system

Abstract

The system according to the embodiment aims to enable owners to detect changes in their dog's health condition at an early stage. [Solution] The system according to the embodiment includes a reception unit, a comparison unit, a detection unit, a collation unit, and a notification unit. The reception unit receives photos of the dog taken by the owner. The comparison unit compares the photos received by the reception unit with previous photos. The detection unit reads the changes detected by the comparison unit. The collation unit compares the changes detected by the detection unit with past case information stored in a database. The notification unit notifies the owner if the collation unit detects an illness or disorder.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult for owners to detect changes in their dog's health at an early stage.

[0005] The system according to the embodiment aims to enable owners to detect changes in their dog's health condition at an early stage. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a comparison unit, a detection unit, a collation unit, and a notification unit. The reception unit receives photos of the dog taken by the owner. The comparison unit compares the photos received by the reception unit with previous photos. The detection unit reads the changes detected by the comparison unit. The collation unit compares the changes detected by the detection unit with past case information stored in a database. The notification unit notifies the owner if the collation unit detects an illness or disorder. [Effects of the Invention]

[0007] The system according to the embodiment can enable owners to detect changes in their dog's health condition early on. [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 health monitoring system according to an embodiment of the present invention monitors the health of pet dogs on a daily basis and supports early detection of illness or ailments. In this health monitoring system, owners add photos of their dogs taken daily to an app, which then compares the newly added photos with previous photos to detect changes. These changes are related to the dog's health, such as weight gain or loss, coat condition, and eye brightness. The app compares these changes with past case information stored in a database to detect possible illness or ailments. If detected, the app notifies the owner and prompts them to take early action. This mechanism allows owners to monitor their dog's health on a daily basis and detect illness or ailments early. For example, owners can add photos of their dogs taken daily to the app. This process is similar to regular photography, requiring no special operations on the part of the owner. For example, the app can simply capture a single scene from a daily life, such as during a walk or while relaxing at home. The app then compares the newly added photos with previous photos to detect changes. These changes are related to the dog's health, such as weight gain or loss, coat condition, and eye brightness. The app uses AI image analysis to detect these changes. For example, weight gain or loss is detected as a change in the dog's body contours and size. The app then compares the detected changes with past case information stored in a database. The database contains previously reported cases of dog illnesses and ailments, and the app uses this information to determine whether the detected changes are signs of illness or ailment. For example, if a sudden weight loss matches past case information, the app determines that the dog may be ill. Finally, if the app detects a possible illness or ailment, it notifies the owner. Notifications are sent via in-app messages or push notifications, encouraging the owner to take early action. For example, a message recommending a visit to a veterinarian may be displayed. This system allows owners to monitor their dog's health on a daily basis and enable early detection of illness or ailments. This health monitoring system allows dogs to monitor their health on a daily basis and supports early detection of illness or ailments.

[0029] A health monitoring system according to an embodiment includes a reception unit, a comparison unit, a detection unit, a collation unit, and a notification unit. The reception unit receives photos of a dog taken by the owner. Photos of a dog taken by the owner include, but are not limited to, photos taken on a daily basis. For example, the reception unit receives photos taken by the owner with a smartphone and uploads them to the app. The reception unit also receives photos taken by the owner with a digital camera and uploads them to the app via a computer. The reception unit can also automatically retrieve and receive photos posted by the owner on social media. For example, the reception unit stores photos taken by the owner in cloud storage, and the app automatically retrieves the photos. The comparison unit compares the photos received by the reception unit with previous photos. For example, the comparison unit can display newly added photos and previously taken photos side by side, allowing users to visually check changes. The comparison unit can also automatically detect changes in photos using AI. For example, the comparison unit can use image analysis technology to detect changes in the dog's weight or coat condition. The detection unit reads the changes detected by the comparison unit. The detection unit performs detailed analysis of changes such as a dog's weight gain or loss, coat condition, and eye brightness. The detection unit uses AI to quantify these changes and store them in a database. For example, the detection unit detects a dog's weight gain or loss as a change in body contour and size, and quantifies the changes. The matching unit compares the changes detected by the detection unit with past case information accumulated in the database. For example, the matching unit determines whether the detected changes are signs of illness or illness based on previously reported case information of dog illnesses or illnesses. The matching unit uses AI to compare the detected changes with the case information and evaluate the possibility of illness or illness. For example, if a sudden weight loss matches past case information, the matching unit determines that there is a possibility of illness. The notification unit notifies the owner if the matching unit detects a possible illness or illness. The notification unit notifies the owner, for example, via in-app messages or push notifications. The notification unit can also notify the owner via email or SMS. For example, the notification unit displays a message recommending that the pet be taken to a veterinary clinic.As a result, the health monitoring system according to the embodiment can monitor the health condition of a dog on a daily basis and assist in the early detection of illness or ailments.

[0030] The collation unit can determine whether the detected change is a sign of illness or ailment based on past case information accumulated in the database. The collation unit can determine whether the detected change is a sign of illness or ailment based on, for example, previously reported case information of dog illnesses or ailments. The collation unit uses AI to compare the case information with the detected change and evaluate the possibility of illness or ailment. For example, if a sudden weight loss matches past case information, the collation unit determines that there is a possibility of illness. This improves the accuracy of determining signs of illness or ailment based on past case information. Some or all of the above-described processing in the collation unit can be performed using, for example, AI, or can be performed without AI. For example, the collation unit can determine signs of illness or ailment using an AI model that inputs the detected change and outputs the possibility of illness or ailment.

[0031] The notification unit can notify the owner when a possible illness or illness is detected. The notification unit notifies the owner, for example, via an in-app message or push notification. The notification unit can also notify the owner via email or SMS. For example, the notification unit displays a message recommending that the owner visit a veterinary clinic. This allows the owner to be promptly notified when a possible illness or illness is detected. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can perform the notification using an AI model that inputs the possible illness or illness and outputs the notification content.

[0032] The detection unit can detect changes in the dog's weight gain or loss, coat condition, and eye brightness. For example, the detection unit detects the dog's weight gain or loss as changes in body contour and size. The detection unit uses AI to quantify these changes and store them in a database. For example, the detection unit detects the dog's weight gain or loss as changes in body contour and size and quantifies the changes. The detection unit can also detect the dog's coat condition as changes in color and texture. For example, the detection unit evaluates the gloss of the dog's coat and the amount of hair loss and quantifies the changes. The detection unit can also detect the brightness of the dog's eyes as changes in light reflection and color. For example, the detection unit evaluates the transparency and light reflection of the dog's eyes and quantifies the changes. This makes it possible to detect specific changes related to the dog's health condition. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can detect changes using an AI model that receives the dog's weight gain or loss, coat condition, and eye brightness as input and quantifies the changes.

[0033] The reception unit can accept photos of the dog taken by the owner on a daily basis. For example, the reception unit accepts photos taken by the owner with a smartphone to be uploaded to the app. The reception unit can also accept photos taken by the owner with a digital camera to be uploaded to the app via a computer. The reception unit can also automatically acquire and accept photos posted by the owner on a social networking site. For example, the reception unit stores photos taken by the owner in cloud storage, and the app automatically acquires the photos. This makes it easy to accept photos taken by the owner on a daily basis. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can accept photos using an AI model that accepts photos, using photos taken by the owner as input.

[0034] The comparison unit can compare the newly added photo with the previous photo. For example, the comparison unit can display the newly added photo side by side with previously taken photos, allowing the user to visually confirm changes. The comparison unit can also automatically detect changes in photos using AI. For example, the comparison unit can use image analysis technology to detect weight gain or loss or changes in the condition of a dog's coat. This allows changes to be detected by comparing the newly added photo with the previous photo. Some or all of the above-described processing in the comparison unit can be performed using AI, for example, or without AI. For example, the comparison unit can compare the photos using an AI model that takes the newly added photo and the previous photo as input and outputs changes.

[0035] The reception unit can analyze the owner's past photography history and select the optimal reception method. For example, the reception unit can analyze the time periods in which the owner frequently took photos in the past and set the reception unit to accept photos during those time periods. If the owner frequently takes photos at a specific location, the reception unit can prioritize accepting photos taken at that location. Furthermore, if the owner tends to take photos at specific events (e.g., walks, meals), the reception unit can also accept photos at those events. This makes it possible to select the optimal reception method based on the owner's past photography history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can select the reception method using an AI model that inputs the owner's past photography history and outputs the optimal reception method.

[0036] When receiving photos, the reception unit can filter them based on the owner's current living situation and areas of interest. For example, if the owner is interested in health, the reception unit can preferentially receive photos related to the owner's health. Also, if the owner is traveling, the reception unit can preferentially receive photos taken at the travel destination. Furthermore, if the owner has adopted a new pet, the reception unit can preferentially receive photos of the new pet. This makes it possible to preferentially receive photos according to the owner's living situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can filter photos using an AI model that inputs the owner's current living situation and areas of interest and outputs filtering results.

[0037] When accepting photos, the reception unit can prioritize accepting photos with high relevance in consideration of the owner's geographical location information. For example, if the owner is in a park, the reception unit can prioritize accepting photos taken in the park. Also, if the owner is at a veterinary clinic, the reception unit can prioritize accepting photos taken at the clinic. Furthermore, if the owner is traveling, the reception unit can also prioritize accepting photos taken at the travel destination. This makes it possible to prioritize accepting photos with high relevance based on the owner's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can accept photos using an AI model that inputs the owner's geographical location information and outputs photos with high relevance.

[0038] When receiving photos, the reception unit can analyze the owner's social media activity and receive related photos. For example, the reception unit can prioritize receiving photos shared by the owner on social media. The reception unit can also prioritize receiving photos of places tagged by the owner on social media. Furthermore, the reception unit can also prioritize receiving photos related to accounts the owner follows on social media. This allows related photos to be preferentially received based on the owner's social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can receive photos using an AI model that inputs the owner's social media activity and outputs related photos.

[0039] During the comparison, the comparison unit can apply an algorithm for detailed analysis of changes such as weight gain or loss and coat condition of the dog. For example, the comparison unit can apply an algorithm that detects weight gain or loss as a change in body contour or size. The comparison unit can also apply an algorithm that detects the condition of the dog's coat as a change in color or texture. The comparison unit can also apply an algorithm that detects the brightness of the dog's eyes as a change in light reflection or color. This allows for detailed analysis of changes such as weight gain or loss and coat condition of the dog. Some or all of the above-described processing in the comparison unit may be performed using, for example, AI, or may be performed without using AI. For example, the comparison unit can analyze the changes in detail using an AI model that receives inputs of weight gain or loss, coat condition, and changes in eye brightness of the dog and outputs the changes.

[0040] During the comparison, the comparison unit can focus on comparing changes in specific parts related to the dog's health condition. For example, the comparison unit can detect and compare the brightness of the dog's eyes as changes in light reflection and color. The comparison unit can also detect and compare the condition of the dog's ears as changes in color and shape. The comparison unit can also detect and compare changes in the wetness and color of the dog's nose. This allows for a focused comparison of changes in specific parts related to the dog's health condition. Some or all of the above-described processing in the comparison unit may be performed using, for example, AI, or may be performed without using AI. For example, the comparison unit can focus on comparing changes in specific parts using an AI model that inputs changes in the brightness of the dog's eyes, the condition of its ears, and the wetness of its nose, and outputs changes.

[0041] The comparison unit can apply different comparison algorithms based on the age and breed of the dog during comparison. For example, the comparison unit can apply different comparison algorithms to young dogs and old dogs. The comparison unit can also apply different comparison algorithms to small dogs and large dogs. Furthermore, the comparison unit can apply a comparison algorithm that takes into account health issues specific to a particular breed. This makes it possible to apply a comparison algorithm depending on the age and breed of the dog. Some or all of the above-mentioned processing in the comparison unit can be performed using, for example, AI, or can be performed without using AI. For example, the comparison unit can apply a comparison algorithm using an AI model that inputs the age and breed of the dog and outputs a comparison algorithm.

[0042] The comparison unit can adjust the comparison criteria based on the dog's activity level and dietary content during comparison. For example, the comparison unit can apply different comparison criteria to active dogs and inactive dogs. The comparison unit can also compare weight gain or loss based on specific dietary content. Furthermore, the comparison unit can compare muscle development and changes in body shape based on the dog's exercise volume. This allows the application of comparison criteria according to the dog's activity level and dietary content. Some or all of the above-described processing in the comparison unit can be performed using, for example, AI, or can be performed without AI. For example, the comparison unit can adjust the comparison criteria using an AI model that inputs the dog's activity level and dietary content and outputs comparison criteria.

[0043] During detection, the detection unit can apply an algorithm for detailed analysis of changes in the dog's weight gain / loss, coat condition, eye brightness, etc. The detection unit can apply, for example, an algorithm that detects the dog's weight gain / loss as a change in body contour or size. The detection unit can also apply an algorithm that detects the dog's coat condition as a change in color or texture. The detection unit can also apply an algorithm that detects the dog's eye brightness as a change in light reflection or color. This allows for detailed analysis of changes in the dog's weight gain / loss, coat condition, eye brightness, etc. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without AI. For example, the detection unit can analyze the changes in detail using an AI model that receives the dog's weight gain / loss, coat condition, and eye brightness as input and outputs the changes.

[0044] During detection, the detection unit can focus on detecting changes in specific areas related to the dog's health condition. For example, the detection unit can detect and compare the brightness of the dog's eyes as light reflection or color changes. The detection unit can also detect and compare the condition of the dog's ears as changes in color or shape. The detection unit can also detect and compare the wetness and color of the dog's nose. This allows for focused detection of changes in specific areas related to the dog's health condition. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without AI. For example, the detection unit can focus on detecting changes in specific areas using an AI model that receives inputs such as changes in the brightness of the dog's eyes, the condition of its ears, and the wetness of its nose, and outputs changes.

[0045] The detection unit can apply different detection algorithms based on the age and breed of the dog during detection. For example, the detection unit can apply different detection algorithms to young dogs and old dogs. The detection unit can also apply different detection algorithms to small dogs and large dogs. Furthermore, the detection unit can apply a detection algorithm that takes into account health issues specific to a particular breed. This allows the detection algorithm to be applied according to the age and breed of the dog. Some or all of the above-mentioned processing in the detection unit can be performed using, for example, AI, or can be performed without using AI. For example, the detection unit can apply a detection algorithm using an AI model that inputs the age and breed of the dog and outputs a detection algorithm.

[0046] The detection unit can adjust the detection criteria based on the dog's activity level and dietary content during detection. For example, the detection unit applies different detection criteria to active dogs and inactive dogs. The detection unit can also detect weight gain or loss based on specific dietary content. Furthermore, the detection unit can detect muscle development and changes in body shape based on the dog's exercise volume. This allows the detection criteria to be applied according to the dog's activity level and dietary content. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can adjust the detection criteria using an AI model that inputs the dog's activity level and dietary content and outputs detection criteria.

[0047] The matching unit can apply an algorithm for analyzing past case information in detail during matching. For example, the matching unit can apply an algorithm for analyzing in detail signs of a specific disease based on the past case information. The matching unit can also apply an algorithm for analyzing in detail signs of a specific disorder based on the past case information. Furthermore, the matching unit can also apply an algorithm for integrating and analyzing multiple pieces of case information based on the past case information. This allows for detailed analysis of past case information. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can analyze past case information in detail using an AI model that receives past case information as input and outputs analysis results.

[0048] During matching, the matching unit can focus on matching information on specific cases related to the dog's health condition. For example, the matching unit can focus on matching case information related to a sudden loss in the dog's weight. The matching unit can also focus on matching case information related to changes in the condition of the dog's coat. Furthermore, the matching unit can also focus on matching case information related to changes in the brightness of the dog's eyes. This makes it possible to focus on matching information on specific cases related to the dog's health condition. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can focus on matching information on specific case information using an AI model that inputs specific case information and outputs matching results.

[0049] During matching, the matching unit can apply different matching algorithms based on the age and breed of the dog. For example, the matching unit can apply different matching algorithms to young dogs and old dogs. The matching unit can also apply different matching algorithms to small dogs and large dogs. Furthermore, the matching unit can also apply a matching algorithm that takes into account health issues specific to a particular breed. This makes it possible to apply a matching algorithm according to the age and breed of the dog. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can apply a matching algorithm using an AI model that inputs the age and breed of the dog and outputs a matching algorithm.

[0050] During matching, the matching unit can adjust the matching criteria based on the dog's activity level and dietary content. For example, the matching unit applies different matching criteria to active dogs and inactive dogs. The matching unit can also match weight gain or loss based on specific dietary content. Furthermore, the matching unit can also match muscle development and changes in body shape based on the dog's exercise volume. This makes it possible to apply matching criteria according to the dog's activity level and dietary content. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can adjust the matching criteria using an AI model that inputs the dog's activity level and dietary content and outputs matching criteria.

[0051] The notification unit can adjust the level of detail of the notification based on the possibility of illness or illness when making a notification. For example, the notification unit provides a brief notification in the case of a mild illness. The notification unit can also provide a detailed notification in the case of a possible severe illness. Furthermore, the notification unit can also provide a notification with an appropriate level of detail in the case of a moderate illness. This makes it possible to adjust the level of detail of the notification depending on the possibility of illness or illness. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can adjust the level of detail of the notification using an AI model that inputs the possibility of illness or illness and outputs the level of detail of the notification.

[0052] When notifying, the notification unit can select the optimal notification method by referring to the owner's past reaction history. For example, the notification unit preferentially uses a notification method to which the owner has responded quickly in the past. The notification unit can also avoid notification methods that the owner has ignored in the past. Furthermore, the notification unit can also use a notification method that the owner has received favorably in the past. This makes it possible to select the optimal notification method based on the owner's past reaction history. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can select the notification method using an AI model that inputs the owner's past reaction history and outputs the optimal notification method.

[0053] The notification unit can select the optimal notification method by taking into consideration the owner's geographical location information when sending a notification. For example, when the owner is at home, the notification unit can prioritize in-app notifications. Furthermore, when the owner is out, the notification unit can prioritize push notifications. Furthermore, when the owner is at a veterinary clinic, the notification unit can also send detailed notifications. This makes it possible to select the optimal notification method based on the owner's geographical location information. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can select the notification method using an AI model that inputs the owner's geographical location information and outputs the optimal notification method.

[0054] At the time of notification, the notification unit can analyze the owner's social media activity and suggest the optimal notification method. For example, if the owner frequently uses social media, the notification unit can send the notification through social media. Also, if the owner is a member of a specific group on social media, the notification unit can send the notification through that group. Furthermore, if the owner follows a specific account on social media, the notification unit can send the notification through that account. This makes it possible to suggest the optimal notification method based on the owner's social media activity. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can suggest the notification method using an AI model that inputs the owner's social media activity and outputs the optimal notification method.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The health monitoring system may further include a voice recognition unit. The voice recognition unit can analyze the tone and content of the voice spoken by the owner to the dog and extract information related to the dog's health condition. For example, if the owner says, "He doesn't seem well," the voice recognition unit acquires that information and provides it to other elements. Also, if the tone of the owner's voice changes when calling the dog's name, the voice recognition unit can detect that change and determine that it may be related to the dog's health condition. Furthermore, the voice recognition unit can analyze the content spoken by the owner about the dog's health condition and detect specific keywords (e.g., cough, loss of appetite). In this way, the voice recognition unit can extract important information related to the dog's health condition from the owner's voice and provide it to other elements.

[0057] The health monitoring system may further include an environmental monitoring unit. The environmental monitoring unit monitors the temperature, humidity, air quality, and other factors in the dog's environment and evaluates the impact of these environmental factors on the dog's health. For example, if the indoor temperature is too high, the environmental monitoring unit may determine that the dog is at risk of heatstroke and notify the owner. If the humidity is too low, the environmental monitoring unit may determine that this could affect the dog's skin and coat condition. Furthermore, if the air quality deteriorates, the environmental monitoring unit may determine that this could affect the dog's respiratory system and prompt the owner to take appropriate measures. In this way, the environmental monitoring unit can monitor environmental factors that affect the dog's health and provide appropriate information to the owner.

[0058] The health monitoring system may further include an exercise amount measurement unit. The exercise amount measurement unit may measure the amount of exercise of the dog and evaluate the health condition based on the data. For example, the exercise amount measurement unit may measure how much the dog walks or runs in a day and provide the data to other elements. If the amount of exercise suddenly decreases, it may be determined that there may be a problem with the dog's health. Furthermore, the exercise amount measurement unit may analyze the dog's exercise pattern and notify the owner if an abnormal pattern is detected. In this way, the exercise amount measurement unit can monitor the dog's exercise amount and use the data to evaluate the health condition.

[0059] The health monitoring system may further include a diet management unit. The diet management unit may record the dog's dietary content and intake amount and evaluate the health condition based on the data. For example, the diet management unit may record the dietary content and intake amount input by the owner and provide the data to other elements. The diet management unit may also determine that an imbalance in the dietary content may affect the dog's health condition. Furthermore, the diet management unit may propose an appropriate diet plan based on the dog's weight and health condition. This allows the diet management unit to monitor the dog's dietary content and intake amount and use the information to evaluate the health condition.

[0060] The health monitoring system may further include a sleep monitoring unit. The sleep monitoring unit may monitor the dog's sleep patterns and evaluate the dog's health condition based on the data. For example, the sleep monitoring unit may measure how much the dog sleeps per day and the quality of the sleep, and provide the data to other elements. If the dog's sleep pattern is disrupted, it may be determined that there may be a problem with the dog's health. Furthermore, the sleep monitoring unit may evaluate the dog's sleeping environment and suggest appropriate improvements. In this way, the sleep monitoring unit may monitor the dog's sleep patterns and use the data to evaluate the dog's health condition.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The reception unit accepts photos of the dog taken by the owner. The app accepts photos taken by the owner with a smartphone or digital camera that can be uploaded to the app via a computer. It can also automatically retrieve and accept photos posted on social media. Step 2: The comparison unit compares the photos received by the reception unit with previous photos. The newly added photos are displayed side by side with previously taken photos, allowing users to visually confirm any changes. AI can also be used to automatically detect changes in photos. For example, image analysis technology can be used to detect weight gain or loss or changes in the condition of a dog's coat. Step 3: The detection unit reads the changes detected by the comparison unit. It analyzes in detail changes in the dog's weight gain or loss, coat condition, and eye brightness, and quantifies these changes and stores them in a database. For example, weight gain or loss can be detected as a change in body shape or size, and these changes can be quantified. Step 4: The matching unit compares the changes detected by the detection unit with past case information stored in a database. Based on previously reported case information of dog illnesses and illnesses, it determines whether the detected changes are signs of illness or illness. AI is used to compare the case information with the detected changes and evaluate the possibility of illness or illness. For example, if a sudden weight loss matches past case information, it is determined that there is a possibility of illness. Step 5: The notification unit notifies the owner if the matching unit detects a possible illness or condition. The owner can be notified via in-app message, push notification, email, or SMS. For example, a message recommending a visit to a veterinarian will be displayed.

[0063] (Example 2) A health monitoring system according to an embodiment of the present invention monitors the health of pet dogs on a daily basis and supports early detection of illness or ailments. In this health monitoring system, owners add photos of their dogs taken daily to an app, which then compares the newly added photos with previous photos to detect changes. These changes are related to the dog's health, such as weight gain or loss, coat condition, and eye brightness. The app compares these changes with past case information stored in a database to detect possible illness or ailments. If detected, the app notifies the owner and prompts them to take early action. This mechanism allows owners to monitor their dog's health on a daily basis and detect illness or ailments early. For example, owners can add photos of their dogs taken daily to the app. This process is similar to regular photography, requiring no special operations on the part of the owner. For example, the app can simply capture a single scene from a daily life, such as during a walk or while relaxing at home. The app then compares the newly added photos with previous photos to detect changes. These changes are related to the dog's health, such as weight gain or loss, coat condition, and eye brightness. The app uses AI image analysis to detect these changes. For example, weight gain or loss is detected as a change in the dog's body contours and size. The app then compares the detected changes with past case information stored in a database. The database contains previously reported cases of dog illnesses and ailments, and the app uses this information to determine whether the detected changes are signs of illness or ailment. For example, if a sudden weight loss matches past case information, the app determines that the dog may be ill. Finally, if the app detects a possible illness or ailment, it notifies the owner. Notifications are sent via in-app messages or push notifications, encouraging the owner to take early action. For example, a message recommending a visit to a veterinarian may be displayed. This system allows owners to monitor their dog's health on a daily basis and enable early detection of illness or ailments. This health monitoring system allows dogs to monitor their health on a daily basis and supports early detection of illness or ailments.

[0064] A health monitoring system according to an embodiment includes a reception unit, a comparison unit, a detection unit, a collation unit, and a notification unit. The reception unit receives photos of a dog taken by the owner. Photos of a dog taken by the owner include, but are not limited to, photos taken on a daily basis. For example, the reception unit receives photos taken by the owner with a smartphone and uploads them to the app. The reception unit also receives photos taken by the owner with a digital camera and uploads them to the app via a computer. The reception unit can also automatically retrieve and receive photos posted by the owner on social media. For example, the reception unit stores photos taken by the owner in cloud storage, and the app automatically retrieves the photos. The comparison unit compares the photos received by the reception unit with previous photos. For example, the comparison unit can display newly added photos and previously taken photos side by side, allowing users to visually check changes. The comparison unit can also automatically detect changes in photos using AI. For example, the comparison unit can use image analysis technology to detect changes in the dog's weight or coat condition. The detection unit reads the changes detected by the comparison unit. The detection unit performs detailed analysis of changes such as a dog's weight gain or loss, coat condition, and eye brightness. The detection unit uses AI to quantify these changes and store them in a database. For example, the detection unit detects a dog's weight gain or loss as a change in body contour and size, and quantifies the changes. The matching unit compares the changes detected by the detection unit with past case information accumulated in the database. For example, the matching unit determines whether the detected changes are signs of illness or illness based on previously reported case information of dog illnesses or illnesses. The matching unit uses AI to compare the detected changes with the case information and evaluate the possibility of illness or illness. For example, if a sudden weight loss matches past case information, the matching unit determines that there is a possibility of illness. The notification unit notifies the owner if the matching unit detects a possible illness or illness. The notification unit notifies the owner, for example, via in-app messages or push notifications. The notification unit can also notify the owner via email or SMS. For example, the notification unit displays a message recommending that the pet be taken to a veterinary clinic.As a result, the health monitoring system according to the embodiment can monitor the health condition of a dog on a daily basis and assist in the early detection of illness or ailments.

[0065] The collation unit can determine whether the detected change is a sign of illness or ailment based on past case information accumulated in the database. The collation unit can determine whether the detected change is a sign of illness or ailment based on, for example, previously reported case information of dog illnesses or ailments. The collation unit uses AI to compare the case information with the detected change and evaluate the possibility of illness or ailment. For example, if a sudden weight loss matches past case information, the collation unit determines that there is a possibility of illness. This improves the accuracy of determining signs of illness or ailment based on past case information. Some or all of the above-described processing in the collation unit can be performed using, for example, AI, or can be performed without AI. For example, the collation unit can determine signs of illness or ailment using an AI model that inputs the detected change and outputs the possibility of illness or ailment.

[0066] The notification unit can notify the owner when a possible illness or illness is detected. The notification unit notifies the owner, for example, via an in-app message or push notification. The notification unit can also notify the owner via email or SMS. For example, the notification unit displays a message recommending that the owner visit a veterinary clinic. This allows the owner to be promptly notified when a possible illness or illness is detected. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can perform the notification using an AI model that inputs the possible illness or illness and outputs the notification content.

[0067] The detection unit can detect changes in the dog's weight gain or loss, coat condition, and eye brightness. For example, the detection unit detects the dog's weight gain or loss as changes in body contour and size. The detection unit uses AI to quantify these changes and store them in a database. For example, the detection unit detects the dog's weight gain or loss as changes in body contour and size and quantifies the changes. The detection unit can also detect the dog's coat condition as changes in color and texture. For example, the detection unit evaluates the gloss of the dog's coat and the amount of hair loss and quantifies the changes. The detection unit can also detect the brightness of the dog's eyes as changes in light reflection and color. For example, the detection unit evaluates the transparency and light reflection of the dog's eyes and quantifies the changes. This makes it possible to detect specific changes related to the dog's health condition. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can detect changes using an AI model that receives the dog's weight gain or loss, coat condition, and eye brightness as input and quantifies the changes.

[0068] The reception unit can accept photos of the dog taken by the owner on a daily basis. For example, the reception unit accepts photos taken by the owner with a smartphone to be uploaded to the app. The reception unit can also accept photos taken by the owner with a digital camera to be uploaded to the app via a computer. The reception unit can also automatically acquire and accept photos posted by the owner on a social networking site. For example, the reception unit stores photos taken by the owner in cloud storage, and the app automatically acquires the photos. This makes it easy to accept photos taken by the owner on a daily basis. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can accept photos using an AI model that accepts photos, using photos taken by the owner as input.

[0069] The comparison unit can compare the newly added photo with the previous photo. For example, the comparison unit can display the newly added photo side by side with previously taken photos, allowing the user to visually confirm changes. The comparison unit can also automatically detect changes in photos using AI. For example, the comparison unit can use image analysis technology to detect weight gain or loss or changes in the condition of a dog's coat. This allows changes to be detected by comparing the newly added photo with the previous photo. Some or all of the above-described processing in the comparison unit can be performed using AI, for example, or without AI. For example, the comparison unit can compare the photos using an AI model that takes the newly added photo and the previous photo as input and outputs changes.

[0070] The reception unit can estimate the owner's emotions and adjust the timing of photo acceptance based on the estimated owner's emotions. For example, if the reception unit is stressed, the app can send a reminder to encourage the owner to take a photo at a relaxed time. Furthermore, if the owner is relaxed, the reception unit can automatically set the timing for the app to accept photos, allowing the owner to capture natural moments. Furthermore, if the owner is busy, the reception unit can have the app predict the optimal timing and send a photo reminder. This allows photos to be accepted at the optimal timing based on the owner's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can adjust the timing of photo acceptance using an AI model that inputs the owner's emotion data and outputs the timing of photo acceptance.

[0071] The reception unit can analyze the owner's past photography history and select the optimal reception method. For example, the reception unit can analyze the time periods in which the owner frequently took photos in the past and set the reception unit to accept photos during those time periods. If the owner frequently takes photos at a specific location, the reception unit can prioritize accepting photos taken at that location. Furthermore, if the owner tends to take photos at specific events (e.g., walks, meals), the reception unit can also accept photos at those events. This makes it possible to select the optimal reception method based on the owner's past photography history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can select the reception method using an AI model that inputs the owner's past photography history and outputs the optimal reception method.

[0072] When receiving photos, the reception unit can filter them based on the owner's current living situation and areas of interest. For example, if the owner is interested in health, the reception unit can preferentially receive photos related to the owner's health. Also, if the owner is traveling, the reception unit can preferentially receive photos taken at the travel destination. Furthermore, if the owner has adopted a new pet, the reception unit can preferentially receive photos of the new pet. This makes it possible to preferentially receive photos according to the owner's living situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can filter photos using an AI model that inputs the owner's current living situation and areas of interest and outputs filtering results.

[0073] The reception unit can estimate the owner's emotions and determine the priority of photos to be received based on the estimated owner's emotions. For example, if the owner is stressed, the reception unit can prioritize receiving photos of relaxed moments. Furthermore, if the owner is happy, the reception unit can prioritize receiving photos of happy moments. Furthermore, if the owner is sad, the reception unit can prioritize receiving photos that are comforting. This allows the priority of photos to be determined according to the owner's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can determine the priority of photos using an AI model that inputs the owner's emotion data and outputs a priority order for the photos.

[0074] When accepting photos, the reception unit can prioritize accepting photos with high relevance in consideration of the owner's geographical location information. For example, if the owner is in a park, the reception unit can prioritize accepting photos taken in the park. Also, if the owner is at a veterinary clinic, the reception unit can prioritize accepting photos taken at the clinic. Furthermore, if the owner is traveling, the reception unit can also prioritize accepting photos taken at the travel destination. This makes it possible to prioritize accepting photos with high relevance based on the owner's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can accept photos using an AI model that inputs the owner's geographical location information and outputs photos with high relevance.

[0075] When receiving photos, the reception unit can analyze the owner's social media activity and receive related photos. For example, the reception unit can prioritize receiving photos shared by the owner on social media. The reception unit can also prioritize receiving photos of places tagged by the owner on social media. Furthermore, the reception unit can also prioritize receiving photos related to accounts the owner follows on social media. This allows related photos to be preferentially received based on the owner's social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can receive photos using an AI model that inputs the owner's social media activity and outputs related photos.

[0076] The comparison unit can estimate the owner's emotions and adjust the photo comparison method based on the estimated owner's emotions. For example, the comparison unit can provide detailed comparison results when the owner is relaxed. Furthermore, the comparison unit can provide concise comparison results when the owner is in a hurry. Furthermore, the comparison unit can provide visually easy-to-understand comparison results when the owner is excited. This allows the photo comparison method to be adjusted according to the owner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the comparison unit can be performed using, for example, AI, or without AI. For example, the comparison unit can adjust the photo comparison method using an AI model that inputs the owner's emotion data and outputs a comparison method.

[0077] During the comparison, the comparison unit can apply an algorithm for detailed analysis of changes such as weight gain or loss and coat condition of the dog. For example, the comparison unit can apply an algorithm that detects weight gain or loss as a change in body contour or size. The comparison unit can also apply an algorithm that detects the condition of the dog's coat as a change in color or texture. The comparison unit can also apply an algorithm that detects the brightness of the dog's eyes as a change in light reflection or color. This allows for detailed analysis of changes such as weight gain or loss and coat condition of the dog. Some or all of the above-described processing in the comparison unit may be performed using, for example, AI, or may be performed without using AI. For example, the comparison unit can analyze the changes in detail using an AI model that receives inputs of weight gain or loss, coat condition, and changes in eye brightness of the dog and outputs the changes.

[0078] During the comparison, the comparison unit can focus on comparing changes in specific parts related to the dog's health condition. For example, the comparison unit can detect and compare the brightness of the dog's eyes as changes in light reflection and color. The comparison unit can also detect and compare the condition of the dog's ears as changes in color and shape. The comparison unit can also detect and compare changes in the wetness and color of the dog's nose. This allows for a focused comparison of changes in specific parts related to the dog's health condition. Some or all of the above-described processing in the comparison unit may be performed using, for example, AI, or may be performed without using AI. For example, the comparison unit can focus on comparing changes in specific parts using an AI model that inputs changes in the brightness of the dog's eyes, the condition of its ears, and the wetness of its nose, and outputs changes.

[0079] The comparison unit can estimate the owner's emotions and adjust the display method of the comparison results based on the estimated owner's emotions. For example, if the owner is nervous, the comparison unit can provide a simple, highly visible display method. Furthermore, if the owner is relaxed, the comparison unit can provide a display method that includes detailed information. Furthermore, if the owner is in a hurry, the comparison unit can provide a display method that focuses on the main points. This makes it possible to adjust the display method of the comparison results according to the owner's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the comparison unit may be performed using, for example, AI, or may be performed without using AI. For example, the comparison unit can adjust the display method of the comparison results using an AI model that inputs the owner's emotion data and outputs a display method.

[0080] The comparison unit can apply different comparison algorithms based on the age and breed of the dog during comparison. For example, the comparison unit can apply different comparison algorithms to young dogs and old dogs. The comparison unit can also apply different comparison algorithms to small dogs and large dogs. Furthermore, the comparison unit can apply a comparison algorithm that takes into account health issues specific to a particular breed. This makes it possible to apply a comparison algorithm depending on the age and breed of the dog. Some or all of the above-mentioned processing in the comparison unit can be performed using, for example, AI, or can be performed without using AI. For example, the comparison unit can apply a comparison algorithm using an AI model that inputs the age and breed of the dog and outputs a comparison algorithm.

[0081] The comparison unit can adjust the comparison criteria based on the dog's activity level and dietary content during comparison. For example, the comparison unit can apply different comparison criteria to active dogs and inactive dogs. The comparison unit can also compare weight gain or loss based on specific dietary content. Furthermore, the comparison unit can compare muscle development and changes in body shape based on the dog's exercise volume. This allows the application of comparison criteria according to the dog's activity level and dietary content. Some or all of the above-described processing in the comparison unit can be performed using, for example, AI, or can be performed without AI. For example, the comparison unit can adjust the comparison criteria using an AI model that inputs the dog's activity level and dietary content and outputs comparison criteria.

[0082] The detection unit can estimate the owner's emotions and determine the priority of changes to be detected based on the estimated owner's emotions. For example, if the owner is stressed, the detection unit can prioritize detecting important changes. Furthermore, if the owner is relaxed, the detection unit can detect detailed changes. Furthermore, if the owner is busy, the detection unit can prioritize detecting brief changes. This allows the priority of changes to be determined according to the owner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the detection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the detection unit can determine the priority of changes to be detected using an AI model that inputs the owner's emotion data and outputs a priority of changes.

[0083] During detection, the detection unit can apply an algorithm for detailed analysis of changes in the dog's weight gain / loss, coat condition, eye brightness, etc. The detection unit can apply, for example, an algorithm that detects the dog's weight gain / loss as a change in body contour or size. The detection unit can also apply an algorithm that detects the dog's coat condition as a change in color or texture. The detection unit can also apply an algorithm that detects the dog's eye brightness as a change in light reflection or color. This allows for detailed analysis of changes in the dog's weight gain / loss, coat condition, eye brightness, etc. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without AI. For example, the detection unit can analyze the changes in detail using an AI model that receives the dog's weight gain / loss, coat condition, and eye brightness as input and outputs the changes.

[0084] During detection, the detection unit can focus on detecting changes in specific areas related to the dog's health condition. For example, the detection unit can detect and compare the brightness of the dog's eyes as light reflection or color changes. The detection unit can also detect and compare the condition of the dog's ears as changes in color or shape. The detection unit can also detect and compare the wetness and color of the dog's nose. This allows for focused detection of changes in specific areas related to the dog's health condition. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without AI. For example, the detection unit can focus on detecting changes in specific areas using an AI model that receives inputs such as changes in the brightness of the dog's eyes, the condition of its ears, and the wetness of its nose, and outputs changes.

[0085] The detection unit can estimate the owner's emotions and adjust the display method of the detection results based on the estimated owner's emotions. For example, if the owner is nervous, the detection unit can provide a simple, highly visible display method. Furthermore, if the owner is relaxed, the detection unit can provide a display method that includes detailed information. Furthermore, if the owner is in a hurry, the detection unit can provide a display method that focuses on the main points. This allows the display method of the detection results to be adjusted according to the owner's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can adjust the display method of the detection results using an AI model that inputs the owner's emotion data and outputs a display method.

[0086] The detection unit can apply different detection algorithms based on the age and breed of the dog during detection. For example, the detection unit can apply different detection algorithms to young dogs and old dogs. The detection unit can also apply different detection algorithms to small dogs and large dogs. Furthermore, the detection unit can apply a detection algorithm that takes into account health issues specific to a particular breed. This allows the detection algorithm to be applied according to the age and breed of the dog. Some or all of the above-mentioned processing in the detection unit can be performed using, for example, AI, or can be performed without using AI. For example, the detection unit can apply a detection algorithm using an AI model that inputs the age and breed of the dog and outputs a detection algorithm.

[0087] The detection unit can adjust the detection criteria based on the dog's activity level and dietary content during detection. For example, the detection unit applies different detection criteria to active dogs and inactive dogs. The detection unit can also detect weight gain or loss based on specific dietary content. Furthermore, the detection unit can detect muscle development and changes in body shape based on the dog's exercise volume. This allows the detection criteria to be applied according to the dog's activity level and dietary content. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can adjust the detection criteria using an AI model that inputs the dog's activity level and dietary content and outputs detection criteria.

[0088] The matching unit can estimate the owner's emotions and determine the priority of the case information to be matched based on the estimated owner's emotions. For example, if the owner is stressed, the matching unit can prioritize matching important case information. Furthermore, if the owner is relaxed, the matching unit can prioritize matching detailed case information. Furthermore, if the owner is busy, the matching unit can prioritize matching concise case information. This makes it possible to determine the priority of the case information to be matched according to the owner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the matching unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the matching unit can determine the priority of the case information to be matched using an AI model that receives the owner's emotion data as input and outputs the priority of the case information.

[0089] The matching unit can apply an algorithm for analyzing past case information in detail during matching. For example, the matching unit can apply an algorithm for analyzing in detail signs of a specific disease based on the past case information. The matching unit can also apply an algorithm for analyzing in detail signs of a specific disorder based on the past case information. Furthermore, the matching unit can also apply an algorithm for integrating and analyzing multiple pieces of case information based on the past case information. This allows for detailed analysis of past case information. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can analyze past case information in detail using an AI model that receives past case information as input and outputs analysis results.

[0090] During matching, the matching unit can focus on matching information on specific cases related to the dog's health condition. For example, the matching unit can focus on matching case information related to a sudden loss in the dog's weight. The matching unit can also focus on matching case information related to changes in the condition of the dog's coat. Furthermore, the matching unit can also focus on matching case information related to changes in the brightness of the dog's eyes. This makes it possible to focus on matching information on specific cases related to the dog's health condition. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can focus on matching information on specific case information using an AI model that inputs specific case information and outputs matching results.

[0091] The matching unit can estimate the owner's emotions and adjust the display method of the matching results based on the estimated owner's emotions. For example, if the owner is nervous, the matching unit can provide a simple, highly visible display method. Furthermore, if the owner is relaxed, the matching unit can provide a display method that includes detailed information. Furthermore, if the owner is in a hurry, the matching unit can provide a display method that focuses on the main points. This makes it possible to adjust the display method of the matching results according to the owner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can adjust the display method of the matching results using an AI model that inputs the owner's emotion data and outputs a display method.

[0092] During matching, the matching unit can apply different matching algorithms based on the age and breed of the dog. For example, the matching unit can apply different matching algorithms to young dogs and old dogs. The matching unit can also apply different matching algorithms to small dogs and large dogs. Furthermore, the matching unit can also apply a matching algorithm that takes into account health issues specific to a particular breed. This makes it possible to apply a matching algorithm according to the age and breed of the dog. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can apply a matching algorithm using an AI model that inputs the age and breed of the dog and outputs a matching algorithm.

[0093] During matching, the matching unit can adjust the matching criteria based on the dog's activity level and dietary content. For example, the matching unit applies different matching criteria to active dogs and inactive dogs. The matching unit can also match weight gain or loss based on specific dietary content. Furthermore, the matching unit can also match muscle development and changes in body shape based on the dog's exercise volume. This makes it possible to apply matching criteria according to the dog's activity level and dietary content. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can adjust the matching criteria using an AI model that inputs the dog's activity level and dietary content and outputs matching criteria.

[0094] The notification unit can estimate the owner's emotions and adjust the notification expression method based on the estimated owner's emotions. For example, if the owner is nervous, the notification unit can use calm expression to notify the owner. Furthermore, if the owner is relaxed, the notification unit can use cheerful expression to notify the owner. Furthermore, if the owner is in a hurry, the notification unit can also use quick and concise expression to notify the owner. This makes it possible to adjust the notification expression method according to the owner's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can adjust the notification expression method using an AI model that inputs the owner's emotion data and outputs the notification expression method.

[0095] The notification unit can adjust the level of detail of the notification based on the possibility of illness or illness when making a notification. For example, the notification unit provides a brief notification in the case of a mild illness. The notification unit can also provide a detailed notification in the case of a possible severe illness. Furthermore, the notification unit can also provide a notification with an appropriate level of detail in the case of a moderate illness. This makes it possible to adjust the level of detail of the notification depending on the possibility of illness or illness. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can adjust the level of detail of the notification using an AI model that inputs the possibility of illness or illness and outputs the level of detail of the notification.

[0096] When notifying, the notification unit can select the optimal notification method by referring to the owner's past reaction history. For example, the notification unit preferentially uses a notification method to which the owner has responded quickly in the past. The notification unit can also avoid notification methods that the owner has ignored in the past. Furthermore, the notification unit can also use a notification method that the owner has received favorably in the past. This makes it possible to select the optimal notification method based on the owner's past reaction history. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can select the notification method using an AI model that inputs the owner's past reaction history and outputs the optimal notification method.

[0097] The notification unit can estimate the owner's emotions and determine the priority of notifications based on the estimated owner's emotions. For example, if the owner is feeling stressed, the notification unit can prioritize important notifications. Furthermore, if the owner is relaxed, the notification unit can provide detailed notifications. Furthermore, if the owner is busy, the notification unit can prioritize brief notifications. This allows the priority of notifications to be determined according to the owner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can determine the priority of notifications using an AI model that inputs the owner's emotion data and outputs the priority of notifications.

[0098] The notification unit can select the optimal notification method by taking into consideration the owner's geographical location information when sending a notification. For example, when the owner is at home, the notification unit can prioritize in-app notifications. Furthermore, when the owner is out, the notification unit can prioritize push notifications. Furthermore, when the owner is at a veterinary clinic, the notification unit can also send detailed notifications. This makes it possible to select the optimal notification method based on the owner's geographical location information. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can select the notification method using an AI model that inputs the owner's geographical location information and outputs the optimal notification method.

[0099] At the time of notification, the notification unit can analyze the owner's social media activity and suggest the optimal notification method. For example, if the owner frequently uses social media, the notification unit can send the notification through social media. Also, if the owner is a member of a specific group on social media, the notification unit can send the notification through that group. Furthermore, if the owner follows a specific account on social media, the notification unit can send the notification through that account. This makes it possible to suggest the optimal notification method based on the owner's social media activity. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can suggest the notification method using an AI model that inputs the owner's social media activity and outputs the optimal notification method. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, comparison unit, detection unit, collation unit, and notification unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives photos of the dog taken by the owner. The comparison unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and compares the newly added photo with previous photos. The detection unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and reads the changes detected by the comparison unit. The collation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and compares the changes detected by the detection unit with past case information accumulated in a database. The notification unit is implemented, for example, by the control unit 46A of the smart device 14 and notifies the owner if a possible illness or disorder is detected. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, comparison unit, detection unit, collation unit, and notification 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 reception unit is realized by the control unit 46A of the smart glasses 214 and receives photos of the dog taken by the owner. The comparison unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and compares the newly added photo with previous photos. The detection unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and reads the changes detected by the comparison unit. The collation unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and compares the changes detected by the detection unit with past case information accumulated in a database. The notification unit is realized, for example, by the control unit 46A of the smart glasses 214 and notifies the owner if a possible illness or disorder is detected. === Hard Collateral 1-3 === Each of the above-described elements, including the reception unit, comparison unit, detection unit, collation unit, and notification unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives photos of the dog taken by the owner. The comparison unit is implemented, for example, by the identification processing unit 290 of the data processing device 12 and compares the newly added photo with previous photos. The detection unit is implemented, for example, by the identification processing unit 290 of the data processing device 12 and reads the changes detected by the comparison unit. The collation unit is implemented, for example, by the identification processing unit 290 of the data processing device 12 and compares the changes detected by the detection unit with past case information accumulated in the database. The notification unit is implemented, for example, by the control unit 46A of the headset terminal 314 and notifies the owner if a possible illness or disorder is detected. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, comparison unit, detection unit, collation unit, and notification unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives photos of the dog taken by the owner. The comparison unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and compares the newly added photo with previous photos. The detection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and reads the changes detected by the comparison unit. The collation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and compares the changes detected by the detection unit with past case information accumulated in a database. The notification unit is realized, for example, by the control unit 46A of the robot 414 and notifies the owner if a possible illness or disorder is detected.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The health monitoring system may further include a voice recognition unit. The voice recognition unit can analyze the tone and content of the voice spoken by the owner to the dog and extract information related to the dog's health condition. For example, if the owner says, "He doesn't seem well," the voice recognition unit acquires that information and provides it to other elements. Also, if the tone of the owner's voice changes when calling the dog's name, the voice recognition unit can detect that change and determine that it may be related to the dog's health condition. Furthermore, the voice recognition unit can analyze the content spoken by the owner about the dog's health condition and detect specific keywords (e.g., cough, loss of appetite). In this way, the voice recognition unit can extract important information related to the dog's health condition from the owner's voice and provide it to other elements.

[0102] The health monitoring system may further include an environmental monitoring unit. The environmental monitoring unit monitors the temperature, humidity, air quality, and other factors in the dog's environment and evaluates the impact of these environmental factors on the dog's health. For example, if the indoor temperature is too high, the environmental monitoring unit may determine that the dog is at risk of heatstroke and notify the owner. If the humidity is too low, the environmental monitoring unit may determine that this could affect the dog's skin and coat condition. Furthermore, if the air quality deteriorates, the environmental monitoring unit may determine that this could affect the dog's respiratory system and prompt the owner to take appropriate measures. In this way, the environmental monitoring unit can monitor environmental factors that affect the dog's health and provide appropriate information to the owner.

[0103] The health monitoring system may further include an exercise amount measurement unit. The exercise amount measurement unit may measure the amount of exercise of the dog and evaluate the health condition based on the data. For example, the exercise amount measurement unit may measure how much the dog walks or runs in a day and provide the data to other elements. If the amount of exercise suddenly decreases, it may be determined that there may be a problem with the dog's health. Furthermore, the exercise amount measurement unit may analyze the dog's exercise pattern and notify the owner if an abnormal pattern is detected. In this way, the exercise amount measurement unit can monitor the dog's exercise amount and use the data to evaluate the health condition.

[0104] The health monitoring system may further include a diet management unit. The diet management unit may record the dog's dietary content and intake amount and evaluate the health condition based on the data. For example, the diet management unit may record the dietary content and intake amount input by the owner and provide the data to other elements. The diet management unit may also determine that an imbalance in the dietary content may affect the dog's health condition. Furthermore, the diet management unit may propose an appropriate diet plan based on the dog's weight and health condition. This allows the diet management unit to monitor the dog's dietary content and intake amount and use the information to evaluate the health condition.

[0105] The health monitoring system may further include a sleep monitoring unit. The sleep monitoring unit may monitor the dog's sleep patterns and evaluate the dog's health condition based on the data. For example, the sleep monitoring unit may measure how much the dog sleeps per day and the quality of the sleep, and provide the data to other elements. If the dog's sleep pattern is disrupted, it may be determined that there may be a problem with the dog's health. Furthermore, the sleep monitoring unit may evaluate the dog's sleeping environment and suggest appropriate improvements. In this way, the sleep monitoring unit may monitor the dog's sleep patterns and use the data to evaluate the dog's health condition.

[0106] The health monitoring system may further include an emotion estimation unit. The emotion estimation unit may estimate the emotion of the owner and adjust the operation of the entire system based on the estimated emotion. For example, the emotion estimation unit may reduce the frequency of notifications from the system when the owner is stressed, and provide detailed information when the owner is relaxed. The emotion estimation unit may also prioritize brief notifications when the owner is busy. Furthermore, the emotion estimation unit may provide positive feedback when the owner is happy. This allows the emotion estimation unit to adjust the operation of the entire system according to the owner's emotion and provide more appropriate support.

[0107] The health monitoring system may further include an emotion estimation unit. The emotion estimation unit may estimate the emotion of the owner and provide information about the dog's health condition based on the estimated emotion. For example, the emotion estimation unit may provide reassuring information when the owner is anxious, and provide detailed health information when the owner is relaxed. The emotion estimation unit may also provide visually easy-to-understand information when the owner is excited. Furthermore, the emotion estimation unit may also provide comforting information when the owner is sad. In this way, the emotion estimation unit may provide information about the dog's health condition according to the owner's emotion.

[0108] The health monitoring system may further include an emotion estimation unit. The emotion estimation unit may estimate the emotion of the owner and provide advice regarding the dog's health condition based on the estimated emotion. For example, if the owner is stressed, the emotion estimation unit may provide advice to relax, and if the owner is relaxed, the emotion estimation unit may provide advice to maintain health. Furthermore, if the owner is busy, the emotion estimation unit may provide concise advice. Furthermore, if the owner is happy, the emotion estimation unit may provide positive advice. In this way, the emotion estimation unit can provide advice regarding the dog's health condition according to the owner's emotion.

[0109] The health monitoring system may further include an emotion estimation unit. The emotion estimation unit may estimate the emotion of the owner and provide a reminder regarding the dog's health condition based on the estimated emotion. For example, the emotion estimation unit may provide a reminder to relax if the owner is stressed, and a reminder to check the dog's health if the owner is relaxed. The emotion estimation unit may also provide a brief reminder if the owner is busy. Furthermore, the emotion estimation unit may also provide a positive reminder if the owner is happy. In this way, the emotion estimation unit can provide a reminder regarding the dog's health condition according to the owner's emotion.

[0110] The health monitoring system may further include an emotion estimation unit. The emotion estimation unit may estimate the emotion of the owner and provide feedback regarding the dog's health condition based on the estimated emotion. For example, the emotion estimation unit may provide feedback to relax if the owner is stressed, and provide detailed health information if the owner is relaxed. The emotion estimation unit may also provide brief feedback if the owner is busy. Furthermore, the emotion estimation unit may also provide positive feedback if the owner is happy. In this way, the emotion estimation unit may provide feedback regarding the dog's health condition according to the owner's emotion.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The reception unit accepts photos of the dog taken by the owner. The app accepts photos taken by the owner with a smartphone or digital camera that can be uploaded to the app via a computer. It can also automatically retrieve and accept photos posted on social media. Step 2: The comparison unit compares the photos received by the reception unit with previous photos. The newly added photos are displayed side by side with previously taken photos, allowing users to visually confirm any changes. AI can also be used to automatically detect changes in photos. For example, image analysis technology can be used to detect weight gain or loss or changes in the condition of a dog's coat. Step 3: The detection unit reads the changes detected by the comparison unit. It analyzes in detail changes in the dog's weight gain or loss, coat condition, and eye brightness, and quantifies these changes and stores them in a database. For example, weight gain or loss can be detected as a change in body shape or size, and these changes can be quantified. Step 4: The matching unit compares the changes detected by the detection unit with past case information stored in a database. Based on previously reported case information of dog illnesses and illnesses, it determines whether the detected changes are signs of illness or illness. AI is used to compare the case information with the detected changes and evaluate the possibility of illness or illness. For example, if a sudden weight loss matches past case information, it is determined that there is a possibility of illness. Step 5: The notification unit notifies the owner if the matching unit detects a possible illness or condition. The owner can be notified via in-app message, push notification, email, or SMS. For example, a message recommending a visit to a veterinarian will be displayed.

[0113] 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.

[0114] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0115] 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.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0131] 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.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0147] 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.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0164] 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.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception desk that accepts photos of dogs taken by owners, a comparison unit that compares the photo received by the reception unit with a previous photo; a detection unit that reads the change detected by the comparison unit; a collation unit that compares the changes detected by the detection unit with past case information stored in a database; and a notification unit that notifies the owner when an illness or disorder is detected by the collation unit. A system characterized by:

2. The collation unit Based on past case information stored in a database, it is determined whether the detected changes are signs of illness or disorder.

2. The system of claim 1.

3. The notification unit Notify owners if potential illness or ailment is detected 2. The system of claim 1.

4. The detection unit Detect changes in your dog's weight gain or loss, coat condition, and eye brightness 2. The system of claim 1.

5. The reception unit Accepts photos of dogs taken by owners on a daily basis 2. The system of claim 1.

6. The comparison unit Compare newly added photos with previous photos 2. The system of claim 1.

7. The reception unit Estimate the owner's emotions and adjust the timing of photo acceptance based on the estimated owner's emotions.

2. The system of claim 1.

8. The reception unit Analyze the owner's past photography history and select the most appropriate reception method 2. The system of claim 1.

Images