System

Abstract

An object of a system according to an embodiment is to support physical condition management and discipline of a pet and selection of a deposit destination when the pet is absent for a long period of time.SOLUTION: A system according to an embodiment includes a physical condition analysis unit, a discipline advice unit, and a deposit destination recommendation unit. The physical condition analysis part analyzes the physical condition of the pet. The discipline advice section advises the discipline of the pet. The deposit destination recommendation unit recommends a deposit destination when the user is absent for a long time.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 made it difficult for pet owners to obtain appropriate information about managing their pet's health, training, and choosing where to leave their pet when they are away for an extended period of time.

[0005] The system according to the embodiment aims to support pet health management, training, and the selection of a place to leave pets when you are away for an extended period of time. [Means for solving the problem]

[0006] The system according to the embodiment includes a physical condition analysis unit, a training advice unit, and a pet care recommendation unit. The physical condition analysis unit analyzes the physical condition of a pet. The training advice unit provides advice on training the pet. The pet care recommendation unit recommends pet care locations for when the owner is away for an extended period of time. [Effects of the Invention]

[0007] The system according to the embodiment can assist in managing the health of pets, training them, and selecting a place to leave them when you are away for an extended period of time. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A pet care system according to an embodiment of the present invention is a system that manages the physical condition of pets, disciplines them, and recommends where to leave them when you are away for an extended period of time. This allows the pet care system to recommend and match pet care methods when pets are not in good health, how to discipline them when they are babies, and where to leave them when you are away for an extended period of time, such as when you are traveling.

[0029] The pet care system according to the embodiment includes a health analysis unit, a training advice unit, and a pet care recommendation unit. The health analysis unit analyzes the pet's health condition. For example, the generation AI analyzes photos and videos of the pet to accurately determine the pet's symptoms. The generation AI can also refer to the pet's past health data and behavioral patterns to provide a more accurate diagnosis. The generation AI can also consider the pet's diet and exercise history to suggest lifestyle improvements. The training advice unit provides advice on pet training. For example, the generation AI can suggest training methods based on the pet's type and age. The generation AI can also refer to the pet's past behavioral data to suggest training methods based on individual behavioral patterns. The generation AI can also use an owner's emotion estimation function to suggest training methods to reduce the owner's stress. The pet care recommendation unit recommends pet care locations when the owner is away for an extended period of time. For example, the generation AI can provide information on pet hotels and pet sitters to suggest pet care locations that meet the owner's needs. The generation AI can also refer to past user reviews and ratings to suggest reliable pet care locations. Furthermore, if a pet requires special care, the generation AI can also prioritize suggesting pet care locations that can provide that care. This allows the pet care system according to the embodiment to centrally manage the pet's health, discipline, and recommend pet care locations. For example, if a pet is unwell, you can quickly respond and protect the pet's health. Proper discipline also makes life with your pet smoother. Furthermore, you can travel with peace of mind without having to worry about where to leave your pet when you're away for an extended period of time.

[0030] The health analysis unit can refer to the pet's past health data or behavioral patterns to make more accurate diagnoses. For example, the health analysis unit collects the pet's past health data, and the generation AI analyzes the health condition based on that data. For example, it refers to past diagnostic results and treatment history and evaluates the correlation with current symptoms. The health analysis unit also analyzes the pet's behavioral patterns, and the generation AI diagnoses the health condition based on that data. For example, it monitors the pet's activity level and food intake and issues an alert if there is an abnormality. The health analysis unit also integrates the pet's health data and behavioral patterns, and the generation AI diagnoses the health condition comprehensively. For example, it compares past health data with current behavioral patterns and suggests countermeasures if there is an abnormality. This improves the accuracy of pet health diagnosis.

[0031] The physical condition analysis unit can make suggestions for improving lifestyle habits by taking into account the pet's diet or exercise history. For example, the physical condition analysis unit analyzes the pet's diet history, and the generation AI makes suggestions for improving lifestyle habits based on that data. For example, if the diet is unbalanced, an appropriate diet plan will be proposed. The physical condition analysis unit also analyzes the pet's exercise history, and the generation AI makes suggestions for improving lifestyle habits based on that data. For example, if the pet is not getting enough exercise, an appropriate exercise plan will be proposed. The physical condition analysis unit also integrates the pet's diet and exercise history, and the generation AI makes comprehensive suggestions for improving lifestyle habits. For example, a health plan that takes into account the balance between diet and exercise will be proposed. This makes it possible to improve the pet's lifestyle habits.

[0032] The discipline advice unit can refer to the pet's past behavioral data and suggest discipline methods based on individual behavioral patterns. For example, the discipline advice unit collects the pet's past behavioral data, and the generation AI suggests discipline methods based on that data. For example, it analyzes past problem behavior patterns and selects an appropriate discipline method. The discipline advice unit also analyzes the pet's behavioral patterns, and the generation AI suggests individual discipline methods based on that data. For example, it suggests training methods to reinforce specific behaviors. The discipline advice unit also integrates the pet's past behavioral data with its current behavioral patterns, and the generation AI suggests comprehensive discipline methods. For example, it selects the optimal discipline method based on past success stories. This makes it possible to discipline a pet based on its individual behavioral patterns.

[0033] The training advice unit can monitor the pet's learning progress in real time and provide appropriate feedback. For example, the training advice unit monitors the pet's learning progress in real time, and the generation AI provides feedback based on that data. For example, the training progress is analyzed and the next step is suggested. The training advice unit also analyzes the pet's learning data, and the generation AI provides appropriate feedback based on that data. For example, if a specific behavior improves, the training advice unit suggests the timing to praise the pet. The training advice unit also monitors the pet's learning progress, and the generation AI adjusts the training method based on that data. For example, if progress is lagging, the training method is suggested to be changed. In this way, appropriate feedback is provided according to the pet's learning progress.

[0034] The pet care recommendation unit can suggest reliable pet care locations by referring to past user reviews or ratings. For example, the pet care recommendation unit collects past user reviews, and the generation AI suggests reliable pet care locations based on that data. For example, it prioritizes recommendations of highly rated pet hotels and pet sitters. The pet care recommendation unit also analyzes user rating data, and the generation AI selects reliable pet care locations based on that data. For example, it suggests pet care locations that meet specific rating criteria. The pet care recommendation unit also integrates past reviews and ratings, and the generation AI suggests reliable pet care locations overall. For example, it selects the optimal pet care location based on multiple highly rated reviews. This allows reliable pet care locations to be suggested.

[0035] The physical condition analysis unit can improve the reliability of the countermeasures by referring to the experiences and reviews of other pet owners. For example, the physical condition analysis unit collects the experiences of other pet owners, and the generation AI proposes countermeasures based on that data. For example, it refers to success stories of pet owners with the same symptoms. The physical condition analysis unit also analyzes the pet owner's reviews, and the generation AI evaluates the reliability of the countermeasures based on that data. For example, it prioritizes suggesting highly rated countermeasures. The physical condition analysis unit also integrates the experiences and reviews of other pet owners, and the generation AI proposes a comprehensive countermeasure. For example, it selects the optimal countermeasure based on multiple success stories. This improves the reliability of the countermeasures.

[0036] The health analysis unit can recommend appropriate supplements or medications according to the pet's health condition. For example, the health analysis unit analyzes the pet's health data, and the generation AI recommends appropriate supplements based on that data. For example, in the case of nutritional deficiencies, a specific vitamin supplement is suggested. The health analysis unit also analyzes the pet's symptoms, and the generation AI recommends appropriate medications based on that data. For example, in the case of skin abnormalities, a specific ointment is suggested. The health analysis unit also integrates the pet's health data and symptoms, and the generation AI comprehensively recommends supplements and medications. For example, it suggests comprehensive supplements that address multiple symptoms. This allows appropriate supplements and medications to be recommended according to the pet's health condition.

[0037] The discipline advice unit can refer to the success stories of other pet owners and suggest effective discipline methods. For example, the discipline advice unit collects success stories of other pet owners, and the generation AI suggests discipline methods based on that data. For example, it refers to success stories of the same type of pet. The discipline advice unit also analyzes the success stories of pet owners, and the generation AI suggests effective discipline methods based on that data. For example, it prioritizes suggesting highly rated training methods. The discipline advice unit also integrates the success stories and reviews of other pet owners, and the generation AI suggests comprehensive discipline methods. For example, it selects the optimal discipline method based on multiple success stories. This allows for the suggestion of effective discipline methods.

[0038] The training advice unit can recommend appropriate training tools or toys according to the type or age of the pet. For example, the training advice unit analyzes the type and age of the pet, and the generation AI recommends appropriate training tools based on that data. For example, it might suggest a specific training mat for a baby dog. The training advice unit also analyzes the age and behavioral patterns of the pet, and the generation AI recommends appropriate toys based on that data. For example, it might suggest a specific scratching post for a baby cat. The training advice unit also integrates the type and age of the pet, and the generation AI comprehensively recommends training tools and toys. For example, it might suggest the optimal tool taking multiple factors into consideration. This allows appropriate training tools and toys to be recommended according to the type and age of the pet.

[0039] The pet boarding recommendation unit can suggest an appropriate pet boarding location based on the pet's type or personality. For example, the pet boarding recommendation unit analyzes the pet's type and personality, and the generation AI suggests an appropriate pet boarding location based on that data. For example, it suggests a pet hotel that suits the personality of a dog. The pet boarding recommendation unit also customizes and suggests an appropriate pet boarding location based on the pet's type and personality data. For example, it suggests a pet sitter that suits the personality of a cat. The pet boarding recommendation unit also integrates the pet's type and personality, and the generation AI suggests an appropriate pet boarding location overall. For example, it selects the optimal pet boarding location taking multiple factors into consideration. This allows the generation AI to suggest an appropriate pet boarding location based on the pet's type and personality.

[0040] If a pet's health condition or special care is required, the pet boarding recommendation unit can suggest the most suitable pet boarding location by taking that information into consideration. For example, the pet boarding recommendation unit analyzes the pet's health condition, and the generation AI uses that data to suggest the most suitable pet boarding location. For example, it may suggest a pet hotel that can address a specific health issue. The pet boarding recommendation unit also customizes and suggests an appropriate pet boarding location based on information about pets that require special care. For example, if a specific medical care is required, it may suggest a pet boarding location that can address that care. The pet boarding recommendation unit also integrates the pet's health condition and special care information, and the generation AI suggests the most suitable pet boarding location overall. For example, it may select a pet boarding location that can address multiple health issues. This allows the generation AI to suggest the most suitable pet boarding location based on the pet's health condition and special care.

[0041] The physical condition analysis unit can provide health management advice to the owner based on the pet's health data. For example, the physical condition analysis unit analyzes the pet's health data, and the generation AI provides health management advice to the owner based on that data. For example, the generation AI suggests an appropriate health management method to the owner based on the pet's diet and exercise data. The physical condition analysis unit also provides specific health management advice to the owner based on the pet's health data. For example, it suggests a diet and exercise plan based on the pet's health condition. The physical condition analysis unit also integrates the pet's health data, and the generation AI provides comprehensive health management advice to the owner. For example, it suggests a health management plan that takes into account the pet's health data and the owner's lifestyle. In this way, health management advice is provided to the owner.

[0042] The physical condition analysis unit can provide information or services to make life with a pet more enjoyable. For example, the physical condition analysis unit has the generation AI provide information to make life with a pet more enjoyable. For example, it suggests information on activities and events that can be enjoyed with a pet. The physical condition analysis unit also has the generation AI provide services to make life with a pet more enjoyable. For example, it suggests information on facilities and services that can be used with a pet. The physical condition analysis unit also has the generation AI provide specific advice to make life with a pet more enjoyable. For example, it suggests games and training methods that can be enjoyed with a pet. In this way, information and services to make life with a pet more enjoyable are provided.

[0043] The physical condition analysis unit can provide the owner with appropriate exercise or dietary advice based on the pet's health data. For example, the physical condition analysis unit analyzes the pet's health data, and the generation AI provides the owner with appropriate exercise advice based on that data. For example, it proposes an exercise plan that can be done together with the pet. The physical condition analysis unit also provides the owner with appropriate dietary advice based on the pet's health data. For example, it proposes a healthy diet plan based on the pet's dietary data. The physical condition analysis unit also integrates the pet's health data, and the generation AI provides the owner with comprehensive exercise and dietary advice. For example, it proposes a balanced lifestyle plan based on the health data of the pet and the owner. This allows the owner to receive appropriate exercise and dietary advice.

[0044] The physical condition analysis unit can provide appropriate health management advice to the owner based on the pet's health data. For example, the physical condition analysis unit analyzes the pet's health data, and the generation AI provides appropriate health management advice to the owner based on that data. For example, it proposes a health management method that takes into account the pet's health condition. Furthermore, the physical condition analysis unit allows the generation AI to provide specific health management advice to the owner based on the pet's health data. For example, it proposes a diet and exercise plan based on the pet's health data. Furthermore, the physical condition analysis unit integrates the pet's health data, and the generation AI provides comprehensive health management advice to the owner. For example, it proposes a health management plan that takes into account the pet's health data and the owner's lifestyle. This allows the owner to receive appropriate health management advice.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The pet care system can also be equipped with a pet nutrition management unit. The nutrition management unit collects data on the pet's diet, and the generation AI can evaluate nutritional balance based on that data. For example, if a specific nutrient is lacking, it can suggest appropriate supplements or ingredients. The nutrition management unit can also link with the pet's physical condition data to provide a meal plan tailored to the pet's health condition. For example, if weight management is necessary, it can suggest a calorie-restricted meal plan. The nutrition management unit can also provide a customized meal plan based on the pet's diet history, taking into account food preferences and allergy information. This helps maintain the pet's health and ensures appropriate nutritional management.

[0047] The pet care system can further include a pet exercise management unit. The exercise management unit collects the pet's exercise data, and the generation AI can evaluate the amount of exercise based on that data. For example, if the pet is not getting enough exercise, it can propose an appropriate exercise plan. The exercise management unit can also link with the pet's physical condition data to provide an exercise plan tailored to the pet's health condition. For example, it can suggest an exercise method that does not put strain on the joints. The exercise management unit can also provide a customized exercise plan that takes into account the pet's exercise preferences and physical fitness level based on the pet's exercise history. This allows for maintaining the pet's health and enabling appropriate exercise management.

[0048] The pet care system can also be equipped with a pet sleep analysis unit. The sleep analysis unit collects the pet's sleep data, and the generation AI can evaluate the quality of the pet's sleep based on that data. For example, if insufficient or excessive sleep is detected, an alert is sent to the owner. The sleep analysis unit can also link with the pet's physical condition data to provide sleep improvement suggestions based on the pet's health condition. For example, it can provide advice on creating a comfortable sleeping environment. The sleep analysis unit can also analyze the pet's sleep patterns based on the pet's sleep history and provide a customized sleep improvement plan. This helps maintain the pet's health and enable appropriate sleep management.

[0049] The pet care system may further include a pet sociability analysis unit. The sociability analysis unit collects data on the pet's interactions with other pets and people, and the generation AI can evaluate the pet's sociability based on that data. For example, if the pet's sociability is low, the sociability analysis unit can suggest an appropriate socialization training method. The sociability analysis unit can also provide specific advice for improving the pet's sociability in conjunction with the pet's behavioral data. For example, it can suggest how to play and interact with other pets. The sociability analysis unit can also provide a customized socialization training plan based on the pet's sociability data, tailored to the pet's personality and preferences. This improves the pet's sociability and enables healthy interactions.

[0050] The pet care system can further include a pet play analysis unit. The play analysis unit collects data on pet play, and the generation AI can evaluate the quality of play based on that data. For example, if play is lacking, it can suggest appropriate play methods. The play analysis unit can also link with pet behavior data to provide play plans based on the pet's health condition. For example, it can suggest play methods to address lack of exercise. The play analysis unit can also provide customized play plans that take into account the pet's play preferences and physical fitness level based on the pet's play history. This allows for maintaining the pet's health and enabling appropriate play management.

[0051] The pet care system can further include a pet learning analysis unit. The learning analysis unit collects the pet's learning data, and the generation AI can evaluate the learning progress based on that data. For example, if the learning is lagging behind, it can suggest an appropriate learning method. The learning analysis unit can also provide specific advice to improve the learning effect by linking with the pet's behavioral data. For example, it can suggest ways to increase motivation for learning. The learning analysis unit can also provide a customized learning plan based on the pet's learning data, tailored to the pet's personality and preferences. This improves the learning effect of the pet and enables appropriate learning management.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The health analysis unit analyzes the pet's health. The generation AI analyzes photos and videos of the pet to accurately determine the pet's symptoms. The generation AI can also refer to the pet's past health data and behavioral patterns to make a more accurate diagnosis. Furthermore, the generation AI can also take into account the pet's diet and exercise history to make suggestions for improving lifestyle habits. Step 2: The training advice section provides advice on pet training. The generation AI suggests training methods appropriate to the pet's type and age. The generation AI can also refer to the pet's past behavioral data to suggest training methods based on individual behavioral patterns. Furthermore, the generation AI can use the owner's emotion estimation function to suggest training methods that will reduce the owner's stress. Step 3: The pet care recommendation unit recommends pet care locations for when the owner is away for an extended period of time. The generation AI provides information on pet hotels and pet sitters, and suggests pet care locations that meet the owner's needs. The generation AI can also refer to past user reviews and ratings to suggest reliable pet care locations. Furthermore, if the pet requires special care, the generation AI can also prioritize suggesting pet care locations that can accommodate that care.

[0054] (Example 2) A pet care system according to an embodiment of the present invention is a system that manages the physical condition of pets, disciplines them, and recommends where to leave them when you are away for an extended period of time. This allows the pet care system to recommend and match pet care methods when pets are not in good health, how to discipline them when they are babies, and where to leave them when you are away for an extended period of time, such as when you are traveling.

[0055] The pet care system according to the embodiment includes a health analysis unit, a training advice unit, and a pet care recommendation unit. The health analysis unit analyzes the pet's health condition. For example, the generation AI analyzes photos and videos of the pet to accurately determine the pet's symptoms. The generation AI can also refer to the pet's past health data and behavioral patterns to provide a more accurate diagnosis. The generation AI can also consider the pet's diet and exercise history to suggest lifestyle improvements. The training advice unit provides advice on pet training. For example, the generation AI can suggest training methods based on the pet's type and age. The generation AI can also refer to the pet's past behavioral data to suggest training methods based on individual behavioral patterns. The generation AI can also use an owner's emotion estimation function to suggest training methods to reduce the owner's stress. The pet care recommendation unit recommends pet care locations when the owner is away for an extended period of time. For example, the generation AI can provide information on pet hotels and pet sitters to suggest pet care locations that meet the owner's needs. The generation AI can also refer to past user reviews and ratings to suggest reliable pet care locations. Furthermore, if a pet requires special care, the generation AI can also prioritize suggesting pet care locations that can provide that care. This allows the pet care system according to the embodiment to centrally manage the pet's health, discipline, and recommend pet care locations. For example, if a pet is unwell, you can quickly respond and protect the pet's health. Proper discipline also makes life with your pet smoother. Furthermore, you can travel with peace of mind without having to worry about where to leave your pet when you're away for an extended period of time.

[0056] The health analysis unit can refer to the pet's past health data or behavioral patterns to make more accurate diagnoses. For example, the health analysis unit collects the pet's past health data, and the generation AI analyzes the health condition based on that data. For example, it refers to past diagnostic results and treatment history and evaluates the correlation with current symptoms. The health analysis unit also analyzes the pet's behavioral patterns, and the generation AI diagnoses the health condition based on that data. For example, it monitors the pet's activity level and food intake and issues an alert if there is an abnormality. The health analysis unit also integrates the pet's health data and behavioral patterns, and the generation AI diagnoses the health condition comprehensively. For example, it compares past health data with current behavioral patterns and suggests countermeasures if there is an abnormality. This improves the accuracy of pet health diagnosis.

[0057] The physical condition analysis unit can simultaneously provide advice to alleviate the owner's anxiety or worry using the owner's emotion estimation function. The physical condition analysis unit, for example, analyzes the owner's emotions in real time, and the generation AI provides advice based on that data. For example, if the owner is feeling anxious, it provides information to reassure the owner. The physical condition analysis unit also customizes advice regarding the pet's physical condition based on the owner's emotion estimation data. For example, it suggests specific ways to deal with symptoms that the owner is worried about. The physical condition analysis unit also analyzes the owner's emotion data, and the generation AI provides advice regarding the pet's physical condition based on that data. For example, if the owner is feeling stressed, it suggests ways to relax. This can alleviate the owner's anxiety and worry.

[0058] The physical condition analysis unit can make suggestions for improving lifestyle habits by taking into account the pet's diet or exercise history. For example, the physical condition analysis unit analyzes the pet's diet history, and the generation AI makes suggestions for improving lifestyle habits based on that data. For example, if the diet is unbalanced, an appropriate diet plan will be proposed. The physical condition analysis unit also analyzes the pet's exercise history, and the generation AI makes suggestions for improving lifestyle habits based on that data. For example, if the pet is not getting enough exercise, an appropriate exercise plan will be proposed. The physical condition analysis unit also integrates the pet's diet and exercise history, and the generation AI makes comprehensive suggestions for improving lifestyle habits. For example, a health plan that takes into account the balance between diet and exercise will be proposed. This makes it possible to improve the pet's lifestyle habits.

[0059] The discipline advice unit can refer to the pet's past behavioral data and suggest discipline methods based on individual behavioral patterns. For example, the discipline advice unit collects the pet's past behavioral data, and the generation AI suggests discipline methods based on that data. For example, it analyzes past problem behavior patterns and selects an appropriate discipline method. The discipline advice unit also analyzes the pet's behavioral patterns, and the generation AI suggests individual discipline methods based on that data. For example, it suggests training methods to reinforce specific behaviors. The discipline advice unit also integrates the pet's past behavioral data with its current behavioral patterns, and the generation AI suggests comprehensive discipline methods. For example, it selects the optimal discipline method based on past success stories. This makes it possible to discipline a pet based on its individual behavioral patterns.

[0060] The discipline advice unit can use the owner's emotion estimation function to suggest discipline methods to reduce the owner's stress. For example, the discipline advice unit analyzes the owner's emotions in real time, and the generation AI suggests discipline methods based on that data. For example, if the owner is feeling stressed, it suggests a simple and effective discipline method. The discipline advice unit also customizes discipline methods to reduce stress based on the owner's emotion estimation data, with the generation AI suggesting such methods. For example, it suggests training methods that allow the owner to relax. The discipline advice unit also analyzes the owner's emotion data, and the generation AI suggests specific discipline methods to reduce stress based on that data. For example, it suggests discipline methods that incorporate play that the owner can enjoy. This provides discipline methods to reduce the owner's stress.

[0061] The training advice unit can monitor the pet's learning progress in real time and provide appropriate feedback. For example, the training advice unit monitors the pet's learning progress in real time, and the generation AI provides feedback based on that data. For example, the training progress is analyzed and the next step is suggested. The training advice unit also analyzes the pet's learning data, and the generation AI provides appropriate feedback based on that data. For example, if a specific behavior improves, the training advice unit suggests the timing to praise the pet. The training advice unit also monitors the pet's learning progress, and the generation AI adjusts the training method based on that data. For example, if progress is lagging, the training method is suggested to be changed. In this way, appropriate feedback is provided according to the pet's learning progress.

[0062] The pet care recommendation unit can suggest reliable pet care locations by referring to past user reviews or ratings. For example, the pet care recommendation unit collects past user reviews, and the generation AI suggests reliable pet care locations based on that data. For example, it prioritizes recommendations of highly rated pet hotels and pet sitters. The pet care recommendation unit also analyzes user rating data, and the generation AI selects reliable pet care locations based on that data. For example, it suggests pet care locations that meet specific rating criteria. The pet care recommendation unit also integrates past reviews and ratings, and the generation AI suggests reliable pet care locations overall. For example, it selects the optimal pet care location based on multiple highly rated reviews. This allows reliable pet care locations to be suggested.

[0063] The pet care recommendation unit can use the owner's emotion estimation function to provide information to reduce the owner's anxiety. For example, the pet care recommendation unit analyzes the owner's emotions in real time, and the generation AI provides information to reduce anxiety based on that data. For example, it provides detailed information about pet care locations that will put the owner at ease. The pet care recommendation unit also customizes information to reduce anxiety based on the owner's emotion estimation data. For example, it suggests specific measures to address the owner's concerns. The pet care recommendation unit also analyzes the owner's emotion data, and the generation AI provides specific information to reduce anxiety based on that data. For example, it provides detailed information about the qualifications and experience of the staff at the pet care location. This provides information to reduce the owner's anxiety.

[0064] The physical condition analysis unit can improve the reliability of the countermeasures by referring to the experiences and reviews of other pet owners. For example, the physical condition analysis unit collects the experiences of other pet owners, and the generation AI proposes countermeasures based on that data. For example, it refers to success stories of pet owners with the same symptoms. The physical condition analysis unit also analyzes the pet owner's reviews, and the generation AI evaluates the reliability of the countermeasures based on that data. For example, it prioritizes suggesting highly rated countermeasures. The physical condition analysis unit also integrates the experiences and reviews of other pet owners, and the generation AI proposes a comprehensive countermeasure. For example, it selects the optimal countermeasure based on multiple success stories. This improves the reliability of the countermeasures.

[0065] The health analysis unit can recommend appropriate supplements or medications according to the pet's health condition. For example, the health analysis unit analyzes the pet's health data, and the generation AI recommends appropriate supplements based on that data. For example, in the case of nutritional deficiencies, a specific vitamin supplement is suggested. The health analysis unit also analyzes the pet's symptoms, and the generation AI recommends appropriate medications based on that data. For example, in the case of skin abnormalities, a specific ointment is suggested. The health analysis unit also integrates the pet's health data and symptoms, and the generation AI comprehensively recommends supplements and medications. For example, it suggests comprehensive supplements that address multiple symptoms. This allows appropriate supplements and medications to be recommended according to the pet's health condition.

[0066] The physical condition analysis unit can use the pet's emotion estimation function to evaluate the pet's stress level and provide advice for stress reduction. The physical condition analysis unit, for example, analyzes the pet's emotions in real time, and the generation AI evaluates the stress level based on that data. For example, it analyzes the pet's behavior and facial expressions to detect signs of stress. The physical condition analysis unit also provides the generation AI with advice for stress reduction based on the pet's emotion estimation data. For example, it suggests ways to create a relaxing environment. The physical condition analysis unit also analyzes the pet's emotion data, and the generation AI suggests specific measures for stress reduction based on that data. For example, it suggests play or exercise to reduce stress. This allows the pet's stress level to be evaluated and advice for stress reduction to be provided.

[0067] The discipline advice unit can refer to the success stories of other pet owners and suggest effective discipline methods. For example, the discipline advice unit collects success stories of other pet owners, and the generation AI suggests discipline methods based on that data. For example, it refers to success stories of the same type of pet. The discipline advice unit also analyzes the success stories of pet owners, and the generation AI suggests effective discipline methods based on that data. For example, it prioritizes suggesting highly rated training methods. The discipline advice unit also integrates the success stories and reviews of other pet owners, and the generation AI suggests comprehensive discipline methods. For example, it selects the optimal discipline method based on multiple success stories. This allows for the suggestion of effective discipline methods.

[0068] The training advice unit can recommend appropriate training tools or toys according to the type or age of the pet. For example, the training advice unit analyzes the type and age of the pet, and the generation AI recommends appropriate training tools based on that data. For example, it might suggest a specific training mat for a baby dog. The training advice unit also analyzes the age and behavioral patterns of the pet, and the generation AI recommends appropriate toys based on that data. For example, it might suggest a specific scratching post for a baby cat. The training advice unit also integrates the type and age of the pet, and the generation AI comprehensively recommends training tools and toys. For example, it might suggest the optimal tool taking multiple factors into consideration. This allows appropriate training tools and toys to be recommended according to the type and age of the pet.

[0069] The discipline advice unit can use the pet emotion estimation function to suggest discipline methods to increase the pet's motivation. For example, the discipline advice unit analyzes the pet's emotions in real time, and the generation AI suggests discipline methods to increase motivation based on that data. For example, it suggests training methods that the pet will enjoy. Furthermore, the discipline advice unit customizes discipline methods to increase motivation based on the pet emotion estimation data. For example, it suggests training methods that use toys that the pet is interested in. Furthermore, the discipline advice unit analyzes the pet's emotion data, and the generation AI suggests specific discipline methods to increase motivation based on that data. For example, it suggests methods to increase motivation by praising the pet. In this way, discipline methods to increase the pet's motivation are suggested.

[0070] The pet boarding recommendation unit can suggest an appropriate pet boarding location based on the pet's type or personality. For example, the pet boarding recommendation unit analyzes the pet's type and personality, and the generation AI suggests an appropriate pet boarding location based on that data. For example, it suggests a pet hotel that suits the personality of a dog. The pet boarding recommendation unit also customizes and suggests an appropriate pet boarding location based on the pet's type and personality data. For example, it suggests a pet sitter that suits the personality of a cat. The pet boarding recommendation unit also integrates the pet's type and personality, and the generation AI suggests an appropriate pet boarding location overall. For example, it selects the optimal pet boarding location taking multiple factors into consideration. This allows the generation AI to suggest an appropriate pet boarding location based on the pet's type and personality.

[0071] If a pet's health condition or special care is required, the pet boarding recommendation unit can suggest the most suitable pet boarding location by taking that information into consideration. For example, the pet boarding recommendation unit analyzes the pet's health condition, and the generation AI uses that data to suggest the most suitable pet boarding location. For example, it may suggest a pet hotel that can address a specific health issue. The pet boarding recommendation unit also customizes and suggests an appropriate pet boarding location based on information about pets that require special care. For example, if a specific medical care is required, it may suggest a pet boarding location that can address that care. The pet boarding recommendation unit also integrates the pet's health condition and special care information, and the generation AI suggests the most suitable pet boarding location overall. For example, it may select a pet boarding location that can address multiple health issues. This allows the generation AI to suggest the most suitable pet boarding location based on the pet's health condition and special care.

[0072] The pet care recommendation unit can use the pet emotion estimation function to suggest pet care locations where the pet can feel safe. For example, the pet care recommendation unit analyzes the pet's emotions in real time, and the generation AI uses that data to suggest pet care locations where the pet can feel safe. For example, it can suggest pet hotels that provide a relaxing environment for pets. The pet care recommendation unit also customizes and suggests pet care locations where the generation AI can feel safe based on the pet emotion estimation data. For example, it can suggest pet care locations where the pet will not feel stressed. The pet care recommendation unit also analyzes the pet emotion data, and the generation AI uses that data to suggest specific pet care locations where the pet can feel safe. For example, it can suggest pet care locations that provide special care that will make the pet feel safe. In this way, pet care locations where the pet can feel safe are suggested.

[0073] The physical condition analysis unit can provide health management advice to the owner based on the pet's health data. For example, the physical condition analysis unit analyzes the pet's health data, and the generation AI provides health management advice to the owner based on that data. For example, the generation AI suggests an appropriate health management method to the owner based on the pet's diet and exercise data. The physical condition analysis unit also provides specific health management advice to the owner based on the pet's health data. For example, it suggests a diet and exercise plan based on the pet's health condition. The physical condition analysis unit also integrates the pet's health data, and the generation AI provides comprehensive health management advice to the owner. For example, it suggests a health management plan that takes into account the pet's health data and the owner's lifestyle. In this way, health management advice is provided to the owner.

[0074] The physical condition analysis unit can use the owner's emotion estimation function to provide advice to reduce the owner's stress. For example, the physical condition analysis unit analyzes the owner's emotions in real time, and the generation AI provides advice to reduce stress based on that data. For example, it suggests ways for the owner to relax. Furthermore, the physical condition analysis unit provides specific advice to reduce stress based on the owner's emotion estimation data. For example, it suggests hobbies and activities that the owner can enjoy. Furthermore, the physical condition analysis unit analyzes the owner's emotion data, and the generation AI provides customized advice to reduce stress based on that data. For example, it suggests ways to create an environment where the owner can relax. This provides advice to reduce the owner's stress.

[0075] The physical condition analysis unit can provide information or services to make life with a pet more enjoyable. For example, the physical condition analysis unit has the generation AI provide information to make life with a pet more enjoyable. For example, it suggests information on activities and events that can be enjoyed with a pet. The physical condition analysis unit also has the generation AI provide services to make life with a pet more enjoyable. For example, it suggests information on facilities and services that can be used with a pet. The physical condition analysis unit also has the generation AI provide specific advice to make life with a pet more enjoyable. For example, it suggests games and training methods that can be enjoyed with a pet. In this way, information and services to make life with a pet more enjoyable are provided.

[0076] The physical condition analysis unit can provide the owner with appropriate exercise or dietary advice based on the pet's health data. For example, the physical condition analysis unit analyzes the pet's health data, and the generation AI provides the owner with appropriate exercise advice based on that data. For example, it proposes an exercise plan that can be done together with the pet. The physical condition analysis unit also provides the owner with appropriate dietary advice based on the pet's health data. For example, it proposes a healthy diet plan based on the pet's dietary data. The physical condition analysis unit also integrates the pet's health data, and the generation AI provides the owner with comprehensive exercise and dietary advice. For example, it proposes a balanced lifestyle plan based on the health data of the pet and the owner. This allows the owner to receive appropriate exercise and dietary advice.

[0077] The physical condition analysis unit can provide appropriate health management advice to the owner based on the pet's health data. For example, the physical condition analysis unit analyzes the pet's health data, and the generation AI provides appropriate health management advice to the owner based on that data. For example, it proposes a health management method that takes into account the pet's health condition. Furthermore, the physical condition analysis unit allows the generation AI to provide specific health management advice to the owner based on the pet's health data. For example, it proposes a diet and exercise plan based on the pet's health data. Furthermore, the physical condition analysis unit integrates the pet's health data, and the generation AI provides comprehensive health management advice to the owner. For example, it proposes a health management plan that takes into account the pet's health data and the owner's lifestyle. This allows the owner to receive appropriate health management advice.

[0078] The physical condition analysis unit can use the owner's emotion estimation function to provide information and services that suit the owner's lifestyle. For example, the physical condition analysis unit analyzes the owner's emotions in real time, and the generation AI provides information that suits the owner's lifestyle based on that data. For example, it suggests ways for the owner to relax. Furthermore, the physical condition analysis unit uses the owner's emotion estimation data to allow the generation AI to provide services that suit the owner's lifestyle. For example, it suggests hobbies and activities that the owner can enjoy. Furthermore, the physical condition analysis unit analyzes the owner's emotion data, and the generation AI uses that data to provide specific information and services that suit the owner's lifestyle. For example, it suggests ways to create an environment where the owner can relax. This allows information and services that suit the owner's lifestyle to be provided.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The pet care system can further include a pet behavior analysis unit. The behavior analysis unit can monitor the pet's behavior in real time and detect abnormal behavior. For example, if the pet exhibits unusual behavior, it can send an alert to the owner. The behavior analysis unit can also analyze the pet's behavioral patterns, and the generating AI can use that data to make suggestions for behavior improvement. For example, it can suggest training methods to reduce excessive barking or unnecessary movements. The behavior analysis unit can also evaluate the pet's stress level based on the pet's behavior data and provide advice on stress reduction. This allows for appropriate management of the pet's behavior and maintenance of its health and happiness.

[0081] The pet care system can also be equipped with a pet nutrition management unit. The nutrition management unit collects data on the pet's diet, and the generation AI can evaluate nutritional balance based on that data. For example, if a specific nutrient is lacking, it can suggest appropriate supplements or ingredients. The nutrition management unit can also link with the pet's physical condition data to provide a meal plan tailored to the pet's health condition. For example, if weight management is necessary, it can suggest a calorie-restricted meal plan. The nutrition management unit can also provide a customized meal plan based on the pet's diet history, taking into account food preferences and allergy information. This helps maintain the pet's health and ensures appropriate nutritional management.

[0082] The pet care system can further include a pet exercise management unit. The exercise management unit collects the pet's exercise data, and the generation AI can evaluate the amount of exercise based on that data. For example, if the pet is not getting enough exercise, it can propose an appropriate exercise plan. The exercise management unit can also link with the pet's physical condition data to provide an exercise plan tailored to the pet's health condition. For example, it can suggest an exercise method that does not put strain on the joints. The exercise management unit can also provide a customized exercise plan that takes into account the pet's exercise preferences and physical fitness level based on the pet's exercise history. This allows for maintaining the pet's health and enabling appropriate exercise management.

[0083] The pet care system can also be equipped with a pet sleep analysis unit. The sleep analysis unit collects the pet's sleep data, and the generation AI can evaluate the quality of the pet's sleep based on that data. For example, if insufficient or excessive sleep is detected, an alert is sent to the owner. The sleep analysis unit can also link with the pet's physical condition data to provide sleep improvement suggestions based on the pet's health condition. For example, it can provide advice on creating a comfortable sleeping environment. The sleep analysis unit can also analyze the pet's sleep patterns based on the pet's sleep history and provide a customized sleep improvement plan. This helps maintain the pet's health and enable appropriate sleep management.

[0084] The pet care system can further include a pet emotion analysis unit. The emotion analysis unit analyzes the pet's facial expressions and behavior, and the generation AI can infer emotions based on that data. For example, if the pet is feeling stressed, an alert is sent to the owner. The emotion analysis unit can also provide advice to reduce stress based on the pet's emotional data. For example, it can suggest ways to create a relaxing environment. The emotion analysis unit can also evaluate the pet's happiness level based on the pet's emotional data and suggest specific measures to improve happiness. This allows for appropriate management of the pet's emotions and maintenance of its health and happiness.

[0085] The pet care system may further include a pet sociability analysis unit. The sociability analysis unit collects data on the pet's interactions with other pets and people, and the generation AI can evaluate the pet's sociability based on that data. For example, if the pet's sociability is low, the sociability analysis unit can suggest an appropriate socialization training method. The sociability analysis unit can also provide specific advice for improving the pet's sociability in conjunction with the pet's behavioral data. For example, it can suggest how to play and interact with other pets. The sociability analysis unit can also provide a customized socialization training plan based on the pet's sociability data, tailored to the pet's personality and preferences. This improves the pet's sociability and enables healthy interactions.

[0086] The pet care system can further include a pet play analysis unit. The play analysis unit collects data on pet play, and the generation AI can evaluate the quality of play based on that data. For example, if play is lacking, it can suggest appropriate play methods. The play analysis unit can also link with pet behavior data to provide play plans based on the pet's health condition. For example, it can suggest play methods to address lack of exercise. The play analysis unit can also provide customized play plans that take into account the pet's play preferences and physical fitness level based on the pet's play history. This allows for maintaining the pet's health and enabling appropriate play management.

[0087] The pet care system can further include a pet learning analysis unit. The learning analysis unit collects the pet's learning data, and the generation AI can evaluate the learning progress based on that data. For example, if the learning is lagging behind, it can suggest an appropriate learning method. The learning analysis unit can also provide specific advice to improve the learning effect by linking with the pet's behavioral data. For example, it can suggest ways to increase motivation for learning. The learning analysis unit can also provide a customized learning plan based on the pet's learning data, tailored to the pet's personality and preferences. This improves the learning effect of the pet and enables appropriate learning management.

[0088] The pet care system can also use a pet emotion estimation function to assess a pet's happiness level and provide advice to improve it. For example, it can analyze a pet's behavior and facial expressions to estimate its happiness level. Based on the pet's happiness data, the generative AI can then suggest specific measures to improve its happiness level. For example, it can suggest games and training methods that the pet will enjoy. It can also provide advice to improve the pet's living environment based on the pet's happiness data. For example, it can suggest ways to create a comfortable living environment. This can improve a pet's happiness level and maintain its health and happiness.

[0089] The pet care system can also use a pet emotion estimation function to assess a pet's stress level and provide advice for stress reduction. For example, it can analyze a pet's behavior and facial expressions to estimate its stress level. Based on the pet's stress level data, the generative AI can then suggest specific measures to reduce stress. For example, it can suggest ways to create a relaxing environment. It can also provide advice for improving a pet's living environment based on the pet's stress level data. For example, it can suggest play or exercise to reduce stress. This allows the system to assess a pet's stress level and provide advice for stress reduction.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The health analysis unit analyzes the pet's health. The generation AI analyzes photos and videos of the pet to accurately determine the pet's symptoms. The generation AI can also refer to the pet's past health data and behavioral patterns to make a more accurate diagnosis. Furthermore, the generation AI can also take into account the pet's diet and exercise history to make suggestions for improving lifestyle habits. Step 2: The training advice section provides advice on pet training. The generation AI suggests training methods appropriate to the pet's type and age. The generation AI can also refer to the pet's past behavioral data to suggest training methods based on individual behavioral patterns. Furthermore, the generation AI can use the owner's emotion estimation function to suggest training methods that will reduce the owner's stress. Step 3: The pet care recommendation unit recommends pet care locations for when the owner is away for an extended period of time. The generation AI provides information on pet hotels and pet sitters, and suggests pet care locations that meet the owner's needs. The generation AI can also refer to past user reviews and ratings to suggest reliable pet care locations. Furthermore, if the pet requires special care, the generation AI can also prioritize suggesting pet care locations that can accommodate that care.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0105] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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."

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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]

[0159] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A system equipped with a generative AI, A physical condition analysis unit that analyzes the physical condition of a pet; The Training Advice Department provides advice on pet training, A storage location recommendation unit that recommends storage locations during long-term absence. A system characterized by:

2. The physical condition analysis unit Referencing the pet's past health data or behavioral patterns to make a more accurate diagnosis 2. The system of claim 1.

3. The physical condition analysis unit and simultaneously providing advice to the pet owner to alleviate the pet owner's anxiety or worry.

2. The system of claim 1.

4. The physical condition analysis unit Considering the pet's dietary or exercise history, we will make lifestyle improvement suggestions.

2. The system of claim 1.

5. The discipline advice unit Refer to the pet's past behavior data and suggest training methods based on individual behavior patterns.

2. The system of claim 1.

Images