system

Abstract

The system according to the embodiment aims to analyze the cries and physical condition of pets and provide the information to owners in an easy-to-understand manner. [Solution] A system according to an embodiment includes an acquisition unit, an analysis unit, and a provision unit. The acquisition unit acquires images and videos of a pet. The analysis unit analyzes the images and videos acquired by the acquisition unit to determine the pet's cries and physical condition. The provision unit provides the information determined by the analysis unit in a specific format.

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 does not adequately capture a pet's cries and physical condition accurately and provide the information to owners in an easy-to-understand manner, so there is room for improvement.

[0005] The system according to the embodiment aims to analyze the cries and physical condition of pets and provide the information to owners in an easy-to-understand manner. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, an analysis unit, and a provision unit. The acquisition unit acquires images and videos of the pet. The analysis unit analyzes the images and videos acquired by the acquisition unit to determine the pet's cries and physical condition. The provision unit provides the information determined by the analysis unit in a specific format. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the cries and physical condition of pets and provide the information to owners in an easy-to-understand manner. [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 system according to an embodiment of the present invention acquires images and videos of a pet, analyzes them using a generation AI to determine the pet's cries and physical condition, and provides the information in an easy-to-understand format. This system acquires images and videos of a pet, analyzes them using a generation AI to determine the pet's cries and physical condition, and provides the determined information to the owner in an easy-to-understand format. For example, to acquire images and videos of a pet, the owner uses a camera to capture images and videos of the pet. Next, the generation AI analyzes the input images and videos to determine the pet's cries and physical condition. The determined information is provided to the owner by the generation AI in an easy-to-understand format. This allows the owner to understand the pet's condition and strengthen communication with the pet. The system analyzes the pet's condition and provides easy-to-understand information to the owner, thereby strengthening communication with the pet. For example, by understanding why the pet is crying, the owner can take appropriate action. Furthermore, if the pet's physical condition is abnormal, early action can be taken. This helps maintain the pet's health and deepen the bond between the pet and the owner.

[0029] The pet analysis system according to the embodiment includes an acquisition unit, an analysis unit, and a provision unit. The acquisition unit acquires images and videos of the pet. Examples of the images and videos of the pet include, but are not limited to, still images, short videos, and live streaming. For example, the acquisition unit is used by a pet owner to capture images and videos of the pet using a camera. The acquisition unit can also acquire images and videos of the pet in real time. For example, the acquisition unit can track the pet's movements using a camera and acquire images and videos in real time. The analysis unit uses a generation AI to analyze the images and videos acquired by the acquisition unit and determine the pet's cries and physical condition. The analysis is performed using, for example, an image analysis algorithm or a sound analysis algorithm, but is not limited to these examples. For example, the analysis unit uses image analysis technology to detect abnormalities in the pet's physical condition or behavior. The analysis unit can also use sound analysis technology to analyze the frequency and pattern of the pet's cries and determine what the pet is trying to communicate. For example, the analysis unit performs frequency analysis of the pet's cries to determine information such as whether the pet is "hungry" or "want to play." The provision unit provides the information determined by the analysis unit to the owner in an easy-to-understand format. The provision may be in the form of, for example, a text message, a voice notification, an in-app notification, or the like, but is not limited to these examples. For example, the provision unit may inform the owner of the pet's condition by text or voice. Furthermore, if there is an abnormality in the pet's health, the provision unit may provide the owner with specific symptoms and measures to deal with the abnormality. For example, if there is an abnormality in the pet's health, the provision unit may notify the owner of the specific symptoms and measures to deal with the abnormality by text message. In this way, the pet analysis system according to the embodiment can analyze the pet's condition and provide the owner with information that is easy to understand. This allows the owner to understand the pet's condition and strengthen communication with the pet.

[0030] The analysis unit can detect abnormalities in the pet's physical condition or behavior using image analysis technology. Image analysis technology includes, but is not limited to, object detection, face recognition, and motion analysis. The analysis unit can detect abnormalities in the pet's physical condition or behavior using, for example, object detection technology. For example, the analysis unit can detect abnormalities on the pet's body surface. The analysis unit can also analyze the pet's facial expression using face recognition technology to detect abnormalities in the pet's physical condition. For example, the analysis unit can analyze the pet's facial expression to detect abnormalities in the pet's physical condition. The analysis unit can also analyze the pet's movements using motion analysis technology to detect abnormalities in the pet's behavior. For example, the analysis unit can analyze the pet's movement patterns to detect abnormalities in the pet's behavior. In this way, image analysis technology can accurately detect abnormalities in the pet's physical condition or behavior. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input image data of the pet into the generation AI and cause the generation AI to detect abnormalities in the pet's physical condition or behavior.

[0031] The analysis unit can use voice analysis technology to analyze the frequency and pattern of the pet's cry and determine what the pet is trying to communicate. Voice analysis technology includes, but is not limited to, frequency analysis, voice recognition, and emotion analysis. For example, the analysis unit can use frequency analysis technology to analyze the frequency of the pet's cry and determine what the pet is trying to communicate. For example, the analysis unit can analyze the frequency of the pet's cry and determine information such as "I'm hungry" or "I want to play." The analysis unit can also use voice recognition technology to analyze the pattern of the pet's cry and determine what the pet is trying to communicate. For example, the analysis unit can analyze the pattern of the pet's cry and determine information such as "I feel anxious" or "I'm happy." The analysis unit can also use emotion analysis technology to analyze the emotion of the pet's cry and determine what the pet is trying to communicate. For example, the analysis unit can analyze the emotion of the pet's cry and determine information such as "I'm angry" or "I'm sad." This allows the use of voice analysis technology to accurately determine the intention of a pet from its cry. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit may input data of the pet's cry into the generation AI and have the generation AI analyze the frequency and pattern of the cry.

[0032] The providing unit can notify the owner of the pet's condition by text or voice. The providing unit can, for example, notify the owner of the pet's condition by using a text message. For example, the providing unit notifies the owner of the pet's condition by a text message. The providing unit can also notify the owner of the pet's condition by using a voice message. For example, the providing unit notifies the owner of the pet's condition by a voice message. The providing unit can also notify the owner of the pet's condition by using an in-app notification. For example, the providing unit notifies the owner of the pet's condition by an in-app notification. This makes it easier for the owner to understand the pet's condition by conveying the pet's condition by text or voice. Some or all of the above-described processing by the providing unit can be performed using, or without, the generation AI. For example, the providing unit can input information determined by the analysis unit into the generation AI and cause the generation AI to generate text or voice.

[0033] The providing unit can provide the owner with specific symptoms and countermeasures when the pet's health condition is abnormal. For example, when the pet's health condition is abnormal, the providing unit notifies the owner of the specific symptoms by a text message. For example, when the pet's health condition is abnormal, the providing unit notifies the owner of the specific symptoms by a text message. The providing unit can also notify the owner of specific countermeasures when the pet's health condition is abnormal by a text message. For example, when the pet's health condition is abnormal, the providing unit notifies the owner of specific countermeasures by a text message. The providing unit can also notify the owner of specific symptoms when the pet's health condition is abnormal by a voice message. For example, when the pet's health condition is abnormal, the providing unit notifies the owner of specific symptoms by a voice message. The providing unit can also notify the owner of specific countermeasures when the pet's health condition is abnormal by a voice message. For example, when the pet's health condition is abnormal, the providing unit notifies the owner of specific countermeasures by a voice message. In this way, when the pet's health condition is abnormal, the specific symptoms and countermeasures are provided, allowing the owner to take appropriate action. Some or all of the above-described processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit may input information determined by the analysis unit to the generation AI and cause the generation AI to generate specific symptoms and countermeasures.

[0034] The acquisition unit can analyze the pet's past behavioral history and select an appropriate acquisition method. The acquisition unit, for example, analyzes the pet's past behavioral history and selects an appropriate acquisition method. The pet's past behavioral history includes, for example, past activity patterns and behavioral frequency, but is not limited to these examples. For example, if the pet was active during a specific time period in the past, the acquisition unit can take a photo during that time period. Furthermore, if the pet often played at a specific location in the past, the acquisition unit can also take a photo at that location. Furthermore, if the pet repeatedly performed a specific behavior in the past, the acquisition unit can also capture that behavior. For example, if the pet was active during a specific time period in the past, the acquisition unit can take a photo during that time period. Furthermore, if the pet often played at a specific location in the past, the acquisition unit can also take a photo at that location. Furthermore, if the pet repeatedly performed a specific behavior in the past, the acquisition unit can also capture that behavior. In this way, the pet's past behavioral history can be analyzed to select an optimal acquisition method. Some or all of the above-described processing in the acquisition unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the acquisition unit can input the pet's past behavioral history data into the generation AI and have the generation AI select the optimal acquisition method.

[0035] The acquisition unit can perform filtering based on the pet's current activity status and environment when acquiring images or videos. For example, the acquisition unit performs filtering based on the pet's current activity status and environment when acquiring images or videos. Filtering includes, but is not limited to, an activity status detection method and environmental condition settings. For example, the acquisition unit prioritizes capturing images of the pet playing when the pet is playing. The acquisition unit can also prioritize capturing images of the pet resting when the pet is resting. The acquisition unit can also prioritize capturing images of the pet eating when the pet is eating. For example, the acquisition unit prioritizes capturing images of the pet playing when the pet is playing. The acquisition unit can also prioritize capturing images of the pet resting when the pet is resting. The acquisition unit can also prioritize capturing images of the pet eating when the pet is eating. By performing filtering based on the pet's current activity status and environment, more appropriate images and videos can be acquired. Some or all of the above-described processing in the acquisition unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the acquisition unit can input the pet's current activity status and environmental data into the generation AI and have the generation AI perform filtering.

[0036] The acquisition unit can select the optimal acquisition means according to the user's input method when acquiring images or videos. For example, when acquiring images or videos, the acquisition unit selects the optimal acquisition means according to the user's input method. User input methods include, but are not limited to, voice input, text input, and gesture input. For example, when a user gives a voice instruction such as "Take a picture of my pet playing," the acquisition unit takes a picture according to the instruction. Furthermore, when a user inputs a text instruction such as "Take a picture of my pet sleeping," the acquisition unit can take a picture according to the instruction. Furthermore, when a user gives a gesture instruction to take a picture, the acquisition unit can take a picture according to the instruction. For example, when a user gives a voice instruction such as "Take a picture of my pet playing," the acquisition unit can take a picture according to the instruction. Furthermore, when a user inputs a text instruction such as "Take a picture of my pet sleeping," the acquisition unit can take a picture according to the instruction. Furthermore, when a user gives a gesture instruction to take a picture, the acquisition unit can take a picture according to the instruction. This allows the optimal acquisition means to be selected according to the user's input method, thereby enabling photography to be performed in line with the user's intentions. Some or all of the above-described processing in the acquisition unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the acquisition unit may input the user's input data into the generation AI and have the generation AI select the optimal acquisition means.

[0037] When acquiring images or videos, the acquisition unit can prioritize acquiring highly relevant data by taking into account the geographical location information of the pet. For example, when acquiring images or videos, the acquisition unit prioritizes acquiring highly relevant data by taking into account the geographical location information of the pet. Geographical location information includes, but is not limited to, GPS data and location information services. For example, if the pet is in a park, the acquisition unit prioritizes acquiring activities at that location. Furthermore, if the pet is at home, the acquisition unit can also prioritize acquiring activities at that location. Furthermore, if the pet is at a veterinary clinic, the acquisition unit can also prioritize acquiring activities at that location. For example, if the pet is in a park, the acquisition unit prioritizes acquiring activities at that location. Furthermore, if the pet is at home, the acquisition unit can also prioritize acquiring activities at that location. Furthermore, if the pet is at a veterinary clinic, the acquisition unit can prioritize acquiring activities at that location. In this way, highly relevant data can be prioritized by taking into account the geographical location information of the pet. Some or all of the above-described processing in the acquisition unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the acquisition unit may input the pet's geographical location information data into the generation AI and cause the generation AI to acquire highly relevant data.

[0038] The acquisition unit can analyze the social media activity of the pet when acquiring images or videos and acquire related data. For example, the acquisition unit can analyze the social media activity of the pet when acquiring images or videos and acquire related data. Social media activity includes, but is not limited to, post content, the number of likes, and comments. For example, the acquisition unit can prioritize acquiring activity at locations where the pet has checked in on social media. The acquisition unit can also analyze the content of the pet's posts on social media and acquire related data. The acquisition unit can also acquire related data by referring to the activity of the pet's friends on social media. For example, the acquisition unit prioritizes acquiring activity at locations where the pet has checked in on social media. The acquisition unit can also analyze the content of the pet's posts on social media and acquire related data. The acquisition unit can also acquire related data by referring to the activity of the pet's friends on social media. In this way, related data can be acquired by analyzing the social media activity of the pet. Some or all of the above-mentioned processing by the acquisition unit may be performed, for example, using a generation AI or without using a generation AI. For example, the acquisition unit can input a pet's social media activity data into the generation AI and cause the generation AI to acquire related data.

[0039] The acquisition unit can customize the acquisition method by reflecting the user's past feedback when acquiring images or videos. For example, the acquisition unit customizes the acquisition method by reflecting the user's past feedback when acquiring images or videos. The user's past feedback includes, but is not limited to, past ratings, comments, and usage history. For example, the acquisition unit prioritizes the use of a shooting method that the user previously preferred. The acquisition unit can also eliminate a shooting method that the user previously avoided. The acquisition unit can also suggest a new shooting method based on the user's past feedback. For example, the acquisition unit prioritizes the use of a shooting method that the user previously preferred. The acquisition unit can also eliminate a shooting method that the user previously avoided. The acquisition unit can also suggest a new shooting method based on the user's past feedback. In this way, the acquisition method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the acquisition unit may be performed using, or without using, a generation AI. For example, the acquisition unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the acquisition method.

[0040] The analysis unit can adjust the level of detail of the analysis based on the pet's health condition during the analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the pet's health condition during the analysis. The level of detail of the analysis includes, but is not limited to, the depth of the analysis and the number of analysis items. For example, the analysis unit provides a concise analysis result when the pet is healthy. The analysis unit can also provide a detailed analysis result when the pet is in poor health. The analysis unit can also provide a detailed analysis result related to a specific illness when the pet has that illness. For example, the analysis unit provides a concise analysis result when the pet is healthy. The analysis unit can also provide a detailed analysis result when the pet is in poor health. The analysis unit can also provide a detailed analysis result related to a specific illness when the pet has that illness. In this way, by adjusting the level of detail of the analysis based on the pet's health condition, appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed, for example, using a generation AI or without using a generation AI. For example, the analysis unit can input data on the pet's health condition into the generation AI and have the generation AI adjust the level of detail of the analysis.

[0041] The analysis unit can apply different analysis algorithms depending on the type and age of the pet during analysis. For example, the analysis unit can apply different analysis algorithms depending on the type and age of the pet during analysis. Analysis algorithms include, but are not limited to, algorithms for dogs, cats, and different ages. For example, the analysis unit can apply an analysis algorithm specifically for dogs to dogs. Furthermore, the analysis unit can apply an analysis algorithm specifically for cats to cats. Furthermore, the analysis unit can apply an analysis algorithm specifically for young pets to young pets. For example, the analysis unit can apply an analysis algorithm specifically for dogs to dogs. Furthermore, the analysis unit can apply an analysis algorithm specifically for cats to cats. Furthermore, the analysis unit can apply an analysis algorithm specifically for young pets to young pets. In this way, by applying an analysis algorithm depending on the type and age of the pet, more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input data on the type and age of the pet into the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0042] The analysis unit can improve the accuracy of the analysis by referring to past analysis results of the pet during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to past analysis results of the pet during analysis. Past analysis results include, but are not limited to, past health checkup results and behavioral patterns. For example, the analysis unit corrects the current analysis result based on the past analysis results of the pet. The analysis unit can also detect abnormalities early based on the past analysis results of the pet. The analysis unit can also optimize the analysis algorithm based on the past analysis results of the pet. For example, the analysis unit corrects the current analysis result based on the past analysis results of the pet. The analysis unit can also detect abnormalities early based on the past analysis results of the pet. The analysis unit can also optimize the analysis algorithm based on the past analysis results of the pet. In this way, the accuracy of the analysis can be improved by referring to the past analysis results of the pet. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI or without using a generation AI. For example, the analysis unit can input past analysis result data of a pet into the generation AI and have the generation AI improve the accuracy of the analysis.

[0043] The analysis unit can determine the analysis priority based on the behavioral history of the pet during analysis. For example, the analysis unit determines the analysis priority based on the behavioral history of the pet during analysis. The behavioral history includes, but is not limited to, past activity patterns and behavioral frequency. For example, if the pet has repeatedly performed a specific behavior in the past, the analysis unit prioritizes analysis related to that behavior. Furthermore, if the pet has been active during a specific time period in the past, the analysis unit can prioritize analysis of that time period. Furthermore, if the pet has often played in a specific location in the past, the analysis unit can prioritize analysis of that location. For example, if the pet has repeatedly performed a specific behavior in the past, the analysis unit prioritizes analysis related to that behavior. Furthermore, if the pet has been active during a specific time period in the past, the analysis unit can prioritize analysis of that time period. Furthermore, if the pet has often played in a specific location in the past, the analysis unit can prioritize analysis of that location. Thus, by determining the analysis priority based on the behavioral history of the pet, important analyses can be prioritized. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit may input the behavior history data of the pet into the generation AI and have the generation AI determine the priority of the analysis.

[0044] The analysis unit can adjust the order of analysis based on pet-related data during analysis. For example, the analysis unit adjusts the order of analysis based on pet-related data during analysis. Related data includes, but is not limited to, health data, behavioral data, and past analysis results. For example, the analysis unit prioritizes important analyses based on the pet's health data. The analysis unit can also prioritize related analyses based on the pet's behavioral data. The analysis unit can also prioritize important analyses based on past analysis results of the pet. For example, the analysis unit prioritizes important analyses based on the pet's health data. The analysis unit can also prioritize related analyses based on the pet's behavioral data. The analysis unit can also prioritize important analyses based on past analysis results of the pet. This enables efficient analysis by adjusting the order of analysis based on the pet's related data. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit may input pet-related data to the generation AI and cause the generation AI to adjust the order of analysis.

[0045] The analysis unit can adjust the use of technical terms in the analysis results according to the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis results according to the user's level of expertise during analysis. The use of technical terms includes, but is not limited to, selecting technical terms and explaining terms. For example, the analysis unit can use detailed technical terms if the user has technical knowledge. Furthermore, the analysis unit can provide analysis results in simple language if the user does not have technical knowledge. Furthermore, the analysis unit can adjust the use of optimal technical terms based on the user's past feedback. For example, the analysis unit can use detailed technical terms if the user has technical knowledge. Furthermore, the analysis unit can provide analysis results in simple language if the user does not have technical knowledge. Furthermore, the analysis unit can adjust the use of optimal technical terms based on the user's past feedback. By adjusting the use of technical terms in the analysis results according to the user's level of expertise, analysis results that are easy for the user to understand can be provided. Some or all of the above-described processing in the analysis unit can be performed, for example, using a generation AI or without a generation AI. For example, the analysis unit can input the user's expertise level data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0046] The providing unit can adjust the level of detail of the provided content based on the pet's health condition when providing information. For example, the providing unit adjusts the level of detail of the provided content based on the pet's health condition when providing information. The level of detail of the provided content includes, but is not limited to, the depth and scope of the information. For example, the providing unit provides concise information when the pet is healthy. Furthermore, the providing unit can provide detailed information when the pet is in poor health. Furthermore, the providing unit can provide detailed information related to a specific illness when the pet has the illness. For example, the providing unit provides concise information when the pet is healthy. Furthermore, the providing unit can provide detailed information when the pet is in poor health. Furthermore, the providing unit can provide detailed information related to a specific illness when the pet has the illness. In this way, by adjusting the level of detail of the provided content based on the pet's health condition, appropriate information can be provided. Some or all of the above-described processing by the providing unit may be performed, for example, using a generation AI or without using a generation AI. For example, the providing unit can input data on the pet's health condition into the generating AI and have the generating AI adjust the level of detail of the provided content.

[0047] The providing unit can apply different providing methods depending on the type and age of the pet when providing information. For example, the providing unit can apply different providing methods depending on the type and age of the pet when providing information. Examples of providing methods include, but are not limited to, methods for dogs, cats, and age-specific methods. For example, the providing unit can apply an information providing method specifically for dogs to a dog. Furthermore, the providing unit can apply an information providing method specifically for cats to a cat. Furthermore, the providing unit can apply an information providing method specifically for young pets to a young pet. For example, the providing unit can apply an information providing method specifically for dogs to a dog. Furthermore, the providing unit can apply an information providing method specifically for cats to a cat. Furthermore, the providing unit can apply an information providing method specifically for young pets to a young pet. This enables more appropriate information to be provided by applying a providing method according to the type and age of the pet. Some or all of the above-described processing by the providing unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the providing unit can input data on the type and age of the pet into the generation AI and cause the generation AI to apply an appropriate providing method.

[0048] The providing unit can improve the provided content by referring to the user's past feedback when providing information. For example, the providing unit improves the provided content by referring to the user's past feedback when providing information. Improvements to the provided content include, but are not limited to, modifying or adding content based on past feedback. For example, the providing unit prioritizes the use of an information providing method that the user previously preferred. The providing unit can also eliminate an information providing method that the user previously avoided. The providing unit can also suggest a new information providing method based on the user's past feedback. For example, the providing unit prioritizes the use of an information providing method that the user previously preferred. The providing unit can also eliminate an information providing method that the user previously avoided. The providing unit can also suggest a new information providing method based on the user's past feedback. This makes it possible to improve the provided content by referring to the user's past feedback and provide optimal information to the user. Some or all of the above-described processing by the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's past feedback data into the generation AI and cause the generation AI to improve the provided content.

[0049] The providing unit can provide optimal information by taking into account the geographical location information of the pet when providing information. For example, the providing unit provides optimal information by taking into account the geographical location information of the pet when providing information. Geographical location information includes, but is not limited to, GPS data and location information services. For example, if the pet is in a park, the providing unit can provide information related to activities at that location. Furthermore, if the pet is at home, the providing unit can provide information related to activities at that location. Furthermore, if the pet is at a veterinary clinic, the providing unit can provide information related to activities at that location. For example, if the pet is in a park, the providing unit can provide information related to activities at that location. Furthermore, if the pet is at home, the providing unit can provide information related to activities at that location. Furthermore, if the pet is at a veterinary clinic, the providing unit can provide information related to activities at that location. This makes it possible to provide optimal information by taking into account the geographical location information of the pet. Some or all of the above-described processing by the providing unit may be performed, for example, using a generation AI or without using a generation AI. For example, the providing unit can input the pet's geographical location information data into the generating AI and cause the generating AI to provide optimal information.

[0050] The providing unit may analyze the social media activity of the pet and provide related information when providing information. For example, the providing unit may analyze the social media activity of the pet and provide related information when providing information. Social media activity includes, but is not limited to, the content of posts, the number of likes, and comments. For example, the providing unit may provide information about places where the pet has checked in on social media. The providing unit may also analyze the content of the pet's social media posts and provide information about related tourist spots and stores. The providing unit may also provide information about related places and events by referring to the activity of the pet's friends on social media. For example, the providing unit may provide information about places where the pet has checked in on social media. The providing unit may also analyze the content of the pet's social media posts and provide information about related tourist spots and stores. The providing unit may also provide information about related places and events by referring to the activity of the pet's friends on social media. In this way, related information can be provided by analyzing the social media activity of the pet. Some or all of the above-described processing by the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input social media activity data of a pet into the generating AI and cause the generating AI to provide related information.

[0051] The providing unit can customize the information provision method by reflecting the user's past feedback when providing information. For example, the providing unit customizes the information provision method by reflecting the user's past feedback when providing information. Customizing the information provision method includes, but is not limited to, adjusting the method based on the past feedback. For example, the providing unit prioritizes the use of an information provision method that the user previously preferred. The providing unit can also eliminate an information provision method that the user previously avoided. The providing unit can also suggest a new information provision method based on the user's past feedback. For example, the providing unit prioritizes the use of an information provision method that the user previously preferred. The providing unit can also eliminate an information provision method that the user previously avoided. The providing unit can also suggest a new information provision method based on the user's past feedback. In this way, the provision method can be customized by reflecting the user's past feedback. Some or all of the above-described processing by the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the information provision method.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The analysis unit can analyze the behavioral patterns of a pet and estimate the pet's stress level. For example, the analysis unit can analyze the frequency and speed of the pet's movements to detect signs of stress. The analysis unit can also analyze the pattern of the pet's cries to detect signs of stress. Furthermore, the analysis unit can analyze the pet's eating and sleeping patterns to detect signs of stress. This allows the pet's stress level to be estimated, allowing the owner to take appropriate measures.

[0054] The providing unit can suggest relaxation methods to the owner based on the pet's stress level. For example, if the pet is feeling stressed, the providing unit can suggest playing relaxing music. The providing unit can also suggest massages or play if the pet is feeling stressed. Furthermore, the providing unit can also suggest specific meals or treats if the pet is feeling stressed. This can reduce the pet's stress and maintain its health.

[0055] The acquisition unit can monitor the behavioral patterns of a pet in real time and issue an alert if an abnormality is detected. For example, the acquisition unit can issue an alert if the pet remains motionless for a longer period of time than usual. The acquisition unit can also issue an alert if the pet becomes abnormally excited. Furthermore, the acquisition unit can also issue an alert if the pet leaves a specific area. This allows for early detection of abnormal pet behavior and allows appropriate action to be taken.

[0056] The analysis unit can monitor a pet's health over the long term and predict health risks. For example, the analysis unit can analyze changes in a pet's weight and food intake to predict the risk of obesity. The analysis unit can also analyze changes in a pet's exercise level to predict joint problems. Furthermore, the analysis unit can analyze changes in a pet's sleep patterns to predict stress and the risk of illness. This allows for early detection of health risks in pets and the implementation of preventative measures.

[0057] The provider can suggest preventive measures to owners based on their pet's health risks. For example, if a pet is at risk of obesity, the provider can suggest an appropriate diet and exercise plan. The provider can also suggest specific supplements and exercises if a pet has joint problems. Furthermore, if a pet is at risk of stress, the provider can suggest relaxation methods and environmental improvements. This helps maintain the pet's health and prevent illness.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The acquisition unit acquires images and videos of the pet. The images and videos of the pet include still images, short videos, live streaming, etc. The acquisition unit allows the owner to take images and videos of the pet using a photographing device, and can also use a camera to track the pet's movements and acquire images and videos in real time. Step 2: The analysis unit uses the generation AI to analyze the images and videos captured by the acquisition unit and determine the pet's cries and physical condition. The analysis is performed using image analysis algorithms and audio analysis algorithms. For example, image analysis technology is used to detect abnormalities in the pet's physical condition and behavior, and audio analysis technology is used to analyze the frequency and pattern of the pet's cries to determine what the pet is trying to communicate. Step 3: The provider provides the information determined by the analyzer to the owner in an easy-to-understand format. This can be in the form of a text message, voice notification, or in-app notification. For example, the provider can inform the owner of the pet's condition via text or voice, and if there is anything abnormal with the pet's health, the provider can notify the owner of the specific symptoms and how to deal with them via text message.

[0060] (Example 2) A system according to an embodiment of the present invention acquires images and videos of a pet, analyzes them using a generation AI to determine the pet's cries and physical condition, and provides the information in an easy-to-understand format. This system acquires images and videos of a pet, analyzes them using a generation AI to determine the pet's cries and physical condition, and provides the determined information to the owner in an easy-to-understand format. For example, to acquire images and videos of a pet, the owner uses a camera to capture images and videos of the pet. Next, the generation AI analyzes the input images and videos to determine the pet's cries and physical condition. The determined information is provided to the owner by the generation AI in an easy-to-understand format. This allows the owner to understand the pet's condition and strengthen communication with the pet. The system analyzes the pet's condition and provides easy-to-understand information to the owner, thereby strengthening communication with the pet. For example, by understanding why the pet is crying, the owner can take appropriate action. Furthermore, if the pet's physical condition is abnormal, early action can be taken. This helps maintain the pet's health and deepen the bond between the pet and the owner.

[0061] The pet analysis system according to the embodiment includes an acquisition unit, an analysis unit, and a provision unit. The acquisition unit acquires images and videos of the pet. Examples of the images and videos of the pet include, but are not limited to, still images, short videos, and live streaming. For example, the acquisition unit is used by a pet owner to capture images and videos of the pet using a camera. The acquisition unit can also acquire images and videos of the pet in real time. For example, the acquisition unit can track the pet's movements using a camera and acquire images and videos in real time. The analysis unit uses a generation AI to analyze the images and videos acquired by the acquisition unit and determine the pet's cries and physical condition. The analysis is performed using, for example, an image analysis algorithm or a sound analysis algorithm, but is not limited to these examples. For example, the analysis unit uses image analysis technology to detect abnormalities in the pet's physical condition or behavior. The analysis unit can also use sound analysis technology to analyze the frequency and pattern of the pet's cries and determine what the pet is trying to communicate. For example, the analysis unit performs frequency analysis of the pet's cries to determine information such as whether the pet is "hungry" or "want to play." The provision unit provides the information determined by the analysis unit to the owner in an easy-to-understand format. The provision may be in the form of, for example, a text message, a voice notification, an in-app notification, or the like, but is not limited to these examples. For example, the provision unit may inform the owner of the pet's condition by text or voice. Furthermore, if there is an abnormality in the pet's health, the provision unit may provide the owner with specific symptoms and measures to deal with the abnormality. For example, if there is an abnormality in the pet's health, the provision unit may notify the owner of the specific symptoms and measures to deal with the abnormality by text message. In this way, the pet analysis system according to the embodiment can analyze the pet's condition and provide the owner with information that is easy to understand. This allows the owner to understand the pet's condition and strengthen communication with the pet.

[0062] The analysis unit can detect abnormalities in the pet's physical condition or behavior using image analysis technology. Image analysis technology includes, but is not limited to, object detection, face recognition, and motion analysis. The analysis unit can detect abnormalities in the pet's physical condition or behavior using, for example, object detection technology. For example, the analysis unit can detect abnormalities on the pet's body surface. The analysis unit can also analyze the pet's facial expression using face recognition technology to detect abnormalities in the pet's physical condition. For example, the analysis unit can analyze the pet's facial expression to detect abnormalities in the pet's physical condition. The analysis unit can also analyze the pet's movements using motion analysis technology to detect abnormalities in the pet's behavior. For example, the analysis unit can analyze the pet's movement patterns to detect abnormalities in the pet's behavior. In this way, image analysis technology can accurately detect abnormalities in the pet's physical condition or behavior. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input image data of the pet into the generation AI and cause the generation AI to detect abnormalities in the pet's physical condition or behavior.

[0063] The analysis unit can use voice analysis technology to analyze the frequency and pattern of the pet's cry and determine what the pet is trying to communicate. Voice analysis technology includes, but is not limited to, frequency analysis, voice recognition, and emotion analysis. For example, the analysis unit can use frequency analysis technology to analyze the frequency of the pet's cry and determine what the pet is trying to communicate. For example, the analysis unit can analyze the frequency of the pet's cry and determine information such as "I'm hungry" or "I want to play." The analysis unit can also use voice recognition technology to analyze the pattern of the pet's cry and determine what the pet is trying to communicate. For example, the analysis unit can analyze the pattern of the pet's cry and determine information such as "I feel anxious" or "I'm happy." The analysis unit can also use emotion analysis technology to analyze the emotion of the pet's cry and determine what the pet is trying to communicate. For example, the analysis unit can analyze the emotion of the pet's cry and determine information such as "I'm angry" or "I'm sad." This allows the use of voice analysis technology to accurately determine the intention of a pet from its cry. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit may input data of the pet's cry into the generation AI and have the generation AI analyze the frequency and pattern of the cry.

[0064] The providing unit can notify the owner of the pet's condition by text or voice. The providing unit can, for example, notify the owner of the pet's condition by using a text message. For example, the providing unit notifies the owner of the pet's condition by a text message. The providing unit can also notify the owner of the pet's condition by using a voice message. For example, the providing unit notifies the owner of the pet's condition by a voice message. The providing unit can also notify the owner of the pet's condition by using an in-app notification. For example, the providing unit notifies the owner of the pet's condition by an in-app notification. This makes it easier for the owner to understand the pet's condition by conveying the pet's condition by text or voice. Some or all of the above-described processing by the providing unit can be performed using, or without, the generation AI. For example, the providing unit can input information determined by the analysis unit into the generation AI and cause the generation AI to generate text or voice.

[0065] The providing unit can provide the owner with specific symptoms and countermeasures when the pet's health condition is abnormal. For example, when the pet's health condition is abnormal, the providing unit notifies the owner of the specific symptoms by a text message. For example, when the pet's health condition is abnormal, the providing unit notifies the owner of the specific symptoms by a text message. The providing unit can also notify the owner of specific countermeasures when the pet's health condition is abnormal by a text message. For example, when the pet's health condition is abnormal, the providing unit notifies the owner of specific countermeasures by a text message. The providing unit can also notify the owner of specific symptoms when the pet's health condition is abnormal by a voice message. For example, when the pet's health condition is abnormal, the providing unit notifies the owner of specific symptoms by a voice message. The providing unit can also notify the owner of specific countermeasures when the pet's health condition is abnormal by a voice message. For example, when the pet's health condition is abnormal, the providing unit notifies the owner of specific countermeasures by a voice message. In this way, when the pet's health condition is abnormal, the specific symptoms and countermeasures are provided, allowing the owner to take appropriate action. Some or all of the above-described processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit may input information determined by the analysis unit to the generation AI and cause the generation AI to generate specific symptoms and countermeasures.

[0066] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring images and videos of the pet based on the estimated user's emotions. The acquisition unit, for example, estimates the user's emotions and adjusts the timing of acquiring images and videos of the pet based on the estimated user's emotions. To estimate the user's emotions, for example, facial expression recognition technology, audio tone analysis technology, behavioral pattern analysis technology, etc. are used. For example, the acquisition unit captures the user's facial expressions with a camera and estimates the emotions using facial expression recognition technology. The acquisition unit can also record the user's voice and estimate the emotions using audio tone analysis technology. The acquisition unit can also analyze the user's behavioral patterns and estimate the emotions using behavioral pattern analysis technology. For example, the acquisition unit analyzes the user's behavioral patterns and estimates the emotions. To adjust the acquisition timing, for example, the timing is set according to the user's emotional state. For example, if the user is feeling stressed, the acquisition unit prioritizes timing to capture images of the pet relaxing. Furthermore, if the user is having fun, the acquisition unit can prioritize timing to capture images of the pet playing. Furthermore, if the user is tired, the acquisition unit can prioritize the timing of capturing images of the pet in a quiet state. This allows the timing of capturing images and videos of the pet to be adjusted according to the user's emotions, allowing for more appropriate timing. Some or all of the above-described processing in the acquisition unit may be performed using, or without, the generation AI. For example, the acquisition unit can input the user's emotion data into the generation AI and have the generation AI adjust the capture timing based on the emotion.

[0067] The acquisition unit can analyze the pet's past behavioral history and select an appropriate acquisition method. The acquisition unit, for example, analyzes the pet's past behavioral history and selects an appropriate acquisition method. The pet's past behavioral history includes, for example, past activity patterns and behavioral frequency, but is not limited to these examples. For example, if the pet was active during a specific time period in the past, the acquisition unit can take a photo during that time period. Furthermore, if the pet often played at a specific location in the past, the acquisition unit can also take a photo at that location. Furthermore, if the pet repeatedly performed a specific behavior in the past, the acquisition unit can also capture that behavior. For example, if the pet was active during a specific time period in the past, the acquisition unit can take a photo during that time period. Furthermore, if the pet often played at a specific location in the past, the acquisition unit can also take a photo at that location. Furthermore, if the pet repeatedly performed a specific behavior in the past, the acquisition unit can also capture that behavior. In this way, the pet's past behavioral history can be analyzed to select an optimal acquisition method. Some or all of the above-described processing in the acquisition unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the acquisition unit can input the pet's past behavioral history data into the generation AI and have the generation AI select the optimal acquisition method.

[0068] The acquisition unit can perform filtering based on the pet's current activity status and environment when acquiring images or videos. For example, the acquisition unit performs filtering based on the pet's current activity status and environment when acquiring images or videos. Filtering includes, but is not limited to, an activity status detection method and environmental condition settings. For example, the acquisition unit prioritizes capturing images of the pet playing when the pet is playing. The acquisition unit can also prioritize capturing images of the pet resting when the pet is resting. The acquisition unit can also prioritize capturing images of the pet eating when the pet is eating. For example, the acquisition unit prioritizes capturing images of the pet playing when the pet is playing. The acquisition unit can also prioritize capturing images of the pet resting when the pet is resting. The acquisition unit can also prioritize capturing images of the pet eating when the pet is eating. By performing filtering based on the pet's current activity status and environment, more appropriate images and videos can be acquired. Some or all of the above-described processing in the acquisition unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the acquisition unit can input the pet's current activity status and environmental data into the generation AI and have the generation AI perform filtering.

[0069] The acquisition unit can select the optimal acquisition means according to the user's input method when acquiring images or videos. For example, when acquiring images or videos, the acquisition unit selects the optimal acquisition means according to the user's input method. User input methods include, but are not limited to, voice input, text input, and gesture input. For example, when a user gives a voice instruction such as "Take a picture of my pet playing," the acquisition unit takes a picture according to the instruction. Furthermore, when a user inputs a text instruction such as "Take a picture of my pet sleeping," the acquisition unit can take a picture according to the instruction. Furthermore, when a user gives a gesture instruction to take a picture, the acquisition unit can take a picture according to the instruction. For example, when a user gives a voice instruction such as "Take a picture of my pet playing," the acquisition unit can take a picture according to the instruction. Furthermore, when a user inputs a text instruction such as "Take a picture of my pet sleeping," the acquisition unit can take a picture according to the instruction. Furthermore, when a user gives a gesture instruction to take a picture, the acquisition unit can take a picture according to the instruction. This allows the optimal acquisition means to be selected according to the user's input method, thereby enabling photography to be performed in line with the user's intentions. Some or all of the above-described processing in the acquisition unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the acquisition unit may input the user's input data into the generation AI and have the generation AI select the optimal acquisition means.

[0070] The acquisition unit can estimate the user's emotions and determine the priority of images and videos to be acquired based on the estimated user emotions. The acquisition unit, for example, estimates the user's emotions and determines the priority of images and videos to be acquired based on the estimated user emotions. To estimate the user's emotions, for example, facial expression recognition technology, audio tone analysis technology, behavioral pattern analysis technology, etc. are used. For example, the acquisition unit captures the user's facial expressions with a camera and estimates the emotions using facial expression recognition technology. The acquisition unit can also record the user's voice and estimate the emotions using audio tone analysis technology. The acquisition unit can also analyze the user's behavioral patterns and estimate the emotions using behavioral pattern analysis technology. For example, the acquisition unit analyzes the user's behavioral patterns and estimates the emotions. To determine the priority of images and videos to be acquired, for example, priority setting is performed based on the user's emotional state. For example, if the user is feeling stressed, the acquisition unit can prioritize acquiring images of a pet that appears relaxed. Also, if the user is having fun, the acquisition unit can prioritize acquiring images of a pet that appears to be playing. Furthermore, if the user is tired, the acquisition unit can prioritize acquiring images of the pet in a calm state. This allows the user to acquire information that meets their needs by determining the priority of images and videos based on the user's emotions. Some or all of the above-described processing in the acquisition unit may be performed using, or without, the generation AI. For example, the acquisition unit can input the user's emotional data into the generation AI and have the generation AI determine the priority based on the emotions.

[0071] When acquiring images or videos, the acquisition unit can prioritize acquiring highly relevant data by taking into account the geographical location information of the pet. For example, when acquiring images or videos, the acquisition unit prioritizes acquiring highly relevant data by taking into account the geographical location information of the pet. Geographical location information includes, but is not limited to, GPS data and location information services. For example, if the pet is in a park, the acquisition unit prioritizes acquiring activities at that location. Furthermore, if the pet is at home, the acquisition unit can also prioritize acquiring activities at that location. Furthermore, if the pet is at a veterinary clinic, the acquisition unit can also prioritize acquiring activities at that location. For example, if the pet is in a park, the acquisition unit prioritizes acquiring activities at that location. Furthermore, if the pet is at home, the acquisition unit can also prioritize acquiring activities at that location. Furthermore, if the pet is at a veterinary clinic, the acquisition unit can prioritize acquiring activities at that location. In this way, highly relevant data can be prioritized by taking into account the geographical location information of the pet. Some or all of the above-described processing in the acquisition unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the acquisition unit may input the pet's geographical location information data into the generation AI and cause the generation AI to acquire highly relevant data.

[0072] The acquisition unit can analyze the social media activity of the pet when acquiring images or videos and acquire related data. For example, the acquisition unit can analyze the social media activity of the pet when acquiring images or videos and acquire related data. Social media activity includes, but is not limited to, post content, the number of likes, and comments. For example, the acquisition unit can prioritize acquiring activity at locations where the pet has checked in on social media. The acquisition unit can also analyze the content of the pet's posts on social media and acquire related data. The acquisition unit can also acquire related data by referring to the activity of the pet's friends on social media. For example, the acquisition unit prioritizes acquiring activity at locations where the pet has checked in on social media. The acquisition unit can also analyze the content of the pet's posts on social media and acquire related data. The acquisition unit can also acquire related data by referring to the activity of the pet's friends on social media. In this way, related data can be acquired by analyzing the social media activity of the pet. Some or all of the above-mentioned processing by the acquisition unit may be performed, for example, using a generation AI or without using a generation AI. For example, the acquisition unit can input a pet's social media activity data into the generation AI and cause the generation AI to acquire related data.

[0073] The acquisition unit can customize the acquisition method by reflecting the user's past feedback when acquiring images or videos. For example, the acquisition unit customizes the acquisition method by reflecting the user's past feedback when acquiring images or videos. The user's past feedback includes, but is not limited to, past ratings, comments, and usage history. For example, the acquisition unit prioritizes the use of a shooting method that the user previously preferred. The acquisition unit can also eliminate a shooting method that the user previously avoided. The acquisition unit can also suggest a new shooting method based on the user's past feedback. For example, the acquisition unit prioritizes the use of a shooting method that the user previously preferred. The acquisition unit can also eliminate a shooting method that the user previously avoided. The acquisition unit can also suggest a new shooting method based on the user's past feedback. In this way, the acquisition method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the acquisition unit may be performed using, or without using, a generation AI. For example, the acquisition unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the acquisition method.

[0074] The analysis unit can estimate the user's emotion and adjust the presentation method of the analysis result based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the presentation method of the analysis result based on the estimated user's emotion. To estimate the user's emotion, for example, facial expression recognition technology, voice tone analysis technology, behavior pattern analysis technology, etc. are used. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using facial expression recognition technology. The analysis unit can also record the user's voice and estimate the emotion using voice tone analysis technology. The analysis unit can also analyze the user's behavior pattern and estimate the emotion using behavior pattern analysis technology. For example, the analysis unit analyzes the user's behavior pattern and estimates the emotion. To adjust the presentation method of the analysis result, for example, the presentation method is set according to the user's emotional state. For example, the analysis unit provides detailed analysis results when the user is relaxed. The analysis unit can provide concise analysis results when the user is in a hurry. The analysis unit can also provide visually stimulating analysis results when the user is excited. This allows the method of expressing the analysis results to be adjusted based on the user's emotions, thereby providing analysis results that are easy for the user to understand. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit may input user emotion data into the generation AI and have the generation AI adjust the method of expressing the analysis results based on the emotion.

[0075] The analysis unit can adjust the level of detail of the analysis based on the pet's health condition during the analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the pet's health condition during the analysis. The level of detail of the analysis includes, but is not limited to, the depth of the analysis and the number of analysis items. For example, the analysis unit provides a concise analysis result when the pet is healthy. The analysis unit can also provide a detailed analysis result when the pet is in poor health. The analysis unit can also provide a detailed analysis result related to a specific illness when the pet has that illness. For example, the analysis unit provides a concise analysis result when the pet is healthy. The analysis unit can also provide a detailed analysis result when the pet is in poor health. The analysis unit can also provide a detailed analysis result related to a specific illness when the pet has that illness. In this way, by adjusting the level of detail of the analysis based on the pet's health condition, appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed, for example, using a generation AI or without using a generation AI. For example, the analysis unit can input data on the pet's health condition into the generation AI and have the generation AI adjust the level of detail of the analysis.

[0076] The analysis unit can apply different analysis algorithms depending on the type and age of the pet during analysis. For example, the analysis unit can apply different analysis algorithms depending on the type and age of the pet during analysis. Analysis algorithms include, but are not limited to, algorithms for dogs, cats, and different ages. For example, the analysis unit can apply an analysis algorithm specifically for dogs to dogs. Furthermore, the analysis unit can apply an analysis algorithm specifically for cats to cats. Furthermore, the analysis unit can apply an analysis algorithm specifically for young pets to young pets. For example, the analysis unit can apply an analysis algorithm specifically for dogs to dogs. Furthermore, the analysis unit can apply an analysis algorithm specifically for cats to cats. Furthermore, the analysis unit can apply an analysis algorithm specifically for young pets to young pets. In this way, by applying an analysis algorithm depending on the type and age of the pet, more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input data on the type and age of the pet into the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0077] The analysis unit can improve the accuracy of the analysis by referring to past analysis results of the pet during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to past analysis results of the pet during analysis. Past analysis results include, but are not limited to, past health checkup results and behavioral patterns. For example, the analysis unit corrects the current analysis result based on the past analysis results of the pet. The analysis unit can also detect abnormalities early based on the past analysis results of the pet. The analysis unit can also optimize the analysis algorithm based on the past analysis results of the pet. For example, the analysis unit corrects the current analysis result based on the past analysis results of the pet. The analysis unit can also detect abnormalities early based on the past analysis results of the pet. The analysis unit can also optimize the analysis algorithm based on the past analysis results of the pet. In this way, the accuracy of the analysis can be improved by referring to the past analysis results of the pet. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI or without using a generation AI. For example, the analysis unit can input past analysis result data of a pet into the generation AI and have the generation AI improve the accuracy of the analysis.

[0078] The analysis unit can estimate the user's emotions and adjust the length of the analysis result based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions and adjusts the length of the analysis result based on the estimated user's emotions. The length of the analysis result includes, but is not limited to, the length of a summary or a detailed report. For example, the analysis unit can provide a short and concise analysis result when the user is in a hurry. The analysis unit can also provide a detailed analysis result when the user is relaxed. The analysis unit can also provide a visually stimulating analysis result when the user is excited. For example, the analysis unit can provide a short and concise analysis result when the user is in a hurry. The analysis unit can also provide a detailed analysis result when the user is relaxed. The analysis unit can also provide a visually stimulating analysis result when the user is excited. By adjusting the length of the analysis result based on the user's emotions, an analysis result of an appropriate length for the user can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's emotional data into the generation AI and have the generation AI adjust the length of the analysis results based on the emotion.

[0079] The analysis unit can determine the analysis priority based on the behavioral history of the pet during analysis. For example, the analysis unit determines the analysis priority based on the behavioral history of the pet during analysis. The behavioral history includes, but is not limited to, past activity patterns and behavioral frequency. For example, if the pet has repeatedly performed a specific behavior in the past, the analysis unit prioritizes analysis related to that behavior. Furthermore, if the pet has been active during a specific time period in the past, the analysis unit can prioritize analysis of that time period. Furthermore, if the pet has often played in a specific location in the past, the analysis unit can prioritize analysis of that location. For example, if the pet has repeatedly performed a specific behavior in the past, the analysis unit prioritizes analysis related to that behavior. Furthermore, if the pet has been active during a specific time period in the past, the analysis unit can prioritize analysis of that time period. Furthermore, if the pet has often played in a specific location in the past, the analysis unit can prioritize analysis of that location. Thus, by determining the analysis priority based on the behavioral history of the pet, important analyses can be prioritized. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit may input the behavior history data of the pet into the generation AI and have the generation AI determine the priority of the analysis.

[0080] The analysis unit can adjust the order of analysis based on pet-related data during analysis. For example, the analysis unit adjusts the order of analysis based on pet-related data during analysis. Related data includes, but is not limited to, health data, behavioral data, and past analysis results. For example, the analysis unit prioritizes important analyses based on the pet's health data. The analysis unit can also prioritize related analyses based on the pet's behavioral data. The analysis unit can also prioritize important analyses based on past analysis results of the pet. For example, the analysis unit prioritizes important analyses based on the pet's health data. The analysis unit can also prioritize related analyses based on the pet's behavioral data. The analysis unit can also prioritize important analyses based on past analysis results of the pet. This enables efficient analysis by adjusting the order of analysis based on the pet's related data. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit may input pet-related data to the generation AI and cause the generation AI to adjust the order of analysis.

[0081] The analysis unit can adjust the use of technical terms in the analysis results according to the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis results according to the user's level of expertise during analysis. The use of technical terms includes, but is not limited to, selecting technical terms and explaining terms. For example, the analysis unit can use detailed technical terms if the user has technical knowledge. Furthermore, the analysis unit can provide analysis results in simple language if the user does not have technical knowledge. Furthermore, the analysis unit can adjust the use of optimal technical terms based on the user's past feedback. For example, the analysis unit can use detailed technical terms if the user has technical knowledge. Furthermore, the analysis unit can provide analysis results in simple language if the user does not have technical knowledge. Furthermore, the analysis unit can adjust the use of optimal technical terms based on the user's past feedback. By adjusting the use of technical terms in the analysis results according to the user's level of expertise, analysis results that are easy for the user to understand can be provided. Some or all of the above-described processing in the analysis unit can be performed, for example, using a generation AI or without a generation AI. For example, the analysis unit can input the user's expertise level data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0082] The providing unit can estimate the user's emotions and adjust the information provision method based on the estimated user emotions. The providing unit, for example, estimates the user's emotions and adjusts the information provision method based on the estimated user emotions. Information provision methods include, but are not limited to, text format, audio format, and visual format. For example, when the user is nervous, the providing unit can provide a simple, highly visible information provision method. Furthermore, when the user is relaxed, the providing unit can provide a detailed information provision method. Furthermore, when the user is in a hurry, the providing unit can provide a basic information provision method. For example, when the user is nervous, the providing unit can provide a simple, highly visible information provision method. Furthermore, when the user is relaxed, the providing unit can provide a detailed information provision method. Furthermore, when the user is in a hurry, the providing unit can provide a basic information provision method. In this way, by adjusting the information provision method based on the user's emotions, it is possible to provide optimal information to the user. Some or all of the above-described processing by the providing unit may be performed, for example, using a generation AI or without using a generation AI. For example, the providing unit can input the user's emotional data into the generating AI and cause the generating AI to adjust the information provision method based on the emotion.

[0083] The providing unit can adjust the level of detail of the provided content based on the pet's health condition when providing information. For example, the providing unit adjusts the level of detail of the provided content based on the pet's health condition when providing information. The level of detail of the provided content includes, but is not limited to, the depth and scope of the information. For example, the providing unit provides concise information when the pet is healthy. Furthermore, the providing unit can provide detailed information when the pet is in poor health. Furthermore, the providing unit can provide detailed information related to a specific illness when the pet has the illness. For example, the providing unit provides concise information when the pet is healthy. Furthermore, the providing unit can provide detailed information when the pet is in poor health. Furthermore, the providing unit can provide detailed information related to a specific illness when the pet has the illness. In this way, by adjusting the level of detail of the provided content based on the pet's health condition, appropriate information can be provided. Some or all of the above-described processing by the providing unit may be performed, for example, using a generation AI or without using a generation AI. For example, the providing unit can input data on the pet's health condition into the generating AI and have the generating AI adjust the level of detail of the provided content.

[0084] The providing unit can apply different providing methods depending on the type and age of the pet when providing information. For example, the providing unit can apply different providing methods depending on the type and age of the pet when providing information. Examples of providing methods include, but are not limited to, methods for dogs, cats, and age-specific methods. For example, the providing unit can apply an information providing method specifically for dogs to a dog. Furthermore, the providing unit can apply an information providing method specifically for cats to a cat. Furthermore, the providing unit can apply an information providing method specifically for young pets to a young pet. For example, the providing unit can apply an information providing method specifically for dogs to a dog. Furthermore, the providing unit can apply an information providing method specifically for cats to a cat. Furthermore, the providing unit can apply an information providing method specifically for young pets to a young pet. This enables more appropriate information to be provided by applying a providing method according to the type and age of the pet. Some or all of the above-described processing by the providing unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the providing unit can input data on the type and age of the pet into the generation AI and cause the generation AI to apply an appropriate providing method.

[0085] The providing unit can improve the provided content by referring to the user's past feedback when providing information. For example, the providing unit improves the provided content by referring to the user's past feedback when providing information. Improvements to the provided content include, but are not limited to, modifying or adding content based on past feedback. For example, the providing unit prioritizes the use of an information providing method that the user previously preferred. The providing unit can also eliminate an information providing method that the user previously avoided. The providing unit can also suggest a new information providing method based on the user's past feedback. For example, the providing unit prioritizes the use of an information providing method that the user previously preferred. The providing unit can also eliminate an information providing method that the user previously avoided. The providing unit can also suggest a new information providing method based on the user's past feedback. This makes it possible to improve the provided content by referring to the user's past feedback and provide optimal information to the user. Some or all of the above-described processing by the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's past feedback data into the generation AI and cause the generation AI to improve the provided content.

[0086] The providing unit can estimate the user's emotions and determine the priority of information provision based on the estimated user emotions. The providing unit, for example, estimates the user's emotions and determines the priority of information provision based on the estimated user emotions. The priority of information provision includes, but is not limited to, important information, interesting information, and concise information. For example, when the user is stressed, the providing unit can prioritize providing important information. Furthermore, when the user is enjoying themselves, the providing unit can prioritize providing interesting information. Furthermore, when the user is tired, the providing unit can prioritize providing concise information. For example, when the user is stressed, the providing unit can prioritize providing important information. Furthermore, when the user is enjoying themselves, the providing unit can prioritize providing interesting information. Furthermore, when the user is tired, the providing unit can prioritize providing concise information. In this way, by determining the priority of information provision based on the user's emotions, it is possible to prioritize providing information that is important to the user. Some or all of the above-described processing in the providing unit may be performed, for example, using a generation AI or without using a generation AI. For example, the providing unit can input the user's emotional data into the generating AI and have the generating AI determine the priority of information provision based on the emotions.

[0087] The providing unit can provide optimal information by taking into account the geographical location information of the pet when providing information. For example, the providing unit provides optimal information by taking into account the geographical location information of the pet when providing information. Geographical location information includes, but is not limited to, GPS data and location information services. For example, if the pet is in a park, the providing unit can provide information related to activities at that location. Furthermore, if the pet is at home, the providing unit can provide information related to activities at that location. Furthermore, if the pet is at a veterinary clinic, the providing unit can provide information related to activities at that location. For example, if the pet is in a park, the providing unit can provide information related to activities at that location. Furthermore, if the pet is at home, the providing unit can provide information related to activities at that location. Furthermore, if the pet is at a veterinary clinic, the providing unit can provide information related to activities at that location. This makes it possible to provide optimal information by taking into account the geographical location information of the pet. Some or all of the above-described processing by the providing unit may be performed, for example, using a generation AI or without using a generation AI. For example, the providing unit can input the pet's geographical location information data into the generating AI and cause the generating AI to provide optimal information.

[0088] The providing unit may analyze the social media activity of the pet and provide related information when providing information. For example, the providing unit may analyze the social media activity of the pet and provide related information when providing information. Social media activity includes, but is not limited to, the content of posts, the number of likes, and comments. For example, the providing unit may provide information about places where the pet has checked in on social media. The providing unit may also analyze the content of the pet's social media posts and provide information about related tourist spots and stores. The providing unit may also provide information about related places and events by referring to the activity of the pet's friends on social media. For example, the providing unit may provide information about places where the pet has checked in on social media. The providing unit may also analyze the content of the pet's social media posts and provide information about related tourist spots and stores. The providing unit may also provide information about related places and events by referring to the activity of the pet's friends on social media. In this way, related information can be provided by analyzing the social media activity of the pet. Some or all of the above-described processing by the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input social media activity data of a pet into the generating AI and cause the generating AI to provide related information.

[0089] The providing unit can customize the information provision method by reflecting the user's past feedback when providing information. For example, the providing unit customizes the information provision method by reflecting the user's past feedback when providing information. Customizing the information provision method includes, but is not limited to, adjusting the method based on the past feedback. For example, the providing unit prioritizes the use of an information provision method that the user previously preferred. The providing unit can also eliminate an information provision method that the user previously avoided. The providing unit can also suggest a new information provision method based on the user's past feedback. For example, the providing unit prioritizes the use of an information provision method that the user previously preferred. The providing unit can also eliminate an information provision method that the user previously avoided. The providing unit can also suggest a new information provision method based on the user's past feedback. In this way, the provision method can be customized by reflecting the user's past feedback. Some or all of the above-described processing by the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the information provision method. === Hard Collateral 1-1 === Each of the multiple elements including the acquisition unit, analysis unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit acquires images and videos of the pet using the camera 42 of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the pet's cries and physical condition using a generation AI. The provision unit is realized by the control unit 46A of the smart device 14 and provides the analysis results to the owner by text message or voice notification. The acquisition unit can estimate the user's emotions and adjust the timing of acquiring images and videos of the pet based on the estimated emotions. === Hard Collateral 1-2 === Each of the multiple elements, including the acquisition unit, analysis unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit acquires images and videos of the pet using the camera 42 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the pet's cries and physical condition using a generation AI. The provision unit is realized by the control unit 46A of the smart glasses 214 and provides the analysis results to the owner by text message or voice notification. The acquisition unit can estimate the user's emotions and adjust the timing of acquiring images and videos of the pet based on the estimated emotions. === Hard Collateral 1-3 === Each of the multiple elements including the acquisition unit, analysis unit, and provision unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the acquisition unit acquires images and videos of the pet using the camera 42 of the headset-type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the pet's cries and physical condition using a generation AI. The provision unit is realized by the control unit 46A of the headset-type terminal 314 and provides the analysis results to the owner by text message or voice notification. The acquisition unit can estimate the user's emotions and adjust the timing of acquiring images and videos of the pet based on the estimated emotions. === Hard Collateral 1-4 === Each of the multiple elements including the acquisition unit, analysis unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit acquires images and videos of the pet using the camera 42 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the pet's cries and physical condition using a generation AI. The provision unit is realized by the control unit 46A of the robot 414 and provides the analysis results to the owner by text message or voice notification. The acquisition unit can estimate the user's emotions and adjust the timing of acquiring images and videos of the pet based on the estimated emotions.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The analysis unit can analyze the behavioral patterns of a pet and estimate the pet's stress level. For example, the analysis unit can analyze the frequency and speed of the pet's movements to detect signs of stress. The analysis unit can also analyze the pattern of the pet's cries to detect signs of stress. Furthermore, the analysis unit can analyze the pet's eating and sleeping patterns to detect signs of stress. This allows the pet's stress level to be estimated, allowing the owner to take appropriate measures.

[0092] The providing unit can suggest relaxation methods to the owner based on the pet's stress level. For example, if the pet is feeling stressed, the providing unit can suggest playing relaxing music. The providing unit can also suggest massages or play if the pet is feeling stressed. Furthermore, the providing unit can also suggest specific meals or treats if the pet is feeling stressed. This can reduce the pet's stress and maintain its health.

[0093] The acquisition unit can monitor the behavioral patterns of a pet in real time and issue an alert if an abnormality is detected. For example, the acquisition unit can issue an alert if the pet remains motionless for a longer period of time than usual. The acquisition unit can also issue an alert if the pet becomes abnormally excited. Furthermore, the acquisition unit can also issue an alert if the pet leaves a specific area. This allows for early detection of abnormal pet behavior and allows appropriate action to be taken.

[0094] The analysis unit can monitor a pet's health over the long term and predict health risks. For example, the analysis unit can analyze changes in a pet's weight and food intake to predict the risk of obesity. The analysis unit can also analyze changes in a pet's exercise level to predict joint problems. Furthermore, the analysis unit can analyze changes in a pet's sleep patterns to predict stress and the risk of illness. This allows for early detection of health risks in pets and the implementation of preventative measures.

[0095] The provider can suggest preventive measures to owners based on their pet's health risks. For example, if a pet is at risk of obesity, the provider can suggest an appropriate diet and exercise plan. The provider can also suggest specific supplements and exercises if a pet has joint problems. Furthermore, if a pet is at risk of stress, the provider can suggest relaxation methods and environmental improvements. This helps maintain the pet's health and prevent illness.

[0096] The analysis unit can estimate the user's emotions and adjust the method for analyzing the pet's behavior based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can prioritize analyzing the pet's relaxed behavior. Also, if the user is having fun, the analysis unit can prioritize analyzing the pet's playful behavior. Furthermore, if the user is tired, the analysis unit can prioritize analyzing the pet's quiet behavior. In this way, by adjusting the analysis of the pet's behavior according to the user's emotions, more appropriate information can be provided.

[0097] The providing unit can estimate the user's emotions and adjust the method of providing pet health information based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide concise and to-the-point health information. Alternatively, if the user is relaxed, the providing unit can provide detailed health information. Furthermore, if the user is excited, the providing unit can provide visually stimulating health information. In this way, by adjusting the method of providing health information according to the user's emotions, it is possible to provide information that is easy for the user to understand.

[0098] The acquisition unit can estimate the user's emotions and adjust the method for acquiring images and videos of the pet based on the estimated user's emotions. For example, if the user is feeling stressed, the acquisition unit can prioritize capturing images of the pet that look relaxed. Also, if the user is having fun, the acquisition unit can prioritize capturing images of the pet that look like they're playing. Furthermore, if the user is tired, the acquisition unit can prioritize capturing images of the pet that look like they're quiet. In this way, by adjusting the method for acquiring images and videos of the pet according to the user's emotions, it is possible to capture images at more appropriate times.

[0099] The providing unit can estimate the user's emotions and provide advice regarding the pet's behavior based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can suggest ways to relax with the pet. If the user is having fun, the providing unit can also suggest ways to play with the pet. Furthermore, if the user is tired, the providing unit can also suggest ways to spend quiet time with the pet. In this way, by providing advice regarding the pet's behavior according to the user's emotions, the relationship between the user and the pet can be strengthened.

[0100] The analysis unit can estimate the user's emotions and adjust the method for analyzing the pet's health state based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can prioritize analyzing the pet's relaxed health state. Alternatively, if the user is having fun, the analysis unit can prioritize analyzing the pet's active health state. Furthermore, if the user is tired, the analysis unit can prioritize analyzing the pet's quiet health state. This allows the pet's health state analysis method to be adjusted according to the user's emotions, thereby providing more appropriate information.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The acquisition unit acquires images and videos of the pet. The images and videos of the pet include still images, short videos, live streaming, etc. The acquisition unit allows the owner to take images and videos of the pet using a photographing device, and can also use a camera to track the pet's movements and acquire images and videos in real time. Step 2: The analysis unit uses the generation AI to analyze the images and videos captured by the acquisition unit and determine the pet's cries and physical condition. The analysis is performed using image analysis algorithms and audio analysis algorithms. For example, image analysis technology is used to detect abnormalities in the pet's physical condition and behavior, and audio analysis technology is used to analyze the frequency and pattern of the pet's cries to determine what the pet is trying to communicate. Step 3: The provider provides the information determined by the analyzer to the owner in an easy-to-understand format. This can be in the form of a text message, voice notification, or in-app notification. For example, the provider can inform the owner of the pet's condition via text or voice, and if there is anything abnormal with the pet's health, the provider can notify the owner of the specific symptoms and how to deal with them via text message.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 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.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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.

[0126] 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.

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

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an 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).

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

[0130] 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.

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

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 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.

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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.

[0142] 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.

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

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an 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).

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

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an acquisition unit for acquiring images and videos of pets; an analysis unit that analyzes the images and videos acquired by the acquisition unit and determines the pet's cry and physical condition; a providing unit that provides the information determined by the analyzing unit in a specific format; Equipped with A system characterized by:

2. The analysis unit Detecting abnormalities in pets' physical condition and behavior using image analysis technology 2. The system of claim 1.

3. The analysis unit Uses audio analysis technology to analyze the frequency and patterns of your pet's cries to determine what they are trying to communicate.

2. The system of claim 1.

4. The providing unit Inform owners of their pet's condition via text or voice 2. The system of claim 1.

5. The providing unit If your pet is in poor health, we will provide you with specific symptoms and how to deal with them.

2. The system of claim 1.

6. The acquisition unit Estimate the user's emotions and adjust the timing of capturing images and videos of pets based on the estimated user emotions.

2. The system of claim 1.

7. The acquisition unit Analyze your pet's past behavioral history and select the appropriate acquisition method 2. The system of claim 1.

8. The acquisition unit When capturing images and videos, they are filtered based on your pet's current activity and environment.

2. The system of claim 1.

Images