system

Abstract

To provide a system that efficiently collects and analyzes behavior patterns and health data on a dog, and provides appropriate information and improvement points.SOLUTION: A system comprises a collection unit, an analysis unit, a provision unit, and a consultation unit. The collection unit collects behavior patterns and health data on a dog using a device or application. The analysis unit analyzes the data collected by the collection unit. The provision unit provides information and improvement points on the basis of the analysis results obtained by the analysis unit. The consultation unit supports online consultations with a veterinarian.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent Document 1 discloses a persona chatbot control method executed by at least one processor, the method comprising the steps of: receiving a user utterance; adding the user utterance to a prompt including an instruction associated with a description of a character of the chatbot; encoding the prompt; and inputting the encoded prompt into a language model to generate a chatbot utterance responding to the user utterance. [Prior Art Literature] [Patent Documents]

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

[0004] In the conventional technology, there has been a problem that it is difficult to efficiently collect and analyze dogs' behavior patterns and health data, and provide appropriate information and improvement points.

[0005] An object of the system according to an embodiment is to efficiently collect and analyze dogs' behavior patterns and health data, and provide appropriate information and improvement points. [Means for Solving the Problem]

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a data provision unit, and a consultation unit. The data collection unit collects dog behavior patterns and health data using a device or app. The analysis unit analyzes the data collected by the data collection unit. The data provision unit provides information and suggestions for improvement based on the analysis results obtained by the analysis unit. The consultation unit supports online consultations with veterinarians. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently collect and analyze dog behavior patterns and health data, and provide appropriate information and suggestions for improvement. [Brief explanation of the drawing]

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

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

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

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

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

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, may be only B, or may be a combination of A and B. In addition, in the present specification, when three or more matters are expressed by connecting them with "and / or", the same concept as that for "A and / or B" applies.

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The dog health management system according to an embodiment of the present invention is a system that collects dog behavior patterns and health data using a device or app and analyzes it using generative AI. This system provides information and suggestions for improvement regarding the dog's health status and behavior. It also provides a service that allows owners to consult with a veterinarian online. For example, data such as the dog's activity level, sleep patterns, and food intake are collected using a device or app. For example, a sensor attached to the dog's collar measures the activity level, and the app records the food intake. This data is input into the generative AI. Next, the generative AI analyzes the collected data. The generative AI analyzes the dog's activity level, sleep patterns, food intake, etc., and provides information regarding the dog's health status and behavior. For example, the generative AI can detect a decrease in the dog's activity level and suggest the possibility of insufficient exercise. Furthermore, the generative AI provides suggestions for improvement based on the analysis results. For example, it can suggest an appropriate amount of exercise for a dog that is not getting enough exercise. It can also suggest improvements to the diet if the food intake is inappropriate. In addition, owners can consult with a veterinarian online through the app. The generating AI provides general advice to owners regarding their questions and their dogs' symptoms, and supports scheduling in-person consultations with veterinarians and handling emergencies when necessary. For example, if an owner asks about their dog's loss of appetite, the generating AI will provide general advice and recommend a consultation with a veterinarian if necessary. This system allows owners to monitor their dog's health in real time and provide appropriate care. Online consultations with veterinarians enable quick responses, making dog health management more efficient. In short, the dog health management system allows owners to monitor their dog's health in real time and provide appropriate care.

[0029] The dog health management system according to this embodiment comprises a data collection unit, an analysis unit, a data provision unit, and a consultation unit. The data collection unit collects dog behavior patterns and health data using devices and apps. For example, the data collection unit can use a sensor attached to the dog's collar to measure its activity level, and an app to record its food intake. The data collection unit can measure the dog's activity level using a sensor attached to the dog's collar. The sensor detects the dog's movement and records steps and exercise time. The data collection unit can also record the dog's food intake using an app. The app records the content and amount of food entered by the owner and stores it in a database. The analysis unit analyzes the data collected by the data collection unit using a generative AI. For example, the analysis unit analyzes the dog's activity level, sleep patterns, food intake, etc., and provides information about the dog's health status and behavior. For example, the analysis unit can use a generative AI to detect a decrease in the dog's activity level and suggest the possibility of insufficient exercise. The analysis unit can also use a generative AI to analyze the dog's sleep patterns and evaluate the quality of sleep. The provision unit provides information and suggestions for improvement based on the analysis results obtained by the analysis unit. For example, the provision unit can suggest an appropriate amount of exercise for a dog that is not getting enough exercise. For example, the provision unit uses generative AI to suggest an appropriate amount of exercise based on the dog's health condition. The provision unit can also suggest improvements to the diet if the amount of food intake is inappropriate. For example, the provision unit uses generative AI to analyze the amount of food intake of a dog and suggests adjusting the nutritional balance or changing the type of food. The consultation unit provides general advice to the owner's questions and the dog's symptoms, and supports scheduling face-to-face consultations with veterinarians and handling emergencies if necessary. For example, the consultation unit uses generative AI to provide general advice to the owner's questions. For example, if the owner asks about the dog's poor appetite, the generative AI will provide general advice and, if necessary, recommend consulting with a veterinarian. As a result, the dog health management system according to this embodiment can grasp the dog's health condition in real time and provide appropriate care. Some or all of the above processing in the consultation unit may be performed using AI, for example, or without using AI.For example, the consultation department can input questions from pet owners into a generating AI, which can then output general advice.

[0030] The data collection unit uses devices and apps to collect dog behavior patterns and health data. Specifically, a sensor attached to the dog's collar measures activity levels, and the app records food intake. The collar sensor has a built-in accelerometer and gyroscope, allowing it to detect the dog's movements in detail. This makes it possible to accurately record steps, exercise time, and even exercise intensity. For example, it can collect data such as how far the dog walked and how fast it ran during a walk. The sensor can also record the dog's rest time and sleep patterns, allowing for an overall understanding of the dog's daily activities. The app records the content and amount of food entered by the owner and stores it in a database. The app has a function to record the type of food (dry food, wet food, homemade food, etc.) and intake in detail, allowing for an accurate understanding of the dog's nutritional intake. Furthermore, the app also records the time and frequency of meals, providing data for analyzing the dog's eating patterns. As a result, the data collection unit can comprehensively collect dog behavior patterns and health data and understand the situation in real time. The collected data is sent to a cloud server, making it accessible to the analysis and provisioning departments. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit uses generative AI to analyze data collected by the collection unit. Specifically, it analyzes the dog's activity level, sleep patterns, and food intake to provide information about the dog's health and behavior. Based on the collected data, the generative AI analyzes the dog's behavior patterns and health in detail. For example, it can detect a decrease in the dog's activity level and suggest the possibility of insufficient exercise. The generative AI can detect abnormal patterns by comparing them with past data, enabling early detection of problems. The generative AI can also analyze the dog's sleep patterns and evaluate the quality of sleep. For example, if a dog wakes up frequently at night, it may suggest stress or a health problem. Furthermore, the generative AI can analyze food intake and suggest adjustments to nutritional balance or changes in the type of food. For example, if a dog is consuming an excessive amount of a particular nutrient, it can suggest reducing that intake. In this way, the analysis unit can quickly and accurately analyze the collected data and provide information about the dog's health and behavior. In addition, the analysis unit can utilize past data and statistical information to perform long-term health management and trend analysis. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term health management, improving the reliability and safety of the entire system.

[0032] The service provider provides information and suggestions for improvement based on the analysis results obtained by the analysis unit. Specifically, it can suggest appropriate exercise levels for dogs that are not getting enough exercise. Using generative AI, it suggests appropriate exercise levels based on the dog's health condition. For example, it can specifically suggest the amount and type of exercise each day, depending on the dog's age, weight, and activity level. The service provider can also suggest improvements to the dog's diet if its food intake is inappropriate. Using generative AI, it analyzes the dog's food intake and suggests adjustments to the nutritional balance or changes to the type of food. For example, if a dog is consuming an excessive amount of a particular nutrient, it can suggest reducing that intake. Also, if a dog is deficient in a particular nutrient, it can suggest a diet to supplement that nutrient. Furthermore, the service provider can also provide care plans tailored to the dog's health condition. For example, if a dog is elderly, it can suggest supplements to maintain joint health and specific exercises. In this way, the service provider can provide specific and practical advice based on the analysis results, supporting the dog's health management. Furthermore, the service provider can also provide specific procedures and schedules to make it easier for owners to implement the suggested advice. This allows the service provider to offer support to pet owners in effectively managing their dogs' health.

[0033] The consultation department provides general advice to pet owners regarding their questions and their dogs' symptoms, and supports them in scheduling in-person consultations with veterinarians and handling emergencies when necessary. Specifically, it uses generative AI to provide general advice to pet owners' questions. For example, if a pet owner asks about their dog's loss of appetite, the generative AI will provide general advice and, if necessary, recommend a consultation with a veterinarian. Based on past data and expertise, the generative AI can generate appropriate answers to pet owners' questions. For example, if a dog's loss of appetite persists, it can suggest dietary changes or ways to reduce stress. The generative AI can also assess the urgency of the dog's symptoms based on the information entered by the pet owner and recommend emergency action if necessary. For example, if a dog suddenly becomes lethargic, it can recommend consulting a veterinarian immediately. Furthermore, the consultation department also provides support for pet owners in scheduling in-person consultations with veterinarians. For example, the generative AI can check the pet owner's schedule and suggest the best appointment time based on the veterinarian's availability. In this way, the consultation department can support pet owners in responding quickly and appropriately to their dog's health problems. Furthermore, the consultation department can collect feedback from pet owners and continuously improve the accuracy and effectiveness of the advice generated by the AI. This allows the consultation department to provide reliable advice to pet owners and support the health management of their dogs.

[0034] The data collection unit measures the dog's activity level using a sensor attached to the dog's collar, and the app can record the dog's food intake. For example, the data collection unit measures the dog's activity level using a sensor attached to the dog's collar. The sensor detects the dog's movement and records the number of steps and exercise time. The data collection unit can also record the dog's food intake using the app. The app records the content and amount of food entered by the owner and stores it in a database. This allows for accurate recording of the dog's activity level and food intake. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from the sensor attached to the dog's collar into a generating AI, which can then analyze the data.

[0035] The analysis unit can analyze a dog's activity level, sleep patterns, and food intake, and provide information about the dog's health and behavior. For example, the analysis unit uses a generative AI to analyze a dog's activity level, sleep patterns, and food intake. The generative AI can detect a decrease in a dog's activity level and suggest the possibility of insufficient exercise. The generative AI can also analyze a dog's sleep patterns and evaluate the quality of sleep. Furthermore, the generative AI can analyze a dog's food intake and suggest adjustments to nutritional balance or changes in the type of food. This allows owners to provide appropriate care by providing information about the dog's health and behavior. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data acquired from the collection unit into the generative AI, which can then analyze the data.

[0036] The service provider can suggest an appropriate amount of exercise for dogs that are not getting enough exercise, based on the analysis results. The service provider can suggest an appropriate amount of exercise for dogs that are not getting enough exercise, for example, by using a generative AI. The generative AI can analyze the dog's activity level data and suggest an appropriate amount of exercise for dogs that are not getting enough exercise. For example, the generative AI can suggest an appropriate amount of exercise based on the dog's age and breed. The generative AI can also adjust the amount of exercise based on the dog's health condition. In this way, by suggesting an appropriate amount of exercise for dogs that are not getting enough exercise, the health of the dogs can be maintained. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input data obtained from the analysis unit into the generative AI, and the generative AI can suggest an appropriate amount of exercise.

[0037] The supply unit can suggest improvements to the diet if the amount of food intake is inappropriate. For example, the supply unit can use a generating AI to suggest improvements to the diet if the amount of food intake is inappropriate. The generating AI can analyze the dog's diet data and suggest adjustments to the nutritional balance or changes in the type of food. For example, the generating AI can suggest appropriate improvements to the diet based on the dog's age and health condition. The generating AI can also analyze the amount of food intake the dog has and make suggestions to prevent overeating or undereating. In this way, the dog's health can be maintained by suggesting improvements to the diet when the amount of food intake is inappropriate. Some or all of the above processing in the supply unit may be performed using AI, for example, or without AI. For example, the supply unit can input data obtained from the analysis unit into the generating AI, and the generating AI can suggest improvements to the diet.

[0038] The consultation department can provide general advice to pet owners regarding their questions and their dogs' symptoms, and can also support scheduling in-person consultations with veterinarians and handling emergencies if necessary. For example, the consultation department can use a generative AI to provide general advice to pet owners' questions. The generative AI can analyze the pet owner's questions and provide general advice. For example, if a pet owner asks about their dog's loss of appetite, the generative AI will provide general advice and, if necessary, recommend a consultation with a veterinarian. The consultation department can also support scheduling in-person consultations with veterinarians and handling emergencies if necessary. For example, the generative AI can schedule an in-person consultation with a veterinarian based on the pet owner's questions and the dog's symptoms. This allows pet owners to quickly consult about their dog's health and, if necessary, arrange in-person consultations with a veterinarian or handle emergencies. Some or all of the above-described processes in the consultation department may be performed using AI, or not. For example, the consultation department can input the pet owner's questions into a generative AI, which can then output general advice.

[0039] The data collection unit can estimate the dog's emotions and adjust the frequency of data collection based on the estimated emotions. The data collection unit estimates the dog's emotions, for example, using generative AI. Generative AI can estimate the dog's emotions by analyzing the dog's behavioral and physiological data. For example, generative AI can analyze changes in the dog's heart rate and activity level to estimate its stress and relaxation state. Generative AI can also analyze the dog's facial expressions and tone of voice to estimate its emotions. The data collection unit adjusts the frequency of data collection based on the estimated emotions of the dog. For example, if the dog is stressed, the frequency of data collection is reduced to lessen the dog's burden. If the dog is relaxed, the frequency of data collection is increased to collect more detailed data. Furthermore, if the dog is excited, the frequency of data collection is temporarily increased to record changes in behavioral patterns in detail. This allows for the reduction of the dog's burden and the collection of detailed data by adjusting the frequency of data collection according to the dog's emotions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI. For example, the data collection unit inputs dog emotional data into a generating AI, which can then adjust the frequency of data collection.

[0040] The data collection unit can analyze a dog's past behavioral patterns and select the optimal timing for data collection. For example, the data collection unit uses a generative AI to analyze the dog's past behavioral patterns. The generative AI analyzes past data and understands the dog's behavioral patterns. For example, if the generative AI has observed that a dog was active during a specific time period in the past, it will concentrate data collection during that time. Similarly, if the generative AI has observed that a dog was resting during a specific time period in the past, it can refrain from collecting data during that time. Furthermore, the generative AI can analyze the dog's past behavioral patterns and intensify data collection on weekends or during specific events. This allows for data collection at the optimal time by analyzing the dog's past behavioral patterns. Some or all of the above-described processes in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can input past behavioral data into the generative AI, which can then select the optimal timing for data collection.

[0041] The data collection unit can filter data based on the dog's current health status and activity level during data collection. For example, the data collection unit can use a generative AI to filter data based on the dog's current health status and activity level. The generative AI can analyze the dog's health and activity data and collect only the necessary data. For example, if the dog is in good health, the generative AI will perform normal data collection. If the dog is in poor health, the generative AI can refrain from collecting data and collect only the necessary data. Furthermore, if the dog's activity level is high, the generative AI can collect detailed data, and if the activity level is low, it can collect only basic data. This allows for the collection of only the necessary data by filtering it according to the dog's health status and activity level. Some or all of the above processing in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can input the dog's health data into the generative AI, which can then filter the data.

[0042] The data collection unit can estimate the dog's emotions and determine the priority of data to collect based on the estimated emotions. The data collection unit estimates the dog's emotions, for example, using a generative AI. The generative AI can estimate the dog's emotions by analyzing the dog's behavioral and physiological data. For example, the generative AI can analyze changes in the dog's heart rate and activity level to estimate its stress and relaxation state. The generative AI can also analyze the dog's facial expressions and tone of voice to estimate its emotions. The data collection unit determines the priority of data to collect based on the estimated emotions of the dog. For example, if the dog is stressed, stress-related data will be collected preferentially. If the dog is relaxed, data on activity level and sleep patterns will be collected preferentially. Furthermore, if the dog is excited, data on changes in behavioral patterns will be collected preferentially. In this way, important data can be collected preferentially by determining the priority of data to collect according to the dog's emotions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input dog emotion data into a generative AI, which can then determine the priority of the data.

[0043] The data collection unit can prioritize the collection of highly relevant data by considering the dog's geographical location during data collection. For example, the data collection unit uses a generative AI to prioritize the collection of highly relevant data by considering the dog's geographical location during data collection. The generative AI can analyze the dog's location information and select highly relevant data. For example, if the dog is in a park, the generative AI will prioritize the collection of data on activity level and exercise patterns. If the dog is at home, the generative AI will prioritize the collection of data on sleep patterns and food intake. Furthermore, if the dog is at a veterinary hospital, the generative AI will prioritize the collection of data on its health status. In this way, highly relevant data can be prioritized by considering the dog's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the dog's location information into the generative AI, which can then select highly relevant data.

[0044] The data collection unit can analyze the social media activity of dog owners and collect relevant data during data collection. For example, the data collection unit can use a generative AI to analyze the social media activity of dog owners and collect relevant data during data collection. The generative AI can analyze the owner's social media posts and select relevant data. For example, if an owner posts about their dog's health on social media, the generative AI can collect data based on that post. Furthermore, if an owner posts about their dog's activities on social media, the generative AI can collect data based on that post. In addition, if an owner posts about their dog's diet on social media, the generative AI can collect data based on that post. This allows for the efficient collection of relevant data by analyzing the owner's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can input the owner's social media posts into the generative AI, which can then select relevant data.

[0045] The analysis unit can estimate the dog's emotions and adjust the analysis algorithm based on the estimated emotions. The analysis unit estimates the dog's emotions using, for example, a generative AI. The generative AI can estimate the dog's emotions by analyzing the dog's behavioral and physiological data. For example, the generative AI can estimate the dog's stress and relaxation states by analyzing changes in the dog's heart rate and activity level. The generative AI can also estimate emotions by analyzing the dog's facial expressions and tone of voice. The analysis unit adjusts the analysis algorithm based on the estimated emotions of the dog. For example, if the dog is stressed, the analysis will focus on stress-related data. If the dog is relaxed, the analysis will focus on activity level and sleep pattern data. Furthermore, if the dog is excited, the analysis will focus on data related to changes in behavioral patterns. By adjusting the analysis algorithm according to the dog's emotions, more accurate analysis results can be obtained. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input dog emotion data into the generative AI, which can then adjust the analysis algorithm.

[0046] The analysis unit can adjust the level of detail of the analysis based on the importance of the dog's health data during the analysis. For example, the analysis unit uses a generating AI to adjust the level of detail of the analysis based on the importance of the dog's health data during the analysis. The generating AI can analyze the dog's health data and adjust the level of detail of the analysis according to its importance. For example, if the dog's health data is important, the generating AI can perform a detailed analysis and provide specific areas for improvement. Also, if the dog's health data is general, the generating AI can perform a basic analysis and provide general advice. Furthermore, if the dog's health data is urgent, the generating AI can quickly perform a detailed analysis and propose emergency measures. In this way, appropriate analysis results can be provided by adjusting the level of detail of the analysis according to the importance of the dog's health data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the dog's health data into the generating AI, and the generating AI can adjust the level of detail of the analysis.

[0047] The analysis unit can apply different analysis algorithms depending on the dog's category during analysis. For example, the analysis unit uses a generative AI to apply different analysis algorithms depending on the dog's category (age, breed, etc.) during analysis. The generative AI can select an appropriate analysis algorithm based on the dog's category. For example, in the case of puppies, the generative AI will focus on growth-related data during analysis. In the case of senior dogs, the generative AI can focus on health-related data during analysis. Furthermore, in the case of specific breeds, the generative AI can consider breed-specific health risks during analysis. By applying analysis algorithms according to the dog's category, more appropriate analysis results can be provided. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input dog category data into the generative AI, which can then select an appropriate analysis algorithm.

[0048] The analysis unit can estimate the dog's emotions and adjust the display method of the analysis results based on the estimated emotions. The analysis unit estimates the dog's emotions, for example, using a generative AI. The generative AI can estimate the dog's emotions by analyzing the dog's behavioral and physiological data. For example, the generative AI can analyze changes in the dog's heart rate and activity level to estimate its stress and relaxation state. The generative AI can also analyze the dog's facial expressions and tone of voice to estimate its emotions. The analysis unit adjusts the display method of the analysis results based on the estimated emotions of the dog. For example, if the dog is stressed, advice for stress reduction will be highlighted. If the dog is relaxed, data on activity level and sleep patterns can be displayed in detail. Furthermore, if the dog is excited, data on changes in behavioral patterns can be highlighted. In this way, by adjusting the display method of the analysis results according to the dog's emotions, information that is easy for the owner to understand can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input dog emotional data into a generating AI, which can then adjust how the analysis results are displayed.

[0049] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit uses a generative AI to determine the priority of analysis based on the data collection timing during analysis. The generative AI can analyze the data collection timing and determine the priority. For example, the generative AI can prioritize the analysis of the latest data to provide real-time information. The generative AI can also analyze current data while referring to past data. Furthermore, the generative AI can prioritize the analysis of data from specific events and evaluate their impact. This allows for the provision of real-time information by determining the priority of analysis based on the data collection timing. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing to the generative AI, and the generative AI can determine the priority of analysis.

[0050] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The analysis unit can adjust the order of analysis based on the relevance of the data during analysis, for example, by using a generative AI. The generative AI can analyze the relevance of the data and adjust the order of analysis. For example, the generative AI can prioritize the analysis of highly relevant data and provide detailed information. The generative AI can also postpone the analysis of less relevant data and analyze important data first. Furthermore, the generative AI can dynamically adjust the order of analysis based on the relevance of the data. This allows for the prioritization of important data by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into the generative AI, and the generative AI can adjust the order of analysis.

[0051] The information provider can estimate the dog's emotions and adjust the way the information is presented based on the estimated emotions. For example, the information provider can use a generative AI to estimate the dog's emotions. The generative AI can analyze the dog's behavioral and physiological data to estimate the dog's emotions. For example, the generative AI can analyze changes in the dog's heart rate and activity level to estimate its stress and relaxation state. The generative AI can also analyze the dog's facial expressions and tone of voice to estimate its emotions. The information provider adjusts the way the information is presented based on the estimated emotions of the dog. For example, if the dog is stressed, it can emphasize and provide advice for stress reduction. If the dog is relaxed, it can provide detailed data on activity level and sleep patterns. Furthermore, if the dog is excited, it can emphasize and provide data on changes in behavioral patterns. In this way, by adjusting the way the information is presented according to the dog's emotions, the information can be provided in a way that is easy for the owner to understand. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input dog emotion data into the generative AI, which can then adjust the way the information is presented.

[0052] The data delivery unit can adjust the level of detail of the information provided based on the importance of the analysis results at the time of delivery. For example, the data delivery unit can use a generating AI to adjust the level of detail of the information provided based on the importance of the analysis results at the time of delivery. The generating AI can analyze the importance of the analysis results and adjust the level of detail of the information. For example, the generating AI can provide detailed information for important analysis results. The generating AI can also provide basic information for general analysis results. Furthermore, the generating AI can quickly provide detailed information for urgent analysis results. In this way, appropriate information can be provided by adjusting the level of detail of the information according to the importance of the analysis results. Some or all of the above processing in the data delivery unit may be performed using AI, for example, or without using AI. For example, the data delivery unit can input the importance of the analysis results into the generating AI, and the generating AI can adjust the level of detail of the information.

[0053] The information provider can apply different information provision algorithms depending on the dog's category at the time of provision. For example, the information provider can use a generative AI to apply different information provision algorithms depending on the dog's category (age, breed, etc.) at the time of provision. The generative AI can select an appropriate information provision algorithm based on the dog's category. For example, the generative AI can focus on providing information about growth for puppies. It can also focus on providing information about health management for senior dogs. Furthermore, the generative AI can provide information about breed-specific health risks for certain breeds. This allows for the provision of more appropriate information by applying information provision algorithms according to the dog's category. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input dog category data into the generative AI, which can then select an appropriate information provision algorithm.

[0054] The information provider can estimate a dog's emotions and determine the priority of the information to provide based on the estimated emotions. The information provider can estimate a dog's emotions using, for example, a generative AI. The generative AI can estimate a dog's emotions by analyzing behavioral and physiological data. For example, the generative AI can estimate a dog's stress or relaxation state by analyzing changes in the dog's heart rate and activity level. The generative AI can also estimate emotions by analyzing the dog's facial expressions and tone of voice. The information provider determines the priority of the information to provide based on the estimated emotions of the dog. For example, if the dog is stressed, information to reduce stress will be provided preferentially. If the dog is relaxed, information on activity level and sleep patterns can be provided preferentially. Furthermore, if the dog is excited, information on changes in behavioral patterns can be provided preferentially. In this way, important information can be provided preferentially by determining the priority of information according to the dog's emotions. Some or all of the above processing in the information provider may be performed using, for example, AI, or without AI. For example, the information provider can input dog emotion data into the generative AI, and the generative AI can determine the priority of the information.

[0055] The data delivery unit can determine the priority of information based on the submission timing of the analysis results at the time of delivery. For example, the data delivery unit can use a generating AI to determine the priority of information based on the submission timing of the analysis results at the time of delivery. The generating AI can analyze the submission timing of the analysis results and determine the priority of information. For example, the generating AI can prioritize providing the latest analysis results and provide real-time information. The generating AI can also provide current information while referring to past analysis results. Furthermore, the generating AI can prioritize providing analysis results for specific events and evaluate their impact. In this way, real-time information can be provided by determining the priority of information based on the submission timing of the analysis results. Some or all of the above processing in the data delivery unit may be performed using AI, for example, or without using AI. For example, the data delivery unit can input the submission timing of the analysis results into the generating AI, and the generating AI can determine the priority of information.

[0056] The information delivery unit can adjust the order of information based on the relevance of the analysis results at the time of delivery. The information delivery unit can adjust the order of information based on the relevance of the analysis results at the time of delivery, for example, by using a generating AI. The generating AI can analyze the relevance of the analysis results and adjust the order of information. For example, the generating AI can prioritize providing highly relevant information and provide detailed information. The generating AI can also postpone less relevant information and provide important information first. Furthermore, the generating AI can dynamically adjust the order of delivery based on the relevance of the information. This allows for the priority provision of important information by adjusting the order of information based on the relevance of the analysis results. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without using AI. For example, the information delivery unit can input the relevance of the analysis results into the generating AI, and the generating AI can adjust the order of information.

[0057] The consultation department can estimate the dog's emotions and adjust the way the consultation is presented based on the estimated emotions. For example, the consultation department uses generative AI to estimate the dog's emotions. Generative AI can analyze the dog's behavioral and physiological data to estimate its emotions. For example, generative AI can analyze changes in the dog's heart rate and activity level to estimate its stress and relaxation state. Generative AI can also analyze the dog's facial expressions and tone of voice to estimate its emotions. The consultation department adjusts the way the consultation is presented based on the estimated emotions of the dog. For example, if the dog is stressed, it will emphasize and provide advice for stress reduction. If the dog is relaxed, it can provide detailed data on its activity level and sleep patterns. Furthermore, if the dog is excited, it can emphasize and provide data on changes in its behavior patterns. By adjusting the way the consultation is presented according to the dog's emotions, the consultation department can provide advice that is easy for the owner to understand. Some or all of the above processing in the consultation department may be performed using AI, for example, or without AI. For example, the consultation department can input dog emotional data into a generating AI, which can then adjust how the consultation content is expressed.

[0058] The consultation department can provide optimal advice by referring to the owner's past consultation history during a consultation. The consultation department can, for example, use a generative AI to refer to the owner's past consultation history during a consultation and provide optimal advice. The generative AI can analyze the owner's past consultation history and provide relevant advice. For example, the generative AI can provide relevant advice based on what the owner has consulted about in the past. The generative AI can also suggest solutions to similar problems from the owner's past consultation history. Furthermore, the generative AI can analyze the owner's past consultation history and provide the most effective advice. In this way, relevant advice can be provided by referring to the owner's past consultation history. Some or all of the above processing in the consultation department may be performed using AI, for example, or without AI. For example, the consultation department can input the owner's past consultation history into the generative AI, and the generative AI can provide optimal advice.

[0059] The consultation department can customize the consultation content based on the dog's current health condition at the time of consultation. The consultation department can customize the consultation content based on the dog's current health condition at the time of consultation, for example, by using a generative AI. The generative AI can analyze the dog's health data and provide appropriate advice. For example, if the dog's health condition is good, the generative AI will provide general advice. If the dog's health condition is poor, the generative AI can propose specific improvement measures. Furthermore, the generative AI can respond quickly if an emergency response is necessary based on the dog's health condition. In this way, appropriate advice can be provided by customizing the consultation content based on the dog's current health condition. Some or all of the above processing in the consultation department may be performed using AI, for example, or without AI. For example, the consultation department can input the dog's health data into the generative AI, and the generative AI can customize the consultation content.

[0060] The consultation department can estimate a dog's emotions and prioritize consultation topics based on the estimated emotions. The consultation department estimates a dog's emotions, for example, using generative AI. Generative AI can estimate a dog's emotions by analyzing behavioral and physiological data. For example, generative AI can analyze changes in a dog's heart rate and activity level to estimate its stress and relaxation state. Generative AI can also analyze a dog's facial expressions and tone of voice to estimate its emotions. The consultation department prioritizes consultation topics based on the estimated emotions of the dog. For example, if a dog is stressed, it will prioritize providing consultation topics to reduce stress. If a dog is relaxed, it will prioritize providing consultation topics related to activity level and sleep patterns. Furthermore, if a dog is excited, it will prioritize providing consultation topics related to changes in behavior patterns. In this way, by prioritizing consultation topics according to the dog's emotions, important consultation topics can be provided preferentially. Some or all of the above processing in the consultation department may be performed using AI, for example, or without using AI. For example, the consultation department can input dog emotional data into a generating AI, which can then determine the priority of the consultation topics.

[0061] The consultation department can provide optimal advice by considering the owner's geographical location information during consultations. For example, the consultation department can use a generative AI to provide optimal advice by considering the owner's geographical location information during consultations. The generative AI can analyze the owner's location information and provide appropriate advice. For example, if the owner lives in an urban area, the generative AI can provide advice suitable for the urban environment. Also, if the owner lives in the suburbs, the generative AI can provide advice suitable for the suburban environment. Furthermore, if the owner is traveling, the generative AI can provide advice suitable for the environment of the travel destination. In this way, by considering the owner's geographical location information, the consultation department can provide optimal advice. Some or all of the above processing in the consultation department may be performed using AI, for example, or without AI. For example, the consultation department can input the owner's location information into the generative AI, and the generative AI can provide optimal advice.

[0062] The consultation department can analyze the owner's social media activity during consultations and provide relevant advice. For example, the consultation department can use generative AI to analyze the owner's social media activity during consultations and provide relevant advice. The generative AI can analyze the owner's social media posts and provide relevant advice. For example, if the owner posts about their dog's health on social media, the generative AI can provide advice based on that content. Furthermore, if the owner posts about their dog's activities on social media, the generative AI can provide advice based on that content. In addition, if the owner posts about their dog's diet on social media, the generative AI can provide advice based on that content. This allows for the efficient provision of relevant advice by analyzing the owner's social media activity. Some or all of the above processing in the consultation department may be performed using AI, or not. For example, the consultation department can input the owner's social media posts into the generative AI, which can then provide relevant advice.

[0063] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0064] The data collection unit can monitor the dog's body temperature and heart rate in real time and issue alerts if abnormalities are detected. For example, if the dog's body temperature rises rapidly, the unit will send a notification to the owner to prompt appropriate action. Also, if the heart rate exceeds the normal range, the unit can detect the abnormality and recommend consultation with a veterinarian. Furthermore, the unit can transmit the dog's body temperature and heart rate data to the analysis unit for a detailed analysis of its health status. This allows for real-time monitoring of the dog's health status and early detection of abnormalities.

[0065] The supply unit can suggest appropriate supplements and nutritional supplements based on the dog's health condition. For example, if the analysis unit detects a nutritional imbalance in the dog, the supply unit will suggest supplements containing specific vitamins and minerals. Also, if the dog is highly active, the supply unit can suggest nutritional supplements for energy replenishment. Furthermore, the supply unit can adjust the type and amount of supplements appropriate according to the dog's age and health condition. This allows for the provision of appropriate nutritional support to maintain the dog's health.

[0066] The data collection unit can detect specific behavioral patterns based on the dog's behavioral data, enabling early detection of abnormal behavior. For example, if a dog is active at an unusual time of day, the unit will detect this as abnormal behavior. Furthermore, if a dog frequently repeats the same behavior in a specific location, the unit can evaluate this behavior as abnormal. In addition, if a dog does not eat at its usual mealtime, the unit can detect this as abnormal behavior and notify the owner. This allows for early detection of abnormal behavior in dogs, enabling appropriate action to be taken.

[0067] The service provider can propose an appropriate exercise plan based on the dog's health condition. For example, if the analysis unit detects that the dog is not getting enough exercise, the service provider will propose a specific exercise plan. Furthermore, the service provider can adjust the type and frequency of exercise according to the dog's age and breed. In addition, based on the dog's health condition, the service provider can customize the exercise plan and suggest exercise within a reasonable range. This allows for the provision of an appropriate exercise plan to maintain the dog's health.

[0068] The analysis unit can analyze long-term health trends based on canine health data. For example, it can track changes in a dog's weight and activity level over a long period to evaluate health trends. It can also analyze changes in a dog's food intake and sleep patterns to understand long-term trends in their health. Furthermore, the analysis unit can predict future health risks and suggest preventive measures based on the dog's health data. This allows owners to understand their dog's long-term health trends and provide appropriate care.

[0069] The following briefly describes the processing flow for example form 1.

[0070] Step 1: The data collection unit uses devices and apps to collect dog behavior patterns and health data. For example, a sensor attached to the dog's collar measures activity levels, and an app records food intake. The sensor detects the dog's movements and records steps and exercise time, while the app records the type and amount of food entered by the owner and stores it in a database. Step 2: The analysis unit uses a generation AI to analyze the data collected by the collection unit. For example, it analyzes the dog's activity level, sleep patterns, and food intake to provide information about the dog's health and behavior. The analysis unit can detect a decrease in the dog's activity level and suggest the possibility of insufficient exercise, or it can analyze the dog's sleep patterns and evaluate the quality of sleep. Step 3: The provision unit provides information and suggestions for improvement based on the analysis results obtained by the analysis unit. For example, it may suggest an appropriate amount of exercise for a dog that is not getting enough exercise, or suggest improvements to the diet if the amount of food intake is inappropriate. Using the generation AI, it is possible to suggest appropriate exercise levels, adjustments to nutritional balance, and changes in the type of food based on the dog's health condition. Step 4: The consultation department provides general advice to owners regarding their questions and their dogs' symptoms, and supports scheduling in-person consultations with veterinarians and handling emergencies when necessary. For example, it uses generated AI to provide general advice to owners' questions and recommends consultations with veterinarians as needed. This allows owners to monitor their dogs' health in real time and provide appropriate care.

[0071] (Example of form 2) The dog health management system according to an embodiment of the present invention is a system that collects dog behavior patterns and health data using a device or app and analyzes it using generative AI. This system provides information and suggestions for improvement regarding the dog's health status and behavior. It also provides a service that allows owners to consult with a veterinarian online. For example, data such as the dog's activity level, sleep patterns, and food intake are collected using a device or app. For example, a sensor attached to the dog's collar measures the activity level, and the app records the food intake. This data is input into the generative AI. Next, the generative AI analyzes the collected data. The generative AI analyzes the dog's activity level, sleep patterns, food intake, etc., and provides information regarding the dog's health status and behavior. For example, the generative AI can detect a decrease in the dog's activity level and suggest the possibility of insufficient exercise. Furthermore, the generative AI provides suggestions for improvement based on the analysis results. For example, it can suggest an appropriate amount of exercise for a dog that is not getting enough exercise. It can also suggest improvements to the diet if the food intake is inappropriate. In addition, owners can consult with a veterinarian online through the app. The generating AI provides general advice to owners regarding their questions and their dogs' symptoms, and supports scheduling in-person consultations with veterinarians and handling emergencies when necessary. For example, if an owner asks about their dog's loss of appetite, the generating AI will provide general advice and recommend a consultation with a veterinarian if necessary. This system allows owners to monitor their dog's health in real time and provide appropriate care. Online consultations with veterinarians enable quick responses, making dog health management more efficient. In short, the dog health management system allows owners to monitor their dog's health in real time and provide appropriate care.

[0072] The dog health management system according to this embodiment comprises a data collection unit, an analysis unit, a data provision unit, and a consultation unit. The data collection unit collects dog behavior patterns and health data using devices and apps. For example, the data collection unit can use a sensor attached to the dog's collar to measure its activity level, and an app to record its food intake. The data collection unit can measure the dog's activity level using a sensor attached to the dog's collar. The sensor detects the dog's movement and records steps and exercise time. The data collection unit can also record the dog's food intake using an app. The app records the content and amount of food entered by the owner and stores it in a database. The analysis unit analyzes the data collected by the data collection unit using a generative AI. For example, the analysis unit analyzes the dog's activity level, sleep patterns, food intake, etc., and provides information about the dog's health status and behavior. For example, the analysis unit can use a generative AI to detect a decrease in the dog's activity level and suggest the possibility of insufficient exercise. The analysis unit can also use a generative AI to analyze the dog's sleep patterns and evaluate the quality of sleep. The provision unit provides information and suggestions for improvement based on the analysis results obtained by the analysis unit. For example, the provision unit can suggest an appropriate amount of exercise for a dog that is not getting enough exercise. For example, the provision unit uses generative AI to suggest an appropriate amount of exercise based on the dog's health condition. The provision unit can also suggest improvements to the diet if the amount of food intake is inappropriate. For example, the provision unit uses generative AI to analyze the amount of food intake of a dog and suggests adjusting the nutritional balance or changing the type of food. The consultation unit provides general advice to the owner's questions and the dog's symptoms, and supports scheduling face-to-face consultations with veterinarians and handling emergencies if necessary. For example, the consultation unit uses generative AI to provide general advice to the owner's questions. For example, if the owner asks about the dog's poor appetite, the generative AI will provide general advice and, if necessary, recommend consulting with a veterinarian. As a result, the dog health management system according to this embodiment can grasp the dog's health condition in real time and provide appropriate care. Some or all of the above processing in the consultation unit may be performed using AI, for example, or without using AI.For example, the consultation department can input questions from pet owners into a generating AI, which can then output general advice.

[0073] The data collection unit uses devices and apps to collect dog behavior patterns and health data. Specifically, a sensor attached to the dog's collar measures activity levels, and the app records food intake. The collar sensor has a built-in accelerometer and gyroscope, allowing it to detect the dog's movements in detail. This makes it possible to accurately record steps, exercise time, and even exercise intensity. For example, it can collect data such as how far the dog walked and how fast it ran during a walk. The sensor can also record the dog's rest time and sleep patterns, allowing for an overall understanding of the dog's daily activities. The app records the content and amount of food entered by the owner and stores it in a database. The app has a function to record the type of food (dry food, wet food, homemade food, etc.) and intake in detail, allowing for an accurate understanding of the dog's nutritional intake. Furthermore, the app also records the time and frequency of meals, providing data for analyzing the dog's eating patterns. As a result, the data collection unit can comprehensively collect dog behavior patterns and health data and understand the situation in real time. The collected data is sent to a cloud server, making it accessible to the analysis and provisioning departments. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0074] The analysis unit uses generative AI to analyze data collected by the collection unit. Specifically, it analyzes the dog's activity level, sleep patterns, and food intake to provide information about the dog's health and behavior. Based on the collected data, the generative AI analyzes the dog's behavior patterns and health in detail. For example, it can detect a decrease in the dog's activity level and suggest the possibility of insufficient exercise. The generative AI can detect abnormal patterns by comparing them with past data, enabling early detection of problems. The generative AI can also analyze the dog's sleep patterns and evaluate the quality of sleep. For example, if a dog wakes up frequently at night, it may suggest stress or a health problem. Furthermore, the generative AI can analyze food intake and suggest adjustments to nutritional balance or changes in the type of food. For example, if a dog is consuming an excessive amount of a particular nutrient, it can suggest reducing that intake. In this way, the analysis unit can quickly and accurately analyze the collected data and provide information about the dog's health and behavior. In addition, the analysis unit can utilize past data and statistical information to perform long-term health management and trend analysis. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term health management, improving the reliability and safety of the entire system.

[0075] The service provider provides information and suggestions for improvement based on the analysis results obtained by the analysis unit. Specifically, it can suggest appropriate exercise levels for dogs that are not getting enough exercise. Using generative AI, it suggests appropriate exercise levels based on the dog's health condition. For example, it can specifically suggest the amount and type of exercise each day, depending on the dog's age, weight, and activity level. The service provider can also suggest improvements to the dog's diet if its food intake is inappropriate. Using generative AI, it analyzes the dog's food intake and suggests adjustments to the nutritional balance or changes to the type of food. For example, if a dog is consuming an excessive amount of a particular nutrient, it can suggest reducing that intake. Also, if a dog is deficient in a particular nutrient, it can suggest a diet to supplement that nutrient. Furthermore, the service provider can also provide care plans tailored to the dog's health condition. For example, if a dog is elderly, it can suggest supplements to maintain joint health and specific exercises. In this way, the service provider can provide specific and practical advice based on the analysis results, supporting the dog's health management. Furthermore, the service provider can also provide specific procedures and schedules to make it easier for owners to implement the suggested advice. This allows the service provider to offer support to pet owners in effectively managing their dogs' health.

[0076] The consultation department provides general advice to pet owners regarding their questions and their dogs' symptoms, and supports them in scheduling in-person consultations with veterinarians and handling emergencies when necessary. Specifically, it uses generative AI to provide general advice to pet owners' questions. For example, if a pet owner asks about their dog's loss of appetite, the generative AI will provide general advice and, if necessary, recommend a consultation with a veterinarian. Based on past data and expertise, the generative AI can generate appropriate answers to pet owners' questions. For example, if a dog's loss of appetite persists, it can suggest dietary changes or ways to reduce stress. The generative AI can also assess the urgency of the dog's symptoms based on the information entered by the pet owner and recommend emergency action if necessary. For example, if a dog suddenly becomes lethargic, it can recommend consulting a veterinarian immediately. Furthermore, the consultation department also provides support for pet owners in scheduling in-person consultations with veterinarians. For example, the generative AI can check the pet owner's schedule and suggest the best appointment time based on the veterinarian's availability. In this way, the consultation department can support pet owners in responding quickly and appropriately to their dog's health problems. Furthermore, the consultation department can collect feedback from pet owners and continuously improve the accuracy and effectiveness of the advice generated by the AI. This allows the consultation department to provide reliable advice to pet owners and support the health management of their dogs.

[0077] The data collection unit measures the dog's activity level using a sensor attached to the dog's collar, and the app can record the dog's food intake. For example, the data collection unit measures the dog's activity level using a sensor attached to the dog's collar. The sensor detects the dog's movement and records the number of steps and exercise time. The data collection unit can also record the dog's food intake using the app. The app records the content and amount of food entered by the owner and stores it in a database. This allows for accurate recording of the dog's activity level and food intake. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from the sensor attached to the dog's collar into a generating AI, which can then analyze the data.

[0078] The analysis unit can analyze a dog's activity level, sleep patterns, and food intake, and provide information about the dog's health and behavior. For example, the analysis unit uses a generative AI to analyze a dog's activity level, sleep patterns, and food intake. The generative AI can detect a decrease in a dog's activity level and suggest the possibility of insufficient exercise. The generative AI can also analyze a dog's sleep patterns and evaluate the quality of sleep. Furthermore, the generative AI can analyze a dog's food intake and suggest adjustments to nutritional balance or changes in the type of food. This allows owners to provide appropriate care by providing information about the dog's health and behavior. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data acquired from the collection unit into the generative AI, which can then analyze the data.

[0079] The service provider can suggest an appropriate amount of exercise for dogs that are not getting enough exercise, based on the analysis results. The service provider can suggest an appropriate amount of exercise for dogs that are not getting enough exercise, for example, by using a generative AI. The generative AI can analyze the dog's activity level data and suggest an appropriate amount of exercise for dogs that are not getting enough exercise. For example, the generative AI can suggest an appropriate amount of exercise based on the dog's age and breed. The generative AI can also adjust the amount of exercise based on the dog's health condition. In this way, by suggesting an appropriate amount of exercise for dogs that are not getting enough exercise, the health of the dogs can be maintained. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input data obtained from the analysis unit into the generative AI, and the generative AI can suggest an appropriate amount of exercise.

[0080] The supply unit can suggest improvements to the diet if the amount of food intake is inappropriate. For example, the supply unit can use a generating AI to suggest improvements to the diet if the amount of food intake is inappropriate. The generating AI can analyze the dog's diet data and suggest adjustments to the nutritional balance or changes in the type of food. For example, the generating AI can suggest appropriate improvements to the diet based on the dog's age and health condition. The generating AI can also analyze the amount of food intake the dog has and make suggestions to prevent overeating or undereating. In this way, the dog's health can be maintained by suggesting improvements to the diet when the amount of food intake is inappropriate. Some or all of the above processing in the supply unit may be performed using AI, for example, or without AI. For example, the supply unit can input data obtained from the analysis unit into the generating AI, and the generating AI can suggest improvements to the diet.

[0081] The consultation department can provide general advice to pet owners regarding their questions and their dogs' symptoms, and can also support scheduling in-person consultations with veterinarians and handling emergencies if necessary. For example, the consultation department can use a generative AI to provide general advice to pet owners' questions. The generative AI can analyze the pet owner's questions and provide general advice. For example, if a pet owner asks about their dog's loss of appetite, the generative AI will provide general advice and, if necessary, recommend a consultation with a veterinarian. The consultation department can also support scheduling in-person consultations with veterinarians and handling emergencies if necessary. For example, the generative AI can schedule an in-person consultation with a veterinarian based on the pet owner's questions and the dog's symptoms. This allows pet owners to quickly consult about their dog's health and, if necessary, arrange in-person consultations with a veterinarian or handle emergencies. Some or all of the above-described processes in the consultation department may be performed using AI, or not. For example, the consultation department can input the pet owner's questions into a generative AI, which can then output general advice.

[0082] The data collection unit can estimate the dog's emotions and adjust the frequency of data collection based on the estimated emotions. The data collection unit estimates the dog's emotions, for example, using generative AI. Generative AI can estimate the dog's emotions by analyzing the dog's behavioral and physiological data. For example, generative AI can analyze changes in the dog's heart rate and activity level to estimate its stress and relaxation state. Generative AI can also analyze the dog's facial expressions and tone of voice to estimate its emotions. The data collection unit adjusts the frequency of data collection based on the estimated emotions of the dog. For example, if the dog is stressed, the frequency of data collection is reduced to lessen the dog's burden. If the dog is relaxed, the frequency of data collection is increased to collect more detailed data. Furthermore, if the dog is excited, the frequency of data collection is temporarily increased to record changes in behavioral patterns in detail. This allows for the reduction of the dog's burden and the collection of detailed data by adjusting the frequency of data collection according to the dog's emotions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI. For example, the data collection unit inputs dog emotional data into a generating AI, which can then adjust the frequency of data collection.

[0083] The data collection unit can analyze a dog's past behavioral patterns and select the optimal timing for data collection. For example, the data collection unit uses a generative AI to analyze the dog's past behavioral patterns. The generative AI analyzes past data and understands the dog's behavioral patterns. For example, if the generative AI has observed that a dog was active during a specific time period in the past, it will concentrate data collection during that time. Similarly, if the generative AI has observed that a dog was resting during a specific time period in the past, it can refrain from collecting data during that time. Furthermore, the generative AI can analyze the dog's past behavioral patterns and intensify data collection on weekends or during specific events. This allows for data collection at the optimal time by analyzing the dog's past behavioral patterns. Some or all of the above-described processes in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can input past behavioral data into the generative AI, which can then select the optimal timing for data collection.

[0084] The data collection unit can filter data based on the dog's current health status and activity level during data collection. For example, the data collection unit can use a generative AI to filter data based on the dog's current health status and activity level. The generative AI can analyze the dog's health and activity data and collect only the necessary data. For example, if the dog is in good health, the generative AI will perform normal data collection. If the dog is in poor health, the generative AI can refrain from collecting data and collect only the necessary data. Furthermore, if the dog's activity level is high, the generative AI can collect detailed data, and if the activity level is low, it can collect only basic data. This allows for the collection of only the necessary data by filtering it according to the dog's health status and activity level. Some or all of the above processing in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can input the dog's health data into the generative AI, which can then filter the data.

[0085] The data collection unit can estimate the dog's emotions and determine the priority of data to collect based on the estimated emotions. The data collection unit estimates the dog's emotions, for example, using a generative AI. The generative AI can estimate the dog's emotions by analyzing the dog's behavioral and physiological data. For example, the generative AI can analyze changes in the dog's heart rate and activity level to estimate its stress and relaxation state. The generative AI can also analyze the dog's facial expressions and tone of voice to estimate its emotions. The data collection unit determines the priority of data to collect based on the estimated emotions of the dog. For example, if the dog is stressed, stress-related data will be collected preferentially. If the dog is relaxed, data on activity level and sleep patterns will be collected preferentially. Furthermore, if the dog is excited, data on changes in behavioral patterns will be collected preferentially. In this way, important data can be collected preferentially by determining the priority of data to collect according to the dog's emotions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input dog emotion data into a generative AI, which can then determine the priority of the data.

[0086] The data collection unit can prioritize the collection of highly relevant data by considering the dog's geographical location during data collection. For example, the data collection unit uses a generative AI to prioritize the collection of highly relevant data by considering the dog's geographical location during data collection. The generative AI can analyze the dog's location information and select highly relevant data. For example, if the dog is in a park, the generative AI will prioritize the collection of data on activity level and exercise patterns. If the dog is at home, the generative AI will prioritize the collection of data on sleep patterns and food intake. Furthermore, if the dog is at a veterinary hospital, the generative AI will prioritize the collection of data on its health status. In this way, highly relevant data can be prioritized by considering the dog's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the dog's location information into the generative AI, which can then select highly relevant data.

[0087] The data collection unit can analyze the social media activity of dog owners and collect relevant data during data collection. For example, the data collection unit can use a generative AI to analyze the social media activity of dog owners and collect relevant data during data collection. The generative AI can analyze the owner's social media posts and select relevant data. For example, if an owner posts about their dog's health on social media, the generative AI can collect data based on that post. Furthermore, if an owner posts about their dog's activities on social media, the generative AI can collect data based on that post. In addition, if an owner posts about their dog's diet on social media, the generative AI can collect data based on that post. This allows for the efficient collection of relevant data by analyzing the owner's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can input the owner's social media posts into the generative AI, which can then select relevant data.

[0088] The analysis unit can estimate the dog's emotions and adjust the analysis algorithm based on the estimated emotions. The analysis unit estimates the dog's emotions using, for example, a generative AI. The generative AI can estimate the dog's emotions by analyzing the dog's behavioral and physiological data. For example, the generative AI can estimate the dog's stress and relaxation states by analyzing changes in the dog's heart rate and activity level. The generative AI can also estimate emotions by analyzing the dog's facial expressions and tone of voice. The analysis unit adjusts the analysis algorithm based on the estimated emotions of the dog. For example, if the dog is stressed, the analysis will focus on stress-related data. If the dog is relaxed, the analysis will focus on activity level and sleep pattern data. Furthermore, if the dog is excited, the analysis will focus on data related to changes in behavioral patterns. By adjusting the analysis algorithm according to the dog's emotions, more accurate analysis results can be obtained. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input dog emotion data into the generative AI, which can then adjust the analysis algorithm.

[0089] The analysis unit can adjust the level of detail of the analysis based on the importance of the dog's health data during the analysis. For example, the analysis unit uses a generating AI to adjust the level of detail of the analysis based on the importance of the dog's health data during the analysis. The generating AI can analyze the dog's health data and adjust the level of detail of the analysis according to its importance. For example, if the dog's health data is important, the generating AI can perform a detailed analysis and provide specific areas for improvement. Also, if the dog's health data is general, the generating AI can perform a basic analysis and provide general advice. Furthermore, if the dog's health data is urgent, the generating AI can quickly perform a detailed analysis and propose emergency measures. In this way, appropriate analysis results can be provided by adjusting the level of detail of the analysis according to the importance of the dog's health data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the dog's health data into the generating AI, and the generating AI can adjust the level of detail of the analysis.

[0090] The analysis unit can apply different analysis algorithms depending on the dog's category during analysis. For example, the analysis unit uses a generative AI to apply different analysis algorithms depending on the dog's category (age, breed, etc.) during analysis. The generative AI can select an appropriate analysis algorithm based on the dog's category. For example, in the case of puppies, the generative AI will focus on growth-related data during analysis. In the case of senior dogs, the generative AI can focus on health-related data during analysis. Furthermore, in the case of specific breeds, the generative AI can consider breed-specific health risks during analysis. By applying analysis algorithms according to the dog's category, more appropriate analysis results can be provided. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input dog category data into the generative AI, which can then select an appropriate analysis algorithm.

[0091] The analysis unit can estimate the dog's emotions and adjust the display method of the analysis results based on the estimated emotions. The analysis unit estimates the dog's emotions, for example, using a generative AI. The generative AI can estimate the dog's emotions by analyzing the dog's behavioral and physiological data. For example, the generative AI can analyze changes in the dog's heart rate and activity level to estimate its stress and relaxation state. The generative AI can also analyze the dog's facial expressions and tone of voice to estimate its emotions. The analysis unit adjusts the display method of the analysis results based on the estimated emotions of the dog. For example, if the dog is stressed, advice for stress reduction will be highlighted. If the dog is relaxed, data on activity level and sleep patterns can be displayed in detail. Furthermore, if the dog is excited, data on changes in behavioral patterns can be highlighted. In this way, by adjusting the display method of the analysis results according to the dog's emotions, information that is easy for the owner to understand can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input dog emotional data into a generating AI, which can then adjust how the analysis results are displayed.

[0092] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit uses a generative AI to determine the priority of analysis based on the data collection timing during analysis. The generative AI can analyze the data collection timing and determine the priority. For example, the generative AI can prioritize the analysis of the latest data to provide real-time information. The generative AI can also analyze current data while referring to past data. Furthermore, the generative AI can prioritize the analysis of data from specific events and evaluate their impact. This allows for the provision of real-time information by determining the priority of analysis based on the data collection timing. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing to the generative AI, and the generative AI can determine the priority of analysis.

[0093] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The analysis unit can adjust the order of analysis based on the relevance of the data during analysis, for example, by using a generative AI. The generative AI can analyze the relevance of the data and adjust the order of analysis. For example, the generative AI can prioritize the analysis of highly relevant data and provide detailed information. The generative AI can also postpone the analysis of less relevant data and analyze important data first. Furthermore, the generative AI can dynamically adjust the order of analysis based on the relevance of the data. This allows for the prioritization of important data by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into the generative AI, and the generative AI can adjust the order of analysis.

[0094] The information provider can estimate the dog's emotions and adjust the way the information is presented based on the estimated emotions. For example, the information provider can use a generative AI to estimate the dog's emotions. The generative AI can analyze the dog's behavioral and physiological data to estimate the dog's emotions. For example, the generative AI can analyze changes in the dog's heart rate and activity level to estimate its stress and relaxation state. The generative AI can also analyze the dog's facial expressions and tone of voice to estimate its emotions. The information provider adjusts the way the information is presented based on the estimated emotions of the dog. For example, if the dog is stressed, it can emphasize and provide advice for stress reduction. If the dog is relaxed, it can provide detailed data on activity level and sleep patterns. Furthermore, if the dog is excited, it can emphasize and provide data on changes in behavioral patterns. In this way, by adjusting the way the information is presented according to the dog's emotions, the information can be provided in a way that is easy for the owner to understand. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input dog emotion data into the generative AI, which can then adjust the way the information is presented.

[0095] The data delivery unit can adjust the level of detail of the information provided based on the importance of the analysis results at the time of delivery. For example, the data delivery unit can use a generating AI to adjust the level of detail of the information provided based on the importance of the analysis results at the time of delivery. The generating AI can analyze the importance of the analysis results and adjust the level of detail of the information. For example, the generating AI can provide detailed information for important analysis results. The generating AI can also provide basic information for general analysis results. Furthermore, the generating AI can quickly provide detailed information for urgent analysis results. In this way, appropriate information can be provided by adjusting the level of detail of the information according to the importance of the analysis results. Some or all of the above processing in the data delivery unit may be performed using AI, for example, or without using AI. For example, the data delivery unit can input the importance of the analysis results into the generating AI, and the generating AI can adjust the level of detail of the information.

[0096] The information provider can apply different information provision algorithms depending on the dog's category at the time of provision. For example, the information provider can use a generative AI to apply different information provision algorithms depending on the dog's category (age, breed, etc.) at the time of provision. The generative AI can select an appropriate information provision algorithm based on the dog's category. For example, the generative AI can focus on providing information about growth for puppies. It can also focus on providing information about health management for senior dogs. Furthermore, the generative AI can provide information about breed-specific health risks for certain breeds. This allows for the provision of more appropriate information by applying information provision algorithms according to the dog's category. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input dog category data into the generative AI, which can then select an appropriate information provision algorithm.

[0097] The information provider can estimate a dog's emotions and determine the priority of the information to provide based on the estimated emotions. The information provider can estimate a dog's emotions using, for example, a generative AI. The generative AI can estimate a dog's emotions by analyzing behavioral and physiological data. For example, the generative AI can estimate a dog's stress or relaxation state by analyzing changes in the dog's heart rate and activity level. The generative AI can also estimate emotions by analyzing the dog's facial expressions and tone of voice. The information provider determines the priority of the information to provide based on the estimated emotions of the dog. For example, if the dog is stressed, information to reduce stress will be provided preferentially. If the dog is relaxed, information on activity level and sleep patterns can be provided preferentially. Furthermore, if the dog is excited, information on changes in behavioral patterns can be provided preferentially. In this way, important information can be provided preferentially by determining the priority of information according to the dog's emotions. Some or all of the above processing in the information provider may be performed using, for example, AI, or without AI. For example, the information provider can input dog emotion data into the generative AI, and the generative AI can determine the priority of the information.

[0098] The data delivery unit can determine the priority of information based on the submission timing of the analysis results at the time of delivery. For example, the data delivery unit can use a generating AI to determine the priority of information based on the submission timing of the analysis results at the time of delivery. The generating AI can analyze the submission timing of the analysis results and determine the priority of information. For example, the generating AI can prioritize providing the latest analysis results and provide real-time information. The generating AI can also provide current information while referring to past analysis results. Furthermore, the generating AI can prioritize providing analysis results for specific events and evaluate their impact. In this way, real-time information can be provided by determining the priority of information based on the submission timing of the analysis results. Some or all of the above processing in the data delivery unit may be performed using AI, for example, or without using AI. For example, the data delivery unit can input the submission timing of the analysis results into the generating AI, and the generating AI can determine the priority of information.

[0099] The information delivery unit can adjust the order of information based on the relevance of the analysis results at the time of delivery. The information delivery unit can adjust the order of information based on the relevance of the analysis results at the time of delivery, for example, by using a generating AI. The generating AI can analyze the relevance of the analysis results and adjust the order of information. For example, the generating AI can prioritize providing highly relevant information and provide detailed information. The generating AI can also postpone less relevant information and provide important information first. Furthermore, the generating AI can dynamically adjust the order of delivery based on the relevance of the information. This allows for the priority provision of important information by adjusting the order of information based on the relevance of the analysis results. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without using AI. For example, the information delivery unit can input the relevance of the analysis results into the generating AI, and the generating AI can adjust the order of information.

[0100] The consultation department can estimate the dog's emotions and adjust the way the consultation is presented based on the estimated emotions. For example, the consultation department uses generative AI to estimate the dog's emotions. Generative AI can analyze the dog's behavioral and physiological data to estimate its emotions. For example, generative AI can analyze changes in the dog's heart rate and activity level to estimate its stress and relaxation state. Generative AI can also analyze the dog's facial expressions and tone of voice to estimate its emotions. The consultation department adjusts the way the consultation is presented based on the estimated emotions of the dog. For example, if the dog is stressed, it will emphasize and provide advice for stress reduction. If the dog is relaxed, it can provide detailed data on its activity level and sleep patterns. Furthermore, if the dog is excited, it can emphasize and provide data on changes in its behavior patterns. By adjusting the way the consultation is presented according to the dog's emotions, the consultation department can provide advice that is easy for the owner to understand. Some or all of the above processing in the consultation department may be performed using AI, for example, or without AI. For example, the consultation department can input dog emotional data into a generating AI, which can then adjust how the consultation content is expressed.

[0101] The consultation department can provide optimal advice by referring to the owner's past consultation history during a consultation. The consultation department can, for example, use a generative AI to refer to the owner's past consultation history during a consultation and provide optimal advice. The generative AI can analyze the owner's past consultation history and provide relevant advice. For example, the generative AI can provide relevant advice based on what the owner has consulted about in the past. The generative AI can also suggest solutions to similar problems from the owner's past consultation history. Furthermore, the generative AI can analyze the owner's past consultation history and provide the most effective advice. In this way, relevant advice can be provided by referring to the owner's past consultation history. Some or all of the above processing in the consultation department may be performed using AI, for example, or without AI. For example, the consultation department can input the owner's past consultation history into the generative AI, and the generative AI can provide optimal advice.

[0102] The consultation department can customize the consultation content based on the dog's current health condition at the time of consultation. The consultation department can customize the consultation content based on the dog's current health condition at the time of consultation, for example, by using a generative AI. The generative AI can analyze the dog's health data and provide appropriate advice. For example, if the dog's health condition is good, the generative AI will provide general advice. If the dog's health condition is poor, the generative AI can propose specific improvement measures. Furthermore, the generative AI can respond quickly if an emergency response is necessary based on the dog's health condition. In this way, appropriate advice can be provided by customizing the consultation content based on the dog's current health condition. Some or all of the above processing in the consultation department may be performed using AI, for example, or without AI. For example, the consultation department can input the dog's health data into the generative AI, and the generative AI can customize the consultation content.

[0103] The consultation department can estimate a dog's emotions and prioritize consultation topics based on the estimated emotions. The consultation department estimates a dog's emotions, for example, using generative AI. Generative AI can estimate a dog's emotions by analyzing behavioral and physiological data. For example, generative AI can analyze changes in a dog's heart rate and activity level to estimate its stress and relaxation state. Generative AI can also analyze a dog's facial expressions and tone of voice to estimate its emotions. The consultation department prioritizes consultation topics based on the estimated emotions of the dog. For example, if a dog is stressed, it will prioritize providing consultation topics to reduce stress. If a dog is relaxed, it will prioritize providing consultation topics related to activity level and sleep patterns. Furthermore, if a dog is excited, it will prioritize providing consultation topics related to changes in behavior patterns. In this way, by prioritizing consultation topics according to the dog's emotions, important consultation topics can be provided preferentially. Some or all of the above processing in the consultation department may be performed using AI, for example, or without using AI. For example, the consultation department can input dog emotional data into a generating AI, which can then determine the priority of the consultation topics.

[0104] The consultation department can provide optimal advice by considering the owner's geographical location information during consultations. For example, the consultation department can use a generative AI to provide optimal advice by considering the owner's geographical location information during consultations. The generative AI can analyze the owner's location information and provide appropriate advice. For example, if the owner lives in an urban area, the generative AI can provide advice suitable for the urban environment. Also, if the owner lives in the suburbs, the generative AI can provide advice suitable for the suburban environment. Furthermore, if the owner is traveling, the generative AI can provide advice suitable for the environment of the travel destination. In this way, by considering the owner's geographical location information, the consultation department can provide optimal advice. Some or all of the above processing in the consultation department may be performed using AI, for example, or without AI. For example, the consultation department can input the owner's location information into the generative AI, and the generative AI can provide optimal advice.

[0105] The consultation department can analyze the owner's social media activity during consultations and provide relevant advice. For example, the consultation department can use generative AI to analyze the owner's social media activity during consultations and provide relevant advice. The generative AI can analyze the owner's social media posts and provide relevant advice. For example, if the owner posts about their dog's health on social media, the generative AI can provide advice based on that content. Furthermore, if the owner posts about their dog's activities on social media, the generative AI can provide advice based on that content. In addition, if the owner posts about their dog's diet on social media, the generative AI can provide advice based on that content. This allows for the efficient provision of relevant advice by analyzing the owner's social media activity. Some or all of the above processing in the consultation department may be performed using AI, or not. For example, the consultation department can input the owner's social media posts into the generative AI, which can then provide relevant advice.

[0106] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0107] The data collection unit can monitor the dog's body temperature and heart rate in real time and issue alerts if abnormalities are detected. For example, if the dog's body temperature rises rapidly, the unit will send a notification to the owner to prompt appropriate action. Also, if the heart rate exceeds the normal range, the unit can detect the abnormality and recommend consultation with a veterinarian. Furthermore, the unit can transmit the dog's body temperature and heart rate data to the analysis unit for a detailed analysis of its health status. This allows for real-time monitoring of the dog's health status and early detection of abnormalities.

[0108] The analysis unit can analyze a dog's behavioral data and assess its stress level. For example, if a dog's activity level suddenly increases, the analysis unit may suggest stress. Similarly, if a dog's sleep pattern is disrupted, the analysis unit can assess this as a sign of stress. Furthermore, if a dog's food intake decreases, the analysis unit may suggest that stress is the cause. This allows owners to understand their dog's stress level and provide appropriate care.

[0109] The supply unit can suggest appropriate supplements and nutritional supplements based on the dog's health condition. For example, if the analysis unit detects a nutritional imbalance in the dog, the supply unit will suggest supplements containing specific vitamins and minerals. Also, if the dog is highly active, the supply unit can suggest nutritional supplements for energy replenishment. Furthermore, the supply unit can adjust the type and amount of supplements appropriate according to the dog's age and health condition. This allows for the provision of appropriate nutritional support to maintain the dog's health.

[0110] The consultation service can estimate a dog's emotions and customize advice for the owner based on that estimation. For example, if a dog is stressed, the consultation service can provide specific advice to reduce stress. If a dog is relaxed, it can provide advice to the owner on how to maintain that state. Furthermore, if a dog is excited, the consultation service can suggest ways to calm it down. This allows owners to provide appropriate care for their dogs according to their emotions.

[0111] The data collection unit can detect specific behavioral patterns based on the dog's behavioral data, enabling early detection of abnormal behavior. For example, if a dog is active at an unusual time of day, the unit will detect this as abnormal behavior. Furthermore, if a dog frequently repeats the same behavior in a specific location, the unit can evaluate this behavior as abnormal. In addition, if a dog does not eat at its usual mealtime, the unit can detect this as abnormal behavior and notify the owner. This allows for early detection of abnormal behavior in dogs, enabling appropriate action to be taken.

[0112] The analysis unit can estimate the dog's emotions and prioritize the analysis results based on the estimated emotions. For example, if the dog is stressed, stress-related data will be prioritized for analysis. If the dog is relaxed, data on activity levels and sleep patterns will be prioritized for analysis. Furthermore, if the dog is excited, data on changes in behavioral patterns will be prioritized for analysis. This allows for the prioritization of important data according to the dog's emotions, providing more accurate analysis results.

[0113] The service provider can propose an appropriate exercise plan based on the dog's health condition. For example, if the analysis unit detects that the dog is not getting enough exercise, the service provider will propose a specific exercise plan. Furthermore, the service provider can adjust the type and frequency of exercise according to the dog's age and breed. In addition, based on the dog's health condition, the service provider can customize the exercise plan and suggest exercise within a reasonable range. This allows for the provision of an appropriate exercise plan to maintain the dog's health.

[0114] The data collection unit can estimate the dog's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the dog is stressed, the frequency of data collection can be reduced to lessen the dog's burden. Conversely, if the dog is relaxed, the frequency of data collection can be increased to collect more detailed data. Furthermore, if the dog is excited, the timing of data collection can be adjusted to record changes in behavioral patterns in detail. This allows for the collection of detailed data while reducing the dog's burden by adjusting the timing of data collection according to the dog's emotions.

[0115] The analysis unit can analyze long-term health trends based on canine health data. For example, it can track changes in a dog's weight and activity level over a long period to evaluate health trends. It can also analyze changes in a dog's food intake and sleep patterns to understand long-term trends in their health. Furthermore, the analysis unit can predict future health risks and suggest preventive measures based on the dog's health data. This allows owners to understand their dog's long-term health trends and provide appropriate care.

[0116] The information provider can estimate the dog's emotions and adjust the format of the information provided based on the estimated emotions. For example, if the dog is stressed, the provider can provide concise advice on stress reduction. If the dog is relaxed, the provider can provide detailed information in a format that is easy for the owner to understand. Furthermore, if the dog is excited, the provider can provide information about changes in behavioral patterns in a visually easy-to-understand manner. In this way, the information format can be adjusted according to the dog's emotions, providing information that is easy for the owner to understand.

[0117] The following briefly describes the processing flow for example form 2.

[0118] Step 1: The data collection unit uses devices and apps to collect dog behavior patterns and health data. For example, a sensor attached to the dog's collar measures activity levels, and an app records food intake. The sensor detects the dog's movements and records steps and exercise time, while the app records the type and amount of food entered by the owner and stores it in a database. Step 2: The analysis unit uses a generation AI to analyze the data collected by the collection unit. For example, it analyzes the dog's activity level, sleep patterns, and food intake to provide information about the dog's health and behavior. The analysis unit can detect a decrease in the dog's activity level and suggest the possibility of insufficient exercise, or it can analyze the dog's sleep patterns and evaluate the quality of sleep. Step 3: The provision unit provides information and suggestions for improvement based on the analysis results obtained by the analysis unit. For example, it may suggest an appropriate amount of exercise for a dog that is not getting enough exercise, or suggest improvements to the diet if the amount of food intake is inappropriate. Using the generation AI, it is possible to suggest appropriate exercise levels, adjustments to nutritional balance, and changes in the type of food based on the dog's health condition. Step 4: The consultation department provides general advice to owners regarding their questions and their dogs' symptoms, and supports scheduling in-person consultations with veterinarians and handling emergencies when necessary. For example, it uses generated AI to provide general advice to owners' questions and recommends consultations with veterinarians as needed. This allows owners to monitor their dogs' health in real time and provide appropriate care.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0121] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] Each of the multiple elements described above, including the data collection unit, analysis unit, provision unit, and consultation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects dog behavior patterns and health data using sensors and apps on the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using generating AI. The provision unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides information and suggestions for improvement based on the analysis results. The consultation unit supports online consultations with veterinarians through apps on the smart device 14. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0123] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0124] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0126] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0127] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0129] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0130] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0131] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0132] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0133] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0137] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0138] Each of the multiple elements described above, including the data collection unit, analysis unit, provision unit, and consultation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects dog behavior patterns and health data using the sensors and app of the smart glasses 214. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using generating AI. The provision unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides information and suggestions for improvement based on the analysis results. The consultation unit supports online consultations with veterinarians through the app of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0139] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0140] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0141] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0142] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0143] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0145] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0146] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0147] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0148] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0149] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0151] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0153] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0154] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and consultation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects dog behavior patterns and health data using the sensors and applications of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using generating AI. The provision unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides information and suggestions for improvement based on the analysis results. The consultation unit supports online consultations with veterinarians through the application of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0155] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0156] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0158] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0159] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0161] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0162] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0163] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0164] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0165] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0166] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0167] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0168] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0169] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0170] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0171] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and consultation unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects dog behavior patterns and health data using the robot 414's sensors and applications. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data using generated AI. The provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and provides information and suggestions for improvement based on the analysis results. The consultation unit supports online consultations with veterinarians through the robot 414's application. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0172] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0173] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0174] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0175] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0176] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0177] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0179] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0180] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0181] 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.

[0182] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0183] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0184] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0185] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0186] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0187] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0188] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0189] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0190] (Note 1) A collection unit that collects dog behavior patterns and health data using a device or app, An analysis unit analyzes the data collected by the aforementioned collection unit, A providing unit that provides information and suggestions for improvement based on the analysis results obtained by the aforementioned analysis unit, It includes a consultation department that supports online consultations with veterinarians. A system characterized by the following features. (Note 2) The aforementioned collection unit is A sensor attached to the dog's collar measures its activity level, and an app records its food intake. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, We analyze a dog's activity level, sleep patterns, and food intake to provide information about the dog's health and behavior. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Based on the analysis results, we will suggest an appropriate amount of exercise for dogs that are not getting enough exercise. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, If your food intake is inappropriate, we will suggest ways to improve your diet. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned consultation department, We provide general advice to pet owners regarding their questions and their dogs' symptoms, and, when necessary, we can help schedule in-person consultations with veterinarians and handle emergencies. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the dog's emotions and adjusts the frequency of data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the dog's past behavioral patterns to select the optimal timing for data collection. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed based on the dog's current health status and activity level. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system estimates the dog's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the system prioritizes collecting highly relevant data, taking into account the dog's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, we analyze the social media activity of dog owners and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the dog's emotions and adjusts the analysis algorithm based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of the dog's health data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the dog's category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the dog's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, We estimate the dog's emotions and adjust how the information we provide is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing the data, we adjust the level of detail of the information provided based on the importance of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing information, different information provision algorithms are applied depending on the dog's category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, The system estimates the dog's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing the data, we will prioritize the information based on the timing of the submission of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing the data, the order of information will be adjusted based on the relevance of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned consultation department, The system estimates the dog's emotions and adjusts the way the consultation is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned consultation department, During consultations, we refer to the owner's past consultation history to provide the most appropriate advice. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned consultation department, During the consultation, we will customize the content of the consultation based on the dog's current health condition. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned consultation department, The system estimates the dog's emotions and prioritizes the consultation topics based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned consultation department, When providing consultations, we take into account the owner's geographical location to offer the most appropriate advice. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned consultation department, During consultations, we analyze the owner's social media activity and provide relevant advice. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects dog behavior patterns and health data using a device or app, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a provisioning unit provides information and suggestions for improvement regarding dog health management using generating AI. It includes a consultation department that supports online consultations with veterinarians, The data collection unit analyzes the dog's behavioral data and physiological data, such as changes in heart rate and activity level, to estimate whether the dog is stressed or relaxed. Based on the estimated emotion of the dog, it adjusts the data collection frequency, reducing it if the dog is estimated to be stressed and increasing it if the dog is estimated to be relaxed. Furthermore, it determines the priority of the data to be collected based on the estimated emotion of the dog; if the dog is estimated to be stressed, it prioritizes the collection of stress-related data, and if the dog is estimated to be relaxed, it prioritizes the collection of activity level and sleep pattern data. The analysis unit analyzes the dog's activity level, sleep patterns, and food intake included in the data collected by the collection unit using generating AI to generate analysis results regarding the dog's health status and behavior. Based on the analysis results, the aforementioned provisioning unit proposes an appropriate amount of exercise for dogs that are not getting enough exercise, and suggests improvements to their diet if their food intake is inappropriate. A system characterized by the following features.

2. The aforementioned collection unit is A sensor attached to the dog's collar measures its activity level, and an app records its food intake. The system according to feature 1.

3. The aforementioned consultation department, We provide general advice to pet owners regarding their questions and their dogs' symptoms, and, when necessary, we can help schedule in-person consultations with veterinarians and handle emergencies. The system according to feature 1.

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