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 technology]

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult to efficiently collect and analyze a dog's behavioral patterns and health data and provide appropriate information and suggestions for improvement.

[0005] The system according to the embodiment aims to efficiently collect and analyze dog behavior patterns and health data, and provide appropriate information and suggestions for improvement. [Means for solving the problem]

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

[0007] The system according to the embodiment can efficiently collect and analyze behavioral patterns and health data of dogs, and provide appropriate information and suggestions for improvement. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

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

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

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) The dog health management system according to the embodiment of the present invention is a system that uses a device or an app to collect dog behavior patterns and health data, and analyzes them using a generation AI. The system provides information and improvements on the dog's health condition and behavior. It also provides a service that allows online consultation with a veterinarian. For example, a device or an app is used to collect data such as the dog's activity level, sleep pattern, and food intake. For example, a sensor attached to the dog's collar measures the activity level, and an app records the food intake. These data are input into the generation AI. The generation AI then analyzes the collected data. The generation AI analyzes the dog's activity level, sleep pattern, food intake, and the like, and provides information on the dog's health condition and behavior. For example, the generation AI can detect a decrease in the dog's activity level and suggest the possibility of insufficient exercise. Furthermore, the generation AI provides improvements based on the analysis results. For example, an appropriate amount of exercise can be suggested for a dog that is not exercising enough. In addition, if the amount of food intake is not appropriate, it is also possible to suggest improvements to the diet. In addition, the owner can consult with a veterinarian online through the app. The generative AI provides general advice to owners' questions and their dog's symptoms, and supports scheduling face-to-face consultations with veterinarians and emergency response if necessary. For example, if an owner asks about their dog's loss of appetite, the generative AI provides general advice and recommends consulting a veterinarian if necessary. This system allows owners to understand their dog's health condition in real time and provide appropriate care. In addition, online consultations with veterinarians allow for quicker responses and more efficient health management of dogs. This allows the dog health management system to understand the dog's health condition in real time and provide appropriate care.

[0029] A dog health management system according to an embodiment includes a collection unit, an analysis unit, a provision unit, and a consultation unit. The collection unit collects the dog's behavioral patterns and health data using a device or an app. For example, the collection unit uses a sensor attached to the dog's collar to measure the amount of activity, and the app records the amount of food intake. For example, the collection unit can measure the amount of activity of the dog using a sensor attached to the dog's collar. The sensor detects the movement of the dog and records the number of steps and the exercise time. The collection unit can also record the amount of food intake of the dog using an app. The app records the content and amount of food input by the owner and stores it in a database. The analysis unit uses a generation AI to analyze the data collected by the collection unit. For example, the analysis unit analyzes the dog's activity level, sleep pattern, food intake, etc., and provides information on the dog's health condition and behavior. For example, the analysis unit can detect a decrease in the dog's activity level using the generation AI and suggest the possibility of lack of exercise. The analysis unit can also analyze the dog's sleep pattern and evaluate the quality of sleep using the generation AI. The providing unit provides information and improvements based on the analysis results obtained by the analyzing unit. The providing unit can, for example, suggest an appropriate amount of exercise for a dog that is not getting enough exercise. The providing unit can, for example, use a generating AI to suggest an appropriate amount of exercise based on the health condition of the dog. In addition, the providing unit can also suggest improvements to the diet if the amount of food intake is not appropriate. The providing unit can, for example, use a generating AI to analyze the amount of food intake of the dog and suggest adjusting the nutritional balance or changing the type of food. The consultation unit provides general advice for the owner's questions and the symptoms of the dog, and supports reservations for face-to-face consultations with veterinarians and emergency responses if necessary. The consultation unit can, for example, use a generating AI to provide general advice for the owner's questions. For example, when the owner asks about the dog's loss of appetite, the consultation unit can provide general advice and recommend consultation with a veterinarian if necessary. As a result, the dog health management system according to the embodiment can grasp the health condition of the dog in real time and provide appropriate care. Part or all of the above-mentioned processing in the consultation unit may be performed using an AI, or may be performed without using an AI.For example, the consultation unit can input an owner's question into the generation AI, which can then output general advice.

[0030] The collection unit uses a device and an app to collect the dog's behavioral patterns and health data. Specifically, a sensor attached to the dog's collar measures the amount of activity, and the app records the amount of food intake. The sensor attached to the collar has a built-in accelerometer and gyroscope, and can detect the dog's movements in detail. This makes it possible to accurately record the number of steps, exercise time, and even the intensity of exercise. For example, data such as how far the dog walked during a walk and how fast it ran can be collected. The sensor can also record the dog's resting time and sleep patterns, which allows the dog's overall daily activity to be understood. 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 the amount of food intake in detail, which allows the dog's nutritional intake status to be accurately understood. In addition, the app also records the time and frequency of meals, providing data for analyzing the dog's eating patterns. This allows the collection unit to comprehensively collect the dog's behavioral patterns and health data and understand the situation in real time. The collected data is sent to a cloud server and made accessible to the analysis and provision units, allowing the collection unit to collect data efficiently and effectively and improve the performance of the entire system.

[0031] The analysis unit uses the generation AI to analyze the data collected by the collection unit. Specifically, it analyzes the dog's activity level, sleep pattern, food intake, etc., and provides information on the dog's health condition and behavior. The generation AI performs a detailed analysis of the dog's behavior pattern and health condition based on the collected data. For example, it can detect a decrease in the dog's activity level and suggest the possibility of lack of exercise. The generation AI can detect abnormal patterns by comparing with past data and detect problems early. The generation AI can also analyze the dog's sleep pattern and evaluate the quality of sleep. For example, if the dog wakes up frequently at night, it can suggest the possibility of stress or health problems. Furthermore, the generation AI can analyze the amount of food intake and suggest adjustments to nutritional balance or changes in the type of food. For example, if the dog is consuming too much of a certain nutrient, it can suggest reducing the intake. This allows the analysis unit to quickly and accurately analyze the collected data and provide information on the dog's health condition and behavior. Furthermore, the analysis unit can utilize past data and statistical information to perform long-term health management and trend analysis. This enables the analysis unit to not only grasp the situation in real time, but also handle long-term health management, improving the reliability and safety of the entire system.

[0032] The provision unit provides information and improvements based on the analysis results obtained by the analysis unit. Specifically, it can suggest an appropriate amount of exercise for a dog that is not getting enough exercise. Using the generation AI, it suggests an appropriate amount of exercise based on the health condition of the dog. For example, it can specifically suggest the amount of daily exercise and the type of exercise according to the age, weight, and activity level of the dog. In addition, the provision unit can also suggest improvements to the diet if the amount of food intake is not appropriate. Using the generation AI, it analyzes the dog's food intake and suggests adjusting the nutritional balance and changing the type of food. For example, if the dog is consuming too much of a specific nutrient, it can suggest reducing the intake. In addition, if the dog is deficient in a specific nutrient, it can suggest a diet to supplement that nutrient. In addition, the provision unit can provide a care plan according to the dog's health condition. For example, if the dog is elderly, it can suggest supplements and specific exercises to maintain joint health. In this way, the provision unit can provide specific and practical advice based on the analysis results and support the health management of the dog. In addition, the provision unit can provide specific procedures and schedules to make it easier for the owner to carry out the suggested advice. This enables the provider to provide support for owners to effectively manage the health of their dogs.

[0033] The consultation section provides general advice to the owner's questions and the dog's symptoms, and supports the reservation of a face-to-face consultation with a veterinarian if necessary and emergency response. Specifically, the generation AI is used to provide general advice to the owner's questions. For example, if the owner asks about the dog's loss of appetite, the generation AI provides general advice and recommends a consultation with a veterinarian if necessary. The generation AI can generate appropriate answers to the owner's questions based on past data and expert knowledge. For example, if the dog's loss of appetite continues, it can suggest a change in diet or a way to reduce stress. In addition, the generation AI can evaluate the urgency of the dog's symptoms based on the information entered by the owner and recommend emergency response if necessary. For example, if the dog suddenly loses energy, it can recommend that the owner consult a veterinarian immediately. In addition, the consultation section also provides support for the owner to make a reservation for a face-to-face consultation with a veterinarian. For example, the generation AI can check the owner's schedule and suggest the best appointment date and time based on the veterinarian's availability. This allows the consultation section to support the owner in quickly and appropriately responding to the dog's health problems. In addition, the consultation department can collect feedback from pet owners and continuously improve the accuracy and effectiveness of the AI's advice, allowing the consultation department to provide reliable advice to pet owners and support their dog's health management.

[0034] The collection unit can measure the amount of activity of the dog using a sensor attached to the dog's collar, and the app can record the amount of food intake. The collection unit can measure the amount of activity of the dog using, for example, a sensor attached to the dog's collar. The sensor detects the movement of the dog and records the number of steps and the exercise time. The collection unit can also record the amount of food intake of the dog using an app. The app records the content and amount of food input by the owner and stores it in a database. This makes it possible to accurately record the amount of activity and the amount of food intake of the dog. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input data acquired from a sensor attached to the dog's collar to a generation AI, and the generation AI can analyze the data.

[0035] The analysis unit can analyze the dog's activity level, sleep pattern, and food intake, and provide information on the dog's health condition and behavior. The analysis unit can analyze the dog's activity level, sleep pattern, and food intake, for example, using a generation AI. The generation AI can detect a decrease in the dog's activity level and suggest the possibility of lack of exercise. The generation AI can also analyze the dog's sleep pattern and evaluate the quality of sleep. Furthermore, the generation AI can analyze the dog's food intake and suggest adjusting the nutritional balance or changing the type of food. This allows the owner to take appropriate care by providing information on the dog's health condition and behavior. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the analysis unit can input data acquired from the collection unit to the generation AI, and the generation AI can analyze the data.

[0036] The providing unit can suggest an appropriate amount of exercise for a dog that is lacking in exercise based on the analysis result. The providing unit, for example, uses a generating AI to suggest an appropriate amount of exercise for a dog that is lacking in exercise. The generating AI can analyze the activity amount data of the dog and suggest an appropriate amount of exercise for the dog that is lacking in exercise. For example, the generating AI can suggest an appropriate amount of exercise based on the age and breed of the dog. The generating AI can also adjust the amount of exercise based on the health condition of the dog. This makes it possible to maintain the health of the dog by suggesting an appropriate amount of exercise for a dog that is lacking in exercise. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the providing unit can input data acquired from the analyzing unit to the generating AI, and the generating AI can suggest an appropriate amount of exercise.

[0037] The provision unit can suggest improvements to the diet if the amount of food intake is not appropriate. The provision unit, for example, uses a generation AI to suggest improvements to the diet if the amount of food intake is not appropriate. The generation AI can analyze the dietary data of the dog and suggest adjustments to the nutritional balance or changes to the type of food. For example, the generation AI can suggest appropriate improvements to the diet based on the age and health condition of the dog. The generation AI can also analyze the amount of food intake of the dog and make suggestions to prevent over-intake or deficiency. This makes it possible to maintain the health of the dog by suggesting improvements to the diet if the amount of food intake is not appropriate. Some or all of the above-mentioned processing in the provision unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the provision unit can input data acquired from the analysis unit to the generation AI, and the generation AI can suggest improvements to the diet.

[0038] The consultation unit provides general advice for the owner's questions and the dog's symptoms, and can support making a reservation for a face-to-face consultation with a veterinarian or responding to an emergency if necessary. The consultation unit provides general advice for the owner's questions, for example, using a generation AI. The generation AI can analyze the owner's questions and provide general advice. For example, if the owner asks about the dog's loss of appetite, the generation AI provides general advice and recommends consulting with a veterinarian if necessary. The consultation unit can also support making a reservation for a face-to-face consultation with a veterinarian or responding to an emergency if necessary. For example, the generation AI can make a reservation for a face-to-face consultation with a veterinarian based on the owner's questions and the dog's symptoms. This allows the owner to quickly consult about the dog's health condition, and enables a face-to-face consultation with a veterinarian or responding to an emergency if necessary. Some or all of the above-mentioned processing in the consultation unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the consultation unit can input the owner's questions to the generation AI, and the generation AI can output general advice.

[0039] The collection unit can estimate the dog's emotions and adjust the frequency of data collection based on the estimated dog's emotions. The collection unit estimates the dog's emotions using, for example, a generation AI. The generation AI can analyze the dog's behavioral data and physiological data to estimate the dog's emotions. For example, the generation AI analyzes changes in the dog's heart rate and activity level to estimate the dog's stressed or relaxed state. The generation AI can also analyze the dog's facial expression and tone of voice to estimate the emotions. The collection unit adjusts the frequency of data collection based on the estimated dog's emotions. For example, if the dog is stressed, the frequency of data collection is reduced to reduce the burden on the dog. Also, if the dog is relaxed, the frequency of data collection can be increased to collect detailed data. Furthermore, if the dog is excited, the frequency of data collection can be temporarily increased to record changes in the behavior pattern in detail. As a result, by adjusting the frequency of data collection according to the dog's emotions, the burden on the dog can be reduced and detailed data can be collected. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input a dog's emotional data into the generation AI, which can then adjust the frequency of data collection.

[0040] The collection unit can analyze the dog's past behavioral patterns and select the optimal data collection timing. The collection unit can analyze the dog's past behavioral patterns, for example, using a generation AI. The generation AI can analyze past data and grasp the dog's behavioral patterns. For example, if the dog was active during a specific time period in the past, the generation AI can focus data collection on that time period. In addition, if the dog was resting during a specific time period in the past, the generation AI can refrain from data collection during that time period. Furthermore, the generation AI can analyze the dog's past behavioral patterns and strengthen data collection on weekends and at specific events. In this way, data can be collected at the optimal timing by analyzing the dog's past behavioral patterns. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the collection unit can input past behavioral data into the generation AI, and the generation AI can select the optimal data collection timing.

[0041] The collection unit can perform filtering based on the dog's current health condition and activity level when collecting data. The collection unit can perform filtering based on the dog's current health condition and activity level when collecting data, for example, using a generation AI. The generation AI can analyze the dog's health data and activity data and collect only necessary data. For example, the generation AI can perform normal data collection when the dog is in good health. In addition, the generation AI can refrain from data collection and collect only necessary data when the dog is in poor health. Furthermore, the generation AI can also collect detailed data when the dog's activity level is high and collect only basic data when the activity level is low. This makes it possible to collect only necessary data by filtering data according to the dog's health condition and activity level. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the collection unit can input the dog's health data to the generation AI, and the generation AI can filter the data.

[0042] The collection unit can estimate the dog's emotions and determine the priority of data to be collected based on the estimated emotions of the dog. The collection unit estimates the dog's emotions, for example, using a generation AI. The generation AI can analyze the behavioral data and physiological data of the dog to estimate the dog's emotions. For example, the generation AI analyzes changes in the dog's heart rate and activity level to estimate the stress or relaxation state. The generation AI can also analyze the dog's facial expression and tone of voice to estimate the emotions. The collection unit determines the priority of data to be collected based on the estimated emotions of the dog. For example, if the dog is stressed, stress-related data is preferentially collected. Also, if the dog is relaxed, data on the activity level and sleep pattern can be preferentially collected. Furthermore, if the dog is excited, data on changes in behavior patterns can be preferentially collected. As a result, important data can be preferentially collected by determining the priority of data to be collected according to the dog's emotions. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the dog's emotion data to the generation AI, and the generation AI can determine the priority of the data.

[0043] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the dog when collecting data. The collection unit, for example, uses a generation AI to preferentially collect highly relevant data in consideration of the geographical location information of the dog when collecting data. The generation AI can analyze the location information of the dog and select highly relevant data. For example, when the dog is in a park, the generation AI preferentially collects data on the amount of activity and the movement pattern. In addition, when the dog is at home, the generation AI can preferentially collect data on the sleep pattern and the amount of food intake. Furthermore, when the dog is at a veterinary clinic, the generation AI can also preferentially collect data on the health condition. In this way, by considering the geographical location information of the dog, highly relevant data can be preferentially collected. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the collection unit can input the location information of the dog to the generation AI, and the generation AI can select highly relevant data.

[0044] The collection unit can analyze the social media activity of the dog owner at the time of data collection and collect related data. The collection unit can use, for example, a generation AI to analyze the social media activity of the dog owner at the time of data collection and collect related data. The generation AI can analyze the owner's social media posts and select related data. For example, when the owner posts on social media about the dog's health, the generation AI can collect data based on the content. In addition, when the owner posts on social media about the dog's activities, the generation AI can collect data based on the content. In addition, when the owner posts on social media about the dog's diet, the generation AI can also collect data based on the content. In this way, by analyzing the owner's social media activity, related data can be efficiently collected. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the owner's social media posts to the generation AI, and the generation AI can select related data.

[0045] The analysis unit can estimate the dog's emotions and adjust the analysis algorithm based on the estimated dog's emotions. The analysis unit estimates the dog's emotions, for example, using a generation AI. The generation AI can analyze the dog's behavioral data and physiological data to estimate the dog's emotions. For example, the generation AI analyzes changes in the dog's heart rate and activity level to estimate the dog's stressed or relaxed state. The generation AI can also analyze the dog's facial expression and tone of voice to estimate the emotions. The analysis unit adjusts the analysis algorithm based on the estimated dog's emotions. For example, if the dog is stressed, the analysis is performed with emphasis on stress-related data. Also, if the dog is relaxed, the analysis can be performed with emphasis on activity level and sleep pattern data. Furthermore, if the dog is excited, the analysis can be performed with emphasis on data related to changes in behavior patterns. As a result, a more accurate analysis result can be obtained by adjusting the analysis algorithm according to the dog's emotions. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit inputs the dog's emotion data into the generation AI, and the generation AI can 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 at the time of analysis. The analysis unit can adjust the level of detail of the analysis based on the importance of the dog's health data at the time of analysis, for example, using a generation AI. The generation AI can analyze the dog's health data and adjust the level of detail of the analysis according to the importance. For example, if the dog's health data is important, the generation AI can perform a detailed analysis and provide specific improvements. In addition, if the dog's health data is general, the generation AI can perform a basic analysis and provide general advice. Furthermore, if the dog's health data is urgent, the generation AI can quickly perform a detailed analysis and propose an emergency response. As a result, by adjusting the level of detail of the analysis according to the importance of the dog's health data, an appropriate analysis result can be provided. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the analysis unit can input the dog's health data to the generation AI, and the generation AI can adjust the level of detail of the analysis.

[0047] The analysis unit can apply different analysis algorithms depending on the dog category during analysis. The analysis unit can apply different analysis algorithms depending on the dog category (age, breed, etc.) during analysis, for example, using the generation AI. The generation AI can select an appropriate analysis algorithm based on the dog category. For example, in the case of a puppy, the generation AI can perform analysis by placing emphasis on data regarding growth. In addition, in the case of an elderly dog, the generation AI can perform analysis by placing emphasis on data regarding the health condition. Furthermore, in the case of a specific breed, the generation AI can also perform analysis by taking into account health risks specific to that breed. In this way, by applying an analysis algorithm depending on the dog category, a more appropriate analysis result can be provided. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input dog category data to the generation AI, and the generation AI can 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 of the dog. The analysis unit estimates the dog's emotions using, for example, a generation AI. The generation AI can analyze the behavioral data and physiological data of the dog to estimate the dog's emotions. For example, the generation AI analyzes changes in the dog's heart rate and activity level to estimate the stress or relaxation state. The generation AI can also analyze the dog's facial expression and tone of voice to estimate the 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 is highlighted and displayed. Also, if the dog is relaxed, data on the activity level and sleep pattern can be displayed in detail. Furthermore, if the dog is excited, data on changes in behavior patterns can be highlighted and displayed. As a result, by adjusting the display method of the analysis results according to the dog's emotions, it is possible to provide information that is easy for the owner to understand. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input a dog's emotional data into the generation AI, which can then adjust the way the analysis results are displayed.

[0049] The analysis unit can determine the priority of the analysis based on the collection time of the data at the time of analysis. The analysis unit can determine the priority of the analysis based on the collection time of the data at the time of analysis, for example, using the generation AI. The generation AI can analyze the collection time of the data and determine the priority. For example, the generation AI can analyze the latest data preferentially and provide real-time information. In addition, the generation AI can analyze the current data while referring to the past data. Furthermore, the generation AI can also analyze the data at the time of a specific event preferentially and evaluate its influence. In this way, by determining the priority of the analysis based on the collection time of the data, real-time information can be provided. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the collection time of the data to the generation AI, and the generation AI can determine the priority of the analysis.

[0050] The analysis unit can adjust the order of analysis based on the relevance of data during analysis. The analysis unit adjusts the order of analysis based on the relevance of data during analysis, for example, using a generation AI. The generation AI can analyze the relevance of data and adjust the order of analysis. For example, the generation AI can analyze highly relevant data preferentially and provide detailed information. In addition, the generation AI can analyze important data first, leaving less relevant data for later. Furthermore, the generation AI can dynamically adjust the order of analysis based on the relevance of data. As a result, important data can be analyzed preferentially by adjusting the order of analysis based on the relevance of data. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the relevance of data to the generation AI, and the generation AI can adjust the order of analysis.

[0051] The providing unit can estimate the dog's emotions and adjust the way of expressing the information to be provided based on the estimated emotions of the dog. The providing unit estimates the dog's emotions, for example, using a generating AI. The generating AI can analyze the behavioral data and physiological data of the dog to estimate the emotions of the dog. For example, the generating AI analyzes the changes in the dog's heart rate and activity level to estimate the stress or relaxation state. The generating AI can also analyze the facial expression and tone of the dog's voice to estimate the emotions. The providing unit adjusts the way of expressing the information to be provided based on the estimated emotions of the dog. For example, if the dog is stressed, advice for reducing stress is provided with emphasis. Also, if the dog is relaxed, data on the activity level and sleep pattern can be provided in detail. Furthermore, if the dog is excited, data on changes in behavior patterns can be provided with emphasis. As a result, by adjusting the way of expressing information according to the emotions of the dog, it is possible to provide information that is easy for the owner to understand. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the providing unit inputs the emotion data of the dog to the generating AI, and the generating AI can adjust the way of expressing the information.

[0052] The providing unit can adjust the level of detail of the information to be provided based on the importance of the analysis result at the time of providing. The providing unit adjusts the level of detail of the information to be provided based on the importance of the analysis result at the time of providing, for example, by using a generating AI. The generating AI can analyze the importance of the analysis result and adjust the level of detail of the information. For example, the generating AI provides detailed information for an important analysis result. In addition, the generating AI can provide basic information for a general analysis result. Furthermore, the generating AI can also quickly provide detailed information for an analysis result with urgency. As a result, by adjusting the level of detail of the information according to the importance of the analysis result, appropriate information can be provided. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the providing unit can input the importance of the analysis result to the generating AI, and the generating AI can adjust the level of detail of the information.

[0053] The providing unit can apply different information provision algorithms according to the dog category at the time of provision. The providing unit applies different information provision algorithms according to the dog category (age, breed, etc.) at the time of provision, for example, using the generating AI. The generating AI can select an appropriate information provision algorithm based on the dog category. For example, in the case of a puppy, the generating AI can provide information on growth with emphasis. In addition, in the case of an elderly dog, the generating AI can provide information on health management with emphasis. Furthermore, in the case of a specific breed, the generating AI can also provide information on health risks specific to that breed. As a result, more appropriate information can be provided by applying an information provision algorithm according to the dog category. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit inputs dog category data to the generating AI, and the generating AI can select an appropriate information provision algorithm.

[0054] The providing unit can estimate the dog's emotion and determine the priority of information to be provided based on the estimated emotion of the dog. The providing unit estimates the emotion of the dog using, for example, a generating AI. The generating AI can analyze the behavioral data and physiological data of the dog to estimate the emotion of the dog. For example, the generating AI analyzes the change in the dog's heart rate and activity level to estimate the stress or relaxation state. The generating AI can also analyze the facial expression and tone of voice of the dog to estimate the emotion. The providing unit determines the priority of information to be provided based on the estimated emotion of the dog. For example, if the dog is stressed, information for stress reduction can be provided preferentially. Also, if the dog is relaxed, information on the activity level and sleep pattern can be provided preferentially. Furthermore, if the dog is excited, information on changes in behavioral patterns can be provided preferentially. As a result, important information can be provided preferentially by determining the priority of information according to the emotion of the dog. Part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit inputs the emotion data of the dog to the generating AI, and the generating AI can determine the priority of the information.

[0055] The providing unit can determine the priority of information based on the time of submission of the analysis result at the time of provision. The providing unit can determine the priority of information based on the time of submission of the analysis result at the time of provision, for example, using the generation AI. The generation AI can analyze the time of submission of the analysis result and determine the priority of information. For example, the generation AI can provide the latest analysis result preferentially and provide real-time information. In addition, the generation AI can provide current information while referring to past analysis results. Furthermore, the generation AI can also provide the analysis result at the time of a specific event preferentially and evaluate its influence. As a result, real-time information can be provided by determining the priority of information based on the time of submission of the analysis result. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the time of submission of the analysis result to the generation AI, and the generation AI can determine the priority of information.

[0056] The providing unit can adjust the order of information based on the relevance of the analysis result at the time of providing. The providing unit adjusts the order of information based on the relevance of the analysis result at the time of providing, for example, by using a generation AI. The generation AI can analyze the relevance of the analysis result and adjust the order of information. For example, the generation AI provides highly relevant information preferentially and provides detailed information. In addition, the generation AI can postpone information with low relevance and provide important information first. Furthermore, the generation AI can dynamically adjust the order of provision based on the relevance of information. As a result, important information can be provided preferentially by adjusting the order of information based on the relevance of the analysis result. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the relevance of the analysis result to the generation AI, and the generation AI can adjust the order of information.

[0057] The consultation unit can estimate the dog's emotions and adjust the way of expressing the consultation content based on the estimated emotion of the dog. The consultation unit estimates the dog's emotions using, for example, a generation AI. The generation AI can analyze the behavioral data and physiological data of the dog to estimate the emotion of the dog. For example, the generation AI analyzes the change in the dog's heart rate and activity level to estimate the stress or relaxation state. The generation AI can also analyze the facial expression and tone of voice of the dog to estimate the emotion. The consultation unit adjusts the way of expressing the consultation content based on the estimated emotion of the dog. For example, if the dog is stressed, advice for stress reduction is emphasized and provided. Also, if the dog is relaxed, data on the activity level and sleep pattern can be provided in detail. Furthermore, if the dog is excited, data on changes in behavior patterns can be emphasized and provided. As a result, by adjusting the way of expressing the consultation content according to the emotion of the dog, it is possible to provide advice that is easy for the owner to understand. A part or all of the above-mentioned processing in the consultation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the consultation department can input data about a dog's emotions into the generation AI, which can then adjust the way the consultation content is expressed.

[0058] The consultation unit can provide optimal advice by referring to the owner's past consultation history at the time of consultation. The consultation unit can provide optimal advice by referring to the owner's past consultation history, for example, using a generation AI. The generation AI can analyze the owner's past consultation history and provide related advice. For example, the generation AI can provide related advice based on the content of the owner's past consultations. In addition, the generation AI can propose a solution to a similar problem from the owner's past consultation history. Furthermore, the generation AI can also analyze the owner's past consultation history and provide the most effective advice. As a result, related advice can be provided by referring to the owner's past consultation history. Part or all of the above-mentioned processing in the consultation unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the consultation unit can input the owner's past consultation history into the generation AI, and the generation AI can provide optimal advice.

[0059] The consultation unit can customize the consultation content based on the dog's current health condition at the time of consultation. The consultation unit customizes the consultation content based on the dog's current health condition at the time of consultation, for example, using a generation AI. The generation AI can analyze the dog's health data and provide appropriate advice. For example, the generation AI provides general advice when the dog's health condition is good. In addition, the generation AI can suggest specific improvement measures when the dog's health condition is poor. Furthermore, the generation AI can also respond quickly if an emergency response is required 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. Part or all of the above-mentioned processing in the consultation unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the consultation unit can input the dog's health data into the generation AI, and the generation AI can customize the consultation content.

[0060] The consultation unit can estimate the dog's emotions and determine the priority of consultation contents based on the estimated emotions of the dog. The consultation unit estimates the dog's emotions using, for example, a generation AI. The generation AI can analyze the behavioral data and physiological data of the dog to estimate the emotions of the dog. For example, the generation AI analyzes changes in the dog's heart rate and activity level to estimate the stress or relaxation state. The generation AI can also analyze the dog's facial expression and tone of voice to estimate the emotions. The consultation unit determines the priority of consultation contents based on the estimated emotions of the dog. For example, if the dog is stressed, consultation contents for stress reduction can be provided preferentially. Also, if the dog is relaxed, consultation contents regarding activity level and sleep patterns can be provided preferentially. Furthermore, if the dog is excited, consultation contents regarding changes in behavior patterns can be provided preferentially. As a result, important consultation contents can be provided preferentially by determining the priority of consultation contents according to the emotions of the dog. A part or all of the above-mentioned processing in the consultation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the consultation department can input data on a dog's emotions into the generation AI, which can then determine the priority of the consultation content.

[0061] The consultation unit can provide optimal consultation content in consideration of the owner's geographical location information at the time of consultation. The consultation unit can provide optimal consultation content in consideration of the owner's geographical location information at the time of consultation, for example, using a generation AI. The generation AI can analyze the owner's location information and provide appropriate advice. For example, if the owner lives in an urban area, the generation AI can provide advice suitable for an urban environment. Also, if the owner lives in a suburban area, the generation AI can provide advice suitable for a suburban environment. Furthermore, if the owner is traveling, the generation AI can also provide advice suitable for the environment of the travel destination. In this way, optimal consultation content can be provided by considering the owner's geographical location information. Part or all of the above-mentioned processing in the consultation unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the consultation unit can input the owner's location information into the generation AI, and the generation AI can provide optimal advice.

[0062] The consultation unit can provide relevant advice by analyzing the social media activity of the owner at the time of consultation. The consultation unit can provide relevant advice by analyzing the social media activity of the owner at the time of consultation, for example, using a generation AI. The generation AI can analyze the social media posts of the owner and provide relevant advice. For example, when the owner posts on social media about the dog's health, the generation AI can provide advice based on the content. In addition, when the owner posts on social media about the dog's activities, the generation AI can provide advice based on the content. In addition, when the owner posts on social media about the dog's diet, the generation AI can also provide advice based on the content. In this way, by analyzing the social media activity of the owner, relevant advice can be provided efficiently. Part or all of the above-mentioned processing in the consultation unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the consultation unit can input the social media posts of the owner to the generation AI, and the generation AI can provide relevant advice.

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

[0064] The collection unit can monitor the dog's body temperature and heart rate in real time and issue an alert if an abnormality is detected. For example, if the dog's body temperature rises suddenly, the collection unit can send a notification to the owner and prompt them to take appropriate action. Also, if the heart rate exceeds the normal range, the collection unit can detect the abnormality and recommend consulting a veterinarian. Furthermore, the collection unit can send the dog's body temperature and heart rate data to the analysis unit for detailed analysis of the dog's health condition. This makes it possible to monitor the dog's health condition in real time and detect abnormalities early on.

[0065] The providing unit can suggest appropriate supplements and nutritional supplements based on the dog's health condition. For example, if the analysis unit detects a lack of nutritional balance in the dog, the providing unit can suggest supplements containing specific vitamins and minerals. In addition, if the dog is highly active, the providing unit can suggest nutritional supplements for energy supply. Furthermore, the providing unit can adjust the type and amount of appropriate supplements according to the dog's age and health condition. This makes it possible to provide appropriate nutritional support for maintaining the dog's health.

[0066] The collection unit can detect specific behavioral patterns based on the dog's behavioral data and detect abnormal behavior early. For example, if the dog is active at a different time than usual, the collection unit can detect this as abnormal behavior. Also, if the dog frequently repeats the same behavior in a specific place, the collection unit can evaluate the behavior as abnormal. Furthermore, if the dog does not eat at its usual mealtime, the collection unit can detect this as abnormal behavior and notify the owner. This allows for early detection of abnormal behavior in the dog and allows appropriate measures to be taken.

[0067] The providing unit can propose an appropriate exercise plan based on the health condition of the dog. For example, if the analysis unit detects that the dog is not active enough, the providing unit proposes a specific exercise plan. In addition, the providing unit can adjust the appropriate type and frequency of exercise according to the age and breed of the dog. Furthermore, the providing unit can customize the exercise plan based on the health condition of the dog and propose exercise within a reasonable range. This makes it possible to provide an appropriate exercise plan for maintaining the health of the dog.

[0068] The analysis unit can analyze long-term health trends based on the dog's health data. For example, it can track changes in the dog's weight and activity over a long period of time and evaluate the health trends. It can also analyze changes in the dog's food intake and sleep patterns to understand long-term trends in the dog's 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 the long-term health trends of their dogs and provide appropriate care.

[0069] The process flow of the first embodiment will be briefly described below.

[0070] Step 1: The collection unit uses devices and apps to collect the dog's behavioral patterns and health data. For example, a sensor attached to the dog's collar measures the dog's activity level, and the app records the dog's food intake. The sensor detects the dog's movements and records the number of steps and exercise time, and the app records the food content and amount entered by the owner and stores them in a database. Step 2: The analysis unit uses the generation AI to analyze the data collected by the collection unit. For example, it analyzes the dog's activity level, sleep patterns, food intake, etc., and provides information on the dog's health and behavior. The analysis unit can detect a decrease in the dog's activity level and suggest the possibility of lack of exercise, analyze the dog's sleep patterns, and evaluate the quality of sleep. Step 3: The provider provides information and improvements based on the analysis results obtained by the analyzer. For example, it can suggest the appropriate amount of exercise for a dog that is not getting enough exercise, or suggest improvements to the dog's diet if the dog's food intake is not appropriate. Using the generator AI, it can suggest the appropriate amount of exercise, adjustments to nutritional balance, and changes to the type of food based on the dog's health condition. Step 4: The consultation section provides general advice to the owner's questions and the dog's symptoms, and supports scheduling a face-to-face consultation with a veterinarian or responding to an emergency if necessary. For example, generative AI can be used to provide general advice to the owner's questions and recommend a consultation with a veterinarian if necessary. This allows the owner to understand the dog's health condition in real time and provide appropriate care.

[0071] (Example 2) The dog health management system according to the embodiment of the present invention is a system that uses a device or an app to collect dog behavior patterns and health data, and analyzes them using a generation AI. The system provides information and improvements on the dog's health condition and behavior. It also provides a service that allows online consultation with a veterinarian. For example, a device or an app is used to collect data such as the dog's activity level, sleep pattern, and food intake. For example, a sensor attached to the dog's collar measures the activity level, and an app records the food intake. These data are input into the generation AI. The generation AI then analyzes the collected data. The generation AI analyzes the dog's activity level, sleep pattern, food intake, and the like, and provides information on the dog's health condition and behavior. For example, the generation AI can detect a decrease in the dog's activity level and suggest the possibility of insufficient exercise. Furthermore, the generation AI provides improvements based on the analysis results. For example, an appropriate amount of exercise can be suggested for a dog that is not exercising enough. In addition, if the amount of food intake is not appropriate, it is also possible to suggest improvements to the diet. In addition, the owner can consult with a veterinarian online through the app. The generative AI provides general advice to owners' questions and their dog's symptoms, and supports scheduling face-to-face consultations with veterinarians and emergency response if necessary. For example, if an owner asks about their dog's loss of appetite, the generative AI provides general advice and recommends consulting a veterinarian if necessary. This system allows owners to understand their dog's health condition in real time and provide appropriate care. In addition, online consultations with veterinarians allow for quicker responses and more efficient health management of dogs. This allows the dog health management system to understand the dog's health condition in real time and provide appropriate care.

[0072] A dog health management system according to an embodiment includes a collection unit, an analysis unit, a provision unit, and a consultation unit. The collection unit collects the dog's behavioral patterns and health data using a device or an app. For example, the collection unit uses a sensor attached to the dog's collar to measure the amount of activity, and the app records the amount of food intake. For example, the collection unit can measure the amount of activity of the dog using a sensor attached to the dog's collar. The sensor detects the movement of the dog and records the number of steps and the exercise time. The collection unit can also record the amount of food intake of the dog using an app. The app records the content and amount of food input by the owner and stores it in a database. The analysis unit uses a generation AI to analyze the data collected by the collection unit. For example, the analysis unit analyzes the dog's activity level, sleep pattern, food intake, etc., and provides information on the dog's health condition and behavior. For example, the analysis unit can detect a decrease in the dog's activity level using the generation AI and suggest the possibility of lack of exercise. The analysis unit can also analyze the dog's sleep pattern and evaluate the quality of sleep using the generation AI. The providing unit provides information and improvements based on the analysis results obtained by the analyzing unit. The providing unit can, for example, suggest an appropriate amount of exercise for a dog that is not getting enough exercise. The providing unit can, for example, use a generating AI to suggest an appropriate amount of exercise based on the health condition of the dog. In addition, the providing unit can also suggest improvements to the diet if the amount of food intake is not appropriate. The providing unit can, for example, use a generating AI to analyze the amount of food intake of the dog and suggest adjusting the nutritional balance or changing the type of food. The consultation unit provides general advice for the owner's questions and the symptoms of the dog, and supports reservations for face-to-face consultations with veterinarians and emergency responses if necessary. The consultation unit can, for example, use a generating AI to provide general advice for the owner's questions. For example, when the owner asks about the dog's loss of appetite, the consultation unit can provide general advice and recommend consultation with a veterinarian if necessary. As a result, the dog health management system according to the embodiment can grasp the health condition of the dog in real time and provide appropriate care. Part or all of the above-mentioned processing in the consultation unit may be performed using an AI, or may be performed without using an AI.For example, the consultation unit can input an owner's question into the generation AI, which can then output general advice.

[0073] The collection unit uses a device and an app to collect the dog's behavioral patterns and health data. Specifically, a sensor attached to the dog's collar measures the amount of activity, and the app records the amount of food intake. The sensor attached to the collar has a built-in accelerometer and gyroscope, and can detect the dog's movements in detail. This makes it possible to accurately record the number of steps, exercise time, and even the intensity of exercise. For example, data such as how far the dog walked during a walk and how fast it ran can be collected. The sensor can also record the dog's resting time and sleep patterns, which allows the dog's overall daily activity to be understood. 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 the amount of food intake in detail, which allows the dog's nutritional intake status to be accurately understood. In addition, the app also records the time and frequency of meals, providing data for analyzing the dog's eating patterns. This allows the collection unit to comprehensively collect the dog's behavioral patterns and health data and understand the situation in real time. The collected data is sent to a cloud server and made accessible to the analysis and provision units, allowing the collection unit to collect data efficiently and effectively and improve the performance of the entire system.

[0074] The analysis unit uses the generation AI to analyze the data collected by the collection unit. Specifically, it analyzes the dog's activity level, sleep pattern, food intake, etc., and provides information on the dog's health condition and behavior. The generation AI performs a detailed analysis of the dog's behavior pattern and health condition based on the collected data. For example, it can detect a decrease in the dog's activity level and suggest the possibility of lack of exercise. The generation AI can detect abnormal patterns by comparing with past data and detect problems early. The generation AI can also analyze the dog's sleep pattern and evaluate the quality of sleep. For example, if the dog wakes up frequently at night, it can suggest the possibility of stress or health problems. Furthermore, the generation AI can analyze the amount of food intake and suggest adjustments to nutritional balance or changes in the type of food. For example, if the dog is consuming too much of a certain nutrient, it can suggest reducing the intake. This allows the analysis unit to quickly and accurately analyze the collected data and provide information on the dog's health condition and behavior. Furthermore, the analysis unit can utilize past data and statistical information to perform long-term health management and trend analysis. This enables the analysis unit to not only grasp the situation in real time, but also handle long-term health management, improving the reliability and safety of the entire system.

[0075] The provision unit provides information and improvements based on the analysis results obtained by the analysis unit. Specifically, it can suggest an appropriate amount of exercise for a dog that is not getting enough exercise. Using the generation AI, it suggests an appropriate amount of exercise based on the health condition of the dog. For example, it can specifically suggest the amount of daily exercise and the type of exercise according to the age, weight, and activity level of the dog. In addition, the provision unit can also suggest improvements to the diet if the amount of food intake is not appropriate. Using the generation AI, it analyzes the dog's food intake and suggests adjusting the nutritional balance and changing the type of food. For example, if the dog is consuming too much of a specific nutrient, it can suggest reducing the intake. In addition, if the dog is deficient in a specific nutrient, it can suggest a diet to supplement that nutrient. In addition, the provision unit can provide a care plan according to the dog's health condition. For example, if the dog is elderly, it can suggest supplements and specific exercises to maintain joint health. In this way, the provision unit can provide specific and practical advice based on the analysis results and support the health management of the dog. In addition, the provision unit can provide specific procedures and schedules to make it easier for the owner to carry out the suggested advice. This enables the provider to provide support for owners to effectively manage the health of their dogs.

[0076] The consultation section provides general advice to the owner's questions and the dog's symptoms, and supports the reservation of a face-to-face consultation with a veterinarian if necessary and emergency response. Specifically, the generation AI is used to provide general advice to the owner's questions. For example, if the owner asks about the dog's loss of appetite, the generation AI provides general advice and recommends a consultation with a veterinarian if necessary. The generation AI can generate appropriate answers to the owner's questions based on past data and expert knowledge. For example, if the dog's loss of appetite continues, it can suggest a change in diet or a way to reduce stress. In addition, the generation AI can evaluate the urgency of the dog's symptoms based on the information entered by the owner and recommend emergency response if necessary. For example, if the dog suddenly loses energy, it can recommend that the owner consult a veterinarian immediately. In addition, the consultation section also provides support for the owner to make a reservation for a face-to-face consultation with a veterinarian. For example, the generation AI can check the owner's schedule and suggest the best appointment date and time based on the veterinarian's availability. This allows the consultation section to support the owner in quickly and appropriately responding to the dog's health problems. In addition, the consultation department can collect feedback from pet owners and continuously improve the accuracy and effectiveness of the AI's advice, allowing the consultation department to provide reliable advice to pet owners and support their dog's health management.

[0077] The collection unit can measure the amount of activity of the dog using a sensor attached to the dog's collar, and the app can record the amount of food intake. The collection unit can measure the amount of activity of the dog using, for example, a sensor attached to the dog's collar. The sensor detects the movement of the dog and records the number of steps and the exercise time. The collection unit can also record the amount of food intake of the dog using an app. The app records the content and amount of food input by the owner and stores it in a database. This makes it possible to accurately record the amount of activity and the amount of food intake of the dog. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input data acquired from a sensor attached to the dog's collar to a generation AI, and the generation AI can analyze the data.

[0078] The analysis unit can analyze the dog's activity level, sleep pattern, and food intake, and provide information on the dog's health condition and behavior. The analysis unit can analyze the dog's activity level, sleep pattern, and food intake, for example, using a generation AI. The generation AI can detect a decrease in the dog's activity level and suggest the possibility of lack of exercise. The generation AI can also analyze the dog's sleep pattern and evaluate the quality of sleep. Furthermore, the generation AI can analyze the dog's food intake and suggest adjusting the nutritional balance or changing the type of food. This allows the owner to take appropriate care by providing information on the dog's health condition and behavior. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the analysis unit can input data acquired from the collection unit to the generation AI, and the generation AI can analyze the data.

[0079] The providing unit can suggest an appropriate amount of exercise for a dog that is lacking in exercise based on the analysis result. The providing unit, for example, uses a generating AI to suggest an appropriate amount of exercise for a dog that is lacking in exercise. The generating AI can analyze the activity amount data of the dog and suggest an appropriate amount of exercise for the dog that is lacking in exercise. For example, the generating AI can suggest an appropriate amount of exercise based on the age and breed of the dog. The generating AI can also adjust the amount of exercise based on the health condition of the dog. This makes it possible to maintain the health of the dog by suggesting an appropriate amount of exercise for a dog that is lacking in exercise. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the providing unit can input data acquired from the analyzing unit to the generating AI, and the generating AI can suggest an appropriate amount of exercise.

[0080] The provision unit can suggest improvements to the diet if the amount of food intake is not appropriate. The provision unit, for example, uses a generation AI to suggest improvements to the diet if the amount of food intake is not appropriate. The generation AI can analyze the dietary data of the dog and suggest adjustments to the nutritional balance or changes to the type of food. For example, the generation AI can suggest appropriate improvements to the diet based on the age and health condition of the dog. The generation AI can also analyze the amount of food intake of the dog and make suggestions to prevent over-intake or deficiency. This makes it possible to maintain the health of the dog by suggesting improvements to the diet if the amount of food intake is not appropriate. Some or all of the above-mentioned processing in the provision unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the provision unit can input data acquired from the analysis unit to the generation AI, and the generation AI can suggest improvements to the diet.

[0081] The consultation unit provides general advice for the owner's questions and the dog's symptoms, and can support making a reservation for a face-to-face consultation with a veterinarian or responding to an emergency if necessary. The consultation unit provides general advice for the owner's questions, for example, using a generation AI. The generation AI can analyze the owner's questions and provide general advice. For example, if the owner asks about the dog's loss of appetite, the generation AI provides general advice and recommends consulting with a veterinarian if necessary. The consultation unit can also support making a reservation for a face-to-face consultation with a veterinarian or responding to an emergency if necessary. For example, the generation AI can make a reservation for a face-to-face consultation with a veterinarian based on the owner's questions and the dog's symptoms. This allows the owner to quickly consult about the dog's health condition, and enables a face-to-face consultation with a veterinarian or responding to an emergency if necessary. Some or all of the above-mentioned processing in the consultation unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the consultation unit can input the owner's questions to the generation AI, and the generation AI can output general advice.

[0082] The collection unit can estimate the dog's emotions and adjust the frequency of data collection based on the estimated dog's emotions. The collection unit estimates the dog's emotions using, for example, a generation AI. The generation AI can analyze the dog's behavioral data and physiological data to estimate the dog's emotions. For example, the generation AI analyzes changes in the dog's heart rate and activity level to estimate the dog's stressed or relaxed state. The generation AI can also analyze the dog's facial expression and tone of voice to estimate the emotions. The collection unit adjusts the frequency of data collection based on the estimated dog's emotions. For example, if the dog is stressed, the frequency of data collection is reduced to reduce the burden on the dog. Also, if the dog is relaxed, the frequency of data collection can be increased to collect detailed data. Furthermore, if the dog is excited, the frequency of data collection can be temporarily increased to record changes in the behavior pattern in detail. As a result, by adjusting the frequency of data collection according to the dog's emotions, the burden on the dog can be reduced and detailed data can be collected. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input a dog's emotional data into the generation AI, which can then adjust the frequency of data collection.

[0083] The collection unit can analyze the dog's past behavioral patterns and select the optimal data collection timing. The collection unit can analyze the dog's past behavioral patterns, for example, using a generation AI. The generation AI can analyze past data and grasp the dog's behavioral patterns. For example, if the dog was active during a specific time period in the past, the generation AI can focus data collection on that time period. In addition, if the dog was resting during a specific time period in the past, the generation AI can refrain from data collection during that time period. Furthermore, the generation AI can analyze the dog's past behavioral patterns and strengthen data collection on weekends and at specific events. In this way, data can be collected at the optimal timing by analyzing the dog's past behavioral patterns. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the collection unit can input past behavioral data into the generation AI, and the generation AI can select the optimal data collection timing.

[0084] The collection unit can perform filtering based on the dog's current health condition and activity level when collecting data. The collection unit can perform filtering based on the dog's current health condition and activity level when collecting data, for example, using a generation AI. The generation AI can analyze the dog's health data and activity data and collect only necessary data. For example, the generation AI can perform normal data collection when the dog is in good health. In addition, the generation AI can refrain from data collection and collect only necessary data when the dog is in poor health. Furthermore, the generation AI can also collect detailed data when the dog's activity level is high and collect only basic data when the activity level is low. This makes it possible to collect only necessary data by filtering data according to the dog's health condition and activity level. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the collection unit can input the dog's health data to the generation AI, and the generation AI can filter the data.

[0085] The collection unit can estimate the dog's emotions and determine the priority of data to be collected based on the estimated emotions of the dog. The collection unit estimates the dog's emotions, for example, using a generation AI. The generation AI can analyze the behavioral data and physiological data of the dog to estimate the dog's emotions. For example, the generation AI analyzes changes in the dog's heart rate and activity level to estimate the stress or relaxation state. The generation AI can also analyze the dog's facial expression and tone of voice to estimate the emotions. The collection unit determines the priority of data to be collected based on the estimated emotions of the dog. For example, if the dog is stressed, stress-related data is preferentially collected. Also, if the dog is relaxed, data on the activity level and sleep pattern can be preferentially collected. Furthermore, if the dog is excited, data on changes in behavior patterns can be preferentially collected. As a result, important data can be preferentially collected by determining the priority of data to be collected according to the dog's emotions. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the dog's emotion data to the generation AI, and the generation AI can determine the priority of the data.

[0086] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the dog when collecting data. The collection unit, for example, uses a generation AI to preferentially collect highly relevant data in consideration of the geographical location information of the dog when collecting data. The generation AI can analyze the location information of the dog and select highly relevant data. For example, when the dog is in a park, the generation AI preferentially collects data on the amount of activity and the movement pattern. In addition, when the dog is at home, the generation AI can preferentially collect data on the sleep pattern and the amount of food intake. Furthermore, when the dog is at a veterinary clinic, the generation AI can also preferentially collect data on the health condition. In this way, by considering the geographical location information of the dog, highly relevant data can be preferentially collected. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the collection unit can input the location information of the dog to the generation AI, and the generation AI can select highly relevant data.

[0087] The collection unit can analyze the social media activity of the dog owner at the time of data collection and collect related data. The collection unit can use, for example, a generation AI to analyze the social media activity of the dog owner at the time of data collection and collect related data. The generation AI can analyze the owner's social media posts and select related data. For example, when the owner posts on social media about the dog's health, the generation AI can collect data based on the content. In addition, when the owner posts on social media about the dog's activities, the generation AI can collect data based on the content. In addition, when the owner posts on social media about the dog's diet, the generation AI can also collect data based on the content. In this way, by analyzing the owner's social media activity, related data can be efficiently collected. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the owner's social media posts to the generation AI, and the generation AI can select related data.

[0088] The analysis unit can estimate the dog's emotions and adjust the analysis algorithm based on the estimated dog's emotions. The analysis unit estimates the dog's emotions, for example, using a generation AI. The generation AI can analyze the dog's behavioral data and physiological data to estimate the dog's emotions. For example, the generation AI analyzes changes in the dog's heart rate and activity level to estimate the dog's stressed or relaxed state. The generation AI can also analyze the dog's facial expression and tone of voice to estimate the emotions. The analysis unit adjusts the analysis algorithm based on the estimated dog's emotions. For example, if the dog is stressed, the analysis is performed with emphasis on stress-related data. Also, if the dog is relaxed, the analysis can be performed with emphasis on activity level and sleep pattern data. Furthermore, if the dog is excited, the analysis can be performed with emphasis on data related to changes in behavior patterns. As a result, a more accurate analysis result can be obtained by adjusting the analysis algorithm according to the dog's emotions. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit inputs the dog's emotion data into the generation AI, and the generation AI can 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 at the time of analysis. The analysis unit can adjust the level of detail of the analysis based on the importance of the dog's health data at the time of analysis, for example, using a generation AI. The generation AI can analyze the dog's health data and adjust the level of detail of the analysis according to the importance. For example, if the dog's health data is important, the generation AI can perform a detailed analysis and provide specific improvements. In addition, if the dog's health data is general, the generation AI can perform a basic analysis and provide general advice. Furthermore, if the dog's health data is urgent, the generation AI can quickly perform a detailed analysis and propose an emergency response. As a result, by adjusting the level of detail of the analysis according to the importance of the dog's health data, an appropriate analysis result can be provided. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the analysis unit can input the dog's health data to the generation AI, and the generation AI can adjust the level of detail of the analysis.

[0090] The analysis unit can apply different analysis algorithms depending on the dog category during analysis. The analysis unit can apply different analysis algorithms depending on the dog category (age, breed, etc.) during analysis, for example, using the generation AI. The generation AI can select an appropriate analysis algorithm based on the dog category. For example, in the case of a puppy, the generation AI can perform analysis by placing emphasis on data regarding growth. In addition, in the case of an elderly dog, the generation AI can perform analysis by placing emphasis on data regarding the health condition. Furthermore, in the case of a specific breed, the generation AI can also perform analysis by taking into account health risks specific to that breed. In this way, by applying an analysis algorithm depending on the dog category, a more appropriate analysis result can be provided. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input dog category data to the generation AI, and the generation AI can 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 of the dog. The analysis unit estimates the dog's emotions using, for example, a generation AI. The generation AI can analyze the behavioral data and physiological data of the dog to estimate the dog's emotions. For example, the generation AI analyzes changes in the dog's heart rate and activity level to estimate the stress or relaxation state. The generation AI can also analyze the dog's facial expression and tone of voice to estimate the 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 is highlighted and displayed. Also, if the dog is relaxed, data on the activity level and sleep pattern can be displayed in detail. Furthermore, if the dog is excited, data on changes in behavior patterns can be highlighted and displayed. As a result, by adjusting the display method of the analysis results according to the dog's emotions, it is possible to provide information that is easy for the owner to understand. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input a dog's emotional data into the generation AI, which can then adjust the way the analysis results are displayed.

[0092] The analysis unit can determine the priority of the analysis based on the collection time of the data at the time of analysis. The analysis unit can determine the priority of the analysis based on the collection time of the data at the time of analysis, for example, using the generation AI. The generation AI can analyze the collection time of the data and determine the priority. For example, the generation AI can analyze the latest data preferentially and provide real-time information. In addition, the generation AI can analyze the current data while referring to the past data. Furthermore, the generation AI can also analyze the data at the time of a specific event preferentially and evaluate its influence. In this way, by determining the priority of the analysis based on the collection time of the data, real-time information can be provided. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the collection time of the data to the generation AI, and the generation AI can determine the priority of the analysis.

[0093] The analysis unit can adjust the order of analysis based on the relevance of data during analysis. The analysis unit adjusts the order of analysis based on the relevance of data during analysis, for example, using a generation AI. The generation AI can analyze the relevance of data and adjust the order of analysis. For example, the generation AI can analyze highly relevant data preferentially and provide detailed information. In addition, the generation AI can analyze important data first, leaving less relevant data for later. Furthermore, the generation AI can dynamically adjust the order of analysis based on the relevance of data. As a result, important data can be analyzed preferentially by adjusting the order of analysis based on the relevance of data. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the relevance of data to the generation AI, and the generation AI can adjust the order of analysis.

[0094] The providing unit can estimate the dog's emotions and adjust the way of expressing the information to be provided based on the estimated emotions of the dog. The providing unit estimates the dog's emotions, for example, using a generating AI. The generating AI can analyze the behavioral data and physiological data of the dog to estimate the emotions of the dog. For example, the generating AI analyzes the changes in the dog's heart rate and activity level to estimate the stress or relaxation state. The generating AI can also analyze the facial expression and tone of the dog's voice to estimate the emotions. The providing unit adjusts the way of expressing the information to be provided based on the estimated emotions of the dog. For example, if the dog is stressed, advice for reducing stress is provided with emphasis. Also, if the dog is relaxed, data on the activity level and sleep pattern can be provided in detail. Furthermore, if the dog is excited, data on changes in behavior patterns can be provided with emphasis. As a result, by adjusting the way of expressing information according to the emotions of the dog, it is possible to provide information that is easy for the owner to understand. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the providing unit inputs the emotion data of the dog to the generating AI, and the generating AI can adjust the way of expressing the information.

[0095] The providing unit can adjust the level of detail of the information to be provided based on the importance of the analysis result at the time of providing. The providing unit adjusts the level of detail of the information to be provided based on the importance of the analysis result at the time of providing, for example, by using a generating AI. The generating AI can analyze the importance of the analysis result and adjust the level of detail of the information. For example, the generating AI provides detailed information for an important analysis result. In addition, the generating AI can provide basic information for a general analysis result. Furthermore, the generating AI can also quickly provide detailed information for an analysis result with urgency. As a result, by adjusting the level of detail of the information according to the importance of the analysis result, appropriate information can be provided. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the providing unit can input the importance of the analysis result to the generating AI, and the generating AI can adjust the level of detail of the information.

[0096] The providing unit can apply different information provision algorithms according to the dog category at the time of provision. The providing unit applies different information provision algorithms according to the dog category (age, breed, etc.) at the time of provision, for example, using the generating AI. The generating AI can select an appropriate information provision algorithm based on the dog category. For example, in the case of a puppy, the generating AI can provide information on growth with emphasis. In addition, in the case of an elderly dog, the generating AI can provide information on health management with emphasis. Furthermore, in the case of a specific breed, the generating AI can also provide information on health risks specific to that breed. As a result, more appropriate information can be provided by applying an information provision algorithm according to the dog category. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit inputs dog category data to the generating AI, and the generating AI can select an appropriate information provision algorithm.

[0097] The providing unit can estimate the dog's emotion and determine the priority of information to be provided based on the estimated emotion of the dog. The providing unit estimates the emotion of the dog using, for example, a generating AI. The generating AI can analyze the behavioral data and physiological data of the dog to estimate the emotion of the dog. For example, the generating AI analyzes the change in the dog's heart rate and activity level to estimate the stress or relaxation state. The generating AI can also analyze the facial expression and tone of voice of the dog to estimate the emotion. The providing unit determines the priority of information to be provided based on the estimated emotion of the dog. For example, if the dog is stressed, information for stress reduction can be provided preferentially. Also, if the dog is relaxed, information on the activity level and sleep pattern can be provided preferentially. Furthermore, if the dog is excited, information on changes in behavioral patterns can be provided preferentially. As a result, important information can be provided preferentially by determining the priority of information according to the emotion of the dog. Part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit inputs the emotion data of the dog to the generating AI, and the generating AI can determine the priority of the information.

[0098] The providing unit can determine the priority of information based on the time of submission of the analysis result at the time of provision. The providing unit can determine the priority of information based on the time of submission of the analysis result at the time of provision, for example, using the generation AI. The generation AI can analyze the time of submission of the analysis result and determine the priority of information. For example, the generation AI can provide the latest analysis result preferentially and provide real-time information. In addition, the generation AI can provide current information while referring to past analysis results. Furthermore, the generation AI can also provide the analysis result at the time of a specific event preferentially and evaluate its influence. As a result, real-time information can be provided by determining the priority of information based on the time of submission of the analysis result. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the time of submission of the analysis result to the generation AI, and the generation AI can determine the priority of information.

[0099] The providing unit can adjust the order of information based on the relevance of the analysis result at the time of providing. The providing unit adjusts the order of information based on the relevance of the analysis result at the time of providing, for example, by using a generation AI. The generation AI can analyze the relevance of the analysis result and adjust the order of information. For example, the generation AI provides highly relevant information preferentially and provides detailed information. In addition, the generation AI can postpone information with low relevance and provide important information first. Furthermore, the generation AI can dynamically adjust the order of provision based on the relevance of information. As a result, important information can be provided preferentially by adjusting the order of information based on the relevance of the analysis result. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the relevance of the analysis result to the generation AI, and the generation AI can adjust the order of information.

[0100] The consultation unit can estimate the dog's emotions and adjust the way of expressing the consultation content based on the estimated emotion of the dog. The consultation unit estimates the dog's emotions using, for example, a generation AI. The generation AI can analyze the behavioral data and physiological data of the dog to estimate the emotion of the dog. For example, the generation AI analyzes the change in the dog's heart rate and activity level to estimate the stress or relaxation state. The generation AI can also analyze the facial expression and tone of voice of the dog to estimate the emotion. The consultation unit adjusts the way of expressing the consultation content based on the estimated emotion of the dog. For example, if the dog is stressed, advice for stress reduction is emphasized and provided. Also, if the dog is relaxed, data on the activity level and sleep pattern can be provided in detail. Furthermore, if the dog is excited, data on changes in behavior patterns can be emphasized and provided. As a result, by adjusting the way of expressing the consultation content according to the emotion of the dog, it is possible to provide advice that is easy for the owner to understand. A part or all of the above-mentioned processing in the consultation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the consultation department can input data about a dog's emotions into the generation AI, which can then adjust the way the consultation content is expressed.

[0101] The consultation unit can provide optimal advice by referring to the owner's past consultation history at the time of consultation. The consultation unit can provide optimal advice by referring to the owner's past consultation history, for example, using a generation AI. The generation AI can analyze the owner's past consultation history and provide related advice. For example, the generation AI can provide related advice based on the content of the owner's past consultations. In addition, the generation AI can propose a solution to a similar problem from the owner's past consultation history. Furthermore, the generation AI can also analyze the owner's past consultation history and provide the most effective advice. As a result, related advice can be provided by referring to the owner's past consultation history. Part or all of the above-mentioned processing in the consultation unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the consultation unit can input the owner's past consultation history into the generation AI, and the generation AI can provide optimal advice.

[0102] The consultation unit can customize the consultation content based on the dog's current health condition at the time of consultation. The consultation unit customizes the consultation content based on the dog's current health condition at the time of consultation, for example, using a generation AI. The generation AI can analyze the dog's health data and provide appropriate advice. For example, the generation AI provides general advice when the dog's health condition is good. In addition, the generation AI can suggest specific improvement measures when the dog's health condition is poor. Furthermore, the generation AI can also respond quickly if an emergency response is required 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. Part or all of the above-mentioned processing in the consultation unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the consultation unit can input the dog's health data into the generation AI, and the generation AI can customize the consultation content.

[0103] The consultation unit can estimate the dog's emotions and determine the priority of consultation contents based on the estimated emotions of the dog. The consultation unit estimates the dog's emotions using, for example, a generation AI. The generation AI can analyze the behavioral data and physiological data of the dog to estimate the emotions of the dog. For example, the generation AI analyzes changes in the dog's heart rate and activity level to estimate the stress or relaxation state. The generation AI can also analyze the dog's facial expression and tone of voice to estimate the emotions. The consultation unit determines the priority of consultation contents based on the estimated emotions of the dog. For example, if the dog is stressed, consultation contents for stress reduction can be provided preferentially. Also, if the dog is relaxed, consultation contents regarding activity level and sleep patterns can be provided preferentially. Furthermore, if the dog is excited, consultation contents regarding changes in behavior patterns can be provided preferentially. As a result, important consultation contents can be provided preferentially by determining the priority of consultation contents according to the emotions of the dog. A part or all of the above-mentioned processing in the consultation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the consultation department can input data on a dog's emotions into the generation AI, which can then determine the priority of the consultation content.

[0104] The consultation unit can provide optimal consultation content in consideration of the owner's geographical location information at the time of consultation. The consultation unit can provide optimal consultation content in consideration of the owner's geographical location information at the time of consultation, for example, using a generation AI. The generation AI can analyze the owner's location information and provide appropriate advice. For example, if the owner lives in an urban area, the generation AI can provide advice suitable for an urban environment. Also, if the owner lives in a suburban area, the generation AI can provide advice suitable for a suburban environment. Furthermore, if the owner is traveling, the generation AI can also provide advice suitable for the environment of the travel destination. In this way, optimal consultation content can be provided by considering the owner's geographical location information. Part or all of the above-mentioned processing in the consultation unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the consultation unit can input the owner's location information into the generation AI, and the generation AI can provide optimal advice.

[0105] The consultation unit can provide relevant advice by analyzing the social media activity of the owner at the time of consultation. The consultation unit can provide relevant advice by analyzing the social media activity of the owner at the time of consultation, for example, using a generation AI. The generation AI can analyze the social media posts of the owner and provide relevant advice. For example, when the owner posts on social media about the dog's health, the generation AI can provide advice based on the content. In addition, when the owner posts on social media about the dog's activities, the generation AI can provide advice based on the content. In addition, when the owner posts on social media about the dog's diet, the generation AI can also provide advice based on the content. In this way, by analyzing the social media activity of the owner, relevant advice can be provided efficiently. Part or all of the above-mentioned processing in the consultation unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the consultation unit can input the social media posts of the owner to the generation AI, and the generation AI can provide relevant advice.

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

[0107] The collection unit can monitor the dog's body temperature and heart rate in real time and issue an alert if an abnormality is detected. For example, if the dog's body temperature rises suddenly, the collection unit can send a notification to the owner and prompt them to take appropriate action. Also, if the heart rate exceeds the normal range, the collection unit can detect the abnormality and recommend consulting a veterinarian. Furthermore, the collection unit can send the dog's body temperature and heart rate data to the analysis unit for detailed analysis of the dog's health condition. This makes it possible to monitor the dog's health condition in real time and detect abnormalities early on.

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

[0109] The providing unit can suggest appropriate supplements and nutritional supplements based on the dog's health condition. For example, if the analysis unit detects a lack of nutritional balance in the dog, the providing unit can suggest supplements containing specific vitamins and minerals. In addition, if the dog is highly active, the providing unit can suggest nutritional supplements for energy supply. Furthermore, the providing unit can adjust the type and amount of appropriate supplements according to the dog's age and health condition. This makes it possible to provide appropriate nutritional support for maintaining the dog's health.

[0110] The consultation unit can estimate the dog's emotions and customize advice for the owner based on the estimated emotions. For example, if the dog is stressed, the consultation unit can provide specific advice for reducing stress. Also, if the dog is relaxed, the consultation unit can provide advice to the owner to maintain that state. Furthermore, if the dog is excited, the consultation unit can suggest ways to calm the dog's excitement. This allows the owner to provide appropriate care according to the dog's emotions.

[0111] The collection unit can detect specific behavioral patterns based on the dog's behavioral data and detect abnormal behavior early. For example, if the dog is active at a different time than usual, the collection unit can detect this as abnormal behavior. Also, if the dog frequently repeats the same behavior in a specific place, the collection unit can evaluate the behavior as abnormal. Furthermore, if the dog does not eat at its usual mealtime, the collection unit can detect this as abnormal behavior and notify the owner. This allows for early detection of abnormal behavior in the dog and allows appropriate measures to be taken.

[0112] The analysis unit can estimate the dog's emotions and determine the priority of the analysis results based on the estimated emotions. For example, if the dog is stressed, stress-related data can be analyzed with priority. If the dog is relaxed, activity level and sleep pattern data can be analyzed with priority. If the dog is excited, data related to changes in behavior patterns can be analyzed with priority. This allows important data to be analyzed with priority according to the dog's emotions, providing more accurate analysis results.

[0113] The providing unit can propose an appropriate exercise plan based on the health condition of the dog. For example, if the analysis unit detects that the dog is not active enough, the providing unit proposes a specific exercise plan. In addition, the providing unit can adjust the appropriate type and frequency of exercise according to the age and breed of the dog. Furthermore, the providing unit can customize the exercise plan based on the health condition of the dog and propose exercise within a reasonable range. This makes it possible to provide an appropriate exercise plan for maintaining the health of the dog.

[0114] The 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 is reduced to reduce the burden on the dog. Also, if the dog is relaxed, the frequency of data collection can be increased to collect detailed data. Furthermore, if the dog is excited, the timing of data collection can be adjusted to record changes in behavioral patterns in detail. In this way, the timing of data collection can be adjusted according to the dog's emotions, and detailed data can be collected while reducing the burden on the dog.

[0115] The analysis unit can analyze long-term health trends based on the dog's health data. For example, it can track changes in the dog's weight and activity over a long period of time and evaluate the health trends. It can also analyze changes in the dog's food intake and sleep patterns to understand long-term trends in the dog's 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 the long-term health trends of their dogs and provide appropriate care.

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

[0117] The process flow of the second embodiment will be briefly described below.

[0118] Step 1: The collection unit uses devices and apps to collect the dog's behavioral patterns and health data. For example, a sensor attached to the dog's collar measures the dog's activity level, and the app records the dog's food intake. The sensor detects the dog's movements and records the number of steps and exercise time, and the app records the food content and amount entered by the owner and stores them in a database. Step 2: The analysis unit uses the generation AI to analyze the data collected by the collection unit. For example, it analyzes the dog's activity level, sleep patterns, food intake, etc., and provides information on the dog's health and behavior. The analysis unit can detect a decrease in the dog's activity level and suggest the possibility of lack of exercise, analyze the dog's sleep patterns, and evaluate the quality of sleep. Step 3: The provider provides information and improvements based on the analysis results obtained by the analyzer. For example, it can suggest the appropriate amount of exercise for a dog that is not getting enough exercise, or suggest improvements to the dog's diet if the dog's food intake is not appropriate. Using the generator AI, it can suggest the appropriate amount of exercise, adjustments to nutritional balance, and changes to the type of food based on the dog's health condition. Step 4: The consultation section provides general advice to the owner's questions and the dog's symptoms, and supports scheduling a face-to-face consultation with a veterinarian or responding to an emergency if necessary. For example, generative AI can be used to provide general advice to the owner's questions and recommend a consultation with a veterinarian if necessary. This allows the owner to understand the dog's health condition 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0121] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0122] Each of the multiple elements including the above-mentioned collection unit, analysis unit, provision unit, and consultation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects behavioral patterns and health data of the dog using a sensor or an app of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using a generation AI. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides information and improvements based on the analysis results. The consultation unit supports online consultation with a veterinarian, for example, through an app of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

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

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

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

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

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0130] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0133] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0138] Each of the multiple elements including the above-mentioned collection unit, analysis unit, provision unit, and consultation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects behavioral patterns and health data of the dog using a sensor or an app of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using a generation AI. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides information and improvements based on the analysis results. The consultation unit supports online consultation with a veterinarian, for example, through the app of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

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

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

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

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

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0146] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0149] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

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

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0154] Each of the multiple elements including the above-mentioned collection unit, analysis unit, provision unit, and consultation unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects behavioral patterns and health data of the dog using a sensor or an app of the headset type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using a generation AI. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides information and improvements based on the analysis results. The consultation unit supports online consultation with a veterinarian, for example, through an app of the headset type terminal 314. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

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

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

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

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

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0161] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0162] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0163] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0164] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0165] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0166] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0167] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[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 control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0171] Each of the multiple elements including the above-mentioned collection unit, analysis unit, provision unit, and consultation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects behavioral patterns and health data of the dog using a sensor or an app of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using a generation AI. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides information and improvements based on the analysis results. The consultation unit supports online consultation with a veterinarian, for example, through an app of the robot 414. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

[0173] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0174] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0175] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0176] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0177] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this 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 the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0179] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

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

[0181] In addition, 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 upon request from the data processing device 12.

[0182] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0183] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

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

[0185] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0186] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0187] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0188] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0189] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0190] (Appendix 1) A collection unit that collects dog behavior patterns and health data using a device or app; an analysis unit that analyzes the data collected by the collection unit; a providing unit that provides information and improvements based on the analysis results obtained by the analyzing unit; A consultation unit that supports online consultations with veterinarians. A system characterized by: (Appendix 2) The collecting unit includes: A sensor attached to the dog's collar measures activity, and the app records food intake. 2. The system of claim 1, (Appendix 3) The analysis unit is Analyzes your dog's activity levels, sleep patterns and food intake to provide information about your dog's health and behavior 2. The system of claim 1, (Appendix 4) The providing unit is Based on the analysis results, we suggest the appropriate amount of exercise for dogs that are lacking in exercise. 2. The system of claim 1, (Appendix 5) The providing unit is If food intake is inadequate, suggest dietary improvements 2. The system of claim 1, (Appendix 6) The consultation department: Provides general advice to pet owners regarding their queries and symptoms in their dogs, and assists with booking face-to-face consultations with veterinarians and emergency response when necessary 2. The system of claim 1, (Appendix 7) The collecting unit includes: Estimate the dog's emotions and adjust the frequency of data collection based on the estimated dog's emotions. 2. The system of claim 1, (Appendix 8) The collecting unit includes: Analyze the dog's past behavioral patterns and select the optimal timing for data collection 2. The system of claim 1, (Appendix 9) The collecting unit includes: Filter data collection based on your dog's current health and activity level 2. The system of claim 1, (Appendix 10) The collecting unit includes: Estimate the dog's emotions and prioritize data collection based on the estimated emotions. 2. The system of claim 1, (Appendix 11) The collecting unit includes: When collecting data, the geographic location of the dog is taken into account to prioritize the collection of more relevant data. 2. The system of claim 1, (Appendix 12) The collecting unit includes: During data collection, we will analyze the social media activity of dog owners and collect relevant data. 2. The system of claim 1, (Appendix 13) The analysis unit is Estimate the dog's emotions and adjust the analysis algorithm based on the estimated emotions of the dog 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, adjust the level of detail based on the importance of your dog's health data 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, different analysis algorithms are applied depending on the dog category. 2. The system of claim 1, (Appendix 16) The analysis unit is Estimate the dog's emotions and adjust the way the analysis results are displayed based on the estimated dog's emotions 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, prioritize analysis based on when the data was collected 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, adjust the order of analysis based on data relevance 2. The system of claim 1, (Appendix 19) The providing unit is Estimate the dog's emotions and adjust the way information is presented based on the dog's emotions. 2. The system of claim 1, (Appendix 20) The providing unit is When providing information, adjust the level of detail provided based on the importance of the analysis results. 2. The system of claim 1, (Appendix 21) The providing unit is When serving, different information serving algorithms are applied depending on the dog category. 2. The system of claim 1, (Appendix 22) The providing unit is Estimate the dog's emotions and prioritize the information to provide based on the estimated emotions of the dog 2. The system of claim 1, (Appendix 23) The providing unit is When provided, information will be prioritized based on when analysis results were submitted. 2. The system of claim 1, (Appendix 24) The providing unit is As it is provided, we will adjust the order of information based on the relevance of the analysis 2. The system of claim 1, (Appendix 25) The consultation department: Estimate the dog's emotions and adjust the way the advice is expressed based on the dog's emotions. 2. The system of claim 1, (Appendix 26) The consultation department: When consulting, the company will provide the best advice by referring to the owner's past consultation history. 2. The system of claim 1, (Appendix 27) The consultation department: During your consultation, we will customize the consultation based on your dog's current health status 2. The system of claim 1, (Appendix 28) The consultation department: Estimate the dog's emotions and prioritize consultations based on the estimated emotions of the dog 2. The system of claim 1, (Appendix 29) The consultation department: When consulting, the service provides the most appropriate consultation content by taking into account the owner's geographic location information. 2. The system of claim 1, (Appendix 30) The consultation department: During consultations, analyze owners' social media activity to provide relevant advice 2. The system of claim 1, [Explanation of symbols]

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

Claims

1. A collection unit that collects dog behavior patterns and health data using a device or app; an analysis unit that analyzes the data collected by the collection unit; a providing unit that provides information and improvements based on the analysis results obtained by the analyzing unit; A consultation unit that supports online consultations with veterinarians. A system characterized by:

2. The collecting unit includes: A sensor attached to the dog's collar measures activity, and the app records food intake.

2. The system of claim 1 .

3. The analysis unit is Analyzes your dog's activity levels, sleep patterns and food intake to provide information about your dog's health and behavior 2. The system of claim 1 .

4. The providing unit is Based on the analysis results, we suggest the appropriate amount of exercise for dogs that are lacking in exercise.

2. The system of claim 1 .

5. The providing unit is If food intake is inadequate, suggest dietary improvements 2. The system of claim 1 .

6. The consultation department: Provides general advice to pet owners regarding their queries and symptoms in their dogs, and assists with booking face-to-face consultations with veterinarians and emergency response when necessary 2. The system of claim 1 .

7. The collecting unit includes: Estimate the dog's emotions and adjust the frequency of data collection based on the estimated dog's emotions.

2. The system of claim 1 .

8. The collecting unit includes: Analyze the dog's past behavioral patterns and select the optimal timing for data collection 2. The system of claim 1 .

9. The collecting unit includes: Filter data collection based on your dog's current health and activity level 2. The system of claim 1 .

10. The collecting unit includes: Estimate the dog's emotions and prioritize data collection based on the estimated emotions.

2. The system of claim 1 .

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