System

Abstract

An object of a system according to an embodiment is to efficiently provide information on a dog.SOLUTION: A system includes an information providing unit, a health management unit, a discipline information unit, a feature information unit, a breeding information unit, and a latest information unit. The information providing unit provides information on the dog using the generated AI. The health management part generates the health management information of the dog provided by the information providing part. The discipline information unit generates discipline information of the dog provided by the information providing unit. The feature information unit generates feature information of the dog type provided by the information providing unit. The breeding information unit generates general information on the breeding of the dog provided by the information providing unit. The up-to-date information section generates up-to-date information about the dog provided by the information providing section.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to efficiently collect and provide information about dogs.

[0005] The system according to the embodiment aims to efficiently provide information about dogs. [Means for solving the problem]

[0006] The system according to the embodiment includes an information providing unit, a health management unit, a training information unit, a characteristic information unit, a breeding information unit, and an latest information unit. The information providing unit provides information about dogs using a generation AI. The health management unit generates health management information about the dogs that is provided by the information providing unit. The training information unit generates training information about the dogs that is provided by the information providing unit. The characteristic information unit generates breed-specific characteristic information that is provided by the information providing unit. The breeding information unit generates general information about dog breeding that is provided by the information providing unit. The latest information unit generates the latest information about the dogs that is provided by the information providing unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently provide information about dogs. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's 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 read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The AI ​​chat system according to the embodiment of the present invention provides information about dogs using a generation AI, allowing dog lovers to quickly and accurately obtain the information they need. This allows the AI ​​chat system to quickly and accurately obtain the information they need, improving time performance.

[0029] An AI chat system according to an embodiment includes an information providing unit, a health management unit, a training information unit, a feature information unit, a breeding information unit, and an up-to-date information unit. The information providing unit provides information about dogs using a generation AI. For example, the generation AI uses a specific model such as GPT-3 or BERT to provide general information and up-to-date information about dog health management, training, breed-specific characteristics, and breeding. The health management unit generates the dog health management information provided by the information providing unit. For example, the generation AI generates appropriate answers to questions about dog diet, exercise, vaccinations, disease symptoms, etc. The training information unit generates dog training information provided by the information providing unit. For example, the generation AI generates appropriate answers to questions about toilet training, measures to prevent excessive barking, how to teach basic commands, etc. The feature information unit generates breed-specific feature information provided by the information providing unit. For example, the generation AI generates appropriate answers to questions about the personality, exercise amount, suitable environment, etc. of a specific dog breed. The breeding information unit generates general information about dog breeding provided by the information providing unit. For example, the generation AI generates appropriate answers to questions about how to choose a dog, how to prepare a breeding environment, and daily care methods. The latest information unit generates the latest information about dogs provided by the information providing unit. For example, the generation AI generates appropriate answers to questions about new breeding methods, the latest health care techniques, and information about dog-related events. This allows the AI ​​chat system according to the embodiment to quickly and accurately obtain the information that dog lovers need.

[0030] The health management unit can generate personalized advice based on the dog's individual health history. For example, when providing health management information for a dog, the generating AI considers the dog's past health checkup results and medical history to propose an individual health management plan. For example, for dogs at high risk of certain diseases, it provides preventive measures and regular checkup schedules. When providing information on dietary management for a dog, the generating AI considers the dog's allergy and weight management history to propose an optimal dietary plan. For example, it recommends an allergy-friendly diet for a dog with a specific allergy. When providing information on exercise management for a dog, the generating AI considers the dog's age and physical strength level to propose the appropriate amount and method of exercise. For example, it recommends exercise methods that do not strain the joints of elderly dogs. This makes it possible to provide advice based on the dog's individual health history.

[0031] The health management unit can propose health management methods appropriate for specific times and locations based on seasonal and regional characteristics. For example, when providing health management information for a dog, the generation AI considers the temperature and humidity of each season and proposes appropriate health management methods. For example, it recommends measures to prevent heatstroke in the summer and measures to protect against the cold in the winter. The health management unit also considers regional characteristics and proposes preventive measures against diseases and parasites that are prevalent in a specific area. For example, if heartworm is common in a specific area, it recommends the use of heartworm preventative medication. Furthermore, to provide seasonal allergy countermeasures, the generation AI considers pollen dispersion conditions and allergen information and proposes appropriate countermeasures. For example, it recommends measures to protect against hay fever in early spring. This makes it possible to provide health management methods tailored to the season and region.

[0032] The health management unit can also simultaneously provide health management information for other pets, making it possible to accommodate users who own multiple pets. For example, when providing health management information for dogs, the generation AI can simultaneously provide health management information for other pets, such as cats and birds. For example, for a user who owns both a dog and a cat, the generation AI can suggest diet and exercise methods suitable for both pets. For users who own multiple pets, the generation AI can integrate health management schedules for each pet and suggest efficient management methods. For example, vaccination and health check schedules can be centrally managed. Furthermore, when providing health management information for other pets, the generation AI can take into account the interactions between pets and suggest appropriate management methods. For example, if a user owns a dog and a bird, the generation AI can recommend creating an environment that does not cause stress for each pet. This makes it possible to accommodate users who own multiple pets.

[0033] The health management section can customize advice based on the owner's lifestyle. For example, if the owner is an outdoorsy person, the generated AI will suggest outdoor activities that the owner can enjoy together with their dog and health management methods. For example, it will introduce precautions and necessary preparations for hiking and camping. If the owner is an indoor person, the generated AI will suggest indoor exercises and health management methods. For example, it will introduce indoor play methods and how to use exercise equipment. The health management section also customizes the dog's diet and exercise schedule based on the owner's lifestyle. For example, it will suggest short, effective exercise methods and easy-to-prepare meal plans for busy owners. This allows the system to provide advice tailored to the owner's lifestyle.

[0034] The training information unit can suggest the optimal training method based on the dog's age and personality. For example, the training information unit uses the generative AI to suggest the optimal training method based on the dog's age. For example, it introduces basic command teaching methods for puppies and advanced training methods for adult dogs. The training information unit also uses the generative AI to suggest individual training plans taking the dog's personality into consideration. For example, it recommends group training for sociable dogs and individual training for introverted dogs. The training information unit also uses the generative AI to adjust the training progress speed and method based on the dog's age and personality. For example, it suggests training at a slower pace for dogs that learn slowly. This makes it possible to provide training methods that are appropriate for the dog's age and personality.

[0035] The training information unit can take into account the owner's past training history and customize advice based on successful or unsuccessful methods. For example, the training information unit can provide advice based on successful methods by the generating AI by taking into account the owner's past training history. For example, it can suggest training methods that were successful in the past. The training information unit can also analyze unsuccessful training methods by the owner in the past and provide advice to help the generating AI avoid those failures. For example, it can suggest alternative methods to avoid methods that failed in the past. The training information unit can also customize a training plan by the generating AI based on the owner's training history. For example, it can suggest the optimal training method by taking into account past successes and failures. This allows it to provide advice based on the owner's past training history.

[0036] The training information unit also simultaneously provides training information for other pets, making it possible to accommodate users who have multiple pets. For example, when providing training information for dogs, the generation AI simultaneously provides training information for other pets, such as cats and birds. For example, for a user who has both a dog and a cat, it suggests training methods suitable for both pets. For users who have multiple pets, the generation AI also integrates training schedules for each pet and suggests efficient management methods. For example, it centrally manages the timing and methods of training. Furthermore, when providing training information for other pets, the generation AI takes into account the interactions between the pets and suggests appropriate training methods. For example, if a user has a dog and a bird, it recommends training methods that do not cause stress to each other. This makes it possible to accommodate users who have multiple pets.

[0037] The training information section can customize advice based on the owner's lifestyle. For example, if the owner is an outdoorsy person, the generated AI will suggest outdoor activities and training methods that the owner can enjoy together with their dog. For example, it will introduce precautions and necessary preparations for hiking and camping. If the owner is an indoor person, the generated AI will suggest training methods and activities that can be done indoors. For example, it will introduce indoor play and training methods. The training information section also customizes the dog's training schedule based on the owner's lifestyle. For example, it will suggest short, effective training methods and easy-to-perform training for busy owners. This allows the system to provide advice tailored to the owner's lifestyle.

[0038] The characteristic information unit can provide a deeper understanding based on the genetic background and history of the breed. For example, when providing characteristic information by breed, the generating AI takes into account the genetic background of the breed and explains specific genetic characteristics and health risks. For example, it introduces genetic diseases that are common in specific breeds and how to prevent them. The characteristic information unit also takes into account the history of the breed, allowing the generating AI to explain the origin and development process of the breed. For example, it introduces the historical background of how a specific breed came to be in its current form. The characteristic information unit also suggests breeding and training methods appropriate for the breed based on the genetic background and history of the breed. For example, it explains that specific exercise and diet are suitable for breeds with specific genetic characteristics. This makes it possible to provide information based on the genetic background and history of the breed.

[0039] The feature information unit provides information on health risks and lifespan for each breed of dog, making it easier for owners to create long-term care plans. For example, the feature information unit allows the generation AI to propose preventive measures for specific diseases and health problems by taking into account the health risks of each breed. For example, it introduces preventive measures for joint diseases and heart diseases that are common in certain breeds. The feature information unit also allows the generation AI to propose long-term care plans based on the average lifespan of each breed. For example, it provides health check and vaccination schedules tailored to the lifespan of a specific breed. The feature information unit also allows the generation AI to propose insurance plans and medical services suitable for owners based on information on the health risks and lifespan of each breed. For example, it introduces insurance plans that address specific health risks. This makes it possible to provide care plans based on the health risks and lifespan of each breed.

[0040] The feature information unit also simultaneously provides breed-specific feature information for other pets, enabling the system to accommodate users who own multiple pets. For example, when providing breed-specific feature information for dogs, the generation AI simultaneously provides breed-specific feature information for other pets, such as cats and birds. For example, for a user who owns both a dog and a cat, the generation AI suggests breeding methods suitable for both pets. Furthermore, for users who own multiple pets, the generation AI integrates the characteristics and breeding methods for each pet to suggest efficient management methods. For example, if a user owns a dog and a bird, the generation AI recommends creating an environment that does not cause stress for either pet. Furthermore, when providing breed-specific feature information for other pets, the generation AI considers the interactions between the pets and suggests appropriate breeding methods. For example, if a user owns a dog and a cat, the generation AI suggests play and training methods that are suitable for each pet. This allows the system to accommodate users who own multiple pets.

[0041] The feature information unit can customize advice based on the owner's lifestyle. For example, if the owner is an outdoorsy person, the generating AI will suggest outdoor activities that can be enjoyed with the dog and how to care for it. For example, it will introduce precautions and necessary preparations for hiking and camping. If the owner is an indoor person, the feature information unit will suggest indoor activities and how to care for the dog. For example, it will introduce indoor play and training methods. The feature information unit also allows the generating AI to customize the dog's care schedule based on the owner's lifestyle. For example, it will suggest short, effective exercise methods and easy-to-prepare meal plans for busy owners. This allows advice to be provided that is tailored to the owner's lifestyle.

[0042] The care information unit can customize advice based on the owner's living environment. For example, if the owner lives in an urban area, the generation AI will provide advice suitable for raising a dog in an urban area. For example, it will introduce exercise methods in small spaces and noise control measures. Furthermore, if the owner lives in the suburbs, the generation AI will provide advice suitable for raising a dog in the suburbs. For example, it will introduce exercise methods in large spaces and precautions to take in natural environments. Furthermore, the care information unit will customize the dog's care schedule based on the owner's living environment. For example, it will suggest short, effective exercise methods in urban areas, and long walks and outdoor activities in suburban areas. This makes it possible to provide advice tailored to the owner's living environment.

[0043] The pet care information unit can also simultaneously provide pet care information for other pets, making it possible to accommodate users who keep multiple pets. For example, when providing general information about dog care, the generation AI can simultaneously provide pet care information for other pets, such as cats and birds. For example, for a user who keeps both a dog and a cat, the generation AI can suggest pet care methods suitable for both pets. For users who keep multiple pets, the generation AI can also integrate the pet care schedules for each pet and suggest efficient management methods. For example, it can centrally manage feeding and health check schedules. Furthermore, when providing pet care information for other pets, the generation AI can consider the interactions between the pets and suggest appropriate pet care methods. For example, if a user keeps a dog and a bird, the generation AI can recommend creating an environment that does not cause stress for each pet. This makes it possible to accommodate users who keep multiple pets.

[0044] The care information section can customize advice based on the owner's lifestyle. For example, if the owner is an outdoorsy type, the generated AI will suggest outdoor activities that can be enjoyed with the dog and how to care for it. For example, it will introduce precautions and necessary preparations for hiking and camping. If the owner is an indoor type, the generated AI will suggest indoor activities and how to care for the dog. For example, it will introduce indoor play and training methods. The care information section also customizes the dog's care schedule based on the owner's lifestyle. For example, it will suggest short and effective exercise methods and easy-to-prepare meal plans for busy owners. This allows the system to provide advice tailored to the owner's lifestyle.

[0045] The latest information section automatically collects research papers and news articles to provide reliable information. For example, the generation AI automatically collects the latest research papers to provide the latest insights into dog health care and breeding methods. For example, it introduces the effectiveness of new vaccinations and the latest training techniques. The latest information section also automatically collects the latest news articles to provide information on the latest trends and events related to dogs. For example, it introduces information on the release of new pet products and information on dog-related events. The generation AI also collects data from reliable sources to provide accurate and up-to-date information to pet owners. For example, it provides information based on articles supervised by veterinarians and announcements from specialist institutions. This makes it possible to provide reliable and up-to-date information.

[0046] The latest information section provides the latest information on specific regions and countries, and can address issues and trends specific to the region. For example, in the latest information section, the generation AI collects the latest information on specific regions and countries and provides health risks and pet care methods specific to those regions. For example, it introduces diseases that are prevalent in specific regions and preventive measures. In addition, in the latest information section, the generation AI collects information on events and new pet products specific to the region to provide trend information for each region. For example, it introduces information on pet events held in the region and information on the release of new products. In addition, in the latest information section, the generation AI collects relevant data to provide the latest information on laws and regulations in specific regions and countries and provide appropriate advice to pet owners. For example, it introduces information on changes to laws and regulations regarding pet care. This makes it possible to provide the latest information that addresses issues and trends specific to the region.

[0047] The latest information section simultaneously provides the latest information on other pets, making it possible to accommodate users who own multiple pets. For example, when providing the latest information on dogs, the generation AI simultaneously provides the latest information on other pets, such as cats and birds. For example, for a user who owns both a dog and a cat, the generation AI suggests the latest health care methods suitable for both pets. For users who own multiple pets, the generation AI also integrates the latest information for each pet and suggests efficient management methods. For example, vaccination and health check schedules can be centrally managed. Furthermore, when providing the latest information on other pets, the generation AI takes into account the interactions between the pets and suggests appropriate management methods. For example, if a user owns a dog and a bird, the generation AI recommends creating an environment that does not cause stress to either pet. This makes it possible to accommodate users who own multiple pets.

[0048] The latest information section can customize advice based on the owner's lifestyle. For example, if the owner is an outdoorsy person, the generated AI will suggest outdoor activities that the owner can enjoy together with their dog and the latest health care methods. For example, it will introduce precautions and necessary preparations for hiking and camping. If the owner is an indoor person, the generated AI will suggest indoor activities and the latest health care methods. For example, it will introduce indoor play and training methods. The latest information section also customizes the dog's health care schedule based on the owner's lifestyle. For example, it will suggest short, effective exercise methods and easy-to-prepare meal plans for busy owners. This allows the generator AI to provide advice tailored to the owner's lifestyle.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The AI ​​chat system can further include a dog behavior analysis unit. The behavior analysis unit can analyze a dog's behavior patterns and detect abnormal behavior. For example, the generative AI collects dog behavior data and compares it with normal behavior patterns to detect abnormal behavior. If abnormal behavior is detected, an alert is provided to the owner to prompt early action. The behavior analysis unit can also provide advice on appropriate training methods and environmental improvements based on the dog's behavior patterns. For example, it can suggest countermeasures for excessive barking or anxious behavior. This makes it possible to monitor a dog's behavior and detect and address problems early.

[0051] The health management department can further analyze a dog's genetic information and provide health management advice based on genetic risk. For example, the generative AI analyzes a dog's genetic information to assess the risk of a specific genetic disease. If the risk is high, preventative measures and a schedule for regular checkups are recommended. The system can also suggest optimal dietary and exercise plans based on the genetic information. For example, a diet containing specific nutrients is recommended for dogs with certain genetic characteristics. This allows for personalized health management advice based on genetic risk.

[0052] The health management unit can also monitor a dog's sleep patterns and suggest an appropriate sleeping environment. For example, the generative AI collects a dog's sleep data and evaluates the quality and quantity of sleep. If insufficient sleep or irregular sleep patterns are detected, it will suggest remedial measures to the owner, such as recommending a quiet environment or the use of appropriate bedding. The health management unit can also adjust the dog's daytime activity schedule based on the dog's sleep patterns, such as suggesting the appropriate amount of exercise and rest time. This allows for comprehensive management of the dog's health.

[0053] The health management unit can also provide advice to improve a dog's social skills. For example, the generative AI can evaluate a dog's social behavior and suggest ways to encourage interaction with other dogs and people. For example, it can recommend playing at a dog park or participating in group training. The health management unit can also provide advice on appropriate training methods and environmental improvements based on the dog's social skills. For example, it can recommend group training for sociable dogs and individual training for introverted dogs. This can improve a dog's social skills and reduce stress.

[0054] The training intelligence can further customize training methods based on a dog's learning style. For example, the generative AI can evaluate a dog's learning style and suggest visual, auditory, and tactile training methods. For example, it can recommend using visual instructions and demonstrations for a dog with a visual learning style. The training intelligence can also adjust the training progress speed and method based on the dog's learning style. For example, it can suggest training at a slower pace for a dog that is a slow learner. This allows for an effective training method that suits a dog's learning style.

[0055] The training information unit can also suggest training methods based on a dog's play preferences. For example, the generative AI can evaluate a dog's play preferences and suggest training methods that incorporate play. For example, for a dog that likes to play with a ball, it can recommend a training method that uses a ball. The training information unit can also adjust the speed and method of training based on the dog's play preferences. For example, learning through play can help maintain a dog's motivation. This makes it possible to provide effective training methods that suit a dog's play preferences.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The information provider uses a generative AI to provide information about dogs. For example, the generative AI uses specific models such as GPT-3 and BERT to provide general information and the latest information on dog health care, training, breed-specific characteristics, and care. Step 2: The health management unit generates the dog's health management information provided by the information provider. For example, the generation AI generates appropriate answers to questions about the dog's diet, exercise, vaccinations, disease symptoms, etc. Step 3: The training information section generates dog training information provided by the information provider. For example, the generation AI generates appropriate answers to questions about toilet training, dealing with excessive barking, and how to teach basic commands. Step 4: The characteristic information unit generates breed-specific characteristic information provided by the information provider. For example, the AI ​​generates appropriate answers to questions about the personality, exercise level, and suitable environment of a specific dog breed. Step 5: The breeding information unit generates general information about dog breeding provided by the information provider. For example, the generation AI generates appropriate answers to questions about how to choose a dog, how to prepare the breeding environment, and how to care for the dog on a daily basis. Step 6: The latest information section generates the latest information about dogs provided by the information provision section. For example, the generation AI generates appropriate answers to questions about new breeding methods, the latest health care technologies, dog event information, etc.

[0058] (Example 2) The AI ​​chat system according to the embodiment of the present invention provides information about dogs using a generation AI, allowing dog lovers to quickly and accurately obtain the information they need. This allows the AI ​​chat system to quickly and accurately obtain the information they need, improving time performance.

[0059] An AI chat system according to an embodiment includes an information providing unit, a health management unit, a training information unit, a feature information unit, a breeding information unit, and an up-to-date information unit. The information providing unit provides information about dogs using a generation AI. For example, the generation AI uses a specific model such as GPT-3 or BERT to provide general information and up-to-date information about dog health management, training, breed-specific characteristics, and breeding. The health management unit generates the dog health management information provided by the information providing unit. For example, the generation AI generates appropriate answers to questions about dog diet, exercise, vaccinations, disease symptoms, etc. The training information unit generates dog training information provided by the information providing unit. For example, the generation AI generates appropriate answers to questions about toilet training, measures to prevent excessive barking, how to teach basic commands, etc. The feature information unit generates breed-specific feature information provided by the information providing unit. For example, the generation AI generates appropriate answers to questions about the personality, exercise amount, suitable environment, etc. of a specific dog breed. The breeding information unit generates general information about dog breeding provided by the information providing unit. For example, the generation AI generates appropriate answers to questions about how to choose a dog, how to prepare a breeding environment, and daily care methods. The latest information unit generates the latest information about dogs provided by the information providing unit. For example, the generation AI generates appropriate answers to questions about new breeding methods, the latest health care techniques, and information about dog-related events. This allows the AI ​​chat system according to the embodiment to quickly and accurately obtain the information that dog lovers need.

[0060] The health management unit can generate personalized advice based on the dog's individual health history. For example, when providing health management information for a dog, the generating AI considers the dog's past health checkup results and medical history to propose an individual health management plan. For example, for dogs at high risk of certain diseases, it provides preventive measures and regular checkup schedules. When providing information on dietary management for a dog, the generating AI considers the dog's allergy and weight management history to propose an optimal dietary plan. For example, it recommends an allergy-friendly diet for a dog with a specific allergy. When providing information on exercise management for a dog, the generating AI considers the dog's age and physical strength level to propose the appropriate amount and method of exercise. For example, it recommends exercise methods that do not strain the joints of elderly dogs. This makes it possible to provide advice based on the dog's individual health history.

[0061] The health management unit can propose health management methods appropriate for specific times and locations based on seasonal and regional characteristics. For example, when providing health management information for a dog, the generation AI considers the temperature and humidity of each season and proposes appropriate health management methods. For example, it recommends measures to prevent heatstroke in the summer and measures to protect against the cold in the winter. The health management unit also considers regional characteristics and proposes preventive measures against diseases and parasites that are prevalent in a specific area. For example, if heartworm is common in a specific area, it recommends the use of heartworm preventative medication. Furthermore, to provide seasonal allergy countermeasures, the generation AI considers pollen dispersion conditions and allergen information and proposes appropriate countermeasures. For example, it recommends measures to protect against hay fever in early spring. This makes it possible to provide health management methods tailored to the season and region.

[0062] The health management unit can use the emotion estimation function to analyze the owner's emotional state and provide health management advice to reduce stress. For example, if the owner is feeling stressed, the health management unit can use the emotion estimation function to have the generating AI suggest ways to spend time with the dog that will help them relax. For example, it can recommend a relaxing massage method or an activity that can be enjoyed together. The health management unit can also analyze the owner's emotional state, and if stress is high, the generating AI can suggest easy-to-implement health management methods. For example, it can provide advice on effective exercise methods that can be done in a short amount of time or simple dietary management. The health management unit can also use the emotion estimation function to have the generating AI suggest new health management methods or challenges if the owner is feeling positive. For example, it can introduce new exercise programs or meal recipes. This makes it possible to provide health management advice that is tailored to the owner's emotional state.

[0063] The health management unit can also simultaneously provide health management information for other pets, making it possible to accommodate users who own multiple pets. For example, when providing health management information for dogs, the generation AI can simultaneously provide health management information for other pets, such as cats and birds. For example, for a user who owns both a dog and a cat, the generation AI can suggest diet and exercise methods suitable for both pets. For users who own multiple pets, the generation AI can integrate health management schedules for each pet and suggest efficient management methods. For example, vaccination and health check schedules can be centrally managed. Furthermore, when providing health management information for other pets, the generation AI can take into account the interactions between pets and suggest appropriate management methods. For example, if a user owns a dog and a bird, the generation AI can recommend creating an environment that does not cause stress for each pet. This makes it possible to accommodate users who own multiple pets.

[0064] The health management section can customize advice based on the owner's lifestyle. For example, if the owner is an outdoorsy person, the generated AI will suggest outdoor activities that the owner can enjoy together with their dog and health management methods. For example, it will introduce precautions and necessary preparations for hiking and camping. If the owner is an indoor person, the generated AI will suggest indoor exercises and health management methods. For example, it will introduce indoor play methods and how to use exercise equipment. The health management section also customizes the dog's diet and exercise schedule based on the owner's lifestyle. For example, it will suggest short, effective exercise methods and easy-to-prepare meal plans for busy owners. This allows the system to provide advice tailored to the owner's lifestyle.

[0065] The health management unit uses the emotion estimation function to analyze the emotional reactions of owners when receiving health management information and can suggest information delivery methods that elicit positive emotions. For example, the health management unit uses the emotion estimation function to analyze the emotional reactions of owners when receiving health management information in real time and suggest information delivery methods that elicit positive emotions. For example, it could introduce encouraging messages or success stories. The health management unit also uses the generation AI to adjust the tone and content of the information provided based on the owner's emotional reaction. For example, it could provide relaxation techniques or simple advice to an owner who is feeling stressed. The health management unit also uses the emotion estimation function to identify information delivery methods that elicit positive emotions from owners and continue to use those methods. For example, it could prioritize information delivery methods that receive a lot of positive feedback. This makes it possible to suggest information delivery methods that correspond to the owner's emotional reaction.

[0066] The training information unit can suggest the optimal training method based on the dog's age and personality. For example, the training information unit uses the generative AI to suggest the optimal training method based on the dog's age. For example, it introduces basic command teaching methods for puppies and advanced training methods for adult dogs. The training information unit also uses the generative AI to suggest individual training plans taking the dog's personality into consideration. For example, it recommends group training for sociable dogs and individual training for introverted dogs. The training information unit also uses the generative AI to adjust the training progress speed and method based on the dog's age and personality. For example, it suggests training at a slower pace for dogs that learn slowly. This makes it possible to provide training methods that are appropriate for the dog's age and personality.

[0067] The training information unit can take into account the owner's past training history and customize advice based on successful or unsuccessful methods. For example, the training information unit can provide advice based on successful methods by the generating AI by taking into account the owner's past training history. For example, it can suggest training methods that were successful in the past. The training information unit can also analyze unsuccessful training methods by the owner in the past and provide advice to help the generating AI avoid those failures. For example, it can suggest alternative methods to avoid methods that failed in the past. The training information unit can also customize a training plan by the generating AI based on the owner's training history. For example, it can suggest the optimal training method by taking into account past successes and failures. This allows it to provide advice based on the owner's past training history.

[0068] The training information unit also simultaneously provides training information for other pets, making it possible to accommodate users who have multiple pets. For example, when providing training information for dogs, the generation AI simultaneously provides training information for other pets, such as cats and birds. For example, for a user who has both a dog and a cat, it suggests training methods suitable for both pets. For users who have multiple pets, the generation AI also integrates training schedules for each pet and suggests efficient management methods. For example, it centrally manages the timing and methods of training. Furthermore, when providing training information for other pets, the generation AI takes into account the interactions between the pets and suggests appropriate training methods. For example, if a user has a dog and a bird, it recommends training methods that do not cause stress to each other. This makes it possible to accommodate users who have multiple pets.

[0069] The training information section can customize advice based on the owner's lifestyle. For example, if the owner is an outdoorsy person, the generated AI will suggest outdoor activities and training methods that the owner can enjoy together with their dog. For example, it will introduce precautions and necessary preparations for hiking and camping. If the owner is an indoor person, the generated AI will suggest training methods and activities that can be done indoors. For example, it will introduce indoor play and training methods. The training information section also customizes the dog's training schedule based on the owner's lifestyle. For example, it will suggest short, effective training methods and easy-to-perform training for busy owners. This allows the system to provide advice tailored to the owner's lifestyle.

[0070] The training information unit uses the emotion estimation function to analyze the emotional reactions of owners when receiving training information and can suggest methods of providing information that will elicit positive emotions. For example, the training information unit uses the emotion estimation function to analyze the emotional reactions of owners when receiving training information in real time and suggest methods of providing information that will elicit positive emotions. For example, it could introduce encouraging messages or success stories. The training information unit also uses the generation AI to adjust the tone and content of the information provided based on the owner's emotional reaction. For example, it could provide relaxation techniques or simple advice to an owner who is feeling stressed. The training information unit also uses the emotion estimation function to identify information delivery methods that elicit positive emotions from owners and continue to use those methods. For example, it could prioritize information delivery methods that receive a lot of positive feedback. This makes it possible to suggest information delivery methods that match the owner's emotional reaction.

[0071] The characteristic information unit can provide a deeper understanding based on the genetic background and history of the breed. For example, when providing characteristic information by breed, the generating AI takes into account the genetic background of the breed and explains specific genetic characteristics and health risks. For example, it introduces genetic diseases that are common in specific breeds and how to prevent them. The characteristic information unit also takes into account the history of the breed, allowing the generating AI to explain the origin and development process of the breed. For example, it introduces the historical background of how a specific breed came to be in its current form. The characteristic information unit also suggests breeding and training methods appropriate for the breed based on the genetic background and history of the breed. For example, it explains that specific exercise and diet are suitable for breeds with specific genetic characteristics. This makes it possible to provide information based on the genetic background and history of the breed.

[0072] The feature information unit provides information on health risks and lifespan for each breed of dog, making it easier for owners to create long-term care plans. For example, the feature information unit allows the generation AI to propose preventive measures for specific diseases and health problems by taking into account the health risks of each breed. For example, it introduces preventive measures for joint diseases and heart diseases that are common in certain breeds. The feature information unit also allows the generation AI to propose long-term care plans based on the average lifespan of each breed. For example, it provides health check and vaccination schedules tailored to the lifespan of a specific breed. The feature information unit also allows the generation AI to propose insurance plans and medical services suitable for owners based on information on the health risks and lifespan of each breed. For example, it introduces insurance plans that address specific health risks. This makes it possible to provide care plans based on the health risks and lifespan of each breed.

[0073] The characteristic information unit can use the emotion estimation function to analyze the owner's emotional state and provide information based on the owner's emotional reaction to a specific dog breed. For example, if the owner has positive feelings toward a specific dog breed, the characteristic information unit can use the emotion estimation function to provide detailed information about the dog breed. For example, the characteristic information unit can introduce the characteristics and breeding methods of a dog breed that the owner is interested in. Furthermore, if the owner analyzes the owner's emotional state and has negative feelings toward a specific dog breed, the characteristic information unit can provide information to clear up misunderstandings about the dog breed. For example, the characteristic information unit can explain characteristics that are easily misunderstood and the owner's actual personality. Furthermore, if the owner has neutral feelings toward a specific dog breed, the characteristic information unit can use the emotion estimation function to provide information that emphasizes the appeal and advantages of the dog breed. For example, the characteristic information unit can introduce the special abilities and suitable environment of the dog breed. This makes it possible to provide dog breed information that suits the owner's emotional state.

[0074] The feature information unit also simultaneously provides breed-specific feature information for other pets, enabling the system to accommodate users who own multiple pets. For example, when providing breed-specific feature information for dogs, the generation AI simultaneously provides breed-specific feature information for other pets, such as cats and birds. For example, for a user who owns both a dog and a cat, the generation AI suggests breeding methods suitable for both pets. Furthermore, for users who own multiple pets, the generation AI integrates the characteristics and breeding methods for each pet to suggest efficient management methods. For example, if a user owns a dog and a bird, the generation AI recommends creating an environment that does not cause stress for either pet. Furthermore, when providing breed-specific feature information for other pets, the generation AI considers the interactions between the pets and suggests appropriate breeding methods. For example, if a user owns a dog and a cat, the generation AI suggests play and training methods that are suitable for each pet. This allows the system to accommodate users who own multiple pets.

[0075] The feature information unit can customize advice based on the owner's lifestyle. For example, if the owner is an outdoorsy person, the generating AI will suggest outdoor activities that can be enjoyed with the dog and how to care for it. For example, it will introduce precautions and necessary preparations for hiking and camping. If the owner is an indoor person, the feature information unit will suggest indoor activities and how to care for the dog. For example, it will introduce indoor play and training methods. The feature information unit also allows the generating AI to customize the dog's care schedule based on the owner's lifestyle. For example, it will suggest short, effective exercise methods and easy-to-prepare meal plans for busy owners. This allows advice to be provided that is tailored to the owner's lifestyle.

[0076] The feature information unit uses the emotion estimation function to analyze the emotional response of the owner when receiving breed-specific feature information and can suggest information delivery methods that elicit positive emotions. For example, the feature information unit uses the emotion estimation function to analyze the emotional response of the owner when receiving breed-specific feature information in real time and suggest information delivery methods that elicit positive emotions. For example, it introduces encouraging messages or success stories. Furthermore, the feature information unit uses the generation AI to adjust the tone and content of the information provided based on the owner's emotional response. For example, it provides relaxation techniques or simple advice to an owner who is feeling stressed. Furthermore, the feature information unit uses the emotion estimation function to identify information delivery methods that elicit positive emotions from the owner and continuously uses those methods. For example, it prioritizes information delivery methods that receive a lot of positive feedback. This makes it possible to suggest information delivery methods that correspond to the owner's emotional response.

[0077] The care information unit can customize advice based on the owner's living environment. For example, if the owner lives in an urban area, the generation AI will provide advice suitable for raising a dog in an urban area. For example, it will introduce exercise methods in small spaces and noise control measures. Furthermore, if the owner lives in the suburbs, the generation AI will provide advice suitable for raising a dog in the suburbs. For example, it will introduce exercise methods in large spaces and precautions to take in natural environments. Furthermore, the care information unit will customize the dog's care schedule based on the owner's living environment. For example, it will suggest short, effective exercise methods in urban areas, and long walks and outdoor activities in suburban areas. This makes it possible to provide advice tailored to the owner's living environment.

[0078] The care information unit can use the emotion estimation function to analyze the owner's emotional state and suggest care methods to reduce stress. For example, if the owner is feeling stressed, the care information unit can use the emotion estimation function to have the generation AI suggest care methods that will help the owner relax. For example, it can introduce games and relaxation methods to reduce stress. The care information unit can also analyze the owner's emotional state, and if stress is high, the generation AI can suggest care methods that are easy to implement. For example, it can provide short, effective care methods and simple daily care. The care information unit can also use the emotion estimation function to identify care methods that the owner feels positive about and continue to use those methods. For example, it can prioritize care methods that receive a lot of positive feedback. This makes it possible to provide care methods that suit the owner's emotional state.

[0079] The pet care information unit can also simultaneously provide pet care information for other pets, making it possible to accommodate users who keep multiple pets. For example, when providing general information about dog care, the generation AI can simultaneously provide pet care information for other pets, such as cats and birds. For example, for a user who keeps both a dog and a cat, the generation AI can suggest pet care methods suitable for both pets. For users who keep multiple pets, the generation AI can also integrate the pet care schedules for each pet and suggest efficient management methods. For example, it can centrally manage feeding and health check schedules. Furthermore, when providing pet care information for other pets, the generation AI can consider the interactions between the pets and suggest appropriate pet care methods. For example, if a user keeps a dog and a bird, the generation AI can recommend creating an environment that does not cause stress for each pet. This makes it possible to accommodate users who keep multiple pets.

[0080] The care information section can customize advice based on the owner's lifestyle. For example, if the owner is an outdoorsy type, the generated AI will suggest outdoor activities that can be enjoyed with the dog and how to care for it. For example, it will introduce precautions and necessary preparations for hiking and camping. If the owner is an indoor type, the generated AI will suggest indoor activities and how to care for the dog. For example, it will introduce indoor play and training methods. The care information section also customizes the dog's care schedule based on the owner's lifestyle. For example, it will suggest short and effective exercise methods and easy-to-prepare meal plans for busy owners. This allows the system to provide advice tailored to the owner's lifestyle.

[0081] The care information unit can use the emotion estimation function to analyze the emotional response of the owner when receiving care information and suggest information delivery methods that will elicit positive emotions. For example, the care information unit can use the emotion estimation function to analyze the emotional response of the owner when receiving care information in real time and suggest information delivery methods that will elicit positive emotions. For example, it can introduce encouraging messages or success stories. The care information unit also uses the generation AI to adjust the tone and content of the information provided based on the owner's emotional response. For example, it can provide relaxation techniques or simple advice to an owner who is feeling stressed. The care information unit also uses the emotion estimation function to identify information delivery methods that elicit positive emotions from the owner and continue to use those methods. For example, it can prioritize information delivery methods that receive a lot of positive feedback. This makes it possible to suggest information delivery methods that correspond to the owner's emotional response.

[0082] The latest information section automatically collects research papers and news articles to provide reliable information. For example, the generation AI automatically collects the latest research papers to provide the latest insights into dog health care and breeding methods. For example, it introduces the effectiveness of new vaccinations and the latest training techniques. The latest information section also automatically collects the latest news articles to provide information on the latest trends and events related to dogs. For example, it introduces information on the release of new pet products and information on dog-related events. The generation AI also collects data from reliable sources to provide accurate and up-to-date information to pet owners. For example, it provides information based on articles supervised by veterinarians and announcements from specialist institutions. This makes it possible to provide reliable and up-to-date information.

[0083] The latest information section provides the latest information on specific regions and countries, and can address issues and trends specific to the region. For example, in the latest information section, the generation AI collects the latest information on specific regions and countries and provides health risks and pet care methods specific to those regions. For example, it introduces diseases that are prevalent in specific regions and preventive measures. In addition, in the latest information section, the generation AI collects information on events and new pet products specific to the region to provide trend information for each region. For example, it introduces information on pet events held in the region and information on the release of new products. In addition, in the latest information section, the generation AI collects relevant data to provide the latest information on laws and regulations in specific regions and countries and provide appropriate advice to pet owners. For example, it introduces information on changes to laws and regulations regarding pet care. This makes it possible to provide the latest information that addresses issues and trends specific to the region.

[0084] The latest information unit can use the emotion estimation function to analyze the emotional state of the owner and provide interesting, up-to-date information. The latest information unit, for example, uses the emotion estimation function to provide the latest information that the owner is likely to be interested in. For example, it introduces the latest research and news related to topics that the owner has positive emotions about. The latest information unit also analyzes the emotional state of the owner and suggests a method of providing information that will interest the owner. For example, if the owner is feeling stressed, it provides relaxing information and positive news. The latest information unit also uses the emotion estimation function to identify the latest information that the owner is likely to be interested in and continuously provides that information. For example, it prioritizes an information provision method that receives a lot of positive feedback. This makes it possible to provide interesting, up-to-date information that matches the owner's emotional state.

[0085] The latest information section simultaneously provides the latest information on other pets, making it possible to accommodate users who own multiple pets. For example, when providing the latest information on dogs, the generation AI simultaneously provides the latest information on other pets, such as cats and birds. For example, for a user who owns both a dog and a cat, the generation AI suggests the latest health care methods suitable for both pets. For users who own multiple pets, the generation AI also integrates the latest information for each pet and suggests efficient management methods. For example, vaccination and health check schedules can be centrally managed. Furthermore, when providing the latest information on other pets, the generation AI takes into account the interactions between the pets and suggests appropriate management methods. For example, if a user owns a dog and a bird, the generation AI recommends creating an environment that does not cause stress to either pet. This makes it possible to accommodate users who own multiple pets.

[0086] The latest information section can customize advice based on the owner's lifestyle. For example, if the owner is an outdoorsy person, the generated AI will suggest outdoor activities that the owner can enjoy together with their dog and the latest health care methods. For example, it will introduce precautions and necessary preparations for hiking and camping. If the owner is an indoor person, the generated AI will suggest indoor activities and the latest health care methods. For example, it will introduce indoor play and training methods. The latest information section also customizes the dog's health care schedule based on the owner's lifestyle. For example, it will suggest short, effective exercise methods and easy-to-prepare meal plans for busy owners. This allows the generator AI to provide advice tailored to the owner's lifestyle.

[0087] The latest information unit uses the emotion estimation function to analyze the emotional reactions of owners when receiving the latest information and can suggest information delivery methods that elicit positive emotions. For example, the latest information unit uses the emotion estimation function to analyze the emotional reactions of owners when receiving the latest information in real time and suggest information delivery methods that elicit positive emotions. For example, it could introduce encouraging messages or success stories. The latest information unit also uses the generation AI to adjust the tone and content of the information provided based on the owner's emotional reaction. For example, it could provide relaxation techniques or simple advice to an owner who is feeling stressed. The latest information unit also uses the emotion estimation function to identify information delivery methods that elicit positive emotions from owners and continuously use those methods. For example, it could prioritize information delivery methods that receive a lot of positive feedback. This makes it possible to suggest information delivery methods that correspond to the owner's emotional reaction.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The AI ​​chat system can further include a dog behavior analysis unit. The behavior analysis unit can analyze a dog's behavior patterns and detect abnormal behavior. For example, the generative AI collects dog behavior data and compares it with normal behavior patterns to detect abnormal behavior. If abnormal behavior is detected, an alert is provided to the owner to prompt early action. The behavior analysis unit can also provide advice on appropriate training methods and environmental improvements based on the dog's behavior patterns. For example, it can suggest countermeasures for excessive barking or anxious behavior. This makes it possible to monitor a dog's behavior and detect and address problems early.

[0090] The health management department can further analyze a dog's genetic information and provide health management advice based on genetic risk. For example, the generative AI analyzes a dog's genetic information to assess the risk of a specific genetic disease. If the risk is high, preventative measures and a schedule for regular checkups are recommended. The system can also suggest optimal dietary and exercise plans based on the genetic information. For example, a diet containing specific nutrients is recommended for dogs with certain genetic characteristics. This allows for personalized health management advice based on genetic risk.

[0091] The health management unit can also monitor a dog's sleep patterns and suggest an appropriate sleeping environment. For example, the generative AI collects a dog's sleep data and evaluates the quality and quantity of sleep. If insufficient sleep or irregular sleep patterns are detected, it will suggest remedial measures to the owner, such as recommending a quiet environment or the use of appropriate bedding. The health management unit can also adjust the dog's daytime activity schedule based on the dog's sleep patterns, such as suggesting the appropriate amount of exercise and rest time. This allows for comprehensive management of the dog's health.

[0092] The health management unit can use its emotion estimation function to analyze the owner's emotional state and suggest ways to communicate with the dog. For example, if the owner is feeling stressed, the generative AI can suggest ways to communicate that will help them relax. For example, it can recommend spending more time playing with or walking the dog. The health management unit can also analyze the owner's emotional state and suggest ways to communicate that will elicit positive emotions. For example, it can recommend increasing interaction with the dog or teaching it new tricks. This can strengthen the relationship between owner and dog and reduce stress.

[0093] The health management unit can also provide advice to improve a dog's social skills. For example, the generative AI can evaluate a dog's social behavior and suggest ways to encourage interaction with other dogs and people. For example, it can recommend playing at a dog park or participating in group training. The health management unit can also provide advice on appropriate training methods and environmental improvements based on the dog's social skills. For example, it can recommend group training for sociable dogs and individual training for introverted dogs. This can improve a dog's social skills and reduce stress.

[0094] The health management unit uses the emotion estimation function to analyze the owner's emotional state and suggest ways to improve their motivation to care for their dog's health. For example, if the owner is losing motivation, the generative AI will provide encouraging messages and introduce success stories. The health management unit also analyzes the owner's emotional state and suggests goal setting methods to elicit positive emotions. For example, it recommends setting short-term goals and providing rewards each time they are achieved. This helps maintain the owner's motivation and enable them to continue caring for their dog's health.

[0095] The training intelligence can further customize training methods based on a dog's learning style. For example, the generative AI can evaluate a dog's learning style and suggest visual, auditory, and tactile training methods. For example, it can recommend using visual instructions and demonstrations for a dog with a visual learning style. The training intelligence can also adjust the training progress speed and method based on the dog's learning style. For example, it can suggest training at a slower pace for a dog that is a slow learner. This allows for an effective training method that suits a dog's learning style.

[0096] The training information unit uses the emotion estimation function to analyze the owner's emotional state and adjust the training progress accordingly. For example, if the owner is feeling stressed, the generative AI will suggest slowing down the training progress. For example, it will recommend teaching short, effective training methods or simple commands. The training information unit also analyzes the owner's emotional state and suggests training methods to elicit positive emotions. For example, it will recommend starting with simple tricks to increase successful experiences. This allows the system to provide training methods that are tailored to the owner's emotional state.

[0097] The training information unit can also suggest training methods based on a dog's play preferences. For example, the generative AI can evaluate a dog's play preferences and suggest training methods that incorporate play. For example, for a dog that likes to play with a ball, it can recommend a training method that uses a ball. The training information unit can also adjust the speed and method of training based on the dog's play preferences. For example, learning through play can help maintain a dog's motivation. This makes it possible to provide effective training methods that suit a dog's play preferences.

[0098] The training information unit can use the emotion estimation function to analyze the owner's emotional state and adjust the training feedback method. For example, if the owner is feeling stressed, the generation AI will suggest providing feedback in a gentle tone. For example, it will recommend feedback that focuses on positive reinforcement. The training information unit can also analyze the owner's emotional state and suggest feedback methods that will elicit positive emotions. For example, it will recommend emphasizing successful experiences and providing feedback that creates a sense of accomplishment. This makes it possible to provide feedback methods that correspond to the owner's emotional state.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The information provider uses a generative AI to provide information about dogs. For example, the generative AI uses specific models such as GPT-3 and BERT to provide general information and the latest information on dog health care, training, breed-specific characteristics, and care. Step 2: The health management unit generates the dog's health management information provided by the information provider. For example, the generation AI generates appropriate answers to questions about the dog's diet, exercise, vaccinations, disease symptoms, etc. Step 3: The training information section generates dog training information provided by the information provider. For example, the generation AI generates appropriate answers to questions about toilet training, dealing with excessive barking, and how to teach basic commands. Step 4: The characteristic information unit generates breed-specific characteristic information provided by the information provider. For example, the AI ​​generates appropriate answers to questions about the personality, exercise level, and suitable environment of a specific dog breed. Step 5: The breeding information unit generates general information about dog breeding provided by the information provider. For example, the generation AI generates appropriate answers to questions about how to choose a dog, how to prepare the breeding environment, and how to care for the dog on a daily basis. Step 6: The latest information section generates the latest information about dogs provided by the information provision section. For example, the generation AI generates appropriate answers to questions about new breeding methods, the latest health care technologies, dog event information, etc.

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0103] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 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.

[0106] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0107] 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.

[0108] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0110] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0111] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0112] 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.

[0113] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0114] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0115] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0116] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0118] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 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.

[0121] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0122] 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.

[0123] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0125] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0126] 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.

[0127] 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.

[0128] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0129] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0137] 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.

[0138] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0140] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0141] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0142] 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.

[0143] 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.

[0144] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0145] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0146] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0147] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0149] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0150] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0151] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0152] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0153] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0154] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0155] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0156] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0157] 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.

[0158] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0159] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0160] 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.

[0161] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0162] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0163] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0164] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0165] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0166] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0167] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0168] 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. An information providing section that provides information about dogs using generative AI; a health management unit that generates dog health management information provided by the information providing unit; a training information unit that generates dog training information provided by the information providing unit; a characteristic information unit that generates characteristic information by breed to be provided by the information providing unit; a breeding information unit that generates general information about dog breeding provided by the information providing unit; an up-to-date information unit that generates up-to-date information about dogs to be provided by the information providing unit; A system characterized by:

2. The health management department The health management information of the other pets is also provided at the same time, and the information is adapted to accommodate users who keep multiple pets.

2. The system of claim 1.

3. The training information unit Recommend the best training method based on the dog's age and personality 2. The system of claim 1.

4. The feature information section Providing a deeper understanding of the breed's genetic background and history 2. The system of claim 1.

5. The breeding information unit Customize advice based on the owner's living environment 2. The system of claim 1.

6. The latest information section includes: Analyzing the owner's emotional state and providing them with interesting updates 2. The system of claim 1.

7. The health management department Analyzing the owner's emotional state and providing health care advice to reduce stress 2. The system of claim 1.

Images