterinarians' diagnoses and is expected to extend the healthy lifespan of pets. In this way, an AI-powered online pet diagnosis system can reduce the burden on both pet owners and pets and provide efficient diagnoses. As a result, an online pet diagnosis system can reduce the burden on pet owners and the burden of vet visits for pets.

[0029] The online pet diagnostic system according to this embodiment comprises a reception unit, a consultation unit, an analysis unit, and a diagnostic unit. The reception unit allows the owner to input basic information and symptoms of their pet. Basic information input by the owner includes, for example, the pet's name, age, breed, and sex. Symptoms include, for example, coughing, loss of appetite, and skin abnormalities. The reception unit provides, for example, an interface for the owner to input basic information and symptoms of their pet. The consultation unit conducts a consultation based on the information input by the reception unit. The consultation unit uses, for example, AI to ask the owner questions such as, "Has your pet vomited?" or "Have there been any changes in your pet's urination?" The consultation unit analyzes the owner's answers to identify the pet's symptoms. The analysis unit analyzes images and videos of the pet based on the information obtained by the consultation unit. The analysis unit uses, for example, AI to analyze information such as whether the pet's eyes are vacant or whether its breathing is normal. The analysis unit identifies the pet's symptoms and determines key points. The diagnostic unit makes a final diagnosis based on the analysis results obtained by the analysis unit. For example, the diagnostic unit uses AI to determine that there is a 70% chance of ureteral stones and a 20% chance of cystitis. The diagnostic unit then has a veterinarian review the AI's judgment and make a final diagnosis. As a result, the online pet diagnostic system according to this embodiment can reduce the burden on pet owners and the burden of veterinary visits for pets.

[0030] The reception section allows pet owners to input basic information and symptoms of their pets. This basic information includes, for example, the pet's name, age, breed, and sex. This information is crucial as fundamental data for accurately understanding the pet's health. Symptoms include, for example, coughing, loss of appetite, and skin abnormalities. These symptoms are used as initial information for evaluating the pet's health. The reception section provides an interface for owners to input basic information and symptoms. This interface is designed to be user-friendly and intuitive, allowing owners to easily input information. For example, dropdown menus and checkboxes are used to enable owners to input information quickly and accurately. The reception section also automatically saves the entered information for subsequent processing, saving owners the trouble of re-entering information they have already entered. Furthermore, the reception section encrypts and stores the entered information to protect privacy, allowing owners to provide information with confidence. The reception section plays a role in quickly and accurately collecting basic pet information and symptoms, improving the overall efficiency of the system.

[0031] The consultation department conducts interviews based on information entered by the reception department. The consultation department uses AI to ask pet owners questions such as, "Has your pet been vomiting?" or "Have there been any changes in your pet's urination?" These questions are important for gaining a more detailed understanding of the pet's symptoms. The consultation department analyzes the pet owner's answers to identify the pet's symptoms. The AI ​​uses natural language processing technology to analyze the pet owner's answers and extract important information. For example, if the pet owner answers, "My pet has lost its appetite recently," the AI ​​extracts the keyword "loss of appetite" and adds it to the symptom list. The AI ​​also generates additional questions based on the pet owner's answers to gather more detailed information. For example, it might ask, "How long has your pet's loss of appetite been ongoing?" This allows the consultation department to gain a detailed understanding of the pet's symptoms and provide the necessary information to the analysis department, which is the next step. Furthermore, the consultation department improves the accuracy of the interviews by selecting the most effective questions based on past consultation data. This allows the medical interview unit to quickly and accurately identify the pet's symptoms, improving the overall diagnostic accuracy of the system.

[0032] The analysis unit analyzes images and videos of pets based on information obtained by the medical interview unit. For example, the analysis unit uses AI to analyze information such as whether the pet's eyes are vacant or whether its breathing is normal. Specifically, it uses image recognition technology to analyze the condition of the pet's eyes and determine whether they are vacant. It also uses video analysis technology to analyze the rhythm and speed of the pet's breathing and determine whether it is normal. In this way, the analysis unit identifies the pet's symptoms and determines key points. Furthermore, the analysis unit utilizes past data and statistical information to more accurately identify the pet's symptoms. For example, by referring to data of pets that have shown similar symptoms in the past and comparing it with the current symptoms of the pet, it can make a more accurate diagnosis. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. In this way, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0033] The diagnostic department makes a final diagnosis based on the analysis results obtained by the analysis department. For example, the diagnostic department uses AI to determine that there is a 70% probability of ureteral stones and a 20% probability of cystitis. Based on the data provided by the analysis department, the AI ​​simulates multiple diagnostic scenarios and identifies the most likely diagnosis. This allows the diagnostic department to diagnose pets quickly and accurately. Furthermore, the diagnostic department has a veterinarian review the AI's judgment and make a final diagnosis. This improves the reliability of the AI's diagnosis and allows for the use of the veterinarian's expertise. The diagnostic department provides the diagnosis results to the pet owner and proposes necessary treatments and measures. For example, if there is a high probability of ureteral stones, it proposes appropriate treatment and preventive measures to the owner. In addition, the diagnostic department can continuously monitor the pet's health based on the diagnosis results and perform re-diagnosis as needed. This allows the diagnostic department to comprehensively support pet health management and reduce the burden on pet owners.

[0034] The analysis unit can analyze X-ray images and test results to help make a final judgment based on multiple cases. For example, the analysis unit can input X-ray images into the AI, which then analyzes the images and detects abnormalities. For example, the analysis unit can input blood test results into the AI, which then identifies abnormal values. For example, the analysis unit can input urine test results into the AI, which then detects abnormalities. This improves the accuracy of diagnosis by analyzing X-ray images and test results. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can improve the accuracy of diagnosis by inputting X-ray images into the AI, which then detects abnormalities.

[0035] The consultation department can make suggestions to reduce the burden on pet owners and the burden of vet visits for pets. For example, the consultation department may suggest to pet owners that they can reduce the number of vet visits by using online diagnostics. For example, the consultation department may suggest to pet owners appropriate care methods according to their pet's symptoms. For example, the consultation department may provide to pet owners advice on pet health management. This can reduce the burden on pet owners and the burden of vet visits for pets. Some or all of the above processing in the consultation department may be performed using AI or not. For example, the consultation department can reduce the burden on pet owners and the burden of vet visits by using AI to suggest appropriate care methods to pet owners.

[0036] The reception desk can refer to the pet's past medical history and provide supplementary information to improve the accuracy of the entered information. For example, the reception desk can automatically supplement the pet's medical history and allergy information from past medical history. For example, the reception desk can automatically suggest information related to the current symptoms based on past medical history. For example, the reception desk can refer to past medical history to detect inconsistencies in the entered information and suggest corrections. This improves the accuracy of the entered information by referring to past medical history. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input past medical history into AI, and the AI ​​can provide supplementary information to improve the accuracy of the entered information.

[0037] The reception system can customize the types and order of information entered by pet owners according to the type and age of the pet. For example, for young pets, the reception system prioritizes input of information related to growth. For elderly pets, the reception system prioritizes input of information related to chronic diseases and regular health checks. For example, the reception system prioritizes input of information related to diseases and symptoms specific to certain types of pets. This enables information input tailored to the type and age of the pet. Some or all of the above processing in the reception system may be performed using AI or not. For example, the reception system can achieve efficient information input by having AI customize the information input according to the type and age of the pet.

[0038] The reception desk can add input fields related to region-specific diseases and symptoms, taking into account the owner's geographical location. For example, the reception desk can automatically add input fields related to diseases prevalent in the region. For example, the reception desk can add input fields related to symptoms associated with the region's climate and environment. For example, the reception desk can add information necessary for treatment based on information from local animal hospitals. This improves the accuracy of diagnosis by inputting information about region-specific diseases and symptoms. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the owner's geographical location into the AI, and the AI ​​can improve the accuracy of diagnosis by adding input fields related to region-specific diseases and symptoms.

[0039] The reception unit can analyze the owner's social media activity and automatically input relevant information. For example, the reception unit can automatically acquire and input pet health information posted by the owner on social media. For example, the reception unit can analyze images and videos of pets shared by the owner on social media and input relevant information. For example, the reception unit can automatically input information about the pet's living environment and habits from the owner's social media activity. This allows for the automatic input of relevant information by analyzing social media activity. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can improve the accuracy of input by inputting the owner's social media activity into AI, which then automatically inputs relevant information.

[0040] The interview unit can refer to past interview data and automatically generate the most appropriate questions for the pet's symptoms. For example, the interview unit can automatically generate questions related to the pet's symptoms from past interview data. For example, the interview unit can suggest questions related to the current symptoms based on past interview data. For example, the interview unit can refer to past interview data to detect inconsistencies in the entered information and suggest corrections. In this way, the optimal questions can be automatically generated by referring to past interview data. Some or all of the above processes in the interview unit may be performed using AI or not. For example, the interview unit can input past interview data into AI, and the AI ​​can automatically generate the optimal questions, thereby achieving efficient interviews.

[0041] The interview function can apply different sets of questions depending on the type and age of the pet during the interview. For example, for young pets, the interview function prioritizes questions related to growth. For older pets, the interview function prioritizes questions related to chronic diseases and regular health checks. For example, the interview function prioritizes questions related to diseases and symptoms specific to certain types of pets. This allows for efficient interviews by applying question sets tailored to the type and age of the pet. Some or all of the above processing in the interview function may be performed using AI or not. For example, the interview function can achieve efficient interviews by applying question sets tailored to the type and age of the pet to AI.

[0042] The consultation unit can customize the questions asked during the consultation, taking into account the owner's lifestyle and occupation. For example, if the owner has a busy job, the consultation unit will prioritize questions that can be answered quickly. If the owner works from home, for example, the consultation unit will ask detailed questions sequentially. The consultation unit will customize questions related to pet health management according to the owner's lifestyle. This enables efficient consultations by providing questions tailored to the owner's lifestyle and occupation. Some or all of the above processing in the consultation unit may be performed using AI or not. For example, the consultation unit can input the owner's lifestyle and occupation information into AI, and the AI ​​can customize the questions to achieve efficient consultations.

[0043] The interview unit can refer to the owner's past interview history and automatically add relevant questions. For example, the interview unit can automatically add questions related to the current symptoms from the past interview history. For example, the interview unit can suggest questions related to the current symptoms based on the past interview history. For example, the interview unit can refer to the past interview history, detect inconsistencies in the entered information, and suggest corrections. This allows relevant questions to be automatically added by referring to the past interview history. Some or all of the above processes in the interview unit may be performed using AI or not. For example, the interview unit can input the past interview history into AI, and the AI ​​can automatically add relevant questions to achieve efficient interviews.

[0044] The analysis unit can refer to past image and video data of the pet and provide supplementary information to improve the accuracy of the analysis. For example, the analysis unit can automatically supplement information on the pet's medical history and allergies from past image and video data. For example, the analysis unit can automatically suggest information related to the current symptoms based on past image and video data. For example, the analysis unit can refer to past image and video data to detect inconsistencies in the analysis results and suggest corrections. This improves the accuracy of the analysis by referring to past image and video data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can improve the accuracy of the analysis by inputting past image and video data into AI, which can then provide supplementary information.

[0045] The analysis unit can apply different analysis algorithms depending on the type and age of the pet during analysis. For example, for young pets, the analysis unit applies an analysis algorithm related to growth. For example, for elderly pets, the analysis unit applies an analysis algorithm related to chronic diseases and regular health checks. For example, the analysis unit applies an analysis algorithm related to diseases and symptoms specific to certain types of pets. By applying an analysis algorithm tailored to the type and age of the pet, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can achieve efficient analysis by having AI apply an analysis algorithm tailored to the type and age of the pet.

[0046] The analysis unit can perform analyses on region-specific diseases and symptoms, taking into account the pet's geographical location. For example, the analysis unit can automatically perform analyses on diseases prevalent in the region. For example, the analysis unit can perform analyses on symptoms related to the region's climate and environment. For example, the analysis unit can perform analyses necessary for treatment based on information from local animal hospitals. This improves the accuracy of diagnosis by performing analyses on region-specific diseases and symptoms. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not. For example, the analysis unit can input the pet's geographical location information into the AI, and the AI ​​can perform analyses on region-specific diseases and symptoms to improve the accuracy of diagnosis.

[0047] The analysis unit can analyze a pet's social media activity and automatically analyze related images and videos. For example, the analysis unit can automatically acquire and analyze images and videos of pets posted by their owners on social media. For example, the analysis unit can analyze health information of pets shared by their owners on social media. For example, the analysis unit can analyze information about the pet's living environment and habits from the owner's social media activity. This allows for the automatic analysis of relevant information by analyzing social media activity. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can improve the accuracy of its analysis by inputting the owner's social media activity into AI, which then automatically analyzes the relevant information.

[0048] The diagnostic unit can automatically generate the optimal diagnosis for a pet's symptoms by referring to past diagnostic data. For example, the diagnostic unit can automatically generate a diagnosis related to the pet's symptoms from past diagnostic data. For example, the diagnostic unit can propose a diagnosis related to the current symptoms based on past diagnostic data. For example, the diagnostic unit can refer to past diagnostic data to detect inconsistencies in the diagnostic results and propose corrections. In this way, the optimal diagnosis can be automatically generated by referring to past diagnostic data. Some or all of the above processes in the diagnostic unit may be performed using AI or not. For example, the diagnostic unit can achieve efficient diagnosis by inputting past diagnostic data into AI, which will then automatically generate the optimal diagnosis.

[0049] The diagnostic unit can apply different diagnostic algorithms depending on the type and age of the pet during diagnosis. For example, for young pets, the diagnostic unit applies a diagnostic algorithm related to growth. For example, for elderly pets, the diagnostic unit applies a diagnostic algorithm related to chronic diseases and regular health checks. For example, the diagnostic unit applies a diagnostic algorithm related to diseases and symptoms specific to certain types of pets. By applying a diagnostic algorithm tailored to the type and age of the pet, the accuracy of the diagnosis is improved. Some or all of the above processing in the diagnostic unit may be performed using AI or not. For example, the diagnostic unit can achieve efficient diagnosis by having AI apply a diagnostic algorithm tailored to the type and age of the pet.

[0050] The diagnostic unit can perform diagnoses regarding region-specific diseases and symptoms by considering the pet's geographical location. For example, the diagnostic unit can automatically perform diagnoses regarding diseases prevalent in the region. For example, the diagnostic unit can perform diagnoses regarding symptoms related to the region's climate and environment. For example, the diagnostic unit can perform diagnoses necessary for treatment based on information from local animal hospitals. This improves the accuracy of the diagnosis by performing diagnoses regarding region-specific diseases and symptoms. Some or all of the above processes in the diagnostic unit may be performed using AI or not. For example, the diagnostic unit can improve the accuracy of the diagnosis by inputting the pet's geographical location information into AI, which then performs diagnoses regarding region-specific diseases and symptoms.

[0051] The diagnostic unit can analyze a pet's social media activity and automatically provide relevant diagnostic results. For example, the diagnostic unit can automatically acquire health information about pets posted by owners on social media and reflect it in the diagnostic results. For example, the diagnostic unit can analyze images and videos of pets shared by owners on social media and provide relevant diagnostic results. For example, the diagnostic unit can reflect information about the pet's living environment and habits from the owner's social media activity in the diagnostic results. In this way, by analyzing social media activity, relevant diagnostic results can be automatically provided. Some or all of the above processing in the diagnostic unit may be performed using AI or not. For example, the diagnostic unit can improve the accuracy of the diagnosis by inputting the owner's social media activity into AI, which then automatically provides relevant diagnostic results.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] The online pet diagnostic system can also include a vaccination management section. This section manages the pet's vaccination history and notifies the owner of the next vaccination date. For example, when the owner enters the pet's vaccination history, the vaccination management section automatically calculates and notifies the owner of the next vaccination date. The vaccination management section can also suggest additional vaccinations based on diseases prevalent in the area. This allows for more efficient pet health management.

[0054] The online pet diagnostic system can also include a nutrition management section. This section manages the pet's diet and nutritional balance, and proposes an appropriate meal plan. For example, when the owner inputs their pet's diet, the nutrition management section evaluates the nutritional balance based on that information and proposes a meal plan to supplement necessary nutrients. Furthermore, the nutrition management section can customize meal plans according to the pet's age and health condition. This allows for more effective maintenance of the pet's health.

[0055] The online pet diagnostic system can also include an exercise management unit. This unit manages the pet's exercise volume and activity level, and suggests an appropriate exercise plan. For example, when the owner inputs the pet's exercise volume, the exercise management unit creates an exercise plan based on that information, suggesting exercises that will help maintain the pet's health. Furthermore, the exercise management unit can customize the exercise plan according to the pet's age and health condition. This allows for more effective maintenance of the pet's health.

[0056] The online pet diagnostic system can also include a stress management section. This section assesses the pet's stress level and provides advice for stress reduction. For example, when an owner inputs information about their pet's behavior or environmental changes, the stress management section uses this information to assess the stress level and provide specific advice for stress reduction. The stress management section can also suggest stress reduction methods tailored to the pet's breed and personality. This allows for more effective stress management for pets.

[0057] The online pet diagnostic system can also be equipped with a behavioral analysis unit. This unit analyzes the pet's behavioral patterns and detects abnormal behavior. For example, if the owner records the pet's behavior, the behavioral analysis unit analyzes the behavioral patterns based on that information and detects abnormal behavior. Furthermore, the behavioral analysis unit can identify the cause of the abnormal behavior and suggest appropriate countermeasures. This allows for more effective pet behavior management.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The reception desk allows pet owners to enter basic information and symptoms of their pets. Basic information includes the pet's name, age, breed, and sex, while symptoms include cough, loss of appetite, and skin abnormalities. The reception desk provides an interface for pet owners to enter this information. Step 2: The consultation department conducts a consultation based on the information entered by the reception department. The consultation department uses AI to ask pet owners questions such as "Has your pet been vomiting?" and "Have you noticed any changes in your pet's urine?", and analyzes the owner's answers to identify the pet's symptoms. Step 3: The analysis unit analyzes images and videos of the pet based on the information obtained by the interview unit. The analysis unit uses AI to analyze information such as whether the pet's eyes are vacant or whether its breathing is normal, to identify the pet's symptoms and determine key points. Step 4: The diagnostic department makes a final diagnosis based on the analysis results obtained by the analysis department. The diagnostic department uses AI to determine that there is a 70% chance of ureteral stones and a 20% chance of cystitis, and a veterinarian confirms the AI's judgment to make a final diagnosis.

[0060] (Example of form 2) The online pet diagnostic system according to an embodiment of the present invention is a system that provides online pet diagnostics and prescriptions for medication to HELPO subscribers who own dogs and cats. The online pet diagnostic system uses AI to perform image diagnosis and interviews with pets, aiming to enable online diagnoses with fewer veterinarians. This will reduce the burden on pet owners and the burden of hospital visits for pets. First, the owner inputs basic information and symptoms of their pet. For example, they input information such as "female, 5 years old, spayed, has been lethargic since yesterday." This information is input into the AI. Next, the AI ​​conducts an interview based on the input information. For example, it asks questions such as "Has the pet vomited?" and "Have there been any changes in urine?", to which the owner answers. The AI ​​analyzes these answers and identifies the pet's symptoms. Furthermore, the AI ​​analyzes images and videos of the pet. For example, it analyzes information such as whether the pet's eyes are vacant or whether its breathing is normal, and determines key points. For example, it may determine that there is a 70% chance of ureteral stones and a 20% chance of cystitis. A veterinarian confirms the AI's judgment and makes a final diagnosis. For example, the system might make a diagnosis such as, "There is a possibility of ureteral stones, so we will remotely prescribe XX. If it does not improve, an X-ray will be necessary, so please come to the clinic." This system makes it possible to reduce the burden on pet owners and the burden of vet visits for pets. For example, even if the pet owner has a job, they can reduce the number of vet visits by using online diagnosis. It also reduces the burden on the pet itself. Furthermore, the AI ​​also supports veterinarians in their diagnoses. For example, it analyzes X-ray images and test results to help make a final decision based on multiple cases. This improves the accuracy of veterinarians' diagnoses and is expected to extend the healthy lifespan of pets. In this way, an AI-powered online pet diagnosis system can reduce the burden on both pet owners and pets and provide efficient diagnoses. As a result, an online pet diagnosis system can reduce the burden on pet owners and the burden of vet visits for pets.

[0061] The online pet diagnostic system according to this embodiment comprises a reception unit, a consultation unit, an analysis unit, and a diagnostic unit. The reception unit allows the owner to input basic information and symptoms of their pet. Basic information input by the owner includes, for example, the pet's name, age, breed, and sex. Symptoms include, for example, coughing, loss of appetite, and skin abnormalities. The reception unit provides, for example, an interface for the owner to input basic information and symptoms of their pet. The consultation unit conducts a consultation based on the information input by the reception unit. The consultation unit uses, for example, AI to ask the owner questions such as, "Has your pet vomited?" or "Have there been any changes in your pet's urination?" The consultation unit analyzes the owner's answers to identify the pet's symptoms. The analysis unit analyzes images and videos of the pet based on the information obtained by the consultation unit. The analysis unit uses, for example, AI to analyze information such as whether the pet's eyes are vacant or whether its breathing is normal. The analysis unit identifies the pet's symptoms and determines key points. The diagnostic unit makes a final diagnosis based on the analysis results obtained by the analysis unit. For example, the diagnostic unit uses AI to determine that there is a 70% chance of ureteral stones and a 20% chance of cystitis. The diagnostic unit then has a veterinarian review the AI's judgment and make a final diagnosis. As a result, the online pet diagnostic system according to this embodiment can reduce the burden on pet owners and the burden of veterinary visits for pets.

[0062] The reception section allows pet owners to input basic information and symptoms of their pets. This basic information includes, for example, the pet's name, age, breed, and sex. This information is crucial as fundamental data for accurately understanding the pet's health. Symptoms include, for example, coughing, loss of appetite, and skin abnormalities. These symptoms are used as initial information for evaluating the pet's health. The reception section provides an interface for owners to input basic information and symptoms. This interface is designed to be user-friendly and intuitive, allowing owners to easily input information. For example, dropdown menus and checkboxes are used to enable owners to input information quickly and accurately. The reception section also automatically saves the entered information for subsequent processing, saving owners the trouble of re-entering information they have already entered. Furthermore, the reception section encrypts and stores the entered information to protect privacy, allowing owners to provide information with confidence. The reception section plays a role in quickly and accurately collecting basic pet information and symptoms, improving the overall efficiency of the system.

[0063] The consultation department conducts interviews based on information entered by the reception department. The consultation department uses AI to ask pet owners questions such as, "Has your pet been vomiting?" or "Have there been any changes in your pet's urination?" These questions are important for gaining a more detailed understanding of the pet's symptoms. The consultation department analyzes the pet owner's answers to identify the pet's symptoms. The AI ​​uses natural language processing technology to analyze the pet owner's answers and extract important information. For example, if the pet owner answers, "My pet has lost its appetite recently," the AI ​​extracts the keyword "loss of appetite" and adds it to the symptom list. The AI ​​also generates additional questions based on the pet owner's answers to gather more detailed information. For example, it might ask, "How long has your pet's loss of appetite been ongoing?" This allows the consultation department to gain a detailed understanding of the pet's symptoms and provide the necessary information to the analysis department, which is the next step. Furthermore, the consultation department improves the accuracy of the interviews by selecting the most effective questions based on past consultation data. This allows the medical interview unit to quickly and accurately identify the pet's symptoms, improving the overall diagnostic accuracy of the system.

[0064] The analysis unit analyzes images and videos of pets based on information obtained by the medical interview unit. For example, the analysis unit uses AI to analyze information such as whether the pet's eyes are vacant or whether its breathing is normal. Specifically, it uses image recognition technology to analyze the condition of the pet's eyes and determine whether they are vacant. It also uses video analysis technology to analyze the rhythm and speed of the pet's breathing and determine whether it is normal. In this way, the analysis unit identifies the pet's symptoms and determines key points. Furthermore, the analysis unit utilizes past data and statistical information to more accurately identify the pet's symptoms. For example, by referring to data of pets that have shown similar symptoms in the past and comparing it with the current symptoms of the pet, it can make a more accurate diagnosis. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. In this way, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0065] The diagnostic department makes a final diagnosis based on the analysis results obtained by the analysis department. For example, the diagnostic department uses AI to determine that there is a 70% probability of ureteral stones and a 20% probability of cystitis. Based on the data provided by the analysis department, the AI ​​simulates multiple diagnostic scenarios and identifies the most likely diagnosis. This allows the diagnostic department to diagnose pets quickly and accurately. Furthermore, the diagnostic department has a veterinarian review the AI's judgment and make a final diagnosis. This improves the reliability of the AI's diagnosis and allows for the use of the veterinarian's expertise. The diagnostic department provides the diagnosis results to the pet owner and proposes necessary treatments and measures. For example, if there is a high probability of ureteral stones, it proposes appropriate treatment and preventive measures to the owner. In addition, the diagnostic department can continuously monitor the pet's health based on the diagnosis results and perform re-diagnosis as needed. This allows the diagnostic department to comprehensively support pet health management and reduce the burden on pet owners.

[0066] The analysis unit can analyze X-ray images and test results to help make a final judgment based on multiple cases. For example, the analysis unit can input X-ray images into the AI, which then analyzes the images and detects abnormalities. For example, the analysis unit can input blood test results into the AI, which then identifies abnormal values. For example, the analysis unit can input urine test results into the AI, which then detects abnormalities. This improves the accuracy of diagnosis by analyzing X-ray images and test results. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can improve the accuracy of diagnosis by inputting X-ray images into the AI, which then detects abnormalities.

[0067] The consultation department can make suggestions to reduce the burden on pet owners and the burden of vet visits for pets. For example, the consultation department may suggest to pet owners that they can reduce the number of vet visits by using online diagnostics. For example, the consultation department may suggest to pet owners appropriate care methods according to their pet's symptoms. For example, the consultation department may provide to pet owners advice on pet health management. This can reduce the burden on pet owners and the burden of vet visits for pets. Some or all of the above processing in the consultation department may be performed using AI or not. For example, the consultation department can reduce the burden on pet owners and the burden of vet visits by using AI to suggest appropriate care methods to pet owners.

[0068] The reception unit can estimate the owner's emotions and adjust the display of the input interface based on the estimated emotions. For example, if the owner is feeling anxious, the reception unit provides a simple and reassuring interface. If the owner is relaxed, the reception unit provides detailed input options and suggests a customizable input method. If the owner is in a hurry, the reception unit prioritizes voice input, allowing for quick input of basic pet information and symptoms. This improves ease of input by providing an interface that responds to the owner's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input image data of the owner captured by a camera into the generative AI and have the generative AI perform the estimation of the owner's emotions.

[0069] The reception desk can refer to the pet's past medical history and provide supplementary information to improve the accuracy of the entered information. For example, the reception desk can automatically supplement the pet's medical history and allergy information from past medical history. For example, the reception desk can automatically suggest information related to the current symptoms based on past medical history. For example, the reception desk can refer to past medical history to detect inconsistencies in the entered information and suggest corrections. This improves the accuracy of the entered information by referring to past medical history. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input past medical history into AI, and the AI ​​can provide supplementary information to improve the accuracy of the entered information.

[0070] The reception system can customize the types and order of information entered by pet owners according to the type and age of the pet. For example, for young pets, the reception system prioritizes input of information related to growth. For elderly pets, the reception system prioritizes input of information related to chronic diseases and regular health checks. For example, the reception system prioritizes input of information related to diseases and symptoms specific to certain types of pets. This enables information input tailored to the type and age of the pet. Some or all of the above processing in the reception system may be performed using AI or not. For example, the reception system can achieve efficient information input by having AI customize the information input according to the type and age of the pet.

[0071] The reception unit can estimate the owner's emotions and determine the priority of input based on the estimated emotions. For example, if the owner is feeling anxious, the reception unit will prioritize inputting the most important information. If the owner is relaxed, the reception unit will prioritize inputting detailed information sequentially. If the owner is in a hurry, the reception unit will prioritize inputting minimal information. This enables efficient information input by determining the priority of input according to the owner's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input image data of the owner taken by a camera into a generative AI and have the generative AI perform the estimation of the owner's emotions.

[0072] The reception desk can add input fields related to region-specific diseases and symptoms, taking into account the owner's geographical location. For example, the reception desk can automatically add input fields related to diseases prevalent in the region. For example, the reception desk can add input fields related to symptoms associated with the region's climate and environment. For example, the reception desk can add information necessary for treatment based on information from local animal hospitals. This improves the accuracy of diagnosis by inputting information about region-specific diseases and symptoms. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the owner's geographical location into the AI, and the AI ​​can improve the accuracy of diagnosis by adding input fields related to region-specific diseases and symptoms.

[0073] The reception unit can analyze the owner's social media activity and automatically input relevant information. For example, the reception unit can automatically acquire and input pet health information posted by the owner on social media. For example, the reception unit can analyze images and videos of pets shared by the owner on social media and input relevant information. For example, the reception unit can automatically input information about the pet's living environment and habits from the owner's social media activity. This allows for the automatic input of relevant information by analyzing social media activity. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can improve the accuracy of input by inputting the owner's social media activity into AI, which then automatically inputs relevant information.

[0074] The interview unit can estimate the owner's emotions and adjust the content and order of interview questions based on the estimated emotions. For example, if the owner is feeling anxious, the interview unit will prioritize asking the most important questions. If the owner is relaxed, the interview unit will ask detailed questions sequentially. If the owner is in a hurry, the interview unit will prioritize asking the fewest questions. This allows for efficient interviews by adjusting the content and order of questions according to the owner's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interview unit may be performed using AI or not. For example, the interview unit can input image data of the owner captured by a camera into the generative AI and have the generative AI perform the estimation of the owner's emotions.

[0075] The interview unit can refer to past interview data and automatically generate the most appropriate questions for the pet's symptoms. For example, the interview unit can automatically generate questions related to the pet's symptoms from past interview data. For example, the interview unit can suggest questions related to the current symptoms based on past interview data. For example, the interview unit can refer to past interview data to detect inconsistencies in the entered information and suggest corrections. In this way, the optimal questions can be automatically generated by referring to past interview data. Some or all of the above processes in the interview unit may be performed using AI or not. For example, the interview unit can input past interview data into AI, and the AI ​​can automatically generate the optimal questions, thereby achieving efficient interviews.

[0076] The interview function can apply different sets of questions depending on the type and age of the pet during the interview. For example, for young pets, the interview function prioritizes questions related to growth. For older pets, the interview function prioritizes questions related to chronic diseases and regular health checks. For example, the interview function prioritizes questions related to diseases and symptoms specific to certain types of pets. This allows for efficient interviews by applying question sets tailored to the type and age of the pet. Some or all of the above processing in the interview function may be performed using AI or not. For example, the interview function can achieve efficient interviews by applying question sets tailored to the type and age of the pet to AI.

[0077] The interview unit can estimate the owner's emotions and adjust the way the interview is answered based on the estimated emotions. For example, if the owner is feeling anxious, the interview unit will provide simple and reassuring answers. If the owner is relaxed, the interview unit will provide detailed answer options and suggest customizable answer methods. If the owner is in a hurry, the interview unit will prioritize voice input to allow for quick answers. This improves the ease of answering by providing answer methods that are tailored to the owner's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interview unit may be performed using AI or not. For example, the interview unit can input image data of the owner captured by a camera into a generative AI and have the generative AI perform the estimation of the owner's emotions.

[0078] The consultation unit can customize the questions asked during the consultation, taking into account the owner's lifestyle and occupation. For example, if the owner has a busy job, the consultation unit will prioritize questions that can be answered quickly. If the owner works from home, for example, the consultation unit will ask detailed questions sequentially. The consultation unit will customize questions related to pet health management according to the owner's lifestyle. This enables efficient consultations by providing questions tailored to the owner's lifestyle and occupation. Some or all of the above processing in the consultation unit may be performed using AI or not. For example, the consultation unit can input the owner's lifestyle and occupation information into AI, and the AI ​​can customize the questions to achieve efficient consultations.

[0079] The interview unit can refer to the owner's past interview history and automatically add relevant questions. For example, the interview unit can automatically add questions related to the current symptoms from the past interview history. For example, the interview unit can suggest questions related to the current symptoms based on the past interview history. For example, the interview unit can refer to the past interview history, detect inconsistencies in the entered information, and suggest corrections. This allows relevant questions to be automatically added by referring to the past interview history. Some or all of the above processes in the interview unit may be performed using AI or not. For example, the interview unit can input the past interview history into AI, and the AI ​​can automatically add relevant questions to achieve efficient interviews.

[0080] The analysis unit can estimate the owner's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the owner is feeling anxious, the analysis unit provides a simple and reassuring display method. For example, if the owner is relaxed, the analysis unit provides detailed analysis results and suggests a customizable display method. For example, if the owner is in a hurry, the analysis unit prioritizes displaying concise analysis results. This improves the understanding of the analysis results by providing a display method that matches the owner's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input image data of the owner captured by a camera into the generative AI and have the generative AI perform the estimation of the owner's emotions.

[0081] The analysis unit can refer to past image and video data of the pet and provide supplementary information to improve the accuracy of the analysis. For example, the analysis unit can automatically supplement information on the pet's medical history and allergies from past image and video data. For example, the analysis unit can automatically suggest information related to the current symptoms based on past image and video data. For example, the analysis unit can refer to past image and video data to detect inconsistencies in the analysis results and suggest corrections. This improves the accuracy of the analysis by referring to past image and video data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can improve the accuracy of the analysis by inputting past image and video data into AI, which can then provide supplementary information.

[0082] The analysis unit can apply different analysis algorithms depending on the type and age of the pet during analysis. For example, for young pets, the analysis unit applies an analysis algorithm related to growth. For example, for elderly pets, the analysis unit applies an analysis algorithm related to chronic diseases and regular health checks. For example, the analysis unit applies an analysis algorithm related to diseases and symptoms specific to certain types of pets. By applying an analysis algorithm tailored to the type and age of the pet, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can achieve efficient analysis by having AI apply an analysis algorithm tailored to the type and age of the pet.

[0083] The analysis unit can estimate the owner's emotions and determine the priority of the analysis results based on the estimated emotions. For example, if the owner is feeling anxious, the analysis unit will prioritize displaying the most important analysis results. If the owner is relaxed, the analysis unit will sequentially display detailed analysis results. If the owner is in a hurry, the analysis unit will prioritize displaying concise analysis results. In this way, by determining priorities according to the owner's emotions, important analysis results can be displayed preferentially. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input image data of the owner captured by a camera into the generative AI and have the generative AI perform the estimation of the owner's emotions.

[0084] The analysis unit can perform analyses on region-specific diseases and symptoms, taking into account the pet's geographical location. For example, the analysis unit can automatically perform analyses on diseases prevalent in the region. For example, the analysis unit can perform analyses on symptoms related to the region's climate and environment. For example, the analysis unit can perform analyses necessary for treatment based on information from local animal hospitals. This improves the accuracy of diagnosis by performing analyses on region-specific diseases and symptoms. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not. For example, the analysis unit can input the pet's geographical location information into the AI, and the AI ​​can perform analyses on region-specific diseases and symptoms to improve the accuracy of diagnosis.

[0085] The analysis unit can analyze a pet's social media activity and automatically analyze related images and videos. For example, the analysis unit can automatically acquire and analyze images and videos of pets posted by their owners on social media. For example, the analysis unit can analyze health information of pets shared by their owners on social media. For example, the analysis unit can analyze information about the pet's living environment and habits from the owner's social media activity. This allows for the automatic analysis of relevant information by analyzing social media activity. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can improve the accuracy of its analysis by inputting the owner's social media activity into AI, which then automatically analyzes the relevant information.

[0086] The diagnostic unit can estimate the owner's emotions and adjust the display method of the diagnostic results based on the estimated emotions. For example, if the owner is feeling anxious, the diagnostic unit provides a simple and reassuring display method. For example, if the owner is relaxed, the diagnostic unit provides detailed diagnostic results and suggests a customizable display method. For example, if the owner is in a hurry, the diagnostic unit prioritizes displaying concise diagnostic results. This improves the understanding of the diagnostic results by providing a display method that matches the owner's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the diagnostic unit may be performed using AI or not. For example, the diagnostic unit can input image data of the owner captured by a camera into the generative AI and have the generative AI perform the estimation of the owner's emotions.

[0087] The diagnostic unit can automatically generate the optimal diagnosis for a pet's symptoms by referring to past diagnostic data. For example, the diagnostic unit can automatically generate a diagnosis related to the pet's symptoms from past diagnostic data. For example, the diagnostic unit can propose a diagnosis related to the current symptoms based on past diagnostic data. For example, the diagnostic unit can refer to past diagnostic data to detect inconsistencies in the diagnostic results and propose corrections. In this way, the optimal diagnosis can be automatically generated by referring to past diagnostic data. Some or all of the above processes in the diagnostic unit may be performed using AI or not. For example, the diagnostic unit can achieve efficient diagnosis by inputting past diagnostic data into AI, which will then automatically generate the optimal diagnosis.

[0088] The diagnostic unit can apply different diagnostic algorithms depending on the type and age of the pet during diagnosis. For example, for young pets, the diagnostic unit applies a diagnostic algorithm related to growth. For example, for elderly pets, the diagnostic unit applies a diagnostic algorithm related to chronic diseases and regular health checks. For example, the diagnostic unit applies a diagnostic algorithm related to diseases and symptoms specific to certain types of pets. By applying a diagnostic algorithm tailored to the type and age of the pet, the accuracy of the diagnosis is improved. Some or all of the above processing in the diagnostic unit may be performed using AI or not. For example, the diagnostic unit can achieve efficient diagnosis by having AI apply a diagnostic algorithm tailored to the type and age of the pet.

[0089] The diagnostic unit can estimate the owner's emotions and prioritize the diagnostic results based on the estimated emotions. For example, if the owner is feeling anxious, the diagnostic unit will prioritize displaying the most important diagnostic results. If the owner is relaxed, the diagnostic unit will sequentially display detailed diagnostic results. If the owner is in a hurry, the diagnostic unit will prioritize displaying concise diagnostic results. In this way, by prioritizing according to the owner's emotions, important diagnostic results can be displayed preferentially. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the diagnostic unit may be performed using AI or not. For example, the diagnostic unit can input image data of the owner captured by a camera into the generative AI and have the generative AI perform the estimation of the owner's emotions.

[0090] The diagnostic unit can perform diagnoses regarding region-specific diseases and symptoms by considering the pet's geographical location. For example, the diagnostic unit can automatically perform diagnoses regarding diseases prevalent in the region. For example, the diagnostic unit can perform diagnoses regarding symptoms related to the region's climate and environment. For example, the diagnostic unit can perform diagnoses necessary for treatment based on information from local animal hospitals. This improves the accuracy of the diagnosis by performing diagnoses regarding region-specific diseases and symptoms. Some or all of the above processes in the diagnostic unit may be performed using AI or not. For example, the diagnostic unit can improve the accuracy of the diagnosis by inputting the pet's geographical location information into AI, which then performs diagnoses regarding region-specific diseases and symptoms.

[0091] The diagnostic unit can analyze a pet's social media activity and automatically provide relevant diagnostic results. For example, the diagnostic unit can automatically acquire health information about pets posted by owners on social media and reflect it in the diagnostic results. For example, the diagnostic unit can analyze images and videos of pets shared by owners on social media and provide relevant diagnostic results. For example, the diagnostic unit can reflect information about the pet's living environment and habits from the owner's social media activity in the diagnostic results. In this way, by analyzing social media activity, relevant diagnostic results can be automatically provided. Some or all of the above processing in the diagnostic unit may be performed using AI or not. For example, the diagnostic unit can improve the accuracy of the diagnosis by inputting the owner's social media activity into AI, which then automatically provides relevant diagnostic results.

[0092] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0093] The online pet diagnostic system can also include a vaccination management section. This section manages the pet's vaccination history and notifies the owner of the next vaccination date. For example, when the owner enters the pet's vaccination history, the vaccination management section automatically calculates and notifies the owner of the next vaccination date. The vaccination management section can also suggest additional vaccinations based on diseases prevalent in the area. This allows for more efficient pet health management.

[0094] The online pet diagnostic system can also include a nutrition management section. This section manages the pet's diet and nutritional balance, and proposes an appropriate meal plan. For example, when the owner inputs their pet's diet, the nutrition management section evaluates the nutritional balance based on that information and proposes a meal plan to supplement necessary nutrients. Furthermore, the nutrition management section can customize meal plans according to the pet's age and health condition. This allows for more effective maintenance of the pet's health.

[0095] The online pet diagnostic system can also include an exercise management unit. This unit manages the pet's exercise volume and activity level, and suggests an appropriate exercise plan. For example, when the owner inputs the pet's exercise volume, the exercise management unit creates an exercise plan based on that information, suggesting exercises that will help maintain the pet's health. Furthermore, the exercise management unit can customize the exercise plan according to the pet's age and health condition. This allows for more effective maintenance of the pet's health.

[0096] The online pet diagnostic system can also include a stress management section. This section assesses the pet's stress level and provides advice for stress reduction. For example, when an owner inputs information about their pet's behavior or environmental changes, the stress management section uses this information to assess the stress level and provide specific advice for stress reduction. The stress management section can also suggest stress reduction methods tailored to the pet's breed and personality. This allows for more effective stress management for pets.

[0097] The online pet diagnostic system can also be equipped with a behavioral analysis unit. This unit analyzes the pet's behavioral patterns and detects abnormal behavior. For example, if the owner records the pet's behavior, the behavioral analysis unit analyzes the behavioral patterns based on that information and detects abnormal behavior. Furthermore, the behavioral analysis unit can identify the cause of the abnormal behavior and suggest appropriate countermeasures. This allows for more effective pet behavior management.

[0098] The online pet diagnostic system can also be equipped with an emotion estimation unit. This unit estimates the pet's emotions from its facial expressions and behavior and provides feedback to the owner. For example, when an owner uploads images or videos of their pet, the emotion estimation unit estimates the pet's emotions based on that information and provides feedback to the owner. The emotion estimation unit can also suggest care methods that are appropriate to the pet's emotions. This allows for more effective management of the pet's emotions.

[0099] The online pet diagnostic system can also be equipped with a communication support unit. This unit assists communication between owners and pets and provides advice to help understand the pet's emotions. For example, when an owner enters questions about communication with their pet, the communication support unit provides advice based on that information. The unit can also suggest communication methods tailored to the pet's emotions. This can lead to a better relationship between owners and their pets.

[0100] The online pet diagnostic system can also include a rehabilitation support section. This section creates and manages the pet's rehabilitation plan. For example, when the owner inputs information about their pet's rehabilitation, the rehabilitation support section creates a rehabilitation plan based on that information and manages its progress. The rehabilitation support section can also suggest rehabilitation methods tailored to the pet's emotional state. This allows for more effective pet rehabilitation.

[0101] The online pet diagnostic system can also include a pet emotional diary function. This function allows owners to record their pet's emotions and behavior in a diary format, tracking changes in their pet's feelings. For example, when an owner records their pet's emotions and behavior in the diary, the emotional diary function analyzes the changes in their pet's emotions based on that information and provides feedback to the owner. The emotional diary function can also suggest care methods tailored to the pet's emotions. This allows for more effective management of pet emotions.

[0102] The online pet diagnostic system can also be equipped with a pet emotional training function. This function understands the pet's emotions and suggests appropriate training methods. For example, when the owner inputs information about the pet's emotions, the emotional training function suggests training methods based on that information. Furthermore, the emotional training function can customize training plans according to the pet's emotions. This allows for more effective management of the pet's emotions.

[0103] The following briefly describes the processing flow for example form 2.

[0104] Step 1: The reception desk allows pet owners to enter basic information and symptoms of their pets. Basic information includes the pet's name, age, breed, and sex, while symptoms include cough, loss of appetite, and skin abnormalities. The reception desk provides an interface for pet owners to enter this information. Step 2: The consultation department conducts a consultation based on the information entered by the reception department. The consultation department uses AI to ask pet owners questions such as "Has your pet been vomiting?" and "Have you noticed any changes in your pet's urine?", and analyzes the owner's answers to identify the pet's symptoms. Step 3: The analysis unit analyzes images and videos of the pet based on the information obtained by the interview unit. The analysis unit uses AI to analyze information such as whether the pet's eyes are vacant or whether its breathing is normal, to identify the pet's symptoms and determine key points. Step 4: The diagnostic department makes a final diagnosis based on the analysis results obtained by the analysis department. The diagnostic department uses AI to determine that there is a 70% chance of ureteral stones and a 20% chance of cystitis, and a veterinarian confirms the AI's judgment to make a final diagnosis.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0107] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0108] Each of the multiple elements described above, including the reception unit, interview unit, analysis unit, and diagnostic unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and provides an interface for the owner to input basic information and symptoms of their pet. The interview unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and uses AI to ask questions to the owner and analyze the answers. The analysis unit acquires images and videos of the pet using, for example, the camera 42 of the smart device 14 and analyzes them using the identification processing unit 290 of the data processing unit 12. The diagnostic unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and makes a final diagnosis based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0109] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0110] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0112] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0113] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0115] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0116] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0117] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0118] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0119] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0123] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0124] Each of the multiple elements described above, including the reception unit, interview unit, analysis unit, and diagnostic unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and provides an interface for the owner to input basic information and symptoms of their pet. The interview unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and uses AI to ask questions to the owner and analyze the answers. The analysis unit acquires images and videos of the pet using, for example, the camera 42 of the smart glasses 214 and analyzes them using the identification processing unit 290 of the data processing unit 12. The diagnostic unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and makes a final diagnosis based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0125] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0126] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0127] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0128] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0129] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0131] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0132] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0133] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0134] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0135] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0137] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements described above, including the reception unit, interview unit, analysis unit, and diagnosis unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and provides an interface for the owner to input basic information and symptoms of their pet. The interview unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and uses AI to ask questions to the owner and analyze the answers. The analysis unit acquires images and videos of the pet using the camera 42 of the headset terminal 314 and analyzes them using the identification processing unit 290 of the data processing unit 12. The diagnosis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and makes a final diagnosis based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0141] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0142] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0149] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0150] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0151] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0152] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0157] Each of the multiple elements described above, including the reception unit, interview unit, analysis unit, and diagnostic unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and provides an interface for the owner to input basic information and symptoms of their pet. The interview unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and uses AI to ask questions to the owner and analyze the answers. The analysis unit acquires images and videos of the pet using, for example, the camera 42 of the robot 414 and analyzes them using the identification processing unit 290 of the data processing unit 12. The diagnostic unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and makes a final diagnosis based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0158] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0159] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0160] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0161] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0162] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0163] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0164] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0165] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0166] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0167] 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.

[0168] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0169] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0170] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0171] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0172] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0173] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0174] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0175] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0176] (Note 1) The reception area is where pet owners enter basic information and symptoms of their pets, A medical interview section conducts a medical interview based on the information entered by the reception section, Based on the information obtained by the aforementioned medical interview unit, an analysis unit analyzes images and videos of the pet. The system includes a diagnostic unit that performs a final diagnosis based on the analysis results obtained by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, We analyze X-ray images and test results to help make final decisions based on multiple cases. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned medical interview section, We will propose ways to reduce the burden on pet owners and lessen the burden of vet visits for pets. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is It estimates the owner's emotions and adjusts the display of the input interface based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is Referencing the pet's past medical history provides supplementary information to improve the accuracy of the entered information. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is Customize the types and order of information entered by the owner according to the pet's breed and age. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the owner's emotions and determines the priority of inputs based on the estimated owner's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Considering the owner's geographical location, we will add input fields related to region-specific diseases and symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is Analyze the owner's social media activity and automatically input relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned medical interview section, The system estimates the owner's emotions and adjusts the content and order of the interview questions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned medical interview section, By referring to past medical history data, the system automatically generates the most appropriate questions based on the pet's symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned medical interview section, During the consultation, different sets of questions are applied depending on the type and age of the pet. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned medical interview section, The system estimates the owner's emotions and adjusts the questionnaire responses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned medical interview section, The questions in the consultation will be customized to take into account the owner's lifestyle and occupation. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned medical interview section, Referencing the owner's past medical history, the system automatically adds relevant questions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the owner's emotions and adjusts the display method of the analysis results based on the estimated emotions of the owner. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, We refer to past images and video data of pets to provide supplementary information to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the type and age of the pet. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, The system estimates the owner's emotions and prioritizes the analysis results based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, We will analyze region-specific diseases and symptoms, taking into account the geographical location of pets. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, Analyzes pets' social media activity and automatically analyzes related images and videos. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned diagnostic unit, The system estimates the owner's emotions and adjusts how the diagnostic results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned diagnostic unit, By referring to past diagnostic data, the system automatically generates the optimal diagnosis based on the pet's symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned diagnostic unit, During diagnosis, different diagnostic algorithms are applied depending on the type and age of the pet. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned diagnostic unit, The system estimates the owner's emotions and prioritizes the diagnostic results based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned diagnostic unit, The diagnosis of region-specific diseases and symptoms is performed taking into account the pet's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned diagnostic unit, It analyzes your pet's social media activity and automatically provides relevant diagnostic results. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception area is where pet owners enter basic information and symptoms of their pets, A medical interview section conducts a medical interview based on the information entered by the reception section, Based on the information obtained by the aforementioned medical interview unit, an analysis unit analyzes images and videos of the pet. The system includes a diagnostic unit that performs a final diagnosis based on the analysis results obtained by the analysis unit. A system characterized by the following features.

2. The aforementioned analysis unit, We analyze X-ray images and test results to help make final decisions based on multiple cases. The system according to feature 1.

3. The aforementioned medical interview section, We will propose ways to reduce the burden on pet owners and lessen the burden of vet visits for pets. The system according to feature 1.

4. The aforementioned reception unit is It estimates the owner's emotions and adjusts the display of the input interface based on the estimated emotions. The system according to feature 1.

5. The aforementioned reception unit is Referencing the pet's past medical history provides supplementary information to improve the accuracy of the entered information. The system according to feature 1.

6. The aforementioned reception unit is Customize the types and order of information entered by the owner according to the pet's breed and age. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the owner's emotions and determines the priority of inputs based on the estimated owner's emotions. The system according to feature 1.

8. The aforementioned reception unit is Considering the owner's geographical location, we will add input fields related to region-specific diseases and symptoms. The system according to feature 1.

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