System

Abstract

An object of a system according to an embodiment is to efficiently perform health management of a dog and predict a disease that the dog is likely to suffer.SOLUTION: A system includes a data collection part, a data analysis part, and a disease prediction part. The data collection unit collects data such as daily physical condition, character, behavior, and food of the dog. The data analysis unit analyzes the data collected by the data collection unit. The disease predictor predicts a disease that the dog is likely to suffer based on the data analyzed by the data analyzer.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have not adequately managed the health of pet dogs, making it difficult to predict the diseases that they are prone to.

[0005] The system according to the embodiment aims to efficiently manage the health of pet dogs and predict diseases to which they are prone. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, a data analysis unit, and a disease prediction unit. The data collection unit collects data on the pet dog's daily physical condition, personality, behavior, diet, etc. The data analysis unit analyzes the data collected by the data collection unit. The disease prediction unit predicts diseases to which the pet dog is prone based on the data analyzed by the data analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently manage the health of pet dogs and predict diseases to which they are prone. [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 health management system according to an embodiment of the present invention collects data on a pet dog's daily physical condition, personality, behavior, diet, etc., and uses a generative AI to analyze the data and predict diseases to which the dog is prone. This allows the health management system to efficiently manage the dog's health and predict diseases to which the dog is prone.

[0029] A health management system according to an embodiment includes a data collection unit, a data analysis unit, and a disease prediction unit. The data collection unit collects data on a pet dog's daily physical condition, personality, behavior, diet, and other information. For example, the data collection unit records the pet dog's body temperature, heart rate, activity level, diet, and excretion status using a sensor or a smart device. The owner can also manually input changes in personality and behavior observed by the pet dog. The data analysis unit analyzes the collected data. For example, the data analysis unit may analyze the data using statistical analysis or a machine learning algorithm. The data analysis unit may also compare the data with past data and data from other dogs to identify abnormal patterns and risk factors. The disease prediction unit predicts diseases to which the pet dog is prone based on the data analyzed by the data analysis unit. For example, the disease prediction unit may identify risk factors and warn of an increased risk of a specific disease. This allows the health management system according to an embodiment to efficiently manage the health of pet dogs and predict diseases to which the pet dog is prone.

[0030] The data collection unit can record body temperature, heart rate, activity level, dietary content, and excretion status using a sensor or a smart device. The data collection unit records data such as the dog's body temperature, heart rate, and activity level using a sensor. For example, a body temperature sensor can be used to measure the dog's body temperature and collect data. A heart rate sensor can also be used to measure the dog's heart rate and collect data. The data collection unit can also record the dog's activity level using a smart device. For example, a smart collar can be used to measure the dog's activity level and collect data. This makes it possible to collect detailed health data about the dog.

[0031] The data analysis unit can compare the data with past data or data from other dogs to identify abnormal patterns or risk factors. The data analysis unit, for example, uses past data to analyze the health of a pet dog. For example, it compares current data based on past health records to identify abnormal patterns. The data analysis unit can also compare data with data from other dogs. For example, it can compare data with data from the same breed to identify abnormal patterns. The data analysis unit can also use machine learning algorithms to identify risk factors. For example, it can identify risk factors and warn of an increased risk of a specific disease. By identifying abnormal patterns or risk factors, measures can be taken early.

[0032] The disease prediction unit can recommend a diet containing nutrients, adjust the amount of exercise, and suggest a health check schedule. The disease prediction unit, for example, recommends a diet containing specific nutrients. For example, it can recommend a diet containing vitamins and minerals. The disease prediction unit can also suggest adjusting the amount of exercise. For example, it can suggest adjusting the exercise time and exercise intensity depending on the activity level of the pet dog. The disease prediction unit can also suggest a schedule for regular health checks. For example, it can suggest schedules for regular health checks and vaccinations. This makes it possible to specifically suggest a health management plan for the pet dog.

[0033] The data collection unit can collect emotional data and analyze the impact of stress levels on pet dogs. The data collection unit, for example, collects emotional data from owners. For example, the owner's facial expressions can be captured with a camera and their emotions can be estimated using image analysis technology. The tone of the owner's voice can also be recorded and their emotions can be estimated using voice analysis technology. The data collection unit can also collect heart rate and sleep patterns to measure the owner's stress level. For example, a smartwatch can be used to measure the owner's heart rate and analyze the stress level. This makes it possible to analyze the impact of the owner's stress on the pet dog.

[0034] When collecting behavioral data, the data collection unit simultaneously records environmental data, allowing the analysis of the impact of environmental factors on health. For example, when collecting behavioral data of a pet dog, the data collection unit simultaneously measures the ambient temperature and humidity. For example, a temperature sensor can be used to measure the ambient temperature and collect data. A humidity sensor can also be used to measure the ambient humidity and collect data. The data collection unit can also measure noise levels. For example, a noise sensor can be used to measure the ambient noise level and collect data. This allows the analysis of the impact of environmental factors on the pet dog's health.

[0035] The data collection unit can use a wearable device to collect data even when the dog is out. For example, the data collection unit attaches the wearable device to the dog's collar and records the dog's activity level and heart rate in real time while the dog is out. For example, the wearable device can be used to measure the dog's activity level and collect data. The data collection unit can also use a wearable device with a GPS function to record the dog's movement route and time spent there. For example, if the dog spends a long time in a particular place, it can analyze whether that place is comfortable for the dog. The data collection unit can also collect environmental data such as the dog's body temperature and humidity through the wearable device. For example, it can investigate whether the dog's body temperature is likely to rise on hot days. This allows data to be collected even when the dog is out.

[0036] The data collection unit can collect data on other pets and build health management systems for different pets. For example, to collect data on the physical condition of cats, the data collection unit develops a wearable device for cats and records their activity levels and heart rates. For example, the data collection unit can measure the cat's activity levels and collect data. The data collection unit can also use a bird sensor to record the weight and feather condition of birds for health management. For example, the data collection unit can measure the weight of birds and collect data. The data collection unit can also integrate data from different pets and build a common health management system. For example, data from dogs, cats, and birds can be centrally managed and the health conditions of each pet can be comprehensively analyzed. This makes it possible to build health management systems for different pets.

[0037] The data analysis unit can add genetic information and perform disease predictions that take genetic risk factors into account. For example, the data analysis unit collects genetic information from pet dogs and inputs it into the generation AI. For example, if a specific gene mutation is present, the data analysis unit can analyze the disease risk associated with that mutation. The data analysis unit can also develop algorithms that predict disease risk for pet dogs based on the genetic information. For example, it can suggest early preventive measures for dogs that are genetically at high risk for heart disease. The data analysis unit can also integrate the pet dog's genetic information with other health data to evaluate overall disease risk. For example, it can combine genetic information and activity data to analyze obesity risk. This makes it possible to perform disease predictions that take genetic risk factors into account.

[0038] The data analysis unit can add past medical history and make disease predictions that take into account the effectiveness of past treatments. For example, the data analysis unit collects the past medical history of a pet dog and inputs it into the generating AI. For example, it can analyze the history of past treatments and medications. The data analysis unit can also develop an algorithm that predicts the risk of disease in a pet dog based on the past medical history. For example, it can evaluate the risk of recurrence for a dog that has previously suffered from a skin disease. The data analysis unit can also take into account the effectiveness of past treatments, allowing the generating AI to propose an optimal treatment plan. For example, it can prioritize treatments that have been effective in the past. This makes it possible to make disease predictions that take into account the effectiveness of past treatments.

[0039] The data analysis unit can add lifestyle data and analyze the impact of owner behavior on the dog's health. For example, the data analysis unit can collect owner lifestyle data and input it into the generation AI. For example, the data analysis unit can record the owner's exercise level and dietary habits and compare them with the dog's health data. The data analysis unit can also develop algorithms to analyze the impact of an owner's lifestyle on the dog's health. For example, it can investigate whether a lack of exercise by the owner also reduces the dog's activity level. The data analysis unit can also integrate the owner's lifestyle data with the dog's health data to evaluate overall health risks. For example, it can analyze the impact of an owner's dietary habits on the dog's weight. This makes it possible to analyze the impact of an owner's behavior on the dog's health.

[0040] The data analysis unit can add regional epidemic information and make predictions that take into account regional disease risks. For example, the data analysis unit collects regional epidemic information and inputs it into the generation AI. For example, it can analyze disease information that is prevalent in a specific region. The data analysis unit can also develop prediction algorithms that take into account regional disease risks. For example, it can evaluate the risk of infectious diseases in a specific region and propose preventive measures. The data analysis unit can also integrate regional epidemic information with pet dog health data to evaluate overall disease risk. For example, it can analyze the impact of regional climatic conditions on disease risk. This makes it possible to make predictions that take into account regional disease risks.

[0041] The disease prediction unit can perform customization taking into account personality or behavioral patterns. For example, the disease prediction unit collects personality data of pet dogs and inputs it into the generation AI. For example, it can propose a plan to increase exercise for active dogs, and provide a relaxing environment for calm dogs. The disease prediction unit can also analyze the behavioral patterns of pet dogs, and the generation AI can propose an optimal health management plan. For example, it can propose a plan to increase nighttime activity for nocturnal dogs. The disease prediction unit can also customize health management plans based on the personality and behavioral patterns of pet dogs. For example, it can propose a plan to encourage interaction with other dogs for sociable dogs. This allows customization that takes into account the individual personality and behavioral patterns of pet dogs.

[0042] The disease prediction unit can reflect the effectiveness of the health management plan and perform optimization. For example, the disease prediction unit collects data on a pet dog's past health management plans and inputs it into the generation AI. For example, it can analyze the effectiveness of past exercise plans and diet plans. The disease prediction unit can also develop an algorithm that allows the generation AI to propose the optimal plan based on the effectiveness of past health management plans. For example, it can prioritize the proposal of plans that have been effective in the past. The disease prediction unit can also integrate data from a pet dog's past health management plans, allowing the generation AI to perform a comprehensive evaluation. For example, it can propose the optimal amount of exercise and diet content based on the effectiveness of past plans. This allows the generation AI to reflect the effectiveness of past health management plans and perform optimization.

[0043] The disease prediction unit can comprehensively manage the health of the owner and their dog, including the owner's health management plan. For example, the disease prediction unit collects the owner's health data and inputs it into the generation AI. For example, it can record the owner's exercise volume and dietary habits and integrate them with the dog's health management plan. The disease prediction unit can also integrate the owner's and dog's health data, allowing the generation AI to propose a comprehensive health management plan. For example, it can propose a plan that allows the owner and dog to exercise together. The disease prediction unit can also customize the dog's health management plan, taking into account the owner's health management plan. For example, if the owner is on a diet, it can also adjust the dog's diet. This allows for comprehensive management of the health of the owner and dog.

[0044] The disease prediction unit can also integrate the health management plans of other pets, allowing for the management of multiple pets at once. For example, the disease prediction unit collects health data of other pets and inputs it into the generation AI. For example, it can record the physical condition data of cats and birds and integrate it with the health management plan of a pet dog. The disease prediction unit can also integrate the health data of multiple pets, allowing the generation AI to propose a comprehensive health management plan. For example, it can propose a plan that allows a dog and a cat to exercise together. The disease prediction unit can also customize the health management plan of a pet dog, taking into account the health management plans of other pets. For example, if a cat requires a specific diet, the diet of the pet dog can also be adjusted. This allows for the management of multiple pets at once.

[0045] The disease prediction unit can make suggestions for improving the dog's living environment in order to extend the dog's lifespan. For example, the disease prediction unit collects data on the dog's living environment and inputs it into the generation AI. For example, it can record the temperature and humidity of the room where the dog spends time and suggest the optimal environment. The disease prediction unit can also analyze the dog's living environment and make suggestions for improvements. For example, it can suggest a temperature and humidity that will keep the dog comfortable. The disease prediction unit can also make comprehensive suggestions for improvements based on the dog's living environment data. For example, it can make suggestions to provide an environment where the dog can relax. In this way, it is possible to extend the dog's lifespan by making suggestions for improving the dog's living environment.

[0046] The disease prediction unit can make suggestions to promote social interactions for the pet dog in order to extend the pet dog's lifespan. For example, the disease prediction unit collects social interaction data for the pet dog and inputs it into the generating AI. For example, it can record how much the pet dog interacts with other dogs and people. The disease prediction unit can also analyze the pet dog's social interactions and make promotion suggestions. For example, it can make suggestions to increase opportunities for the pet dog to play with other dogs. The disease prediction unit can also make comprehensive interaction promotion suggestions based on the social interaction data for the pet dog. For example, it can suggest events and places where the pet dog can interact with other dogs and people. This makes it possible to extend the lifespan by making suggestions to promote social interactions for the pet dog.

[0047] The disease prediction unit can make suggestions for improving the owner's lifestyle habits. For example, the disease prediction unit collects lifestyle data about the owner and inputs it into the generation AI. For example, it can record the owner's amount of exercise and dietary content and make suggestions that will have a positive impact on the health of the pet dog. The disease prediction unit can also analyze the owner's lifestyle habits and make suggestions for improvement. For example, it can make suggestions that if the owner leads a healthy lifestyle, the health of the pet dog will also improve. The disease prediction unit can also make comprehensive suggestions for improvement based on the owner's lifestyle data. For example, if the owner is not getting enough exercise, it can make a suggestion for the owner to exercise together with the pet dog. This makes it possible to make suggestions for improving the owner's lifestyle habits.

[0048] The disease prediction unit can suggest travel or outdoor activities based on the dog's health condition. For example, the disease prediction unit collects health condition data of the dog and inputs it into the generation AI. For example, the disease prediction unit can record the dog's physical condition and activity level and suggest optimal travel or outdoor activities. The disease prediction unit can also analyze the dog's health condition and suggest travel or outdoor activities using the generation AI. For example, it can suggest places and activities where the dog can relax. The disease prediction unit can also make comprehensive travel or outdoor activity suggestions based on the dog's health condition data. For example, it can suggest activities and places that the dog can enjoy. This makes it possible to suggest travel or outdoor activities based on the dog's health condition.

[0049] The data analysis unit can continuously monitor data, predict changes in the dog's health, and issue advance warnings. For example, the data analysis unit can continuously monitor the dog's health data, and if the generating AI detects an abnormality, it can issue an advance warning. For example, it can notify the user if it detects an abnormality in body temperature or heart rate. The data analysis unit can also develop an algorithm that enables the generating AI to predict changes in the dog's health and issue an advance warning. For example, it can issue an alert if it detects a sudden decrease in activity level. The data analysis unit can also enable the generating AI to evaluate overall health risks based on the dog's health data and issue an advance warning. For example, it can issue an alert if the dog continues to lose its appetite. This makes it possible to predict changes in the dog's health and issue an advance warning.

[0050] The data analysis unit can continuously monitor data and provide real-time advice in response to changes in the dog's health condition. For example, the data analysis unit can continuously monitor the dog's health data, and the generation AI can provide advice in real time. For example, if the dog's activity level decreases, advice can be given to encourage exercise. The data analysis unit can also develop an algorithm that allows the generation AI to provide advice in real time in response to changes in the dog's health condition. For example, if the dog has a persistent loss of appetite, advice can be given to adjust the dog's diet. The data analysis unit can also allow the generation AI to provide comprehensive advice in real time based on the dog's health data. For example, if the dog's body temperature rises, cooling methods can be suggested. This allows real-time advice to be provided in response to changes in the dog's health condition.

[0051] The data analysis unit can simultaneously monitor the owner's health data and provide comprehensive health management. For example, the data analysis unit continuously monitors the owner's health data, and the generation AI integrates it with the dog's health data to provide comprehensive health management. For example, the data analysis unit can record the owner's exercise volume and dietary habits and integrate them into the dog's health management plan. The data analysis unit can also develop algorithms that allow the generation AI to analyze the owner's health data and reflect this in the dog's health management plan. For example, if the owner is not getting enough exercise, the generation AI can suggest an exercise plan for the owner and the dog. The data analysis unit can also integrate the owner's health data and the dog's health data, allowing the generation AI to propose a comprehensive health management plan. For example, the generation AI can propose a plan for the owner and dog to live a healthy lifestyle together. This allows the owner's health data to be simultaneously monitored and comprehensive health management to be provided.

[0052] The data analysis unit can simultaneously monitor the data of other pets and manage the health of multiple pets at once. For example, the data analysis unit continuously monitors the health data of other pets, and the generation AI integrates it with the dog's health data to provide comprehensive health management. For example, the data analysis unit can record the physical condition data of cats and birds and integrate it into the dog's health management plan. The data analysis unit can also develop algorithms that allow the generation AI to analyze the health data of other pets and reflect it in the dog's health management plan. For example, if the cat requires a specific diet, the dog's diet can also be adjusted. The data analysis unit can also integrate the health data of other pets with the dog's health data, allowing the generation AI to propose a comprehensive health management plan. For example, it can propose a plan for multiple pets to live a healthy life together. This allows the data of other pets to be monitored simultaneously and the health of multiple pets to be managed at once.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The health management system can also collect sleep data from pet dogs and analyze their sleep quality. For example, it can monitor pet dogs' movements while they sleep and record the depth of their sleep and the frequency of sleep interruptions. It can also analyze pet dogs' sleep patterns and suggest appropriate sleeping environments. For example, providing a quiet environment and appropriate temperature can improve pet dogs' sleep quality. This allows for a comprehensive approach to pet dog health management that takes sleep quality into account.

[0055] The data collection unit can also collect olfactory data from pet dogs and analyze the impact of changes in olfactory sense on their health. For example, it can record whether a pet dog becomes sensitive to a particular smell. The olfactory data can also be used to analyze changes in a pet dog's appetite and behavior. For example, it can investigate whether a particular smell stimulates a pet dog's appetite. This allows the use of olfactory data to gain a more detailed understanding of a pet dog's health condition.

[0056] The data analysis unit can also analyze social behavior data of pet dogs to evaluate the impact of social interactions on their health. For example, it can record how often pet dogs interact with other dogs and people and analyze the relationship between the frequency of social interactions and their health status. Furthermore, if social interactions are low, it can make suggestions to promote social interactions. For example, it can suggest increasing opportunities for pet dogs to visit dog parks and play with other dogs. This allows for comprehensive management of pet dogs' social health.

[0057] The disease prediction unit can also analyze a dog's genetic information and propose a health management plan that takes genetic risk factors into account. For example, if a specific gene mutation is present, the system can evaluate the disease risk associated with that mutation and propose preventive measures. It can also customize a dog's diet and exercise plan based on the genetic information. For example, for a dog with a genetically high risk of obesity, it can suggest a low-calorie diet and moderate exercise. This makes it possible to provide an individualized health management plan that takes genetic risk factors into account.

[0058] The data collection unit can record detailed dietary data for your dog and analyze its nutritional balance. For example, it can record the ingredients your dog consumes and the amount of each ingredient to evaluate the nutrient balance. It can also analyze changes in your dog's weight and health based on the dietary data. For example, it can investigate whether certain ingredients affect your dog's weight gain. This allows for more detailed dietary management of your dog.

[0059] The data collection unit can record detailed exercise data of pet dogs and analyze the quality of the exercise. For example, it can record the dog's heart rate and activity level while exercising and evaluate the effectiveness of the exercise. It can also analyze changes in the dog's physical strength and endurance based on the exercise data. For example, it can investigate how much a particular exercise contributes to improving the dog's physical strength. This allows for more detailed exercise management of pet dogs.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The data collection unit collects data on the dog's daily physical condition, personality, behavior, diet, etc. For example, sensors and smart devices are used to record the dog's body temperature, heart rate, activity level, dietary content, and excretion status. Owners can also manually input any changes in personality or behavior they observe. Step 2: The data analysis unit analyzes the collected data. For example, the data analysis unit may analyze the data using statistical analysis or machine learning algorithms. The data analysis unit may also compare the data with past data or data from other dogs to identify abnormal patterns or risk factors. Step 3: The disease prediction unit predicts diseases that the pet dog is likely to suffer from based on the data analyzed by the data analysis unit. For example, the disease prediction unit identifies risk factors and warns of an increased risk of a specific disease.

[0062] (Example 2) A health management system according to an embodiment of the present invention collects data on a pet dog's daily physical condition, personality, behavior, diet, etc., and uses a generative AI to analyze the data and predict diseases to which the dog is prone. This allows the health management system to efficiently manage the dog's health and predict diseases to which the dog is prone.

[0063] A health management system according to an embodiment includes a data collection unit, a data analysis unit, and a disease prediction unit. The data collection unit collects data on a pet dog's daily physical condition, personality, behavior, diet, and other information. For example, the data collection unit records the pet dog's body temperature, heart rate, activity level, diet, and excretion status using a sensor or a smart device. The owner can also manually input changes in personality and behavior observed by the pet dog. The data analysis unit analyzes the collected data. For example, the data analysis unit may analyze the data using statistical analysis or a machine learning algorithm. The data analysis unit may also compare the data with past data and data from other dogs to identify abnormal patterns and risk factors. The disease prediction unit predicts diseases to which the pet dog is prone based on the data analyzed by the data analysis unit. For example, the disease prediction unit may identify risk factors and warn of an increased risk of a specific disease. This allows the health management system according to an embodiment to efficiently manage the health of pet dogs and predict diseases to which the pet dog is prone.

[0064] The data collection unit can record body temperature, heart rate, activity level, dietary content, and excretion status using a sensor or a smart device. The data collection unit records data such as the dog's body temperature, heart rate, and activity level using a sensor. For example, a body temperature sensor can be used to measure the dog's body temperature and collect data. A heart rate sensor can also be used to measure the dog's heart rate and collect data. The data collection unit can also record the dog's activity level using a smart device. For example, a smart collar can be used to measure the dog's activity level and collect data. This makes it possible to collect detailed health data about the dog.

[0065] The data analysis unit can compare the data with past data or data from other dogs to identify abnormal patterns or risk factors. The data analysis unit, for example, uses past data to analyze the health of a pet dog. For example, it compares current data based on past health records to identify abnormal patterns. The data analysis unit can also compare data with data from other dogs. For example, it can compare data with data from the same breed to identify abnormal patterns. The data analysis unit can also use machine learning algorithms to identify risk factors. For example, it can identify risk factors and warn of an increased risk of a specific disease. By identifying abnormal patterns or risk factors, measures can be taken early.

[0066] The disease prediction unit can recommend a diet containing nutrients, adjust the amount of exercise, and suggest a health check schedule. The disease prediction unit, for example, recommends a diet containing specific nutrients. For example, it can recommend a diet containing vitamins and minerals. The disease prediction unit can also suggest adjusting the amount of exercise. For example, it can suggest adjusting the exercise time and exercise intensity depending on the activity level of the pet dog. The disease prediction unit can also suggest a schedule for regular health checks. For example, it can suggest schedules for regular health checks and vaccinations. This makes it possible to specifically suggest a health management plan for the pet dog.

[0067] The data collection unit can collect emotional data and analyze the impact of stress levels on pet dogs. The data collection unit, for example, collects emotional data from owners. For example, the owner's facial expressions can be captured with a camera and their emotions can be estimated using image analysis technology. The tone of the owner's voice can also be recorded and their emotions can be estimated using voice analysis technology. The data collection unit can also collect heart rate and sleep patterns to measure the owner's stress level. For example, a smartwatch can be used to measure the owner's heart rate and analyze the stress level. This makes it possible to analyze the impact of the owner's stress on the pet dog.

[0068] When collecting behavioral data, the data collection unit simultaneously records environmental data, allowing the analysis of the impact of environmental factors on health. For example, when collecting behavioral data of a pet dog, the data collection unit simultaneously measures the ambient temperature and humidity. For example, a temperature sensor can be used to measure the ambient temperature and collect data. A humidity sensor can also be used to measure the ambient humidity and collect data. The data collection unit can also measure noise levels. For example, a noise sensor can be used to measure the ambient noise level and collect data. This allows the analysis of the impact of environmental factors on the pet dog's health.

[0069] The data collection unit can use the emotion estimation function to estimate emotions from facial expressions or tone of voice, and utilize the emotion data for health management. For example, the data collection unit can capture a pet dog's facial expression with a camera and estimate the emotion using image analysis technology. For example, it can analyze the difference in facial expressions when the pet dog is happy and when it is anxious. The data collection unit can also record the tone of the pet dog's voice and estimate the emotion using voice analysis technology. For example, it can analyze the difference in the voice when the pet dog is excited and when it is relaxed. The data collection unit can also utilize the emotion data for health management and analyze the impact of emotional states on health. For example, it can investigate whether the pet dog's physical condition deteriorates when it is under stress. In this way, the pet dog's emotion data can be utilized for health management.

[0070] The data collection unit can use a wearable device to collect data even when the dog is out. For example, the data collection unit attaches the wearable device to the dog's collar and records the dog's activity level and heart rate in real time while the dog is out. For example, the wearable device can be used to measure the dog's activity level and collect data. The data collection unit can also use a wearable device with a GPS function to record the dog's movement route and time spent there. For example, if the dog spends a long time in a particular place, it can analyze whether that place is comfortable for the dog. The data collection unit can also collect environmental data such as the dog's body temperature and humidity through the wearable device. For example, it can investigate whether the dog's body temperature is likely to rise on hot days. This allows data to be collected even when the dog is out.

[0071] The data collection unit can collect data on other pets and build health management systems for different pets. For example, to collect data on the physical condition of cats, the data collection unit develops a wearable device for cats and records their activity levels and heart rates. For example, the data collection unit can measure the cat's activity levels and collect data. The data collection unit can also use a bird sensor to record the weight and feather condition of birds for health management. For example, the data collection unit can measure the weight of birds and collect data. The data collection unit can also integrate data from different pets and build a common health management system. For example, data from dogs, cats, and birds can be centrally managed and the health conditions of each pet can be comprehensively analyzed. This makes it possible to build health management systems for different pets.

[0072] The data collection unit can use the emotion estimation function to estimate the emotion of the owner when entering the health condition of their dog in real time, thereby improving the reliability of the input content. For example, the data collection unit analyzes facial expressions and voice when the owner enters the health condition of their dog, and estimates the emotion in real time. For example, if the owner is feeling anxious, the input content can be carefully considered. The data collection unit can also use the emotion estimation function to record the owner's emotional state and evaluate the reliability of the input content. For example, input content in a positive emotional state can be preferentially adopted. The data collection unit can also provide feedback to improve the reliability of the input content based on the owner's emotional data. For example, if the owner's emotions are unstable, a message can be displayed prompting the owner to re-enter the information. This makes it possible to improve the reliability of the input content by taking the owner's emotions into consideration.

[0073] The data analysis unit can add genetic information and perform disease predictions that take genetic risk factors into account. For example, the data analysis unit collects genetic information from pet dogs and inputs it into the generation AI. For example, if a specific gene mutation is present, the data analysis unit can analyze the disease risk associated with that mutation. The data analysis unit can also develop algorithms that predict disease risk for pet dogs based on the genetic information. For example, it can suggest early preventive measures for dogs that are genetically at high risk for heart disease. The data analysis unit can also integrate the pet dog's genetic information with other health data to evaluate overall disease risk. For example, it can combine genetic information and activity data to analyze obesity risk. This makes it possible to perform disease predictions that take genetic risk factors into account.

[0074] The data analysis unit can add past medical history and make disease predictions that take into account the effectiveness of past treatments. For example, the data analysis unit collects the past medical history of a pet dog and inputs it into the generating AI. For example, it can analyze the history of past treatments and medications. The data analysis unit can also develop an algorithm that predicts the risk of disease in a pet dog based on the past medical history. For example, it can evaluate the risk of recurrence for a dog that has previously suffered from a skin disease. The data analysis unit can also take into account the effectiveness of past treatments, allowing the generating AI to propose an optimal treatment plan. For example, it can prioritize treatments that have been effective in the past. This makes it possible to make disease predictions that take into account the effectiveness of past treatments.

[0075] The data analysis unit can use the emotion estimation function to analyze the emotional state and evaluate the impact of emotional changes on disease risk. For example, to analyze the emotional state of a pet dog, the data analysis unit records facial expressions and tone of voice and estimates emotions using the emotion estimation function. For example, it can analyze whether the risk of disease increases during periods of stress. The data analysis unit can also evaluate the correlation between the emotional state of a pet dog and disease risk based on the emotion estimation data. For example, it can investigate whether the dog's physical condition deteriorates during periods of emotional instability. The data analysis unit can also use the emotional data of a pet dog for health management and perform a detailed analysis of the impact of the emotional state on disease risk. For example, it can evaluate whether the dog's health is good during periods of positive emotions. This makes it possible to evaluate the impact of emotional changes in a pet dog on disease risk.

[0076] The data analysis unit can add lifestyle data and analyze the impact of owner behavior on the dog's health. For example, the data analysis unit can collect owner lifestyle data and input it into the generation AI. For example, the data analysis unit can record the owner's exercise level and dietary habits and compare them with the dog's health data. The data analysis unit can also develop algorithms to analyze the impact of an owner's lifestyle on the dog's health. For example, it can investigate whether a lack of exercise by the owner also reduces the dog's activity level. The data analysis unit can also integrate the owner's lifestyle data with the dog's health data to evaluate overall health risks. For example, it can analyze the impact of an owner's dietary habits on the dog's weight. This makes it possible to analyze the impact of an owner's behavior on the dog's health.

[0077] The data analysis unit can add regional epidemic information and make predictions that take into account regional disease risks. For example, the data analysis unit collects regional epidemic information and inputs it into the generation AI. For example, it can analyze disease information that is prevalent in a specific region. The data analysis unit can also develop prediction algorithms that take into account regional disease risks. For example, it can evaluate the risk of infectious diseases in a specific region and propose preventive measures. The data analysis unit can also integrate regional epidemic information with pet dog health data to evaluate overall disease risk. For example, it can analyze the impact of regional climatic conditions on disease risk. This makes it possible to make predictions that take into account regional disease risks.

[0078] The data analysis unit can use the emotion estimation function to estimate the emotions of the owner when receiving the analysis results of the generation AI and provide feedback according to the emotions. For example, when the owner receives the analysis results of the generation AI, the data analysis unit analyzes the owner's facial expressions and voice to estimate the emotions in real time. For example, if the owner is feeling anxious, it can provide reassuring feedback. The data analysis unit can also use the emotion estimation function to record the owner's emotional state and adjust the content of the analysis result feedback. For example, it can prioritize feedback for positive emotional states. The data analysis unit can also personalize the analysis result feedback based on the owner's emotional data. For example, if the owner's emotions are unstable, it can provide detailed explanations or additional support. This makes it possible to provide feedback according to the owner's emotions.

[0079] The disease prediction unit can perform customization taking into account personality or behavioral patterns. For example, the disease prediction unit collects personality data of pet dogs and inputs it into the generation AI. For example, it can propose a plan to increase exercise for active dogs, and provide a relaxing environment for calm dogs. The disease prediction unit can also analyze the behavioral patterns of pet dogs, and the generation AI can propose an optimal health management plan. For example, it can propose a plan to increase nighttime activity for nocturnal dogs. The disease prediction unit can also customize health management plans based on the personality and behavioral patterns of pet dogs. For example, it can propose a plan to encourage interaction with other dogs for sociable dogs. This allows customization that takes into account the individual personality and behavioral patterns of pet dogs.

[0080] The disease prediction unit can reflect the effectiveness of the health management plan and perform optimization. For example, the disease prediction unit collects data on a pet dog's past health management plans and inputs it into the generation AI. For example, it can analyze the effectiveness of past exercise plans and diet plans. The disease prediction unit can also develop an algorithm that allows the generation AI to propose the optimal plan based on the effectiveness of past health management plans. For example, it can prioritize the proposal of plans that have been effective in the past. The disease prediction unit can also integrate data from a pet dog's past health management plans, allowing the generation AI to perform a comprehensive evaluation. For example, it can propose the optimal amount of exercise and diet content based on the effectiveness of past plans. This allows the generation AI to reflect the effectiveness of past health management plans and perform optimization.

[0081] The disease prediction unit can use the emotion estimation function to propose a health management plan based on the owner's emotional state, thereby reducing stress. For example, the disease prediction unit analyzes the owner's emotional state, and the generation AI proposes an optimal health management plan. For example, if the owner is feeling stressed, it can suggest relaxing activities. The disease prediction unit can also use the emotion estimation function to collect emotional data from the owner and customize a health management plan. For example, it can prioritize the proposal of a plan based on a positive emotional state. The disease prediction unit can also provide feedback based on the owner's emotional state and propose a health management plan to reduce stress. For example, if the owner's emotions are unstable, it can provide a relaxing environment. This makes it possible to propose a health management plan based on the owner's emotional state, thereby reducing stress.

[0082] The disease prediction unit can comprehensively manage the health of the owner and their dog, including the owner's health management plan. For example, the disease prediction unit collects the owner's health data and inputs it into the generation AI. For example, it can record the owner's exercise volume and dietary habits and integrate them with the dog's health management plan. The disease prediction unit can also integrate the owner's and dog's health data, allowing the generation AI to propose a comprehensive health management plan. For example, it can propose a plan that allows the owner and dog to exercise together. The disease prediction unit can also customize the dog's health management plan, taking into account the owner's health management plan. For example, if the owner is on a diet, it can also adjust the dog's diet. This allows for comprehensive management of the health of the owner and dog.

[0083] The disease prediction unit can also integrate the health management plans of other pets, allowing for the management of multiple pets at once. For example, the disease prediction unit collects health data of other pets and inputs it into the generation AI. For example, it can record the physical condition data of cats and birds and integrate it with the health management plan of a pet dog. The disease prediction unit can also integrate the health data of multiple pets, allowing the generation AI to propose a comprehensive health management plan. For example, it can propose a plan that allows a dog and a cat to exercise together. The disease prediction unit can also customize the health management plan of a pet dog, taking into account the health management plans of other pets. For example, if a cat requires a specific diet, the diet of the pet dog can also be adjusted. This allows for the management of multiple pets at once.

[0084] The disease prediction unit can use the emotion estimation function to monitor the owner's emotions when implementing the health management plan and make suggestions to elicit positive emotions. For example, the disease prediction unit can analyze facial expressions and voice to monitor emotions in real time when the owner is implementing the health management plan. For example, it can preferentially suggest activities that evoke positive emotions in the owner. The disease prediction unit can also use the emotion estimation function to collect emotional data on the owner and customize the health management plan. For example, it can preferentially suggest plans for a positive emotional state. The disease prediction unit can also provide feedback according to the owner's emotional state and suggest health management plans to elicit positive emotions. For example, if the owner's emotions are unstable, it can provide a relaxing environment. This makes it possible to make suggestions to elicit positive emotions when the owner is implementing the health management plan.

[0085] The disease prediction unit can make suggestions for improving the dog's living environment in order to extend the dog's lifespan. For example, the disease prediction unit collects data on the dog's living environment and inputs it into the generation AI. For example, it can record the temperature and humidity of the room where the dog spends time and suggest the optimal environment. The disease prediction unit can also analyze the dog's living environment and make suggestions for improvements. For example, it can suggest a temperature and humidity that will keep the dog comfortable. The disease prediction unit can also make comprehensive suggestions for improvements based on the dog's living environment data. For example, it can make suggestions to provide an environment where the dog can relax. In this way, it is possible to extend the dog's lifespan by making suggestions for improving the dog's living environment.

[0086] The disease prediction unit can make suggestions to promote social interactions for the pet dog in order to extend the pet dog's lifespan. For example, the disease prediction unit collects social interaction data for the pet dog and inputs it into the generating AI. For example, it can record how much the pet dog interacts with other dogs and people. The disease prediction unit can also analyze the pet dog's social interactions and make promotion suggestions. For example, it can make suggestions to increase opportunities for the pet dog to play with other dogs. The disease prediction unit can also make comprehensive interaction promotion suggestions based on the social interaction data for the pet dog. For example, it can suggest events and places where the pet dog can interact with other dogs and people. This makes it possible to extend the lifespan by making suggestions to promote social interactions for the pet dog.

[0087] The disease prediction unit can use the emotion estimation function to suggest activities to strengthen the emotional bond between the owner and the dog, thereby contributing to extending the dog's lifespan. For example, the disease prediction unit can collect emotional data on the owner and the dog and input it into the generation AI. For example, it can record the time the owner and the dog spend together and their emotional state at that time. The disease prediction unit can also use the emotion estimation function to suggest activities to strengthen the emotional bond between the owner and the dog. For example, it can suggest increasing the amount of time the owner and the dog spend together. The disease prediction unit can also use the emotion estimation function to suggest activities to strengthen the emotional bond between the owner and the dog. For example, it can suggest increasing the amount of time the owner and the dog spend together. The disease prediction unit can also use the generation AI to make comprehensive suggestions to strengthen the bond based on the emotional data on the owner and the dog. For example, it can suggest providing an environment where the owner and the dog can relax together. This can contribute to extending the dog's lifespan by suggesting activities to strengthen the emotional bond between the owner and the dog.

[0088] The disease prediction unit can make suggestions for improving the owner's lifestyle habits. For example, the disease prediction unit collects lifestyle data about the owner and inputs it into the generation AI. For example, it can record the owner's amount of exercise and dietary content and make suggestions that will have a positive impact on the health of the pet dog. The disease prediction unit can also analyze the owner's lifestyle habits and make suggestions for improvement. For example, it can make suggestions that if the owner leads a healthy lifestyle, the health of the pet dog will also improve. The disease prediction unit can also make comprehensive suggestions for improvement based on the owner's lifestyle data. For example, if the owner is not getting enough exercise, it can make a suggestion for the owner to exercise together with the pet dog. This makes it possible to make suggestions for improving the owner's lifestyle habits.

[0089] The disease prediction unit can suggest travel or outdoor activities based on the dog's health condition. For example, the disease prediction unit collects health condition data of the dog and inputs it into the generation AI. For example, the disease prediction unit can record the dog's physical condition and activity level and suggest optimal travel or outdoor activities. The disease prediction unit can also analyze the dog's health condition and suggest travel or outdoor activities using the generation AI. For example, it can suggest places and activities where the dog can relax. The disease prediction unit can also make comprehensive travel or outdoor activity suggestions based on the dog's health condition data. For example, it can suggest activities and places that the dog can enjoy. This makes it possible to suggest travel or outdoor activities based on the dog's health condition.

[0090] The disease prediction unit can use the emotion estimation function to make suggestions to improve the quality of time the owner spends with their dog. For example, the disease prediction unit collects emotional data about the time the owner and their dog spend together and inputs it into the generation AI. For example, it can record the emotional state of the owner and their dog during the time they spend together and make suggestions to improve the quality. The disease prediction unit can also use the emotion estimation function to make suggestions to improve the quality of time the owner and their dog spend together. For example, it can suggest activities that the owner can enjoy together. The disease prediction unit can also make comprehensive quality improvement suggestions based on the emotional data of the owner and their dog. For example, it can make suggestions to provide an environment where the owner and their dog can relax. This makes it possible to make suggestions to improve the quality of time the owner spends with their dog.

[0091] The data analysis unit can continuously monitor data, predict changes in the dog's health, and issue advance warnings. For example, the data analysis unit can continuously monitor the dog's health data, and if the generating AI detects an abnormality, it can issue an advance warning. For example, it can notify the user if it detects an abnormality in body temperature or heart rate. The data analysis unit can also develop an algorithm that enables the generating AI to predict changes in the dog's health and issue an advance warning. For example, it can issue an alert if it detects a sudden decrease in activity level. The data analysis unit can also enable the generating AI to evaluate overall health risks based on the dog's health data and issue an advance warning. For example, it can issue an alert if the dog continues to lose its appetite. This makes it possible to predict changes in the dog's health and issue an advance warning.

[0092] The data analysis unit can continuously monitor data and provide real-time advice in response to changes in the dog's health condition. For example, the data analysis unit can continuously monitor the dog's health data, and the generation AI can provide advice in real time. For example, if the dog's activity level decreases, advice can be given to encourage exercise. The data analysis unit can also develop an algorithm that allows the generation AI to provide advice in real time in response to changes in the dog's health condition. For example, if the dog has a persistent loss of appetite, advice can be given to adjust the dog's diet. The data analysis unit can also allow the generation AI to provide comprehensive advice in real time based on the dog's health data. For example, if the dog's body temperature rises, cooling methods can be suggested. This allows real-time advice to be provided in response to changes in the dog's health condition.

[0093] The data analysis unit can use the emotion estimation function to monitor the owner's emotional state and suggest relaxation methods when stress increases. For example, the data analysis unit can continuously monitor the owner's emotional state and suggest relaxation methods when stress increases. For example, it can suggest activities that will help the owner relax when the owner is feeling stressed. The data analysis unit can also use the emotion estimation function to collect the owner's emotional data and develop an algorithm that suggests relaxation methods when stress increases. For example, it can provide an environment where the owner can relax. The data analysis unit can also use the generation AI to suggest comprehensive relaxation methods based on the owner's emotional state. For example, it can suggest music or aromas that will help the owner relax. This makes it possible to monitor the owner's emotional state and suggest relaxation methods when stress increases.

[0094] The data analysis unit can simultaneously monitor the owner's health data and provide comprehensive health management. For example, the data analysis unit continuously monitors the owner's health data, and the generation AI integrates it with the dog's health data to provide comprehensive health management. For example, the data analysis unit can record the owner's exercise volume and dietary habits and integrate them into the dog's health management plan. The data analysis unit can also develop algorithms that allow the generation AI to analyze the owner's health data and reflect this in the dog's health management plan. For example, if the owner is not getting enough exercise, the generation AI can suggest an exercise plan for the owner and the dog. The data analysis unit can also integrate the owner's health data and the dog's health data, allowing the generation AI to propose a comprehensive health management plan. For example, the generation AI can propose a plan for the owner and dog to live a healthy lifestyle together. This allows the owner's health data to be simultaneously monitored and comprehensive health management to be provided.

[0095] The data analysis unit can simultaneously monitor the data of other pets and manage the health of multiple pets at once. For example, the data analysis unit continuously monitors the health data of other pets, and the generation AI integrates it with the dog's health data to provide comprehensive health management. For example, the data analysis unit can record the physical condition data of cats and birds and integrate it into the dog's health management plan. The data analysis unit can also develop algorithms that allow the generation AI to analyze the health data of other pets and reflect it in the dog's health management plan. For example, if the cat requires a specific diet, the dog's diet can also be adjusted. The data analysis unit can also integrate the health data of other pets with the dog's health data, allowing the generation AI to propose a comprehensive health management plan. For example, it can propose a plan for multiple pets to live a healthy life together. This allows the data of other pets to be monitored simultaneously and the health of multiple pets to be managed at once.

[0096] The data analysis unit can use the emotion estimation function to monitor the owner's emotions when receiving feedback from the generative AI and provide feedback that elicits positive emotions. For example, the data analysis unit can analyze facial expressions and voice to monitor emotions in real time when the owner receives feedback from the generative AI. For example, it can prioritize providing feedback that the owner feels positive about. The data analysis unit can also use the emotion estimation function to collect emotional data from the owner and develop an algorithm that adjusts the content of the feedback. For example, it can prioritize providing feedback that indicates a positive emotional state. The data analysis unit can also provide feedback according to the owner's emotional state and suggest feedback that elicits positive emotions. For example, if the owner's emotions are unstable, it can provide reassuring feedback. This makes it possible to provide feedback that elicits positive emotions when the owner receives feedback from the generative AI.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] The health management system can also collect sleep data from pet dogs and analyze their sleep quality. For example, it can monitor pet dogs' movements while they sleep and record the depth of their sleep and the frequency of sleep interruptions. It can also analyze pet dogs' sleep patterns and suggest appropriate sleeping environments. For example, providing a quiet environment and appropriate temperature can improve pet dogs' sleep quality. This allows for a comprehensive approach to pet dog health management that takes sleep quality into account.

[0099] The data collection unit can also collect olfactory data from pet dogs and analyze the impact of changes in olfactory sense on their health. For example, it can record whether a pet dog becomes sensitive to a particular smell. The olfactory data can also be used to analyze changes in a pet dog's appetite and behavior. For example, it can investigate whether a particular smell stimulates a pet dog's appetite. This allows the use of olfactory data to gain a more detailed understanding of a pet dog's health condition.

[0100] The data analysis unit can also analyze social behavior data of pet dogs to evaluate the impact of social interactions on their health. For example, it can record how often pet dogs interact with other dogs and people and analyze the relationship between the frequency of social interactions and their health status. Furthermore, if social interactions are low, it can make suggestions to promote social interactions. For example, it can suggest increasing opportunities for pet dogs to visit dog parks and play with other dogs. This allows for comprehensive management of pet dogs' social health.

[0101] The disease prediction unit can also analyze a dog's genetic information and propose a health management plan that takes genetic risk factors into account. For example, if a specific gene mutation is present, the system can evaluate the disease risk associated with that mutation and propose preventive measures. It can also customize a dog's diet and exercise plan based on the genetic information. For example, for a dog with a genetically high risk of obesity, it can suggest a low-calorie diet and moderate exercise. This makes it possible to provide an individualized health management plan that takes genetic risk factors into account.

[0102] The data collection unit can collect emotional data from pet dogs and analyze the impact of emotional changes on their health. For example, it can analyze a pet dog's facial expressions and tone of voice to estimate its emotional state. It can also evaluate the pet dog's stress level and happiness based on the emotional data. For example, if stress levels are high, it can suggest providing a relaxing environment for the pet dog. This allows for health management that takes the pet dog's emotional state into consideration.

[0103] The data collection unit can record detailed dietary data for your dog and analyze its nutritional balance. For example, it can record the ingredients your dog consumes and the amount of each ingredient to evaluate the nutrient balance. It can also analyze changes in your dog's weight and health based on the dietary data. For example, it can investigate whether certain ingredients affect your dog's weight gain. This allows for more detailed dietary management of your dog.

[0104] The data collection unit can collect emotional data from pet dogs and analyze the impact of emotional changes on their health. For example, it can analyze a pet dog's facial expressions and tone of voice to estimate its emotional state. It can also evaluate the pet dog's stress level and happiness based on the emotional data. For example, if stress levels are high, it can suggest providing a relaxing environment for the pet dog. This allows for health management that takes the pet dog's emotional state into consideration.

[0105] The data collection unit can record detailed exercise data of pet dogs and analyze the quality of the exercise. For example, it can record the dog's heart rate and activity level while exercising and evaluate the effectiveness of the exercise. It can also analyze changes in the dog's physical strength and endurance based on the exercise data. For example, it can investigate how much a particular exercise contributes to improving the dog's physical strength. This allows for more detailed exercise management of pet dogs.

[0106] The data collection unit can collect emotional data from pet dogs and analyze the impact of emotional changes on their health. For example, it can analyze a pet dog's facial expressions and tone of voice to estimate its emotional state. It can also evaluate the pet dog's stress level and happiness based on the emotional data. For example, if stress levels are high, it can suggest providing a relaxing environment for the pet dog. This allows for health management that takes the pet dog's emotional state into consideration.

[0107] The data collection unit can collect emotional data from pet dogs and analyze the impact of emotional changes on their health. For example, it can analyze a pet dog's facial expressions and tone of voice to estimate its emotional state. It can also evaluate the pet dog's stress level and happiness based on the emotional data. For example, if stress levels are high, it can suggest providing a relaxing environment for the pet dog. This allows for health management that takes the pet dog's emotional state into consideration.

[0108] The processing flow of the second embodiment will be briefly explained below.

[0109] Step 1: The data collection unit collects data on the dog's daily physical condition, personality, behavior, diet, etc. For example, sensors and smart devices are used to record the dog's body temperature, heart rate, activity level, dietary content, and excretion status. Owners can also manually input any changes in personality or behavior they observe. Step 2: The data analysis unit analyzes the collected data. For example, the data analysis unit may analyze the data using statistical analysis or machine learning algorithms. The data analysis unit may also compare the data with past data or data from other dogs to identify abnormal patterns or risk factors. Step 3: The disease prediction unit predicts diseases that the pet dog is likely to suffer from based on the data analyzed by the data analysis unit. For example, the disease prediction unit identifies risk factors and warns of an increased risk of a specific disease.

[0110] 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.

[0111] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0112] 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.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] 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.

[0128] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0129] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0139] 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.

[0140] 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.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] 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.

[0143] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] In the robot 414, 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 robot 414 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.

[0155] 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.

[0156] 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.

[0157] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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. [Explanation of symbols]

[0177] 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 data collection department collects data on the pet dog's daily physical condition, personality, behavior, food, etc. a data analysis unit that analyzes the data collected by the data collection unit; a disease prediction unit that predicts diseases that pet dogs are likely to suffer from based on the data analyzed by the data analysis unit. A system characterized by:

2. The data collection unit Body temperature, heart rate, activity level, dietary content, and excretion status are recorded using sensors or smart devices.

2. The system of claim 1.

3. The data analysis unit Compare with historical data or data from other dogs to identify any unusual patterns or risk factors 2. The system of claim 1.

4. The disease prediction unit Recommend nutritious meals, exercise regimens, and health check schedules 2. The system of claim 1.

5. The data collection unit Collecting emotional data and analyzing the impact of stress levels on the pet dog 2. The system of claim 1.

6. The data collection unit When collecting behavioral data, environmental data will be recorded simultaneously to analyze the impact of environmental factors on health.

2. The system of claim 1.

7. The data collection unit Emotions are estimated from facial expressions or tone of voice, and emotional data is utilized for health management.

2. The system of claim 1.

8. The data collection unit A wearable device can be used to collect the data even when the pet dog is out and about.

2. The system of claim 1.

Images