System

Abstract

To provide a system according to an embodiment that provides a proper diagnosis or improvement method without going to an animal hospital with respect to a body condition failure of an animal or a change in behavior thereof .SOLUTION: A system according to an embodiment comprises: a reception unit; an analysis unit; an identification unit; and a proposal unit. The reception unit is configured to receive an input of a change in behavior of an animal or a body condition thereof. The analysis unit is configured to analyze information received by the reception unit. The identification unit is configured to identify a disease on the basis of the information analyzed by the analysis unit. The proposal unit is configured to propose an improvement method on the basis of the disease identified by the identification unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to obtain an appropriate diagnosis or treatment for an animal's poor health or changes in behavior without taking it to a veterinary hospital.

[0005] The system according to the embodiment aims to provide an appropriate diagnosis and remedial method for an animal's poor physical condition or change in behavior without the need to visit a veterinary clinic. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, an analyzing unit, an identifying unit, and a proposing unit. The receiving unit receives input of changes in the animal's behavior and physical condition. The analyzing unit analyzes the information received by the receiving unit. The identifying unit identifies a disease based on the information analyzed by the analyzing unit. The proposing unit proposes an improvement method based on the disease identified by the identifying unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide an appropriate diagnosis and remedial method for an animal's poor physical condition or change in behavior without the need to visit a veterinary clinic. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention can detect unusual behavior or poor health in family pets, such as dogs, cats, and other animals, and provide advice on what is causing the illness and how to improve the condition without requiring a visit to the veterinarian. Owners input changes in their pet's behavior or physical condition, and AI analyzes the information to identify possible illnesses and suggest solutions. For example, if an owner inputs information such as "My dog ​​has no appetite" or "My cat is vomiting frequently," the AI ​​analyzes the information, identifies possible digestive issues or stress, and provides advice on dietary changes, specific medications, environmental improvements, and stress reduction. This allows owners to understand their pet's health and take appropriate action without visiting the veterinarian.

[0029] An animal health management system according to an embodiment includes a reception unit, an analysis unit, an identification unit, and a suggestion unit. The reception unit receives input of changes in an animal's behavior or physical condition. For example, an owner may input information such as "my dog ​​has no appetite" or "my cat frequently vomits." The reception unit may receive information such as an animal's appetite, excretion, behavioral patterns, and body temperature. The analysis unit analyzes the information received by the reception unit. For example, the analysis unit references a database of changes in an animal's behavior or physical condition and uses data analysis techniques and algorithms to identify possible illnesses. The identification unit identifies possible illnesses based on the information analyzed by the analysis unit. For example, the identification unit uses a diagnostic algorithm to identify possible digestive problems or stress. The suggestion unit suggests improvement methods based on the illness identified by the identification unit. The suggestion unit provides advice, for example, on dietary changes, the use of specific medications, environmental improvements, and stress reduction. This allows the animal health management system according to an embodiment to understand the animal's health condition and take appropriate measures without the owner having to visit a veterinary clinic. Some or all of the above-described processing in the proposing unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposing unit may use an AI model to propose an improvement method based on the disease identified by the identifying unit.

[0030] The reception unit accepts inputs of changes in an animal's behavior and physical condition. For example, an owner can input information such as "My dog ​​has no appetite" or "My cat is vomiting frequently." The reception unit can also accept information such as an animal's appetite, excretion, behavioral patterns, and body temperature. Specifically, an owner accesses a dedicated application or website using a smartphone or computer and inputs detailed information about the animal's condition. The input information is sent to a cloud server and stored in a database. The reception unit can automatically classify the input information and associate it with basic information such as the animal's type, age, and gender. Furthermore, the reception unit can provide multiple input methods to improve the reliability of the input information. For example, voice input or image recognition technology can be used to make it easier for owners to input information. The reception unit also has a periodic reminder function that can prompt owners to regularly report their animal's condition. This allows the reception unit to collect detailed and accurate information about the animal's health condition and improve the accuracy of the entire system.

[0031] The analysis unit analyzes the information received by the reception unit. For example, the analysis unit references a database of changes in animal behavior and physical condition and uses data analysis methods and algorithms to identify possible illnesses. Specifically, the analysis unit uses machine learning algorithms to detect abnormal patterns based on past data on animal behavior patterns and changes in physical condition. For example, if an animal's loss of appetite or frequency of vomiting exceeds the normal range, the analysis unit determines this as an abnormality and performs further analysis. The analysis unit can simultaneously analyze multiple parameters related to the animal's health and find correlations. For example, if an increase in body temperature and a loss of appetite occur simultaneously, the analysis unit determines that this may be a sign of an infection. The analysis unit can also take into account basic information such as the animal's species, age, and gender. This allows the analysis unit to perform highly accurate analysis tailored to the individual characteristics of each animal. Furthermore, the analysis unit can continuously monitor changes in the animal's health based on data updated in real time. This allows the analysis unit to detect abnormalities early and support prompt response.

[0032] The identification unit identifies possible illnesses based on the information analyzed by the analysis unit. The identification unit uses diagnostic algorithms to identify possible digestive problems or stress, for example. Specifically, it uses rule-based diagnostic systems and machine learning models to evaluate the possibility of specific illnesses or health problems based on data on changes in the animal's behavior and physical condition. For example, if an animal frequently vomits, the identification unit suspects a digestive problem and makes a diagnosis by referring to related symptoms and past data. Furthermore, if the animal's behavioral patterns suddenly change, the identification unit considers the possibility that stress or changes in the environment are the cause. The identification unit comprehensively evaluates multiple factors related to the animal's health condition and can identify the most likely illness or health problem. Furthermore, the identification unit has an interface that presents the diagnosis results to the owner in an easy-to-understand manner and can provide specific advice and next steps based on the diagnosis results. This allows the identification unit to make a quick and accurate diagnosis of the animal's health condition and provide information for the owner to take appropriate measures.

[0033] The suggestion unit proposes improvement methods based on the illness identified by the identification unit. The suggestion unit provides advice, for example, on dietary changes, the use of specific medications, environmental improvements, and stress reduction. Specifically, it uses an AI model to propose optimal treatments and measures for identified illnesses and health problems. For example, if a digestive problem is identified, the suggestion unit suggests a digestible diet and the use of specific supplements. Furthermore, if stress is identified as the cause, the suggestion unit provides specific advice on environmental improvements and stress reduction. The suggestion unit can make individual suggestions taking into account basic information such as the animal's species, age, and gender. Furthermore, the suggestion unit can provide specific steps and precautions for the owner to implement the proposed improvement methods. For example, it can provide detailed guidelines on the use and dosage of specific medications and dietary changes. The suggestion unit can also collect feedback from owners after they implement the proposed improvement methods, thereby continuously improving the accuracy and effectiveness of its suggestions. This allows the suggestion unit to provide specific and practical advice to improve the animal's health and support owners in taking appropriate measures.

[0034] The analysis unit may include a database unit that references a database related to changes in animal behavior and physical condition. The database unit, for example, accumulates data related to changes in animal behavior and physical condition, and the analysis unit references the data to improve the accuracy of the analysis. The database may include information such as the animal's appetite, excretion, behavioral patterns, and body temperature. The database unit can always provide the latest information by adding the latest research data and related literature, for example. This allows the analysis unit to improve the accuracy of the analysis by referencing the database related to changes in animal behavior and physical condition. Some or all of the above-described processing in the database unit may be performed using, for example, AI, or may be performed without using AI. For example, the database unit may classify and search data using AI.

[0035] The suggestion unit may include an advice unit that provides specific advice based on the identified illness. For example, the advice unit provides the owner with specific behavioral instructions, medication administration methods, environmental adjustment methods, etc., based on the identified illness. For example, if a digestive problem is identified, the advice unit suggests changing the diet or using a specific medication. Furthermore, if stress is identified as the cause, the advice unit provides advice for improving the environment or reducing stress. In this way, the suggestion unit provides specific advice based on the identified illness, allowing the owner to take appropriate measures. Some or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI. For example, the advice unit may generate the content of advice to the owner using AI.

[0036] The reception unit can receive information on the animal's appetite, excretion, behavioral patterns, and body temperature. For example, an owner can input information such as the animal's appetite, excretion, behavioral patterns, and body temperature into the reception unit. This information can also be collected automatically, for example, using a sensor. For example, a sensor that measures the amount of food eaten can be used to monitor changes in the animal's appetite. Furthermore, a sensor that measures the amount and frequency of excretion can be used to detect abnormalities in the animal's excretion. Furthermore, a sensor that detects the animal's movement can be used to monitor the animal's behavioral patterns. A sensor that measures body temperature can be used to monitor changes in body temperature. By receiving detailed information about the animal, the reception unit can improve the accuracy of the analysis. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can analyze the input information using AI and evaluate the animal's health condition.

[0037] The identification unit can identify possible digestive problems or stress. The identification unit uses a diagnostic algorithm to identify possible digestive problems or stress based on, for example, changes in the animal's behavior or physical condition. For example, if the animal shows a loss of appetite, the identification unit can identify possible digestive problems or stress. The identification unit can analyze information such as the animal's vomiting frequency, food intake, and changes in behavioral patterns to identify possible digestive problems or stress. By identifying possible digestive problems or stress, the identification unit can suggest appropriate remedial measures. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without AI. For example, the identification unit can use AI to analyze changes in the animal's behavior or physical condition to identify possible digestive problems or stress.

[0038] The suggestion unit may suggest a change in diet or the use of a specific medication. For example, if a digestive problem is identified, the suggestion unit may suggest a change in diet or the use of a specific medication. For example, by changing the animal's diet, the digestive problem can be improved. The suggestion unit may, for example, suggest a diet containing specific nutrients. The suggestion unit may also suggest the use of a specific medication. For example, it may suggest a medication for improving the digestive problem. As a result, the suggestion unit may improve the health condition of the animal by suggesting a change in diet or the use of a specific medication. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may use AI to evaluate the health condition of the animal and suggest an appropriate diet or the use of medication.

[0039] The suggestion unit can provide advice for improving the animal's environment or reducing stress. For example, if stress is identified as the cause, the suggestion unit can provide advice for improving the animal's environment or reducing stress. For example, the suggestion unit can suggest specific methods for improving the animal's living environment. The suggestion unit can provide advice such as expanding the animal's living space, providing a quiet environment, and encouraging appropriate exercise. The suggestion unit can also suggest specific measures for reducing stress. For example, the suggestion unit can suggest play or relaxation methods to reduce the animal's stress. By providing advice for improving the environment or reducing stress, the suggestion unit can reduce the animal's stress and improve its health. Some or all of the above-described processing in the suggestion unit can be performed, for example, using AI or without AI. For example, the suggestion unit can use AI to evaluate the animal's stress level and provide advice for appropriate environmental improvement or stress reduction.

[0040] The reception unit can analyze the owner's past input history and select the optimal input method. For example, the reception unit can prioritize and suggest input methods (such as voice and text) that the owner has frequently used in the past. For example, if the owner has frequently used voice input in the past, the reception unit can prioritize and suggest voice input. The reception unit can also automatically customize the input form based on information previously input by the owner. For example, it can provide an input form that displays only necessary items based on information previously input by the owner. Furthermore, the reception unit can prompt the owner to input data at a specific time period based on the owner's past input history. For example, if the owner has previously input data at a specific time period, it can send a notification prompting the owner to input data at that time period. In this way, the reception unit can analyze the owner's past input history to select the optimal input method and improve input efficiency. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the owner's past input history into AI and have the AI ​​select the optimal input method.

[0041] The reception unit can monitor changes in the animal's behavior and physical condition in real time and automatically input the data. For example, the reception unit can detect the animal's movements using a sensor and automatically input any abnormalities. For example, if the animal's movements are abnormal, the reception unit can automatically input the information. The reception unit can also periodically measure the animal's body temperature and automatically input any abnormalities. For example, if the animal's body temperature exceeds the normal range, the information can be automatically input. Furthermore, the reception unit can monitor the animal's food intake and automatically input any abnormalities. For example, if the animal's food intake is lower than normal, the information can be automatically input. This allows the reception unit to monitor changes in the animal's behavior and physical condition in real time, automatically input any abnormalities, and respond quickly. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can input data acquired by a sensor into AI and have the AI ​​detect abnormalities and automatically input the data.

[0042] The reception unit can prioritize inputting information about diseases specific to a region, taking into account the owner's geographical location information. The reception unit, for example, prioritizes inputting information about diseases that are prevalent in the region where the owner lives. For example, if a particular disease is prevalent in the region where the owner lives, the reception unit can prioritize inputting information about that disease. Furthermore, if the owner is traveling, the reception unit can prioritize inputting information about diseases specific to the region to which the owner is traveling. For example, if a particular disease is prevalent in the region to which the owner is traveling, the reception unit can prioritize inputting information about that disease. Furthermore, if the owner moves, the reception unit can prioritize inputting information about diseases in the new region. For example, if the owner moves to a new region, the reception unit can prioritize inputting information about diseases that are prevalent in that region. This allows the reception unit to prioritize inputting information about diseases specific to a region by taking into account the owner's geographical location information, enabling appropriate response. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without AI. For example, the reception unit can input the owner's geographical location information into the AI ​​and have the AI ​​perform a process that prioritizes inputting information about diseases specific to that region.

[0043] The reception unit can analyze the owner's social media activities and input related behavioral and physical changes. The reception unit, for example, analyzes photos and videos of the animal posted by the owner on social media and inputs any abnormalities. For example, the reception unit can detect changes in the animal's behavior and physical condition from photos and videos of the animal posted by the owner on social media and input the information. The reception unit can also input changes in the animal's behavior and physical condition based on information shared by the owner on social media. For example, the reception unit can detect changes in the animal's behavior and physical condition from information shared by the owner on social media and input the information. Furthermore, the reception unit can complement the input content based on information obtained from animal-related accounts followed by the owner on social media. For example, the reception unit can input changes in the animal's behavior and physical condition based on information obtained from animal-related accounts followed by the owner. This allows the reception unit to input related behavioral and physical changes by analyzing the owner's social media activities, thereby providing more accurate information. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or without AI. For example, the reception department can input social media data into the AI ​​and have the AI ​​perform a process to detect changes in the animal's behavior and physical condition.

[0044] The analysis unit can analyze changes in the animal's behavior and physical condition over time to identify abnormal patterns. For example, the analysis unit can analyze changes in the animal's food intake over time to identify abnormal patterns. For example, if the animal's food intake decreases over a certain period of time, the pattern can be identified. The analysis unit can also analyze changes in the animal's body temperature over time to identify abnormal patterns. For example, if the animal's body temperature increases over a certain period of time, the pattern can be identified. The analysis unit can also analyze changes in the animal's behavioral patterns over time to identify abnormal patterns. For example, if the animal's activity level decreases over a certain period of time, the pattern can be identified. In this way, by analyzing changes in the animal's behavior and physical condition over time, the analysis unit can identify abnormal patterns and take early action. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input data on the animal's behavior and physical condition into AI and have the AI ​​perform time series analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the type and age of the animal. For example, the analysis unit can apply different analysis algorithms to dogs and cats to identify possible illnesses. For example, dogs have dog-specific illnesses and cats have cat-specific illnesses, so an appropriate analysis algorithm can be applied for each. The analysis unit can also apply different analysis algorithms to young and old animals. For example, young animals have diseases specific to growth periods, and old animals have age-related illnesses, so an appropriate analysis algorithm can be applied for each. Furthermore, the analysis unit can apply an analysis algorithm that takes into account illnesses specific to a particular breed. For example, certain dog and cat breeds have genetically specific illnesses, so an appropriate analysis algorithm can be applied. This allows the analysis unit to provide more accurate analysis results by applying an analysis algorithm appropriate for the type and age of the animal. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input data according to the type and age of the animal into AI and have the AI ​​select an appropriate analysis algorithm.

[0046] The analysis unit can analyze changes in the animal's behavior and physical condition by comparing it with other animals. The analysis unit can, for example, identify abnormal patterns by comparing it with other animals of the same breed. For example, it can determine whether a particular behavior or change in physical condition is abnormal compared with other animals of the same breed. The analysis unit can also identify abnormal patterns by comparing it with other animals of the same age. For example, it can determine whether a particular behavior or change in physical condition is abnormal compared with other animals of the same age. Furthermore, the analysis unit can also identify abnormal patterns by comparing it with other animals living in the same area. For example, it can determine whether a particular behavior or change in physical condition is abnormal compared with other animals living in the same area. This makes it easier for the analysis unit to identify abnormal patterns by analyzing it in comparison with other animals. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI, or without AI. For example, the analysis unit can input data of other animals into AI and have the AI ​​perform a comparative analysis.

[0047] The analysis unit can analyze changes in the animal's behavior and physical condition by comparing them with related literature. For example, the analysis unit can analyze changes in the animal's behavior by comparing them with related academic papers. For example, the analysis unit can refer to related academic papers to determine whether the changes in the animal's behavior are related to a specific disease. The analysis unit can also analyze changes in the animal's physical condition by comparing them with related medical books. For example, the analysis unit can refer to related medical books to determine whether the changes in the animal's physical condition are related to a specific disease. Furthermore, the analysis unit can analyze the animal's symptoms by comparing them with related online databases. For example, the analysis unit can refer to related online databases to determine whether the animal's symptoms are related to a specific disease. This allows the analysis unit to provide more accurate analysis results by analyzing them with related literature. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input data from related literature into AI and have the AI ​​perform a cross-check analysis.

[0048] The identification unit can identify changes in an animal's behavior or physical condition as multiple possible illnesses. For example, the identification unit can identify an animal's loss of appetite as a possible digestive problem or stress. For example, it can determine whether the animal's loss of appetite is related to either a digestive problem or stress. The identification unit can also identify an animal's vomiting as a possible digestive problem or poisoning. For example, it can determine whether the animal's vomiting is related to either a digestive problem or poisoning. Furthermore, the identification unit can identify an animal's lethargy as a possible infection or nutritional deficiency. For example, it can determine whether the animal's lethargy is related to either an infection or nutritional deficiency. This allows the identification unit to identify changes in an animal's behavior or physical condition as multiple possible illnesses, enabling a broader range of diagnoses. Some or all of the above-described processing in the identification unit may be performed using, or without, AI. For example, the identification unit can input data on the animal's behavior and physical condition into AI and cause the AI ​​to execute processing to identify multiple possible illnesses.

[0049] The identification unit can identify the possibility of illness by taking into account the animal's past medical history. For example, the identification unit can refer to the animal's past medical history to identify the possibility of recurrence. For example, if the animal has had digestive problems in the past, the identification unit can identify the possibility of recurrence. The identification unit can also refer to the animal's past medical history to identify the possibility of related illnesses. For example, if the animal has had stress-related problems in the past, the identification unit can identify the possibility of related illnesses. Furthermore, the identification unit can refer to the animal's past medical history to identify the possibility of illness by taking into account the effectiveness of treatment. For example, if the animal has received a specific treatment in the past and it was effective, the identification unit can determine whether that treatment needs to be repeated. This makes it easier for the identification unit to identify the possibility of recurrence or related illnesses by taking into account the animal's past medical history. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without AI. For example, the identification unit can input the animal's past medical history data into AI and cause the AI ​​to execute processing to identify the possibility of illness.

[0050] The identification unit can identify the possibility of a disease by taking into account the geographical distribution of animals. For example, the identification unit can identify the possibility of a disease that is prevalent in the area where the animal lives. For example, if a specific disease is prevalent in the area where the animal lives, the identification unit can identify the possibility of that disease. Furthermore, if the animal is traveling, the identification unit can identify the possibility of a disease specific to the area to which the animal is traveling. For example, if a specific disease is prevalent in the area to which the animal is traveling, the identification unit can identify the possibility of that disease. Furthermore, if the animal moves, the identification unit can identify the possibility of a disease in the new area. For example, if the animal moves to a new area, the identification unit can identify the possibility of a disease that is prevalent in that area. This makes it easier for the identification unit to identify the possibility of a disease specific to the area by taking into account the geographical distribution of animals. Some or all of the above-mentioned processing in the identification unit may be performed using AI, for example, or may be performed without using AI. For example, the identification unit can input geographical distribution data of animals into AI and cause the AI ​​to execute processing to identify the possibility of a disease.

[0051] The identification unit can identify changes in the animal's behavior or physical condition by comparing them with a related database. For example, the identification unit can identify changes in the animal's behavior by comparing them with a related database. For example, the identification unit can refer to the related database to determine whether the changes in the animal's behavior are related to a specific disease. The identification unit can also identify changes in the animal's physical condition by comparing them with a related database. For example, the identification unit can refer to the related database to determine whether the changes in the animal's physical condition are related to a specific disease. The identification unit can also identify symptoms of the animal by comparing them with a related database. For example, the identification unit can refer to the related database to determine whether the symptoms of the animal are related to a specific disease. This allows the identification unit to provide more accurate identification results by comparing them with the related database. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input data from the related database into AI and have the AI ​​perform a comparison analysis.

[0052] When making a suggestion, the suggestion unit can adjust the level of detail of the suggestion based on the severity of the illness. For example, the suggestion unit may make simple suggestions for a mild illness. For example, for a mild digestive problem, the suggestion unit may suggest dietary changes or light exercise. The suggestion unit may also make detailed suggestions for a severe illness. For example, for a severe infection, the suggestion unit may suggest the use of specific medication or a consultation with a specialist. The suggestion unit may also make moderately detailed suggestions for a moderate illness. For example, for moderate stress, the suggestion unit may suggest specific measures for improving the environment or reducing stress. This allows the suggestion unit to adjust the level of detail of the suggestion depending on the severity of the illness, thereby enabling appropriate responses. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit may input illness severity data into AI and cause the AI ​​to execute a process of adjusting the level of detail of the suggestion.

[0053] When making a proposal, the suggestion unit can apply different proposal algorithms depending on the type and age of the animal. For example, the suggestion unit applies different proposal algorithms to dogs and cats to make appropriate proposals. For example, dogs have dog-specific diseases and cats have cat-specific diseases, so a proposal algorithm appropriate for each can be applied. The suggestion unit can also apply different proposal algorithms to young animals and old animals. For example, young animals have diseases specific to their growth period, and old animals have diseases associated with aging, so a proposal algorithm appropriate for each can be applied. Furthermore, the suggestion unit can apply a proposal algorithm that takes into account diseases specific to a specific breed. For example, certain dog breeds and cat breeds have genetically specific diseases, so a proposal algorithm appropriate for that can be applied. As a result, the suggestion unit can make more appropriate proposals by applying a proposal algorithm appropriate for the type and age of the animal. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data on the type and age of the animal into AI and cause the AI ​​to execute a process of selecting an appropriate proposal algorithm.

[0054] When making a suggestion, the suggestion unit can make an optimal suggestion by taking into account the geographical location information of the animal. For example, the suggestion unit makes a suggestion for a disease that is prevalent in the area where the owner lives. For example, if a specific disease is prevalent in the area where the owner lives, the suggestion unit can make a suggestion for that disease. Furthermore, if the owner is traveling, the suggestion unit can make a suggestion for a disease that is specific to the area the owner is traveling to. For example, if a specific disease is prevalent in the area where the owner is traveling, the suggestion unit can make a suggestion for that disease. Furthermore, if the owner moves, the suggestion unit can make a suggestion for a disease in the new area. For example, if the owner moves to a new area, the suggestion unit can make a suggestion for a disease that is prevalent in that area. This enables the suggestion unit to make an appropriate suggestion for a disease that is specific to the area by taking into account the geographical location information of the animal. Some or all of the above-mentioned processing by the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the geographical location information of the animal to AI and cause the AI ​​to execute a process of making an optimal suggestion.

[0055] When making a suggestion, the suggestion unit may compare the animal's behavior or changes in physical condition with related literature. For example, the suggestion unit may compare the animal's behavioral changes with related academic papers to make a suggestion. For example, the suggestion unit may refer to related academic papers to determine whether the animal's behavioral changes are related to a specific disease. The suggestion unit may also compare the animal's physical condition changes with related medical books to make a suggestion. For example, the suggestion unit may refer to related medical books to determine whether the animal's physical condition changes are related to a specific disease. Furthermore, the suggestion unit may compare the animal's symptoms with related online databases to make a suggestion. For example, the suggestion unit may refer to related online databases to determine whether the animal's symptoms are related to a specific disease. This allows the suggestion unit to make more accurate suggestions by comparing the symptoms with related literature. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit may input data from related literature into AI and have the AI ​​perform a cross-check analysis.

[0056] The database unit can add the latest research data on changes in animal behavior and physical condition to the database. For example, the database unit can add data on changes in animal behavior from the latest academic papers. For example, new findings on changes in animal behavior based on the latest academic papers can be added to the database. The database unit can also add data on changes in animal physical condition from the latest medical books. For example, new findings on changes in animal physical condition based on the latest medical books can be added to the database. Furthermore, the database unit can add data on animal symptoms from the latest online database. For example, new findings on animal symptoms can be added to the database based on the latest online database. In this way, the database unit improves the accuracy of analysis by adding the latest research data. Some or all of the above-mentioned processing in the database unit can be performed, for example, using AI, or can be performed without using AI. For example, the database unit can input the latest research data into AI and cause the AI ​​to execute the process of adding it to the database.

[0057] The database unit can classify and store data according to the type and age of animals in the database. The database unit, for example, classifies and stores data for dogs and cats. For example, by classifying and storing data for dogs and cats separately, analysis appropriate for each animal is possible. The database unit can also classify and store data for young animals and old animals. For example, by classifying and storing data for young animals and old animals separately, analysis appropriate for each age is possible. Furthermore, the database unit can classify and store data specific to a particular breed. For example, by classifying and storing data specific to a particular breed of dog or cat separately, analysis appropriate for each breed is possible. In this way, the database unit classifies and stores data according to the type and age of animals, enabling more accurate analysis. Some or all of the above-described processing in the database unit may be performed using, for example, AI, or may be performed without AI. For example, the database unit can input data on the type and age of animals into AI and have the AI ​​classify and store the data.

[0058] The database unit can add data regarding the geographical distribution of animals to the database. For example, the database unit can add data regarding the area where the animal lives. For example, data regarding the climate and environment of the area where the animal lives can be added to the database. Furthermore, if the animal is traveling, the database unit can add data regarding the area where the animal travels. For example, data regarding the climate and environment of the area where the animal travels can be added to the database. Furthermore, if the animal moves, the database unit can add data regarding the new area. For example, if the animal moves to a new area, data regarding the climate and environment of that area can be added to the database. In this way, by adding data regarding the geographical distribution, the database unit can analyze diseases specific to the area. Some or all of the above-mentioned processing in the database unit can be performed using, for example, AI, or can be performed without using AI. For example, the database unit can input the geographical distribution data into AI and cause the AI ​​to execute processing to add the data to the database.

[0059] The database unit can add related literature related to changes in animal behavior and physical condition to the database. The database unit can add, for example, academic papers related to changes in animal behavior. For example, the latest academic papers related to changes in animal behavior can be added to the database. The database unit can also add medical books related to changes in animal physical condition. For example, the latest medical books related to changes in animal physical condition can be added to the database. Furthermore, the database unit can also add online databases related to animal symptoms. For example, the latest online databases related to animal symptoms can be added to the database. In this way, the database unit can improve the accuracy of analysis by adding related literature. Some or all of the above-mentioned processing in the database unit can be performed using, for example, AI, or can be performed without using AI. For example, the database unit can input data from related literature into AI and have the AI ​​execute the process of adding it to the database.

[0060] When providing advice, the advice unit can adjust the level of detail of the advice based on the severity of the illness. For example, in the case of a mild illness, the advice unit provides simple advice. For example, for a mild digestive problem, the advice unit can suggest dietary changes or light exercise. The advice unit can also provide detailed advice in the case of a severe illness. For example, for a severe infection, the advice unit can suggest the use of a specific medication or a consultation with a specialist. Furthermore, in the case of a moderate illness, the advice unit can provide advice with moderate detail. For example, for moderate stress, the advice unit can suggest specific measures for improving the environment or reducing stress. This allows the advice unit to adjust the level of detail of the advice depending on the severity of the illness, enabling appropriate responses. Some or all of the above-mentioned processing in the advice unit may be performed using, or without, AI. For example, the advice unit may input illness severity data into AI and cause the AI ​​to execute processing to adjust the level of detail of the advice.

[0061] When providing advice, the advice unit can apply different advice algorithms depending on the type and age of the animal. The advice unit can apply different advice algorithms to dogs and cats, for example, to provide appropriate advice. For example, dogs have dog-specific diseases and cats have cat-specific diseases, so an appropriate advice algorithm can be applied for each. The advice unit can also apply different advice algorithms to young animals and old animals. For example, young animals have diseases specific to their growth period, and old animals have diseases associated with aging, so an appropriate advice algorithm can be applied for each. Furthermore, the advice unit can apply an advice algorithm that takes into account diseases specific to a specific breed. For example, certain dog breeds and cat breeds have genetically specific diseases, so an appropriate advice algorithm can be applied. This allows the advice unit to provide more appropriate advice by applying an advice algorithm depending on the type and age of the animal. Some or all of the above-described processing in the advice unit may be performed using, or without, AI. For example, the advice unit can input data on the type and age of the animal into AI and cause the AI ​​to execute a process of selecting an appropriate advice algorithm.

[0062] When giving advice, the advice unit can provide optimal advice by taking into account the geographical location information of the animal. For example, the advice unit provides advice on diseases that are prevalent in the area where the owner lives. For example, if a specific disease is prevalent in the area where the owner lives, the advice unit can provide advice on that disease. Furthermore, if the owner is traveling, the advice unit can provide advice on diseases that are specific to the area the owner is traveling to. For example, if a specific disease is prevalent in the area where the owner is traveling, the advice unit can provide advice on that disease. Furthermore, if the owner moves, the advice unit can provide advice on diseases that are prevalent in the new area. For example, if the owner moves to a new area, the advice unit can provide advice on diseases that are prevalent in that area. This enables the advice unit to provide appropriate advice on diseases that are specific to the area by taking into account the geographical location information of the animal. Some or all of the above-mentioned processing by the advice unit may be performed using AI, for example, or may be performed without using AI. For example, the advice unit can input the geographical location information of the animal into AI and cause the AI ​​to execute processing to provide optimal advice.

[0063] When providing advice, the advice unit can compare the animal's behavior and changes in physical condition with relevant literature to provide advice. For example, the advice unit can compare the animal's behavioral changes with relevant academic papers to provide advice. For example, the advice unit can refer to relevant academic papers to determine whether the animal's behavioral changes are related to a specific disease. The advice unit can also compare the animal's physical condition changes with relevant medical books to provide advice. For example, the advice unit can refer to relevant medical books to determine whether the animal's physical condition changes are related to a specific disease. Furthermore, the advice unit can compare the animal's symptoms with relevant online databases to provide advice. For example, the advice unit can refer to relevant online databases to determine whether the animal's symptoms are related to a specific disease. This allows the advice unit to provide more accurate advice by comparing the symptoms with relevant literature. Some or all of the above-described processing in the advice unit may be performed using, or without, AI. For example, the advice unit may input data from relevant literature into AI and have the AI ​​perform a cross-check analysis.

[0064] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0065] The analysis unit can monitor changes in the animal's behavior and physical condition in real time and immediately notify the owner if an abnormality is detected. For example, if the animal's body temperature rises suddenly or the amount of food eaten suddenly decreases, the information can be analyzed in real time and the owner can be notified. In addition, if the animal's behavior pattern differs from normal, a notification can be sent immediately. This allows the owner to respond quickly and manage the animal's health more effectively. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input data acquired by a sensor into AI and have the AI ​​detect and notify abnormalities.

[0066] The suggestion unit can provide preventive advice based on changes in the animal's behavior and physical condition. For example, if the animal is in a stressful environment, it can suggest environmental improvement measures to reduce stress. Also, if the animal is prone to a particular disease, it can provide advice on diet and exercise to prevent that disease. It can also provide advice on health management during seasonal changes. This allows owners to take measures in advance and maintain the health of their animals. Some or all of the above-mentioned processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the animal's behavior and physical condition into AI and cause the AI ​​to execute processing to generate preventive advice.

[0067] The reception unit can receive information about changes in an animal's behavior or physical condition via voice input. For example, an owner can input information such as "My dog ​​is not feeling well" or "My cat has no appetite" via voice. Voice input improves convenience because it allows the owner to input information without using their hands. In addition, voice recognition technology can be used to convert the input voice into text and send it to the analysis unit. This allows the reception unit to quickly and accurately receive information about changes in an animal's behavior or physical condition. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can use AI to execute voice recognition technology to convert the voice input into text.

[0068] The identification unit can refer to the animal's past data when analyzing changes in the animal's behavior or physical condition. For example, if the animal has shown similar symptoms in the past, the identification unit can refer to that data to identify the correlation with the current symptoms. Also, by referring to the animal's past treatment history, the optimal treatment method for the current symptoms can be identified. Furthermore, by referring to the animal's past behavioral patterns, the cause of the current abnormal behavior can be identified. This allows the identification unit to make a more accurate diagnosis. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the animal's past data into AI and have the AI ​​execute a process to analyze the correlation with the current symptoms.

[0069] The suggestion unit can send reminders to owners for regular health checks based on changes in the animal's behavior and physical condition. For example, even if the animal's physical condition is stable, sending a reminder to encourage regular health checks can enable early detection and prevention. Furthermore, if any abnormalities are observed in the animal's behavior or physical condition, a reminder can be sent immediately to encourage a prompt response. Furthermore, the content of the reminder can be customized according to the type and age of the animal. This allows owners to more effectively manage their animal's health. Some or all of the above-mentioned processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data on the animal's behavior and physical condition into AI and have the AI ​​send the reminder.

[0070] The processing flow of the first embodiment will be briefly explained below.

[0071] Step 1: The reception unit accepts input about changes in an animal's behavior and physical condition. For example, an owner can enter information such as "My dog ​​has no appetite" or "My cat is vomiting frequently." The reception unit can accept information about an animal's appetite, excretion, behavioral patterns, body temperature, etc. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit refers to a database of changes in animal behavior and physical condition, and uses data analysis methods and algorithms to identify possible illnesses. Step 3: The identification unit identifies possible illnesses based on the information analyzed by the analysis unit. The identification unit uses diagnostic algorithms to identify possible digestive problems or stress. Step 4: The suggestion unit suggests improvement methods based on the illness identified by the identification unit. The suggestion unit provides advice on dietary changes, the use of specific medications, environmental improvements, and stress reduction. The processing in the suggestion unit may be performed using AI or without AI.

[0072] (Example 2) A system according to an embodiment of the present invention can detect unusual behavior or poor health in family pets, such as dogs, cats, and other animals, and provide advice on what is causing the illness and how to improve the condition without requiring a visit to the veterinarian. Owners input changes in their pet's behavior or physical condition, and AI analyzes the information to identify possible illnesses and suggest solutions. For example, if an owner inputs information such as "My dog ​​has no appetite" or "My cat is vomiting frequently," the AI ​​analyzes the information, identifies possible digestive issues or stress, and provides advice on dietary changes, specific medications, environmental improvements, and stress reduction. This allows owners to understand their pet's health and take appropriate action without visiting the veterinarian.

[0073] An animal health management system according to an embodiment includes a reception unit, an analysis unit, an identification unit, and a suggestion unit. The reception unit receives input of changes in an animal's behavior or physical condition. For example, an owner may input information such as "my dog ​​has no appetite" or "my cat frequently vomits." The reception unit may receive information such as an animal's appetite, excretion, behavioral patterns, and body temperature. The analysis unit analyzes the information received by the reception unit. For example, the analysis unit references a database of changes in an animal's behavior or physical condition and uses data analysis techniques and algorithms to identify possible illnesses. The identification unit identifies possible illnesses based on the information analyzed by the analysis unit. For example, the identification unit uses a diagnostic algorithm to identify possible digestive problems or stress. The suggestion unit suggests improvement methods based on the illness identified by the identification unit. The suggestion unit provides advice, for example, on dietary changes, the use of specific medications, environmental improvements, and stress reduction. This allows the animal health management system according to an embodiment to understand the animal's health condition and take appropriate measures without the owner having to visit a veterinary clinic. Some or all of the above-described processing in the proposing unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposing unit may use an AI model to propose an improvement method based on the disease identified by the identifying unit.

[0074] The reception unit accepts inputs of changes in an animal's behavior and physical condition. For example, an owner can input information such as "My dog ​​has no appetite" or "My cat is vomiting frequently." The reception unit can also accept information such as an animal's appetite, excretion, behavioral patterns, and body temperature. Specifically, an owner accesses a dedicated application or website using a smartphone or computer and inputs detailed information about the animal's condition. The input information is sent to a cloud server and stored in a database. The reception unit can automatically classify the input information and associate it with basic information such as the animal's type, age, and gender. Furthermore, the reception unit can provide multiple input methods to improve the reliability of the input information. For example, voice input or image recognition technology can be used to make it easier for owners to input information. The reception unit also has a periodic reminder function that can prompt owners to regularly report their animal's condition. This allows the reception unit to collect detailed and accurate information about the animal's health condition and improve the accuracy of the entire system.

[0075] The analysis unit analyzes the information received by the reception unit. For example, the analysis unit references a database of changes in animal behavior and physical condition and uses data analysis methods and algorithms to identify possible illnesses. Specifically, the analysis unit uses machine learning algorithms to detect abnormal patterns based on past data on animal behavior patterns and changes in physical condition. For example, if an animal's loss of appetite or frequency of vomiting exceeds the normal range, the analysis unit determines this as an abnormality and performs further analysis. The analysis unit can simultaneously analyze multiple parameters related to the animal's health and find correlations. For example, if an increase in body temperature and a loss of appetite occur simultaneously, the analysis unit determines that this may be a sign of an infection. The analysis unit can also take into account basic information such as the animal's species, age, and gender. This allows the analysis unit to perform highly accurate analysis tailored to the individual characteristics of each animal. Furthermore, the analysis unit can continuously monitor changes in the animal's health based on data updated in real time. This allows the analysis unit to detect abnormalities early and support prompt response.

[0076] The identification unit identifies possible illnesses based on the information analyzed by the analysis unit. The identification unit uses diagnostic algorithms to identify possible digestive problems or stress, for example. Specifically, it uses rule-based diagnostic systems and machine learning models to evaluate the possibility of specific illnesses or health problems based on data on changes in the animal's behavior and physical condition. For example, if an animal frequently vomits, the identification unit suspects a digestive problem and makes a diagnosis by referring to related symptoms and past data. Furthermore, if the animal's behavioral patterns suddenly change, the identification unit considers the possibility that stress or changes in the environment are the cause. The identification unit comprehensively evaluates multiple factors related to the animal's health condition and can identify the most likely illness or health problem. Furthermore, the identification unit has an interface that presents the diagnosis results to the owner in an easy-to-understand manner and can provide specific advice and next steps based on the diagnosis results. This allows the identification unit to make a quick and accurate diagnosis of the animal's health condition and provide information for the owner to take appropriate measures.

[0077] The suggestion unit proposes improvement methods based on the illness identified by the identification unit. The suggestion unit provides advice, for example, on dietary changes, the use of specific medications, environmental improvements, and stress reduction. Specifically, it uses an AI model to propose optimal treatments and measures for identified illnesses and health problems. For example, if a digestive problem is identified, the suggestion unit suggests a digestible diet and the use of specific supplements. Furthermore, if stress is identified as the cause, the suggestion unit provides specific advice on environmental improvements and stress reduction. The suggestion unit can make individual suggestions taking into account basic information such as the animal's species, age, and gender. Furthermore, the suggestion unit can provide specific steps and precautions for the owner to implement the proposed improvement methods. For example, it can provide detailed guidelines on the use and dosage of specific medications and dietary changes. The suggestion unit can also collect feedback from owners after they implement the proposed improvement methods, thereby continuously improving the accuracy and effectiveness of its suggestions. This allows the suggestion unit to provide specific and practical advice to improve the animal's health and support owners in taking appropriate measures.

[0078] The analysis unit may include a database unit that references a database related to changes in animal behavior and physical condition. The database unit, for example, accumulates data related to changes in animal behavior and physical condition, and the analysis unit references the data to improve the accuracy of the analysis. The database may include information such as the animal's appetite, excretion, behavioral patterns, and body temperature. The database unit can always provide the latest information by adding the latest research data and related literature, for example. This allows the analysis unit to improve the accuracy of the analysis by referencing the database related to changes in animal behavior and physical condition. Some or all of the above-described processing in the database unit may be performed using, for example, AI, or may be performed without using AI. For example, the database unit may classify and search data using AI.

[0079] The suggestion unit may include an advice unit that provides specific advice based on the identified illness. For example, the advice unit provides the owner with specific behavioral instructions, medication administration methods, environmental adjustment methods, etc., based on the identified illness. For example, if a digestive problem is identified, the advice unit suggests changing the diet or using a specific medication. Furthermore, if stress is identified as the cause, the advice unit provides advice for improving the environment or reducing stress. In this way, the suggestion unit provides specific advice based on the identified illness, allowing the owner to take appropriate measures. Some or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI. For example, the advice unit may generate the content of advice to the owner using AI.

[0080] The reception unit can receive information on the animal's appetite, excretion, behavioral patterns, and body temperature. For example, an owner can input information such as the animal's appetite, excretion, behavioral patterns, and body temperature into the reception unit. This information can also be collected automatically, for example, using a sensor. For example, a sensor that measures the amount of food eaten can be used to monitor changes in the animal's appetite. Furthermore, a sensor that measures the amount and frequency of excretion can be used to detect abnormalities in the animal's excretion. Furthermore, a sensor that detects the animal's movement can be used to monitor the animal's behavioral patterns. A sensor that measures body temperature can be used to monitor changes in body temperature. By receiving detailed information about the animal, the reception unit can improve the accuracy of the analysis. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can analyze the input information using AI and evaluate the animal's health condition.

[0081] The identification unit can identify possible digestive problems or stress. The identification unit uses a diagnostic algorithm to identify possible digestive problems or stress based on, for example, changes in the animal's behavior or physical condition. For example, if the animal shows a loss of appetite, the identification unit can identify possible digestive problems or stress. The identification unit can analyze information such as the animal's vomiting frequency, food intake, and changes in behavioral patterns to identify possible digestive problems or stress. By identifying possible digestive problems or stress, the identification unit can suggest appropriate remedial measures. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without AI. For example, the identification unit can use AI to analyze changes in the animal's behavior or physical condition to identify possible digestive problems or stress.

[0082] The suggestion unit may suggest a change in diet or the use of a specific medication. For example, if a digestive problem is identified, the suggestion unit may suggest a change in diet or the use of a specific medication. For example, by changing the animal's diet, the digestive problem can be improved. The suggestion unit may, for example, suggest a diet containing specific nutrients. The suggestion unit may also suggest the use of a specific medication. For example, it may suggest a medication for improving the digestive problem. As a result, the suggestion unit may improve the health condition of the animal by suggesting a change in diet or the use of a specific medication. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may use AI to evaluate the health condition of the animal and suggest an appropriate diet or the use of medication.

[0083] The suggestion unit can provide advice for improving the animal's environment or reducing stress. For example, if stress is identified as the cause, the suggestion unit can provide advice for improving the animal's environment or reducing stress. For example, the suggestion unit can suggest specific methods for improving the animal's living environment. The suggestion unit can provide advice such as expanding the animal's living space, providing a quiet environment, and encouraging appropriate exercise. The suggestion unit can also suggest specific measures for reducing stress. For example, the suggestion unit can suggest play or relaxation methods to reduce the animal's stress. By providing advice for improving the environment or reducing stress, the suggestion unit can reduce the animal's stress and improve its health. Some or all of the above-described processing in the suggestion unit can be performed, for example, using AI or without AI. For example, the suggestion unit can use AI to evaluate the animal's stress level and provide advice for appropriate environmental improvement or stress reduction.

[0084] The reception unit can estimate the owner's emotions and adjust the timing of input based on the estimated owner's emotions. For example, the reception unit captures the owner's facial expression with a camera and estimates the owner's emotions using an emotion estimation algorithm. For example, if the owner is feeling anxious, the reception unit can send a notification prompting the owner to input information immediately. If the owner is relaxed, the notification prompting the owner to input information can be delayed. Furthermore, if the owner is busy, a reminder can be set to input information later. This allows the reception unit to adjust the timing of input according to the owner's emotions, allowing information to be input at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can input image data of the owner taken with a camera into the generation AI and have the generation AI estimate the owner's emotions.

[0085] The reception unit can analyze the owner's past input history and select the optimal input method. For example, the reception unit can prioritize and suggest input methods (such as voice and text) that the owner has frequently used in the past. For example, if the owner has frequently used voice input in the past, the reception unit can prioritize and suggest voice input. The reception unit can also automatically customize the input form based on information previously input by the owner. For example, it can provide an input form that displays only necessary items based on information previously input by the owner. Furthermore, the reception unit can prompt the owner to input data at a specific time period based on the owner's past input history. For example, if the owner has previously input data at a specific time period, it can send a notification prompting the owner to input data at that time period. In this way, the reception unit can analyze the owner's past input history to select the optimal input method and improve input efficiency. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the owner's past input history into AI and have the AI ​​select the optimal input method.

[0086] The reception unit can monitor changes in the animal's behavior and physical condition in real time and automatically input the data. For example, the reception unit can detect the animal's movements using a sensor and automatically input any abnormalities. For example, if the animal's movements are abnormal, the reception unit can automatically input the information. The reception unit can also periodically measure the animal's body temperature and automatically input any abnormalities. For example, if the animal's body temperature exceeds the normal range, the information can be automatically input. Furthermore, the reception unit can monitor the animal's food intake and automatically input any abnormalities. For example, if the animal's food intake is lower than normal, the information can be automatically input. This allows the reception unit to monitor changes in the animal's behavior and physical condition in real time, automatically input any abnormalities, and respond quickly. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can input data acquired by a sensor into AI and have the AI ​​detect abnormalities and automatically input the data.

[0087] The reception unit can estimate the owner's emotions and prioritize input content based on the estimated owner's emotions. For example, the reception unit captures the owner's facial expression with a camera and estimates the owner's emotions using an emotion estimation algorithm. For example, if the owner is feeling anxious, the reception unit can prompt the owner to prioritize inputting important information. Also, if the owner is relaxed, the reception unit can prompt the owner to prioritize inputting detailed information. Furthermore, if the owner is busy, the reception unit can prompt the owner to prioritize inputting simple information. In this way, the reception unit can prioritize inputting important information by determining the priority of input content according to the owner's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can input image data of the owner taken with a camera into the generation AI and have the generation AI estimate the owner's emotions.

[0088] The reception unit can prioritize inputting information about diseases specific to a region, taking into account the owner's geographical location information. The reception unit, for example, prioritizes inputting information about diseases that are prevalent in the region where the owner lives. For example, if a particular disease is prevalent in the region where the owner lives, the reception unit can prioritize inputting information about that disease. Furthermore, if the owner is traveling, the reception unit can prioritize inputting information about diseases specific to the region to which the owner is traveling. For example, if a particular disease is prevalent in the region to which the owner is traveling, the reception unit can prioritize inputting information about that disease. Furthermore, if the owner moves, the reception unit can prioritize inputting information about diseases in the new region. For example, if the owner moves to a new region, the reception unit can prioritize inputting information about diseases that are prevalent in that region. This allows the reception unit to prioritize inputting information about diseases specific to a region by taking into account the owner's geographical location information, enabling appropriate response. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without AI. For example, the reception unit can input the owner's geographical location information into the AI ​​and have the AI ​​perform a process that prioritizes inputting information about diseases specific to that region.

[0089] The reception unit can analyze the owner's social media activities and input related behavioral and physical changes. The reception unit, for example, analyzes photos and videos of the animal posted by the owner on social media and inputs any abnormalities. For example, the reception unit can detect changes in the animal's behavior and physical condition from photos and videos of the animal posted by the owner on social media and input the information. The reception unit can also input changes in the animal's behavior and physical condition based on information shared by the owner on social media. For example, the reception unit can detect changes in the animal's behavior and physical condition from information shared by the owner on social media and input the information. Furthermore, the reception unit can complement the input content based on information obtained from animal-related accounts followed by the owner on social media. For example, the reception unit can input changes in the animal's behavior and physical condition based on information obtained from animal-related accounts followed by the owner. This allows the reception unit to input related behavioral and physical changes by analyzing the owner's social media activities, thereby providing more accurate information. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or without AI. For example, the reception department can input social media data into the AI ​​and have the AI ​​perform a process to detect changes in the animal's behavior and physical condition.

[0090] The analysis unit can estimate the owner's emotions and adjust the accuracy of the analysis based on the estimated owner's emotions. For example, the analysis unit captures the owner's facial expressions with a camera and estimates the owner's emotions using an emotion estimation algorithm. For example, if the owner is feeling anxious, detailed data can be used to improve the accuracy of the analysis. Also, if the owner is relaxed, the accuracy of the analysis can be adjusted to provide quick results. Furthermore, if the owner is busy, the accuracy of the analysis can be adjusted to provide concise results. In this way, the analysis unit can adjust the accuracy of the analysis according to the owner's emotions and provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI or without AI. For example, the analysis unit can input image data of the owner taken with a camera into the generation AI and have the generation AI estimate the owner's emotions.

[0091] The analysis unit can analyze changes in the animal's behavior and physical condition over time to identify abnormal patterns. For example, the analysis unit can analyze changes in the animal's food intake over time to identify abnormal patterns. For example, if the animal's food intake decreases over a certain period of time, the pattern can be identified. The analysis unit can also analyze changes in the animal's body temperature over time to identify abnormal patterns. For example, if the animal's body temperature increases over a certain period of time, the pattern can be identified. The analysis unit can also analyze changes in the animal's behavioral patterns over time to identify abnormal patterns. For example, if the animal's activity level decreases over a certain period of time, the pattern can be identified. In this way, by analyzing changes in the animal's behavior and physical condition over time, the analysis unit can identify abnormal patterns and take early action. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input data on the animal's behavior and physical condition into AI and have the AI ​​perform time series analysis.

[0092] The analysis unit can apply different analysis algorithms depending on the type and age of the animal. For example, the analysis unit can apply different analysis algorithms to dogs and cats to identify possible illnesses. For example, dogs have dog-specific illnesses and cats have cat-specific illnesses, so an appropriate analysis algorithm can be applied for each. The analysis unit can also apply different analysis algorithms to young and old animals. For example, young animals have diseases specific to growth periods, and old animals have age-related illnesses, so an appropriate analysis algorithm can be applied for each. Furthermore, the analysis unit can apply an analysis algorithm that takes into account illnesses specific to a particular breed. For example, certain dog and cat breeds have genetically specific illnesses, so an appropriate analysis algorithm can be applied. This allows the analysis unit to provide more accurate analysis results by applying an analysis algorithm appropriate for the type and age of the animal. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input data according to the type and age of the animal into AI and have the AI ​​select an appropriate analysis algorithm.

[0093] The analysis unit can estimate the owner's emotions and adjust the display method of the analysis results based on the estimated owner's emotions. For example, the analysis unit captures the owner's facial expressions with a camera and estimates the owner's emotions using an emotion estimation algorithm. For example, if the owner is feeling anxious, detailed analysis results can be displayed. If the owner is relaxed, concise analysis results can be displayed. Furthermore, if the owner is busy, analysis results that focus on the main points can be displayed. In this way, the analysis unit can provide more appropriate information by adjusting the display method of the analysis results according to the owner's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input image data of the owner taken with a camera into the generation AI and have the generation AI estimate the owner's emotions.

[0094] The analysis unit can analyze changes in the animal's behavior and physical condition by comparing it with other animals. The analysis unit can, for example, identify abnormal patterns by comparing it with other animals of the same breed. For example, it can determine whether a particular behavior or change in physical condition is abnormal compared with other animals of the same breed. The analysis unit can also identify abnormal patterns by comparing it with other animals of the same age. For example, it can determine whether a particular behavior or change in physical condition is abnormal compared with other animals of the same age. Furthermore, the analysis unit can also identify abnormal patterns by comparing it with other animals living in the same area. For example, it can determine whether a particular behavior or change in physical condition is abnormal compared with other animals living in the same area. This makes it easier for the analysis unit to identify abnormal patterns by analyzing it in comparison with other animals. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI, or without AI. For example, the analysis unit can input data of other animals into AI and have the AI ​​perform a comparative analysis.

[0095] The analysis unit can analyze changes in the animal's behavior and physical condition by comparing them with related literature. For example, the analysis unit can analyze changes in the animal's behavior by comparing them with related academic papers. For example, the analysis unit can refer to related academic papers to determine whether the changes in the animal's behavior are related to a specific disease. The analysis unit can also analyze changes in the animal's physical condition by comparing them with related medical books. For example, the analysis unit can refer to related medical books to determine whether the changes in the animal's physical condition are related to a specific disease. Furthermore, the analysis unit can analyze the animal's symptoms by comparing them with related online databases. For example, the analysis unit can refer to related online databases to determine whether the animal's symptoms are related to a specific disease. This allows the analysis unit to provide more accurate analysis results by analyzing them with related literature. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input data from related literature into AI and have the AI ​​perform a cross-check analysis.

[0096] The identification unit can estimate the owner's emotions and adjust the specific criteria based on the estimated owner's emotions. For example, the identification unit captures the owner's facial expression with a camera and estimates the owner's emotions using an emotion estimation algorithm. For example, if the owner is feeling anxious, the specific criteria can be set stricter. If the owner is relaxed, the specific criteria can be set looser. Furthermore, if the owner is busy, the specific criteria can be set simpler. This allows the identification unit to adjust the specific criteria according to the owner's emotions and provide more appropriate identification results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input image data of the owner captured with a camera into the generation AI and cause the generation AI to estimate the owner's emotions.

[0097] The identification unit can identify changes in an animal's behavior or physical condition as multiple possible illnesses. For example, the identification unit can identify an animal's loss of appetite as a possible digestive problem or stress. For example, it can determine whether the animal's loss of appetite is related to either a digestive problem or stress. The identification unit can also identify an animal's vomiting as a possible digestive problem or poisoning. For example, it can determine whether the animal's vomiting is related to either a digestive problem or poisoning. Furthermore, the identification unit can identify an animal's lethargy as a possible infection or nutritional deficiency. For example, it can determine whether the animal's lethargy is related to either an infection or nutritional deficiency. This allows the identification unit to identify changes in an animal's behavior or physical condition as multiple possible illnesses, enabling a broader range of diagnoses. Some or all of the above-described processing in the identification unit may be performed using, or without, AI. For example, the identification unit can input data on the animal's behavior and physical condition into AI and cause the AI ​​to execute processing to identify multiple possible illnesses.

[0098] The identification unit can identify the possibility of illness by taking into account the animal's past medical history. For example, the identification unit can refer to the animal's past medical history to identify the possibility of recurrence. For example, if the animal has had digestive problems in the past, the identification unit can identify the possibility of recurrence. The identification unit can also refer to the animal's past medical history to identify the possibility of related illnesses. For example, if the animal has had stress-related problems in the past, the identification unit can identify the possibility of related illnesses. Furthermore, the identification unit can refer to the animal's past medical history to identify the possibility of illness by taking into account the effectiveness of treatment. For example, if the animal has received a specific treatment in the past and it was effective, the identification unit can determine whether that treatment needs to be repeated. This makes it easier for the identification unit to identify the possibility of recurrence or related illnesses by taking into account the animal's past medical history. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without AI. For example, the identification unit can input the animal's past medical history data into AI and cause the AI ​​to execute processing to identify the possibility of illness.

[0099] The identification unit can estimate the owner's emotions and adjust the display order of the identification results based on the estimated owner's emotions. For example, the identification unit captures the owner's facial expression with a camera and estimates the owner's emotions using an emotion estimation algorithm. For example, if the owner is feeling anxious, important identification results can be displayed preferentially. Furthermore, if the owner is relaxed, detailed identification results can be displayed. Furthermore, if the owner is busy, concise identification results can be displayed. In this way, the identification unit can prioritize providing important information by adjusting the display order of the identification results according to the owner's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, an AI. For example, the identification unit can input image data of the owner captured with a camera to the generation AI and cause the generation AI to estimate the owner's emotions.

[0100] The identification unit can identify the possibility of a disease by taking into account the geographical distribution of animals. For example, the identification unit can identify the possibility of a disease that is prevalent in the area where the animal lives. For example, if a specific disease is prevalent in the area where the animal lives, the identification unit can identify the possibility of that disease. Furthermore, if the animal is traveling, the identification unit can identify the possibility of a disease specific to the area to which the animal is traveling. For example, if a specific disease is prevalent in the area to which the animal is traveling, the identification unit can identify the possibility of that disease. Furthermore, if the animal moves, the identification unit can identify the possibility of a disease in the new area. For example, if the animal moves to a new area, the identification unit can identify the possibility of a disease that is prevalent in that area. This makes it easier for the identification unit to identify the possibility of a disease specific to the area by taking into account the geographical distribution of animals. Some or all of the above-mentioned processing in the identification unit may be performed using AI, for example, or may be performed without using AI. For example, the identification unit can input geographical distribution data of animals into AI and cause the AI ​​to execute processing to identify the possibility of a disease.

[0101] The identification unit can identify changes in the animal's behavior or physical condition by comparing them with a related database. For example, the identification unit can identify changes in the animal's behavior by comparing them with a related database. For example, the identification unit can refer to the related database to determine whether the changes in the animal's behavior are related to a specific disease. The identification unit can also identify changes in the animal's physical condition by comparing them with a related database. For example, the identification unit can refer to the related database to determine whether the changes in the animal's physical condition are related to a specific disease. The identification unit can also identify symptoms of the animal by comparing them with a related database. For example, the identification unit can refer to the related database to determine whether the symptoms of the animal are related to a specific disease. This allows the identification unit to provide more accurate identification results by comparing them with the related database. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input data from the related database into AI and have the AI ​​perform a comparison analysis.

[0102] The suggestion unit can estimate the owner's emotions and adjust the way the suggestion is expressed based on the estimated owner's emotions. For example, the suggestion unit captures the owner's facial expression with a camera and estimates the owner's emotions using an emotion estimation algorithm. For example, if the owner is feeling anxious, the suggestion unit can make a suggestion in gentle words. If the owner is relaxed, the suggestion unit can make a detailed suggestion. If the owner is busy, the suggestion unit can make a concise suggestion. This allows the suggestion unit to adjust the way the suggestion is expressed depending on the owner's emotions, thereby making more appropriate suggestions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input image data of the owner captured with a camera to the generation AI and cause the generation AI to estimate the owner's emotions.

[0103] When making a suggestion, the suggestion unit can adjust the level of detail of the suggestion based on the severity of the illness. For example, the suggestion unit may make simple suggestions for a mild illness. For example, for a mild digestive problem, the suggestion unit may suggest dietary changes or light exercise. The suggestion unit may also make detailed suggestions for a severe illness. For example, for a severe infection, the suggestion unit may suggest the use of specific medication or a consultation with a specialist. The suggestion unit may also make moderately detailed suggestions for a moderate illness. For example, for moderate stress, the suggestion unit may suggest specific measures for improving the environment or reducing stress. This allows the suggestion unit to adjust the level of detail of the suggestion depending on the severity of the illness, thereby enabling appropriate responses. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit may input illness severity data into AI and cause the AI ​​to execute a process of adjusting the level of detail of the suggestion.

[0104] When making a proposal, the suggestion unit can apply different proposal algorithms depending on the type and age of the animal. For example, the suggestion unit applies different proposal algorithms to dogs and cats to make appropriate proposals. For example, dogs have dog-specific diseases and cats have cat-specific diseases, so a proposal algorithm appropriate for each can be applied. The suggestion unit can also apply different proposal algorithms to young animals and old animals. For example, young animals have diseases specific to their growth period, and old animals have diseases associated with aging, so a proposal algorithm appropriate for each can be applied. Furthermore, the suggestion unit can apply a proposal algorithm that takes into account diseases specific to a specific breed. For example, certain dog breeds and cat breeds have genetically specific diseases, so a proposal algorithm appropriate for that can be applied. As a result, the suggestion unit can make more appropriate proposals by applying a proposal algorithm appropriate for the type and age of the animal. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data on the type and age of the animal into AI and cause the AI ​​to execute a process of selecting an appropriate proposal algorithm.

[0105] The suggestion unit can estimate the owner's emotions and determine the priority of suggestions based on the estimated owner's emotions. For example, the suggestion unit captures the owner's facial expression with a camera and estimates the owner's emotions using an emotion estimation algorithm. For example, if the owner is feeling anxious, important suggestions can be prioritized. Also, if the owner is relaxed, detailed suggestions can be made. Furthermore, if the owner is busy, brief suggestions can be made. In this way, the suggestion unit can prioritize suggestions according to the owner's emotions, thereby prioritizing important suggestions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input image data of the owner captured with a camera to the generation AI and cause the generation AI to estimate the owner's emotions.

[0106] When making a suggestion, the suggestion unit can make an optimal suggestion by taking into account the geographical location information of the animal. For example, the suggestion unit makes a suggestion for a disease that is prevalent in the area where the owner lives. For example, if a specific disease is prevalent in the area where the owner lives, the suggestion unit can make a suggestion for that disease. Furthermore, if the owner is traveling, the suggestion unit can make a suggestion for a disease that is specific to the area the owner is traveling to. For example, if a specific disease is prevalent in the area where the owner is traveling, the suggestion unit can make a suggestion for that disease. Furthermore, if the owner moves, the suggestion unit can make a suggestion for a disease in the new area. For example, if the owner moves to a new area, the suggestion unit can make a suggestion for a disease that is prevalent in that area. This enables the suggestion unit to make an appropriate suggestion for a disease that is specific to the area by taking into account the geographical location information of the animal. Some or all of the above-mentioned processing by the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the geographical location information of the animal to AI and cause the AI ​​to execute a process of making an optimal suggestion.

[0107] When making a suggestion, the suggestion unit may compare the animal's behavior or changes in physical condition with related literature. For example, the suggestion unit may compare the animal's behavioral changes with related academic papers to make a suggestion. For example, the suggestion unit may refer to related academic papers to determine whether the animal's behavioral changes are related to a specific disease. The suggestion unit may also compare the animal's physical condition changes with related medical books to make a suggestion. For example, the suggestion unit may refer to related medical books to determine whether the animal's physical condition changes are related to a specific disease. Furthermore, the suggestion unit may compare the animal's symptoms with related online databases to make a suggestion. For example, the suggestion unit may refer to related online databases to determine whether the animal's symptoms are related to a specific disease. This allows the suggestion unit to make more accurate suggestions by comparing the symptoms with related literature. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit may input data from related literature into AI and have the AI ​​perform a cross-check analysis.

[0108] The database unit can estimate the owner's emotions and adjust the database reference method based on the estimated owner's emotions. For example, the database unit captures the owner's facial expressions with a camera and estimates the owner's emotions using an emotion estimation algorithm. For example, if the owner is feeling anxious, detailed data can be referenced preferentially. If the owner is relaxed, concise data can be referenced preferentially. If the owner is busy, data that highlights the main points can be referenced preferentially. This allows the database unit to provide more appropriate information by adjusting the database reference method according to the owner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the database unit may be performed using AI, for example, or without AI. For example, the database unit can input image data of the owner captured with a camera into the generation AI and cause the generation AI to estimate the owner's emotions.

[0109] The database unit can add the latest research data on changes in animal behavior and physical condition to the database. For example, the database unit can add data on changes in animal behavior from the latest academic papers. For example, new findings on changes in animal behavior based on the latest academic papers can be added to the database. The database unit can also add data on changes in animal physical condition from the latest medical books. For example, new findings on changes in animal physical condition based on the latest medical books can be added to the database. Furthermore, the database unit can add data on animal symptoms from the latest online database. For example, new findings on animal symptoms can be added to the database based on the latest online database. In this way, the database unit improves the accuracy of analysis by adding the latest research data. Some or all of the above-mentioned processing in the database unit can be performed, for example, using AI, or can be performed without using AI. For example, the database unit can input the latest research data into AI and cause the AI ​​to execute the process of adding it to the database.

[0110] The database unit can classify and store data according to the type and age of animals in the database. The database unit, for example, classifies and stores data for dogs and cats. For example, by classifying and storing data for dogs and cats separately, analysis appropriate for each animal is possible. The database unit can also classify and store data for young animals and old animals. For example, by classifying and storing data for young animals and old animals separately, analysis appropriate for each age is possible. Furthermore, the database unit can classify and store data specific to a particular breed. For example, by classifying and storing data specific to a particular breed of dog or cat separately, analysis appropriate for each breed is possible. In this way, the database unit classifies and stores data according to the type and age of animals, enabling more accurate analysis. Some or all of the above-described processing in the database unit may be performed using, for example, AI, or may be performed without AI. For example, the database unit can input data on the type and age of animals into AI and have the AI ​​classify and store the data.

[0111] The database unit can estimate the owner's emotions and adjust the display order of database search results based on the estimated owner's emotions. For example, the database unit captures the owner's facial expressions with a camera and estimates the owner's emotions using an emotion estimation algorithm. For example, if the owner is feeling anxious, important data can be displayed preferentially. Also, if the owner is relaxed, detailed data can be displayed. Furthermore, if the owner is busy, concise data can be displayed preferentially. In this way, the database unit can prioritize providing important information by adjusting the display order of search results according to the owner's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the database unit can be performed using, for example, AI, or without AI. For example, the database unit can input image data of the owner captured with a camera into the generation AI and cause the generation AI to estimate the owner's emotions.

[0112] The database unit can add data regarding the geographical distribution of animals to the database. For example, the database unit can add data regarding the area where the animal lives. For example, data regarding the climate and environment of the area where the animal lives can be added to the database. Furthermore, if the animal is traveling, the database unit can add data regarding the area where the animal travels. For example, data regarding the climate and environment of the area where the animal travels can be added to the database. Furthermore, if the animal moves, the database unit can add data regarding the new area. For example, if the animal moves to a new area, data regarding the climate and environment of that area can be added to the database. In this way, by adding data regarding the geographical distribution, the database unit can analyze diseases specific to the area. Some or all of the above-mentioned processing in the database unit can be performed using, for example, AI, or can be performed without using AI. For example, the database unit can input the geographical distribution data into AI and cause the AI ​​to execute processing to add the data to the database.

[0113] The database unit can add related literature related to changes in animal behavior and physical condition to the database. The database unit can add, for example, academic papers related to changes in animal behavior. For example, the latest academic papers related to changes in animal behavior can be added to the database. The database unit can also add medical books related to changes in animal physical condition. For example, the latest medical books related to changes in animal physical condition can be added to the database. Furthermore, the database unit can also add online databases related to animal symptoms. For example, the latest online databases related to animal symptoms can be added to the database. In this way, the database unit can improve the accuracy of analysis by adding related literature. Some or all of the above-mentioned processing in the database unit can be performed using, for example, AI, or can be performed without using AI. For example, the database unit can input data from related literature into AI and have the AI ​​execute the process of adding it to the database.

[0114] The advice unit can estimate the owner's emotions and adjust the way the advice is expressed based on the estimated owner's emotions. For example, the advice unit captures the owner's facial expression with a camera and estimates the owner's emotions using an emotion estimation algorithm. For example, if the owner is feeling anxious, the advice unit can provide gentle advice. If the owner is relaxed, the advice unit can provide detailed advice. If the owner is busy, the advice unit can provide concise advice. This allows the advice unit to adjust the way the advice is expressed depending on the owner's emotions, thereby providing more appropriate advice. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the advice unit can input image data of the owner captured with a camera into the generation AI and cause the generation AI to estimate the owner's emotions.

[0115] When providing advice, the advice unit can adjust the level of detail of the advice based on the severity of the illness. For example, in the case of a mild illness, the advice unit provides simple advice. For example, for a mild digestive problem, the advice unit can suggest dietary changes or light exercise. The advice unit can also provide detailed advice in the case of a severe illness. For example, for a severe infection, the advice unit can suggest the use of a specific medication or a consultation with a specialist. Furthermore, in the case of a moderate illness, the advice unit can provide advice with moderate detail. For example, for moderate stress, the advice unit can suggest specific measures for improving the environment or reducing stress. This allows the advice unit to adjust the level of detail of the advice depending on the severity of the illness, enabling appropriate responses. Some or all of the above-mentioned processing in the advice unit may be performed using, or without, AI. For example, the advice unit may input illness severity data into AI and cause the AI ​​to execute processing to adjust the level of detail of the advice.

[0116] When providing advice, the advice unit can apply different advice algorithms depending on the type and age of the animal. The advice unit can apply different advice algorithms to dogs and cats, for example, to provide appropriate advice. For example, dogs have dog-specific diseases and cats have cat-specific diseases, so an appropriate advice algorithm can be applied for each. The advice unit can also apply different advice algorithms to young animals and old animals. For example, young animals have diseases specific to their growth period, and old animals have diseases associated with aging, so an appropriate advice algorithm can be applied for each. Furthermore, the advice unit can apply an advice algorithm that takes into account diseases specific to a specific breed. For example, certain dog breeds and cat breeds have genetically specific diseases, so an appropriate advice algorithm can be applied. This allows the advice unit to provide more appropriate advice by applying an advice algorithm depending on the type and age of the animal. Some or all of the above-described processing in the advice unit may be performed using, or without, AI. For example, the advice unit can input data on the type and age of the animal into AI and cause the AI ​​to execute a process of selecting an appropriate advice algorithm.

[0117] The advice unit can estimate the owner's emotions and determine the priority of advice based on the estimated owner's emotions. For example, the advice unit captures the owner's facial expression with a camera and estimates the owner's emotions using an emotion estimation algorithm. For example, if the owner is feeling anxious, important advice can be given priority. Also, if the owner is relaxed, detailed advice can be given. Furthermore, if the owner is busy, concise advice can be given priority. In this way, the advice unit can determine the priority of advice according to the owner's emotions, thereby giving priority to important advice. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the advice unit may be performed using an AI, for example, or without an AI. For example, the advice unit can input image data of the owner captured with a camera to the generation AI and cause the generation AI to estimate the owner's emotions.

[0118] When giving advice, the advice unit can provide optimal advice by taking into account the geographical location information of the animal. For example, the advice unit provides advice on diseases that are prevalent in the area where the owner lives. For example, if a specific disease is prevalent in the area where the owner lives, the advice unit can provide advice on that disease. Furthermore, if the owner is traveling, the advice unit can provide advice on diseases that are specific to the area the owner is traveling to. For example, if a specific disease is prevalent in the area where the owner is traveling, the advice unit can provide advice on that disease. Furthermore, if the owner moves, the advice unit can provide advice on diseases that are prevalent in the new area. For example, if the owner moves to a new area, the advice unit can provide advice on diseases that are prevalent in that area. This enables the advice unit to provide appropriate advice on diseases that are specific to the area by taking into account the geographical location information of the animal. Some or all of the above-mentioned processing by the advice unit may be performed using AI, for example, or may be performed without using AI. For example, the advice unit can input the geographical location information of the animal into AI and cause the AI ​​to execute processing to provide optimal advice.

[0119] When providing advice, the advice unit can compare the animal's behavior and changes in physical condition with relevant literature to provide advice. For example, the advice unit can compare the animal's behavioral changes with relevant academic papers to provide advice. For example, the advice unit can refer to relevant academic papers to determine whether the animal's behavioral changes are related to a specific disease. The advice unit can also compare the animal's physical condition changes with relevant medical books to provide advice. For example, the advice unit can refer to relevant medical books to determine whether the animal's physical condition changes are related to a specific disease. Furthermore, the advice unit can compare the animal's symptoms with relevant online databases to provide advice. For example, the advice unit can refer to relevant online databases to determine whether the animal's symptoms are related to a specific disease. This allows the advice unit to provide more accurate advice by comparing the symptoms with relevant literature. Some or all of the above-described processing in the advice unit may be performed using, or without, AI. For example, the advice unit may input data from relevant literature into AI and have the AI ​​perform a cross-check analysis.

[0120] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0121] The analysis unit can monitor changes in the animal's behavior and physical condition in real time and immediately notify the owner if an abnormality is detected. For example, if the animal's body temperature rises suddenly or the amount of food eaten suddenly decreases, the information can be analyzed in real time and the owner can be notified. In addition, if the animal's behavior pattern differs from normal, a notification can be sent immediately. This allows the owner to respond quickly and manage the animal's health more effectively. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input data acquired by a sensor into AI and have the AI ​​detect and notify abnormalities.

[0122] The suggestion unit can provide preventive advice based on changes in the animal's behavior and physical condition. For example, if the animal is in a stressful environment, it can suggest environmental improvement measures to reduce stress. Also, if the animal is prone to a particular disease, it can provide advice on diet and exercise to prevent that disease. It can also provide advice on health management during seasonal changes. This allows owners to take measures in advance and maintain the health of their animals. Some or all of the above-mentioned processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the animal's behavior and physical condition into AI and cause the AI ​​to execute processing to generate preventive advice.

[0123] The reception unit can receive information about changes in an animal's behavior or physical condition via voice input. For example, an owner can input information such as "My dog ​​is not feeling well" or "My cat has no appetite" via voice. Voice input improves convenience because it allows the owner to input information without using their hands. In addition, voice recognition technology can be used to convert the input voice into text and send it to the analysis unit. This allows the reception unit to quickly and accurately receive information about changes in an animal's behavior or physical condition. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can use AI to execute voice recognition technology to convert the voice input into text.

[0124] The identification unit can refer to the animal's past data when analyzing changes in the animal's behavior or physical condition. For example, if the animal has shown similar symptoms in the past, the identification unit can refer to that data to identify the correlation with the current symptoms. Also, by referring to the animal's past treatment history, the optimal treatment method for the current symptoms can be identified. Furthermore, by referring to the animal's past behavioral patterns, the cause of the current abnormal behavior can be identified. This allows the identification unit to make a more accurate diagnosis. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the animal's past data into AI and have the AI ​​execute a process to analyze the correlation with the current symptoms.

[0125] The suggestion unit can send reminders to owners for regular health checks based on changes in the animal's behavior and physical condition. For example, even if the animal's physical condition is stable, sending a reminder to encourage regular health checks can enable early detection and prevention. Furthermore, if any abnormalities are observed in the animal's behavior or physical condition, a reminder can be sent immediately to encourage a prompt response. Furthermore, the content of the reminder can be customized according to the type and age of the animal. This allows owners to more effectively manage their animal's health. Some or all of the above-mentioned processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data on the animal's behavior and physical condition into AI and have the AI ​​send the reminder.

[0126] When analyzing changes in the animal's behavior or physical condition, the analysis unit can estimate the owner's emotions and adjust the display method of the analysis results based on the estimated owner's emotions. For example, if the owner is feeling anxious, detailed analysis results can be displayed. Furthermore, if the owner is relaxed, concise analysis results can be displayed. Furthermore, if the owner is busy, analysis results that focus on the main points can be displayed. This allows the analysis unit to provide more appropriate information by adjusting the display method of the analysis results according to the owner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input image data of the owner taken with a camera into the generation AI and cause the generation AI to estimate the owner's emotions.

[0127] The suggestion unit can estimate the owner's emotions and adjust the way the suggestion is expressed based on the estimated owner's emotions. For example, if the owner is feeling anxious, the suggestion unit can make gentle suggestions. If the owner is relaxed, the suggestion unit can make detailed suggestions. If the owner is busy, the suggestion unit can make concise suggestions. This allows the suggestion unit to adjust the way the suggestion is expressed depending on the owner's emotions, thereby making more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input image data of the owner taken with a camera into the generation AI and cause the generation AI to estimate the owner's emotions.

[0128] The reception unit can estimate the owner's emotions and adjust the timing of input based on the estimated owner's emotions. For example, if the owner is feeling anxious, it can send a notification prompting the owner to input information immediately. Furthermore, if the owner is relaxed, it can delay the notification prompting the owner to input information. Furthermore, if the owner is busy, it can set a reminder to input information later. This allows the reception unit to adjust the timing of input according to the owner's emotions, allowing the owner to input information at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can input image data of the owner taken with a camera into the generation AI and cause the generation AI to estimate the owner's emotions.

[0129] The identification unit can estimate the owner's emotions and adjust the specific criteria based on the estimated owner's emotions. For example, if the owner is feeling anxious, the specific criteria can be set stricter. Furthermore, if the owner is relaxed, the specific criteria can be set looser. Furthermore, if the owner is busy, the specific criteria can be set simpler. This allows the identification unit to provide more appropriate identification results by adjusting the specific criteria according to the owner's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or without AI. For example, the identification unit can input image data of the owner captured by a camera into the generation AI and cause the generation AI to estimate the owner's emotions.

[0130] The suggestion unit can estimate the owner's emotions and prioritize suggestions based on the estimated owner's emotions. For example, if the owner is feeling anxious, important suggestions can be prioritized. If the owner is relaxed, detailed suggestions can be prioritized. If the owner is busy, brief suggestions can be prioritized. As a result, the suggestion unit can prioritize suggestions according to the owner's emotions, thereby prioritizing important suggestions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, or may be performed without AI. For example, the suggestion unit can input image data of the owner taken with a camera into the generation AI and cause the generation AI to estimate the owner's emotions.

[0131] The processing flow of the second embodiment will be briefly explained below.

[0132] Step 1: The reception unit accepts input about changes in an animal's behavior and physical condition. For example, an owner can enter information such as "My dog ​​has no appetite" or "My cat is vomiting frequently." The reception unit can accept information about an animal's appetite, excretion, behavioral patterns, body temperature, etc. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit refers to a database of changes in animal behavior and physical condition, and uses data analysis methods and algorithms to identify possible illnesses. Step 3: The identification unit identifies possible illnesses based on the information analyzed by the analysis unit. The identification unit uses diagnostic algorithms to identify possible digestive problems or stress. Step 4: The suggestion unit suggests improvement methods based on the illness identified by the identification unit. The suggestion unit provides advice on dietary changes, the use of specific medications, environmental improvements, and stress reduction. The processing in the suggestion unit may be performed using AI or without AI.

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats including voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. The AIs other than the generation AI are, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but are not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or in whole by AI, but are not limited to these examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0135] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements, including the above-described reception unit, analysis unit, identification unit, and suggestion unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14, allowing the owner to input information about changes in the animal's behavior and physical condition. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the information received by the reception unit. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies the possibility of illness based on the analyzed information. The suggestion unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and suggests an improvement method based on the identified illness. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications are possible.

[0137] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0138] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in one or more data formats, such as voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0151] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] Each of the multiple elements, including the above-described reception unit, analysis unit, identification unit, and suggestion unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214, allowing the owner to input changes in the animal's behavior and physical condition by voice. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the information received by the reception unit. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies the possibility of illness based on the analyzed information. The suggestion unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and suggests an improvement method based on the identified illness. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications are possible.

[0153] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0154] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0155] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

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

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

[0160] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0161] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0162] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0163] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

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

[0165] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0166] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in one or more data formats, such as voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0167] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0168] Each of the multiple elements including the above-described reception unit, analysis unit, identification unit, and suggestion unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314, allowing the owner to input changes in the animal's behavior and physical condition by voice. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the information received by the reception unit. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies the possibility of illness based on the analyzed information. The suggestion unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and suggests an improvement method based on the identified illness. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications are possible.

[0169] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0170] 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.

[0171] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0172] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

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

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

[0176] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0177] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0178] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0179] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0180] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0181] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0182] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0183] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in one or more data formats, such as voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0184] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0185] Each of the multiple elements including the above-mentioned reception unit, analysis unit, identification unit, and suggestion unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414, allowing the owner to input changes in the animal's behavior and physical condition by voice. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the information received by the reception unit. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies the possibility of illness based on the analyzed information. The suggestion unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and suggests an improvement method based on the identified illness. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications are possible.

[0186] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0187] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0188] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0189] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0190] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0191] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0192] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0193] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0194] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0195] 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.

[0196] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0197] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0198] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0199] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0200] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0201] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0202] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0203] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0204] (Appendix 1) a reception unit that receives input of changes in the behavior and physical condition of the animal; an analysis unit that analyzes the information received by the reception unit; an identification unit that identifies a disease based on the information analyzed by the analysis unit; a suggestion unit that suggests an improvement method based on the disease identified by the identification unit. A system characterized by: (Appendix 2) The analysis unit Equipped with a database section that references a database on animal behavior and changes in physical condition 2. The system of claim 1. (Appendix 3) The proposal unit An advice section provides specific advice based on the identified illness. 2. The system of claim 1. (Appendix 4) 10. The system of claim 1, wherein the system receives information about an animal's appetite, excretion, behavioral patterns, and body temperature. (Appendix 5) 10. The system of claim 1, wherein the system identifies possible digestive issues or stress. (Appendix 6) 10. The system of claim 1, wherein the system suggests dietary changes or the use of specific medications. (Appendix 7) 10. The system of claim 1, wherein the system provides advice for improving the environment or reducing stress. (Appendix 8) The reception unit Estimate the owner's emotions and adjust the timing of inputs based on the estimated emotions. 2. The system of claim 1. (Appendix 9) The reception unit Analyze the owner's past input history and select the optimal input method 2. The system of claim 1. (Appendix 10) The reception unit Real-time monitoring of animal behavior and changes in physical condition, and automatic input 2. The system of claim 1. (Appendix 11) The reception unit Estimate the owner's emotions and prioritize input content based on the estimated emotions. 2. The system of claim 1. (Appendix 12) The reception unit Taking into account the owner's geographic location, information on diseases specific to that region is entered first. 2. The system of claim 1. (Appendix 13) The reception unit Analyze owners' social media activity and record any relevant behavioral or physical changes 2. The system of claim 1. (Appendix 14) The analysis unit Estimate the owner's emotions and adjust the accuracy of the analysis based on the estimated emotions of the owner. 2. The system of claim 1. (Appendix 15) The analysis unit Analyzing changes in animal behavior and physical condition over time to identify abnormal patterns 2. The system of claim 1. (Appendix 16) The analysis unit Applying different analysis algorithms depending on the species and age of the animal 2. The system of claim 1. (Appendix 17) The analysis unit Estimate the owner's emotions and adjust the display method of the analysis results based on the estimated owner's emotions. 2. The system of claim 1. (Appendix 18) The analysis unit Analyzing changes in animal behavior and physical condition in comparison with other animals 2. The system of claim 1. (Appendix 19) The analysis unit Analyze changes in animal behavior and physical condition by comparing them with relevant literature 2. The system of claim 1. (Appendix 20) The identification unit Inferring the owner's emotions and adjusting certain criteria based on the estimated owner's emotions 2. The system of claim 1. (Appendix 21) The identification unit Identifying changes in animal behavior and physical condition as possible indications of multiple illnesses 2. The system of claim 1. (Appendix 22) The identification unit Consider the animal's past medical history to identify possible illnesses 2. The system of claim 1. (Appendix 23) The identification unit Inferring the owner's emotions and adjusting the display order of specific results based on the estimated owner's emotions 2. The system of claim 1. (Appendix 24) The identification unit Identifying potential diseases by considering the geographic distribution of animals 2. The system of claim 1. (Appendix 25) The identification unit Identifying changes in animal behavior and physical condition by comparing them with relevant databases 2. The system of claim 1. (Appendix 26) The proposal unit Estimate the owner's emotions and adjust the way suggestions are presented based on the estimated emotions of the owner. 2. The system of claim 1. (Appendix 27) The proposal unit When making suggestions, adjust the level of detail of the suggestions based on the severity of the illness 2. The system of claim 1. (Appendix 28) The proposal unit When making suggestions, different suggestion algorithms are applied depending on the type and age of the animal. 2. The system of claim 1. (Appendix 29) The proposal unit Estimate the owner's emotions and prioritize suggestions based on the estimated emotions 2. The system of claim 1. (Appendix 30) The proposal unit When making suggestions, the geographic location of the animal is taken into account to make the best suggestions. 2. The system of claim 1. (Appendix 31) The proposal unit When proposing, compare the animal's behavior and changes in physical condition with relevant literature. 2. The system of claim 1. (Appendix 32) The database unit Inferring the owner's emotions and adjusting the database reference method based on the estimated owner's emotions 3. The system of claim 2. (Appendix 33) The database unit Adding the latest research data on animal behavior and changes in physical condition to the database 3. The system of claim 2. (Appendix 34) The database unit The database stores data according to the type and age of the animals. 3. The system of claim 2. (Appendix 35) The database unit Inferring the owner's emotions and adjusting the order in which search results are displayed based on the estimated owner's emotions 3. The system of claim 2. (Appendix 36) The database unit Add data about the geographic distribution of animals to the database 3. The system of claim 2. (Appendix 37) The database unit Add relevant literature on animal behavior and physical condition changes to the database 3. The system of claim 2. (Appendix 38) The advice unit Estimate the owner's emotions and adjust the way advice is presented based on the estimated emotions. 4. The system of claim 3. (Appendix 39) The advice unit When giving advice, adjust the level of detail based on the severity of the illness 4. The system of claim 3. (Appendix 40) The advice unit When giving advice, different advice algorithms are applied depending on the type and age of the animal. 4. The system of claim 3. (Appendix 41) The advice unit Estimate the owner's emotions and prioritize advice based on the estimated emotions. 4. The system of claim 3. (Appendix 42) The advice unit When giving advice, the geographic location of the animal is taken into consideration to provide optimal advice. 4. The system of claim 3. (Appendix 43) The advice unit When giving advice, we compare the animal's behavior and changes in physical condition with relevant literature. 4. The system of claim 3. [Explanation of symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception unit that receives input of changes in the behavior and physical condition of an animal; an analysis unit that analyzes the information received by the reception unit; an identification unit that identifies a disease based on the information analyzed by the analysis unit; A suggestion unit that proposes an improvement method based on the disease identified by the identification unit. A system characterized by:

2. The analysis unit is Equipped with a database section that refers to a database on animal behavior and changes in physical condition 2. The system of claim 1 .

3. The suggestion unit, Provides an advice section that provides specific advice based on the identified illness 2. The system of claim 1 .

4. The reception unit is Accepts information on animals' appetites, excretions, behavioral patterns, and body temperatures 2. The system of claim 1 .

5. The identification unit is Identifying possible digestive issues or stress 2. The system of claim 1 .

6. The suggestion unit, Suggest dietary changes or the use of certain medications 2. The system of claim 1 .

7. The suggestion unit, Offering advice on improving the environment or reducing stress 2. The system of claim 1 .

8. The reception unit is Estimate the owner's emotions and adjust the timing of inputs based on the estimated emotions of the owner 2. The system of claim 1 .

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