System

Abstract

To provide a system that, from an image of a cat, automatically estimates the type of the cat and a sign of a disease.SOLUTION: A system according to an embodiment comprises a receiving unit, an estimation unit, and a display unit. The receiving unit uploads an image of a cat. The estimation unit estimates the type of the cat and a sign of a disease on the basis of the image uploaded by the receiving unit. The display unit displays information estimated by the estimation unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

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

[0004] With conventional technology, it was difficult to automatically guess a cat's species or signs of illness from an image.

[0005] The system of the embodiment aims to automatically infer the type of cat and signs of illness from an image of the cat. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an estimation unit, and a display unit. The reception unit uploads an image of a cat. The estimation unit estimates the cat's breed and symptoms of illness based on the image uploaded by the reception unit. The display unit displays the information estimated by the estimation unit. Effect of the Invention

[0007] The system according to the embodiment can automatically infer the type of cat and signs of illness from an image of the cat. [Brief description of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A cat image analysis system according to an embodiment of the present invention is a system that, when a user uploads an image of a cat, infers the cat's type and emotions, and also detects signs of illness. Furthermore, when a user labels a cat, the cat's name and the age determined by the user are also displayed. This system promotes communication between users by creating a place to brag about cats, and provides a place to solve worries about cats. For example, a user uploads an image of a cat. This image is input to a generation AI, which infers the cat's type, emotions, and signs of illness. For example, cat types include Persian cats and Siamese cats, and emotions such as joy, sadness, and anger are inferred. In addition, signs of illness such as eye abnormalities and skin conditions are detected. Next, a user labels a cat, and the cat's name and the age determined by the user are displayed. For example, if a user labels the name "Mike", the name is displayed. In addition, if a user determines the cat's age to be "3 years old", the age is also displayed. In addition, by creating a place to brag about cats, communication between users is promoted. Users can share images and information about their cats with other users and interact with them through comments and likes. This provides a place where users can solve their cat-related worries. For example, users can receive advice from other users about their cat's health condition. This system allows users to infer the cat's species, emotions, and signs of illness simply by uploading a cat image, and displays information labeled by the user, thereby promoting communication between users and providing a place where users can solve their cat-related worries. This cat image analysis system allows users to infer the cat's species, emotions, and signs of illness simply by uploading a cat image, and displays information labeled by the user, thereby promoting communication between users and providing a place where users can solve their cat-related worries.

[0029] The cat image analysis system according to the embodiment includes a reception unit, an estimation unit, and a display unit. The reception unit allows a user to upload an image of a cat. For example, the user can upload an image of a cat using a smartphone or a personal computer. The reception unit inputs the uploaded image to the generation AI. The estimation unit uses the generation AI to estimate the type, emotion, and signs of illness of the cat based on the uploaded image. For example, the generation AI analyzes the image of the cat and estimates the type, such as Persian cat or Siamese cat. In addition, the generation AI analyzes the facial expression and behavior of the cat and estimates emotions such as joy, sadness, and anger. Furthermore, the generation AI analyzes the condition of the cat's eyes and skin to detect signs of illness. The display unit displays the information estimated by the estimation unit. For example, the display unit displays the type, emotion, and signs of illness of the cat to the user. The display unit includes an input unit for the user to label the cat. The user can label the name and age of the cat using the input unit. For example, if the user inputs the name "Mike", the name is displayed. Also, when a user inputs the cat's age as "3 years old", the age is also displayed. The display unit displays information for promoting communication between users. For example, the display unit provides a function for sharing cat images and information uploaded by the user with other users. The user can comment on or like the cat images of other users. This promotes communication between users. As a result, the cat image analysis system according to the embodiment can promote communication between users and provide a place to solve worries about cats by inferring the cat's type, emotions, and signs of illness and displaying the information labeled by the user simply by uploading an image of the cat.

[0030] The reception unit allows users to upload images of cats. For example, users can upload images of cats using a smartphone or computer. The reception unit inputs the uploaded images into the generation AI. Specifically, the user selects an image of a cat through a dedicated application or website and presses the upload button, and the image is sent to the system. The reception unit has a function to automatically check the format and resolution of the image and convert it to an appropriate format if necessary. For example, it accepts images in JPEG or PNG format and corrects them if the resolution is low. The reception unit also has a function to notify the user that the image upload is complete. This allows the user to check whether the image has been uploaded successfully. In addition, the reception unit temporarily stores the uploaded image and checks the quality of the image before inputting it into the generation AI. For example, if the image is blurry or the cat is not clearly visible, the user can be prompted to upload the image again. This allows the generation AI to perform analysis based on high-quality images, improving the accuracy of the estimation results.

[0031] The estimation unit uses the generation AI to infer the cat's type, emotions, and signs of illness based on the uploaded image. For example, the generation AI analyzes the image of the cat and infers the type, such as Persian or Siamese. The generation AI also analyzes the cat's facial expressions and behavior to infer emotions such as joy, sadness, and anger. The generation AI also analyzes the condition of the cat's eyes and skin to detect signs of illness. Specifically, the generation AI uses deep learning technology to analyze the cat's image with a multi-layered neural network. First, as preprocessing of the image, noise is removed and contrast is adjusted to make it suitable for analysis. Next, image features are extracted to capture the features of the cat's face and body. For example, the shape of the ears, eye color, and fur pattern are analyzed, and the type of cat is inferred based on these features. In addition, a facial expression analysis algorithm is used to capture subtle changes in the cat's face and infer emotions. For example, the degree to which the eyes are open, the shape of the mouth, the direction of the ears, etc. are analyzed to determine whether the cat is happy or sad. In addition, the generative AI performs detailed analysis of the cat's eye and skin conditions to detect signs of illness. For example, it detects abnormalities such as bloodshot eyes, skin inflammation, and hair loss, and determines whether these are signs of illness. This allows the estimation unit to extract information from multiple angles from cat images uploaded by users, and to infer the cat's species, emotions, and signs of illness with a high degree of accuracy.

[0032] The display unit displays the information estimated by the estimation unit. For example, the display unit displays the cat's type, emotions, and signs of illness to the user. The display unit includes an input unit for the user to label the cat. The user can use the input unit to label the cat's name and age. For example, if the user inputs the name "Mike", the name is displayed. Also, if the user inputs the cat's age as "3 years old", the age is also displayed. The display unit displays information to promote communication between users. For example, the display unit provides a function for sharing cat images and information uploaded by the user with other users. The user can comment on or like the cat images of other users. Specifically, the display unit employs a graphical user interface (GUI) to visually display the information received from the estimation unit in an easy-to-understand manner. For example, the cat's type, emotions, and signs of illness are displayed with icons and colors, allowing the user to understand at a glance. In addition, the labeling function reflects the information input by the user in real time and displays it as the cat's profile. Furthermore, the display unit provides a function like a social media to promote communication between users. For example, a user can share information and thoughts about cats by leaving a comment on an image of another user's cat or pressing "Like". In addition, the display unit can provide a privacy setting for a user to share information about his / her cat with other users and control the disclosure range of the information. In this way, the display unit can effectively display the information from the estimation unit and promote communication between users, thereby providing a place to solve worries about cats.

[0033] The estimation unit can estimate the type of cat. For example, the estimation unit uses a generation AI to estimate the type of cat based on a cat image. For example, the generation AI analyzes the shape of the cat's face, coat color, body type, etc., and estimates the type, such as Persian cat, Siamese cat, or American shorthair. The generation AI has learned a large amount of cat image data, and is capable of highly accurate type estimation. For example, the generation AI estimates the type by extracting the features of the cat's face and comparing them with known cat types. The generation AI can also analyze the cat's body type and coat color patterns and estimate the type based on them. This allows the estimation unit to accurately estimate the type of cat.

[0034] The estimation unit can estimate the cat's emotions. The estimation unit estimates the cat's emotions based on an image of a cat, for example, using a generation AI. For example, the generation AI analyzes the cat's facial expressions and behavior to estimate emotions such as joy, sadness, and anger. The generation AI has learned a large amount of cat image data and is capable of highly accurate emotion estimation. For example, the generation AI analyzes the cat's facial expressions, such as the shape of the cat's eyes and the movement of the mouth, and estimates emotions based on the analysis. The generation AI can also analyze the cat's behavioral patterns and estimate emotions based on the analysis. This allows the estimation unit to accurately estimate the cat's emotions.

[0035] The estimation unit can detect signs of illness in cats. The estimation unit detects signs of illness in cats using, for example, a generation AI based on images of cats. For example, the generation AI analyzes the condition of the cat's eyes and skin to detect signs of illness. The generation AI has learned a large amount of cat image data and is capable of detecting signs of illness with high accuracy. For example, the generation AI analyzes the bloodshot eyes and amount of tears of the cat and detects signs of illness based on that. The generation AI can also analyze the condition of the cat's skin and the amount of hair loss and detect signs of illness based on that. This allows the estimation unit to detect signs of illness in cats at an early stage.

[0036] The cat image analysis system includes an input unit for a user to label a cat. The input unit provides an interface for a user to label a cat's name and age. For example, a user can input a cat's name using the input unit. The input unit provides, for example, a text input field, allowing the user to freely input a name. The input unit also provides a field for inputting the cat's age. For example, when a user inputs the cat's age as "3 years old", the age is displayed. The input unit can also provide an option for the user to label the cat's gender and other information. For example, a drop-down menu is provided for the user to select the cat's gender. This allows the user to easily label information such as the cat's name, age, and gender. A part or all of the above-mentioned processing in the input unit may be performed, for example, using AI, or may be performed without using AI. For example, the input unit can input the information input by the user to the generation AI, and the generation AI can analyze and display the information.

[0037] The display unit can display the name and age of the cat. For example, the display unit displays the name and age of the cat that are labeled by the user using the input unit. For example, the display unit can display the name and age below the image of the cat. The display unit displays the name and age in a highly visible font. For example, the display unit displays the name in a large font size and displays the age below in a small font size. The display unit can also adjust the display position of the name and age. For example, the display unit can display the name at the top of the image and the age at the bottom of the image. This allows the display unit to display the name and age of the cat in a highly visible format.

[0038] The display unit can display information for promoting communication between users. The display unit provides, for example, a function for sharing cat images and information uploaded by the user with other users. For example, the display unit displays a button for commenting on or liking a cat image uploaded by the user. The user can leave a comment on or like an image of a cat of another user. The display unit also provides a share button for the user to share an image of his / her cat with other users. For example, the user can share an image of his / her cat on social media or by email by pressing the share button. This allows the display unit to display information for promoting communication between users.

[0039] The reception unit can analyze the user's past image upload history and select the optimal upload method. The reception unit, for example, analyzes the user's past image upload history and selects the optimal upload method. For example, the reception unit preferentially suggests an upload method (such as voice or text) that the user has frequently used in the past. The reception unit can also predict and suggest an upload method to be used in a specific time period from the user's past upload history. Furthermore, the reception unit can also suggest the optimal upload method based on the type of image that the user has uploaded in the past. For example, the reception unit selects the optimal upload method based on the type of cat image that the user has uploaded in the past. This allows the reception unit to suggest the optimal upload method based on the user's past history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's past upload history data to the generation AI and cause the generation AI to select the optimal upload method.

[0040] The reception unit can perform filtering based on the user's current field of interest when uploading images. The reception unit, for example, identifies the user's current field of interest and filters images based on the field. For example, the reception unit analyzes the user's search history and browsing history, and preferentially uploads related images based on the type of cat in which the user is currently interested. The reception unit can also filter related images based on the health condition of the cat in which the user is interested. Furthermore, the reception unit can filter related images based on the emotions of the cat in which the user is interested. For example, the reception unit preferentially uploads related images based on the type and health condition of cats recently searched by the user. This allows the reception unit to preferentially upload related images based on the user's interest. A part or all of the above-mentioned 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 input the user's field of interest data to the generation AI and cause the generation AI to execute image filtering.

[0041] When uploading images, the reception unit can preferentially upload images with high relevance in consideration of the geographical location information of the user. The reception unit, for example, acquires the geographical location information of the user and preferentially uploads images with high relevance based on the information. For example, when the user is in a specific area, the reception unit preferentially uploads images of cats related to the area. In addition, when the user is traveling, the reception unit can also preferentially upload images of cats related to the travel destination. Furthermore, when the user is at home, the reception unit can also preferentially upload images of cats related to the vicinity of the user's home. For example, the reception unit acquires geographical location information using the user's GPS data or IP address, and selects related images based on the information. This allows the reception unit to preferentially upload images with high relevance based on the geographical location information of the user. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the geographical location information data of the user to the generation AI and cause the generation AI to select images with high relevance.

[0042] The reception unit can analyze the social media activity of the user at the time of uploading an image and upload related images. The reception unit, for example, analyzes the social media activity of the user and uploads related images based on the analysis. For example, the reception unit preferentially uploads related cat images based on images shared by the user on social media. The reception unit can also preferentially upload related cat images based on accounts the user follows on social media. Furthermore, the reception unit can also preferentially upload related cat images based on images that the user has "liked" on social media. For example, the reception unit collects social media activity data of the user and selects related images based on the collected data. This allows the reception unit to preferentially upload related images based on the social media activity of the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the social media activity data of the user to the generation AI and cause the generation AI to select related images.

[0043] The estimation unit can adjust the level of detail of the estimation based on the importance of the cat image at the time of estimation. The estimation unit, for example, evaluates the importance of the cat image and adjusts the level of detail of the estimation based on the evaluation. For example, the estimation unit provides a detailed estimation result in the case of an important image. In addition, the estimation unit can also provide a concise estimation result in the case of a general image. Furthermore, in the case of an image related to a specific event, the estimation unit can also provide a detailed estimation result related to the event. For example, the estimation unit evaluates the resolution of the cat image and the rarity of the content, and adjusts the level of detail of the estimation based on the evaluation. Thereby, the estimation unit can provide an estimation result with an optimal level of detail according to the importance of the cat image. Part or all of the above-mentioned processing in the estimation unit may be performed, for example, using AI, or may be performed without using AI. For example, the estimation unit can input cat image data to the generation AI and cause the generation AI to adjust the level of detail of the estimation.

[0044] The estimation unit can apply different estimation algorithms depending on the type and condition of the cat at the time of estimation. The estimation unit applies an optimal estimation algorithm depending on, for example, the type and condition of the cat. For example, in the case of a Persian cat, the estimation unit applies an estimation algorithm specialized for Persian cats. In addition, in the case of a Siamese cat, the estimation unit can also apply an estimation algorithm specialized for Siamese cats. Furthermore, the estimation unit can apply different estimation algorithms depending on the health condition of the cat. For example, the estimation unit selects an optimal estimation algorithm based on the type and health condition of the cat. This allows the estimation unit to apply an optimal estimation algorithm depending on the type and condition of the cat. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input the type and condition data of the cat to the generation AI and cause the generation AI to execute the application of the estimation algorithm.

[0045] The estimation unit can determine the priority of estimation based on the time when the cat image was taken at the time of estimation. The estimation unit, for example, evaluates the time when the cat image was taken and determines the priority of estimation based on the time. For example, the estimation unit preferentially estimates images taken recently. The estimation unit can also preferentially estimate images related to a specific event. Furthermore, the estimation unit can preferentially estimate images within a period specified by the user. For example, the estimation unit evaluates the time of shooting using metadata and timestamps of the cat image and determines the priority of estimation based on the evaluation. Thereby, the estimation unit can provide estimation results in an optimal order based on the time when the cat image was taken. Part or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input the shooting time data of the cat image to the generation AI and cause the generation AI to execute the determination of the priority of estimation.

[0046] The estimation unit can improve the accuracy of the estimation by referring to the cat-related information at the time of estimation. The estimation unit, for example, refers to the cat-related information and improves the accuracy of the estimation based on the information. For example, the estimation unit improves the accuracy of the estimation by referring to a database related to the type of cat. The estimation unit can also improve the accuracy of the estimation by referring to a database related to the health condition of the cat. Furthermore, the estimation unit can improve the accuracy of the estimation by referring to a database related to the cat's emotions. For example, the estimation unit improves the accuracy of the estimation by referring to the cat's breeding history and health record. Thereby, the estimation unit can improve the accuracy of the estimation by referring to the cat-related information. Part or all of the above-mentioned processing in the estimation unit may be performed, for example, using AI or may be performed without using AI. For example, the estimation unit can input the cat-related information data to the generation AI and cause the generation AI to execute the improvement of the accuracy of the estimation.

[0047] The display unit can select the optimal display method by referring to the user's past browsing history at the time of display. The display unit, for example, refers to the user's past browsing history and selects the optimal display method based on the past browsing history. For example, the display unit displays related information based on the type of cat that the user has viewed in the past. The display unit can also predict and suggest information to be displayed in a specific time period from the user's past browsing history. Furthermore, the display unit can display related information based on health information that the user has viewed in the past. For example, the display unit acquires past browsing history using the user's browser history or app usage history, and selects the optimal display method based on the acquired browsing history. Thereby, the display unit can provide the optimal display method based on the user's past browsing history. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input the user's past browsing history data to the generation AI and cause the generation AI to select the optimal display method.

[0048] The display unit can apply different display formats depending on the type and condition of the cat when displaying. The display unit applies the optimal display format depending on, for example, the type and condition of the cat. For example, in the case of a Persian cat, the display unit applies a display format specialized for Persian cats. In addition, in the case of a Siamese cat, the display unit can also apply a display format specialized for Siamese cats. Furthermore, the display unit can apply different display formats depending on the health condition of the cat. For example, the display unit selects the optimal display format based on the type and health condition of the cat. In this way, the display unit can provide the optimal display format depending on the type and condition of the cat. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input the type and condition data of the cat to the generation AI and cause the generation AI to apply the display format.

[0049] The display unit can select the optimal display method in consideration of the user's device information at the time of display. The display unit, for example, acquires the user's device information and selects the optimal display method based on the information. For example, when the user is using a smartphone, the display unit provides a display method that matches the screen size. In addition, when the user is using a tablet, the display unit can also provide a display method optimized for a large screen. Furthermore, when the user is using a smartwatch, the display unit can also provide a simple and highly visible display method. For example, the display unit evaluates the type and screen size of the user's device and selects the optimal display method based on the evaluation. Thereby, the display unit can provide the optimal display method based on the user's device information. Part or all of the above-mentioned processing in the display unit may be performed, for example, using AI or may be performed without using AI. For example, the display unit can input the user's device information data to the generation AI and cause the generation AI to select the optimal display method.

[0050] The display unit can analyze the user's social media activity and display related information at the time of display. The display unit, for example, analyzes the user's social media activity and displays related information based on the analysis. For example, the display unit displays related cat information based on information shared by the user on social media. The display unit can also display related cat information based on accounts the user follows on social media. Furthermore, the display unit can display related cat information based on information that the user "likes" on social media. For example, the display unit collects the user's social media activity data and selects related information based on the collected data. This allows the display unit to provide related information based on the user's social media activity. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI or may be performed without using AI. For example, the display unit can input the user's social media activity data to the generation AI and cause the generation AI to select related information.

[0051] The input unit can select the optimal input method by referring to the user's past input history at the time of input. The input unit, for example, refers to the user's past input history and selects the optimal input method based on the past input history. For example, the input unit preferentially suggests an input method (such as voice or text) that the user has used frequently in the past. The input unit can also predict and suggest an input method to be used in a specific time period from the user's past input history. Furthermore, the input unit can also suggest the optimal input method based on the type of information the user has input in the past. For example, the input unit evaluates the user's input content and input frequency, and selects the optimal input method based on the evaluation. This allows the input unit to provide the optimal input method based on the user's past input history. A part or all of the above-mentioned processing in the input unit may be performed, for example, using AI, or may be performed without using AI. For example, the input unit can input the user's past input history data to the generation AI and cause the generation AI to select the optimal input method.

[0052] The input unit can select the optimal input method in consideration of the device information of the user at the time of input. The input unit, for example, acquires the device information of the user and selects the optimal input method based on the device information. For example, when the user is using a smartphone, the input unit provides an input method that matches the screen size. In addition, when the user is using a tablet, the input unit can also provide an input method optimized for a large screen. Furthermore, when the user is using a smartwatch, the input unit can also provide an input method that is simple and has high visibility. For example, the input unit evaluates the type and screen size of the user's device and selects the optimal input method based on the evaluation. Thereby, the input unit can provide the optimal input method based on the device information of the user. Part or all of the above-mentioned processing in the input unit may be performed, for example, using AI or may be performed without using AI. For example, the input unit can input the device information data of the user to the generation AI and cause the generation AI to select the optimal input method.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The reception unit can automatically optimize the resolution and file size of an image when the user uploads an image of a cat. For example, the reception unit analyzes the resolution of an uploaded image and adjusts the resolution as necessary. The reception unit can also compress the file size of the image to shorten the upload time. Furthermore, the reception unit can convert the format of the image and save it in a format optimal for the system. In this way, the reception unit can support the user to smoothly upload images.

[0055] The estimation unit can also analyze the cat's meow when inferring the cat's type. For example, the estimation unit identifies the cat's type by recording the cat's meow and analyzing the audio data. The estimation unit can also learn the patterns of cat meows and grasp the characteristics of each type. Furthermore, the estimation unit can analyze the frequency and volume of the meow and infer the type based on that. This allows the estimation unit to accurately infer the cat's type by utilizing not only images but also audio data.

[0056] The estimation unit can refer to the cat's diet and exercise data when detecting signs of illness. For example, the estimation unit collects the cat's diet record and exercise data and detects signs of illness based on the data. The estimation unit can also analyze changes in the cat's appetite and activity level to detect abnormalities at an early stage. Furthermore, the estimation unit can refer to the cat's weight and body fat percentage data and evaluate the cat's health condition based on the data. This allows the estimation unit to more accurately detect signs of illness by utilizing the cat's lifestyle data.

[0057] The input unit can support voice input when the user labels the name and age of the cat. For example, the input unit allows the user to input the name and age of the cat by voice. The input unit can also convert the user's voice into text using voice recognition technology and display it as labeling information. Furthermore, the input unit can learn the user's pronunciation and accent to improve the accuracy of the voice input. In this way, the input unit can support the user to more easily label the name and age of the cat.

[0058] The display unit can provide a real-time chat function for cat images to promote communication between users. For example, the display unit provides a function that allows users to not only leave comments on cat images but also chat with other users in real time. The display unit can also analyze the content of the chat and automatically provide related information and advice. Furthermore, the display unit can also save the chat history so that it can be referenced later. In this way, the display unit can make communication between users more active and promote the exchange of information about cats.

[0059] The reception unit can analyze the user's past image upload history and suggest the optimal upload timing. For example, the reception unit can analyze the time periods in which the user frequently uploaded images in the past, and encourage the user to upload during those time periods. The reception unit can also predict and suggest timing related to specific days of the week or events from the user's past upload history. Furthermore, the reception unit can also suggest the optimal upload timing based on the user's life rhythm. This allows the reception unit to suggest the optimal upload timing based on the user's past history.

[0060] The reception unit can suggest related content based on the user's current field of interest when uploading an image. For example, the reception unit can analyze the user's search history and browsing history to suggest related cat information and articles. The reception unit can also suggest related videos and images based on the type and health condition of cats in which the user is interested. Furthermore, the reception unit can also suggest related advice and care methods based on the emotions of cats in which the user is interested. This allows the reception unit to suggest related content based on the user's interests.

[0061] The process flow of the first embodiment will be briefly described below.

[0062] Step 1: The reception unit receives a user's image of a cat. For example, the user can upload a cat image using a smartphone or computer. The reception unit inputs the uploaded image into the generation AI. Step 2: The estimation unit uses the generation AI to infer the cat's species, emotions, and signs of illness based on the uploaded image. For example, the generation AI analyzes the image of a cat and infers the species, such as Persian or Siamese. The generation AI also analyzes the cat's facial expressions and behavior to infer emotions, such as joy, sadness, and anger. Furthermore, the generation AI analyzes the condition of the cat's eyes and skin to detect signs of illness. Step 3: The display unit displays the information inferred by the estimation unit. For example, the display unit displays the cat's type, emotions, and signs of illness to the user. The display unit includes an input unit for the user to label the cat. The user can use the input unit to label the cat's name and age. For example, if the user inputs the name "Mike", the name is displayed. Also, if the user inputs the cat's age as "3 years old", the age is also displayed. The display unit displays information to promote communication between users. For example, the display unit provides a function for the user to share cat images and information uploaded by the user with other users. The user can comment on or like other users' cat images. This promotes communication between users.

[0063] (Example 2) A cat image analysis system according to an embodiment of the present invention is a system that, when a user uploads an image of a cat, infers the cat's type and emotions, and also detects signs of illness. Furthermore, when a user labels a cat, the cat's name and the age determined by the user are also displayed. This system promotes communication between users by creating a place to brag about cats, and provides a place to solve worries about cats. For example, a user uploads an image of a cat. This image is input to a generation AI, which infers the cat's type, emotions, and signs of illness. For example, cat types include Persian cats and Siamese cats, and emotions such as joy, sadness, and anger are inferred. In addition, signs of illness such as eye abnormalities and skin conditions are detected. Next, a user labels a cat, and the cat's name and the age determined by the user are displayed. For example, if a user labels the name "Mike", the name is displayed. In addition, if a user determines the cat's age to be "3 years old", the age is also displayed. In addition, by creating a place to brag about cats, communication between users is promoted. Users can share images and information about their cats with other users and interact with them through comments and likes. This provides a place where users can solve their cat-related worries. For example, users can receive advice from other users about their cat's health condition. This system allows users to infer the cat's species, emotions, and signs of illness simply by uploading a cat image, and displays information labeled by the user, thereby promoting communication between users and providing a place where users can solve their cat-related worries. This cat image analysis system allows users to infer the cat's species, emotions, and signs of illness simply by uploading a cat image, and displays information labeled by the user, thereby promoting communication between users and providing a place where users can solve their cat-related worries.

[0064] The cat image analysis system according to the embodiment includes a reception unit, an estimation unit, and a display unit. The reception unit allows a user to upload an image of a cat. For example, the user can upload an image of a cat using a smartphone or a personal computer. The reception unit inputs the uploaded image to the generation AI. The estimation unit uses the generation AI to estimate the type, emotion, and signs of illness of the cat based on the uploaded image. For example, the generation AI analyzes the image of the cat and estimates the type, such as Persian cat or Siamese cat. In addition, the generation AI analyzes the facial expression and behavior of the cat and estimates emotions such as joy, sadness, and anger. Furthermore, the generation AI analyzes the condition of the cat's eyes and skin to detect signs of illness. The display unit displays the information estimated by the estimation unit. For example, the display unit displays the type, emotion, and signs of illness of the cat to the user. The display unit includes an input unit for the user to label the cat. The user can label the name and age of the cat using the input unit. For example, if the user inputs the name "Mike", the name is displayed. Also, when a user inputs the cat's age as "3 years old", the age is also displayed. The display unit displays information for promoting communication between users. For example, the display unit provides a function for sharing cat images and information uploaded by the user with other users. The user can comment on or like the cat images of other users. This promotes communication between users. As a result, the cat image analysis system according to the embodiment can promote communication between users and provide a place to solve worries about cats by inferring the cat's type, emotions, and signs of illness and displaying the information labeled by the user simply by uploading an image of the cat.

[0065] The reception unit allows users to upload images of cats. For example, users can upload images of cats using a smartphone or computer. The reception unit inputs the uploaded images into the generation AI. Specifically, the user selects an image of a cat through a dedicated application or website and presses the upload button, and the image is sent to the system. The reception unit has a function to automatically check the format and resolution of the image and convert it to an appropriate format if necessary. For example, it accepts images in JPEG or PNG format and corrects them if the resolution is low. The reception unit also has a function to notify the user that the image upload is complete. This allows the user to check whether the image has been uploaded successfully. In addition, the reception unit temporarily stores the uploaded image and checks the quality of the image before inputting it into the generation AI. For example, if the image is blurry or the cat is not clearly visible, the user can be prompted to upload the image again. This allows the generation AI to perform analysis based on high-quality images, improving the accuracy of the estimation results.

[0066] The estimation unit uses the generation AI to infer the cat's type, emotions, and signs of illness based on the uploaded image. For example, the generation AI analyzes the image of the cat and infers the type, such as Persian or Siamese. The generation AI also analyzes the cat's facial expressions and behavior to infer emotions such as joy, sadness, and anger. The generation AI also analyzes the condition of the cat's eyes and skin to detect signs of illness. Specifically, the generation AI uses deep learning technology to analyze the cat's image with a multi-layered neural network. First, as preprocessing of the image, noise is removed and contrast is adjusted to make it suitable for analysis. Next, image features are extracted to capture the features of the cat's face and body. For example, the shape of the ears, eye color, and fur pattern are analyzed, and the type of cat is inferred based on these features. In addition, a facial expression analysis algorithm is used to capture subtle changes in the cat's face and infer emotions. For example, the degree to which the eyes are open, the shape of the mouth, the direction of the ears, etc. are analyzed to determine whether the cat is happy or sad. In addition, the generative AI performs detailed analysis of the cat's eye and skin conditions to detect signs of illness. For example, it detects abnormalities such as bloodshot eyes, skin inflammation, and hair loss, and determines whether these are signs of illness. This allows the estimation unit to extract information from multiple angles from cat images uploaded by users, and to infer the cat's species, emotions, and signs of illness with a high degree of accuracy.

[0067] The display unit displays the information estimated by the estimation unit. For example, the display unit displays the cat's type, emotions, and signs of illness to the user. The display unit includes an input unit for the user to label the cat. The user can use the input unit to label the cat's name and age. For example, if the user inputs the name "Mike", the name is displayed. Also, if the user inputs the cat's age as "3 years old", the age is also displayed. The display unit displays information to promote communication between users. For example, the display unit provides a function for sharing cat images and information uploaded by the user with other users. The user can comment on or like the cat images of other users. Specifically, the display unit employs a graphical user interface (GUI) to visually display the information received from the estimation unit in an easy-to-understand manner. For example, the cat's type, emotions, and signs of illness are displayed with icons and colors, allowing the user to understand at a glance. In addition, the labeling function reflects the information input by the user in real time and displays it as the cat's profile. Furthermore, the display unit provides a function like a social media to promote communication between users. For example, a user can share information and thoughts about cats by leaving a comment on an image of another user's cat or pressing "Like". In addition, the display unit can provide a privacy setting for a user to share information about his / her cat with other users and control the disclosure range of the information. In this way, the display unit can effectively display the information from the estimation unit and promote communication between users, thereby providing a place to solve worries about cats.

[0068] The estimation unit can estimate the type of cat. For example, the estimation unit uses a generation AI to estimate the type of cat based on a cat image. For example, the generation AI analyzes the shape of the cat's face, coat color, body type, etc., and estimates the type, such as Persian cat, Siamese cat, or American shorthair. The generation AI has learned a large amount of cat image data, and is capable of highly accurate type estimation. For example, the generation AI estimates the type by extracting the features of the cat's face and comparing them with known cat types. The generation AI can also analyze the cat's body type and coat color patterns and estimate the type based on them. This allows the estimation unit to accurately estimate the type of cat.

[0069] The estimation unit can estimate the cat's emotions. The estimation unit estimates the cat's emotions based on an image of a cat, for example, using a generation AI. For example, the generation AI analyzes the cat's facial expressions and behavior to estimate emotions such as joy, sadness, and anger. The generation AI has learned a large amount of cat image data and is capable of highly accurate emotion estimation. For example, the generation AI analyzes the cat's facial expressions, such as the shape of the cat's eyes and the movement of the mouth, and estimates emotions based on the analysis. The generation AI can also analyze the cat's behavioral patterns and estimate emotions based on the analysis. This allows the estimation unit to accurately estimate the cat's emotions.

[0070] The estimation unit can detect signs of illness in cats. The estimation unit detects signs of illness in cats using, for example, a generation AI based on images of cats. For example, the generation AI analyzes the condition of the cat's eyes and skin to detect signs of illness. The generation AI has learned a large amount of cat image data and is capable of detecting signs of illness with high accuracy. For example, the generation AI analyzes the bloodshot eyes and amount of tears of the cat and detects signs of illness based on that. The generation AI can also analyze the condition of the cat's skin and the amount of hair loss and detect signs of illness based on that. This allows the estimation unit to detect signs of illness in cats at an early stage.

[0071] The cat image analysis system includes an input unit for a user to label a cat. The input unit provides an interface for a user to label a cat's name and age. For example, a user can input a cat's name using the input unit. The input unit provides, for example, a text input field, allowing the user to freely input a name. The input unit also provides a field for inputting the cat's age. For example, when a user inputs the cat's age as "3 years old", the age is displayed. The input unit can also provide an option for the user to label the cat's gender and other information. For example, a drop-down menu is provided for the user to select the cat's gender. This allows the user to easily label information such as the cat's name, age, and gender. A part or all of the above-mentioned processing in the input unit may be performed, for example, using AI, or may be performed without using AI. For example, the input unit can input the information input by the user to the generation AI, and the generation AI can analyze and display the information.

[0072] The display unit can display the name and age of the cat. For example, the display unit displays the name and age of the cat that are labeled by the user using the input unit. For example, the display unit can display the name and age below the image of the cat. The display unit displays the name and age in a highly visible font. For example, the display unit displays the name in a large font size and displays the age below in a small font size. The display unit can also adjust the display position of the name and age. For example, the display unit can display the name at the top of the image and the age at the bottom of the image. This allows the display unit to display the name and age of the cat in a highly visible format.

[0073] The display unit can display information for promoting communication between users. The display unit provides, for example, a function for sharing cat images and information uploaded by the user with other users. For example, the display unit displays a button for commenting on or liking a cat image uploaded by the user. The user can leave a comment on or like an image of a cat of another user. The display unit also provides a share button for the user to share an image of his / her cat with other users. For example, the user can share an image of his / her cat on social media or by email by pressing the share button. This allows the display unit to display information for promoting communication between users.

[0074] The reception unit can estimate the user's emotion and adjust the timing of uploading images based on the estimated user's emotion. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on changes in facial expression, and simplifies and speeds up image uploading when the user is stressed. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the reception unit analyzes the tone and speed of the voice, and when the user is relaxed, provides detailed upload options and suggests a customizable upload method. Furthermore, the reception unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on fluctuations in heart rate, and when the user is in a hurry, prioritizes voice input and enables quick image uploading. This allows the reception unit to upload images at the optimal timing according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may 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 reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0075] The reception unit can analyze the user's past image upload history and select the optimal upload method. The reception unit, for example, analyzes the user's past image upload history and selects the optimal upload method. For example, the reception unit preferentially suggests an upload method (such as voice or text) that the user has frequently used in the past. The reception unit can also predict and suggest an upload method to be used in a specific time period from the user's past upload history. Furthermore, the reception unit can also suggest the optimal upload method based on the type of image the user has uploaded in the past. For example, the reception unit selects the optimal upload method based on the type of cat image the user has uploaded in the past. This allows the reception unit to suggest the optimal upload method based on the user's past history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's past upload history data to the generation AI and cause the generation AI to select the optimal upload method.

[0076] The reception unit can perform filtering based on the user's current field of interest when uploading images. The reception unit, for example, identifies the user's current field of interest and filters images based on the field. For example, the reception unit analyzes the user's search history and browsing history, and preferentially uploads related images based on the type of cat in which the user is currently interested. The reception unit can also filter related images based on the health condition of the cat in which the user is interested. Furthermore, the reception unit can filter related images based on the emotions of the cat in which the user is interested. For example, the reception unit preferentially uploads related images based on the type and health condition of cats recently searched by the user. This allows the reception unit to preferentially upload related images based on the user's interest. A part or all of the above-mentioned 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 input the user's field of interest data to the generation AI and cause the generation AI to execute image filtering.

[0077] The reception unit can estimate the user's emotion and determine the priority of images to be uploaded based on the estimated user's emotion. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on changes in facial expression, and if the user is happy, preferentially uploads images with positive emotions. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the reception unit analyzes the tone and speed of the voice, and preferentially uploads images that are comforting to the user if the user is sad. Furthermore, the reception unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on fluctuations in heart rate, and if the user is excited, preferentially uploads images that allow the user to share the excitement. This allows the reception unit to preferentially upload optimal images according to the user's emotion. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may 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 reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit may input user emotion data to the generation AI and cause the generation AI to determine the priority order of images.

[0078] When uploading images, the reception unit can preferentially upload images with high relevance in consideration of the geographical location information of the user. The reception unit, for example, acquires the geographical location information of the user and preferentially uploads images with high relevance based on the information. For example, when the user is in a specific area, the reception unit preferentially uploads images of cats related to the area. In addition, when the user is traveling, the reception unit can also preferentially upload images of cats related to the travel destination. Furthermore, when the user is at home, the reception unit can also preferentially upload images of cats related to the vicinity of the user's home. For example, the reception unit acquires geographical location information using the user's GPS data or IP address, and selects related images based on the information. This allows the reception unit to preferentially upload images with high relevance based on the geographical location information of the user. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the geographical location information data of the user to the generation AI and cause the generation AI to select images with high relevance.

[0079] The reception unit can analyze the social media activity of the user at the time of uploading an image and upload related images. The reception unit, for example, analyzes the social media activity of the user and uploads related images based on the analysis. For example, the reception unit preferentially uploads related cat images based on images shared by the user on social media. The reception unit can also preferentially upload related cat images based on accounts the user follows on social media. Furthermore, the reception unit can also preferentially upload related cat images based on images that the user has "liked" on social media. For example, the reception unit collects social media activity data of the user and selects related images based on the collected data. This allows the reception unit to preferentially upload related images based on the social media activity of the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the social media activity data of the user to the generation AI and cause the generation AI to select related images.

[0080] The estimation unit can estimate the user's emotion and adjust the expression method of the estimation result based on the estimated user's emotion. For example, the estimation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the estimation unit calculates an emotion score based on changes in facial expression, and provides a detailed estimation result when the user is relaxed. The estimation unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the estimation unit analyzes the tone and speed of the voice, and provides a concise estimation result when the user is in a hurry. Furthermore, the estimation unit can collect the user's biodata (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the estimation unit calculates an emotion score based on fluctuations in heart rate, and provides an estimation result with a visually stimulating effect when the user is excited. This allows the estimation unit to provide the estimation result in an optimal expression method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may 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 estimation unit may be performed, for example, using AI, or may be performed without using AI. For example, the estimation unit may input user emotion data to the generation AI and cause the generation AI to adjust the expression method of the estimation result.

[0081] The estimation unit can adjust the level of detail of the estimation based on the importance of the cat image at the time of estimation. The estimation unit, for example, evaluates the importance of the cat image and adjusts the level of detail of the estimation based on the evaluation. For example, the estimation unit provides a detailed estimation result in the case of an important image. In addition, the estimation unit can also provide a concise estimation result in the case of a general image. Furthermore, in the case of an image related to a specific event, the estimation unit can also provide a detailed estimation result related to the event. For example, the estimation unit evaluates the resolution of the cat image and the rarity of the content, and adjusts the level of detail of the estimation based on the evaluation. Thereby, the estimation unit can provide an estimation result with an optimal level of detail according to the importance of the cat image. Part or all of the above-mentioned processing in the estimation unit may be performed, for example, using AI, or may be performed without using AI. For example, the estimation unit can input cat image data to the generation AI and cause the generation AI to adjust the level of detail of the estimation.

[0082] The estimation unit can apply different estimation algorithms depending on the type and condition of the cat at the time of estimation. The estimation unit applies an optimal estimation algorithm depending on, for example, the type and condition of the cat. For example, in the case of a Persian cat, the estimation unit applies an estimation algorithm specialized for Persian cats. In addition, in the case of a Siamese cat, the estimation unit can also apply an estimation algorithm specialized for Siamese cats. Furthermore, the estimation unit can apply different estimation algorithms depending on the health condition of the cat. For example, the estimation unit selects an optimal estimation algorithm based on the type and health condition of the cat. This allows the estimation unit to apply an optimal estimation algorithm depending on the type and condition of the cat. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input the type and condition data of the cat to the generation AI and cause the generation AI to execute the application of the estimation algorithm.

[0083] The estimation unit can estimate the user's emotion and adjust the display order of the estimation results based on the estimated user's emotion. For example, the estimation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the estimation unit calculates an emotion score based on changes in facial expression, and if the user is happy, preferentially displays positive estimation results. The estimation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the estimation unit analyzes the tone and speed of the voice, and preferentially displays comforting estimation results if the user is sad. Furthermore, the estimation unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the estimation unit calculates an emotion score based on fluctuations in heart rate, and if the user is excited, preferentially displays estimation results that can share the excitement. This allows the estimation unit to display the estimation results in an optimal order according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may 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 estimation unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the estimation unit may input user emotion data to the generation AI and cause the generation AI to adjust the display order of the estimation results.

[0084] The estimation unit can determine the priority of estimation based on the time when the cat image was taken at the time of estimation. The estimation unit, for example, evaluates the time when the cat image was taken and determines the priority of estimation based on the time. For example, the estimation unit preferentially estimates images taken recently. The estimation unit can also preferentially estimate images related to a specific event. Furthermore, the estimation unit can preferentially estimate images within a period specified by the user. For example, the estimation unit evaluates the time of shooting using metadata and timestamps of the cat image and determines the priority of estimation based on the evaluation. Thereby, the estimation unit can provide estimation results in an optimal order based on the time when the cat image was taken. Part or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input the shooting time data of the cat image to the generation AI and cause the generation AI to execute the determination of the priority of estimation.

[0085] The estimation unit can improve the accuracy of the estimation by referring to the cat-related information at the time of estimation. The estimation unit, for example, refers to the cat-related information and improves the accuracy of the estimation based on the information. For example, the estimation unit improves the accuracy of the estimation by referring to a database related to the type of cat. The estimation unit can also improve the accuracy of the estimation by referring to a database related to the health condition of the cat. Furthermore, the estimation unit can improve the accuracy of the estimation by referring to a database related to the cat's emotions. For example, the estimation unit improves the accuracy of the estimation by referring to the cat's breeding history and health record. Thereby, the estimation unit can improve the accuracy of the estimation by referring to the cat-related information. Part or all of the above-mentioned processing in the estimation unit may be performed, for example, using AI or may be performed without using AI. For example, the estimation unit can input the cat-related information data to the generation AI and cause the generation AI to execute the improvement of the accuracy of the estimation.

[0086] The display unit can estimate the user's emotion and adjust the display content based on the estimated user's emotion. For example, the display unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the display unit calculates an emotion score based on changes in facial expression, and provides a simple and highly visible display method when the user is nervous. The display unit can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the display unit analyzes the tone and speed of the voice, and provides a display method including detailed information when the user is relaxed. Furthermore, the display unit can collect the user's biodata (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the display unit calculates an emotion score based on fluctuations in heart rate, and provides a display method that focuses on the main points when the user is in a hurry. This allows the display unit to provide optimal display content according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may 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 display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit may input user emotion data to the generation AI and cause the generation AI to adjust the display content.

[0087] The display unit can select the optimal display method by referring to the user's past browsing history at the time of display. The display unit, for example, refers to the user's past browsing history and selects the optimal display method based on the past browsing history. For example, the display unit displays related information based on the type of cat that the user has viewed in the past. The display unit can also predict and suggest information to be displayed in a specific time period from the user's past browsing history. Furthermore, the display unit can display related information based on health information that the user has viewed in the past. For example, the display unit acquires past browsing history using the user's browser history or app usage history, and selects the optimal display method based on the acquired browsing history. Thereby, the display unit can provide the optimal display method based on the user's past browsing history. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input the user's past browsing history data to the generation AI and cause the generation AI to select the optimal display method.

[0088] The display unit can apply different display formats depending on the type and condition of the cat when displaying. The display unit applies the optimal display format depending on, for example, the type and condition of the cat. For example, in the case of a Persian cat, the display unit applies a display format specialized for Persian cats. In addition, in the case of a Siamese cat, the display unit can also apply a display format specialized for Siamese cats. Furthermore, the display unit can apply different display formats depending on the health condition of the cat. For example, the display unit selects the optimal display format based on the type and health condition of the cat. In this way, the display unit can provide the optimal display format depending on the type and condition of the cat. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input the type and condition data of the cat to the generation AI and cause the generation AI to apply the display format.

[0089] The display unit can estimate the user's emotion and determine the display priority order based on the estimated user's emotion. For example, the display unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the display unit calculates an emotion score based on changes in facial expression, and when the user is happy, it preferentially displays positive information. The display unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the display unit analyzes the tone and speed of the voice, and when the user is sad, it preferentially displays comforting information. Furthermore, the display unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the display unit calculates an emotion score based on fluctuations in heart rate, and when the user is excited, it preferentially displays information that allows the user to share the excitement. This allows the display unit to display information in an optimal order according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may 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 display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit may input user emotion data to the generation AI and cause the generation AI to determine display priorities.

[0090] The display unit can select the optimal display method in consideration of the user's device information at the time of display. The display unit, for example, acquires the user's device information and selects the optimal display method based on the information. For example, when the user is using a smartphone, the display unit provides a display method that matches the screen size. In addition, when the user is using a tablet, the display unit can also provide a display method optimized for a large screen. Furthermore, when the user is using a smartwatch, the display unit can also provide a simple and highly visible display method. For example, the display unit evaluates the type and screen size of the user's device and selects the optimal display method based on the evaluation. Thereby, the display unit can provide the optimal display method based on the user's device information. Part or all of the above-mentioned processing in the display unit may be performed, for example, using AI or may be performed without using AI. For example, the display unit can input the user's device information data to the generation AI and cause the generation AI to select the optimal display method.

[0091] The display unit can analyze the user's social media activity and display related information at the time of display. The display unit, for example, analyzes the user's social media activity and displays related information based on the analysis. For example, the display unit displays related cat information based on information shared by the user on social media. The display unit can also display related cat information based on accounts the user follows on social media. Furthermore, the display unit can display related cat information based on information that the user "likes" on social media. For example, the display unit collects the user's social media activity data and selects related information based on the collected data. This allows the display unit to provide related information based on the user's social media activity. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI or may be performed without using AI. For example, the display unit can input the user's social media activity data to the generation AI and cause the generation AI to select related information.

[0092] The input unit can estimate the user's emotion and adjust the input method based on the estimated user's emotion. For example, the input unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the input unit calculates an emotion score based on changes in facial expression, and if the user is nervous, provides a simple interface and minimizes input procedures. The input unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the input unit analyzes the tone and speed of the voice, and if the user is relaxed, provides detailed input options and suggests a customizable input method. Furthermore, the input unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the input unit calculates an emotion score based on fluctuations in heart rate, and if the user is in a hurry, prioritizes voice input and enables quick input. This allows the input unit to provide the optimal input method according to the user's emotion. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation 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-mentioned processing in the input unit may be performed, for example, using AI, or may be performed without using AI. For example, the input unit may input the user's emotion data to the generation AI and cause the generation AI to adjust the input method.

[0093] The input unit can select the optimal input method by referring to the user's past input history at the time of input. The input unit, for example, refers to the user's past input history and selects the optimal input method based on the past input history. For example, the input unit preferentially suggests an input method (such as voice or text) that the user has used frequently in the past. The input unit can also predict and suggest an input method to be used in a specific time period from the user's past input history. Furthermore, the input unit can also suggest the optimal input method based on the type of information the user has input in the past. For example, the input unit evaluates the user's input content and input frequency, and selects the optimal input method based on the evaluation. This allows the input unit to provide the optimal input method based on the user's past input history. A part or all of the above-mentioned processing in the input unit may be performed, for example, using AI, or may be performed without using AI. For example, the input unit can input the user's past input history data to the generation AI and cause the generation AI to select the optimal input method.

[0094] The input unit can estimate the user's emotion and determine the priority of input based on the estimated user's emotion. For example, the input unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the input unit calculates an emotion score based on changes in facial expression, and if the user is happy, preferentially encourages the input of positive information. The input unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the input unit analyzes the tone and speed of the voice, and preferentially encourages the input of comforting information if the user is sad. Furthermore, the input unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the input unit calculates an emotion score based on fluctuations in heart rate, and if the user is excited, preferentially encourages the input of information that can share the excitement. This allows the input unit to input information in an optimal order according to the user's emotion. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation 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-mentioned processing in the input unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the input unit may input user emotion data to the generation AI and cause the generation AI to determine the priority order of the input.

[0095] The input unit can select the optimal input method in consideration of the device information of the user at the time of input. The input unit, for example, acquires the device information of the user and selects the optimal input method based on the device information. For example, when the user is using a smartphone, the input unit provides an input method that matches the screen size. In addition, when the user is using a tablet, the input unit can also provide an input method optimized for a large screen. Furthermore, when the user is using a smartwatch, the input unit can also provide an input method that is simple and has high visibility. For example, the input unit evaluates the type and screen size of the user's device and selects the optimal input method based on the evaluation. Thereby, the input unit can provide the optimal input method based on the device information of the user. Part or all of the above-mentioned processing in the input unit may be performed, for example, using AI or may be performed without using AI. For example, the input unit can input the device information data of the user to the generation AI and cause the generation AI to select the optimal input method.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The reception unit can automatically optimize the resolution and file size of an image when the user uploads an image of a cat. For example, the reception unit analyzes the resolution of an uploaded image and adjusts the resolution as necessary. The reception unit can also compress the file size of the image to shorten the upload time. Furthermore, the reception unit can convert the format of the image and save it in a format optimal for the system. In this way, the reception unit can support the user to smoothly upload images.

[0098] The estimation unit can also analyze the cat's meow when inferring the cat's type. For example, the estimation unit identifies the cat's type by recording the cat's meow and analyzing the audio data. The estimation unit can also learn the patterns of cat meows and grasp the characteristics of each type. Furthermore, the estimation unit can analyze the frequency and volume of the meow and infer the type based on that. This allows the estimation unit to accurately infer the cat's type by utilizing not only images but also audio data.

[0099] The estimation unit can monitor the cat's body temperature and heart rate when inferring the cat's emotions. For example, the estimation unit collects biological data of the cat using a body temperature sensor and a heart rate monitor. The estimation unit can also analyze the collected biological data to infer the cat's emotional state. Furthermore, the estimation unit can also monitor fluctuations in the cat's body temperature and heart rate in real time to detect changes in emotions. This allows the estimation unit to more accurately infer emotions by utilizing the cat's biological data.

[0100] The estimation unit can refer to the cat's diet and exercise data when detecting signs of illness. For example, the estimation unit collects the cat's diet record and exercise data and detects signs of illness based on the data. The estimation unit can also analyze changes in the cat's appetite and activity level to detect abnormalities at an early stage. Furthermore, the estimation unit can refer to the cat's weight and body fat percentage data and evaluate the cat's health condition based on the data. This allows the estimation unit to more accurately detect signs of illness by utilizing the cat's lifestyle data.

[0101] The input unit can support voice input when the user labels the name and age of the cat. For example, the input unit allows the user to input the name and age of the cat by voice. The input unit can also convert the user's voice into text using voice recognition technology and display it as labeling information. Furthermore, the input unit can learn the user's pronunciation and accent to improve the accuracy of the voice input. In this way, the input unit can support the user to more easily label the name and age of the cat.

[0102] The display unit can change the display style according to the user's emotions when displaying the cat's name and age. For example, if the user is happy, the display unit can display the name and age in bright colors and large fonts. If the user is sad, the display unit can display the name and age in subdued colors and small fonts. Furthermore, the display unit can add animation effects according to the user's emotions. This allows the display unit to display the cat's name and age in a display style that matches the user's emotions.

[0103] The display unit can provide a real-time chat function for cat images to promote communication between users. For example, the display unit provides a function that allows users to not only leave comments on cat images but also chat with other users in real time. The display unit can also analyze the content of the chat and automatically provide related information and advice. Furthermore, the display unit can also save the chat history so that it can be referenced later. In this way, the display unit can make communication between users more active and promote the exchange of information about cats.

[0104] The reception unit can estimate the user's emotions and display a message to increase engagement when uploading images based on the estimated user's emotions. For example, when the user is happy, the reception unit can display a positive message to encourage uploading. When the user is sad, the reception unit can display an encouraging message to ease the user's feelings. Furthermore, when the user is excited, the reception unit can display an exciting message to increase the user's motivation to upload. In this way, the reception unit can smoothly upload images by displaying a message according to the user's emotions.

[0105] The reception unit can analyze the user's past image upload history and suggest the optimal upload timing. For example, the reception unit can analyze the time periods in which the user frequently uploaded images in the past, and encourage the user to upload during those time periods. The reception unit can also predict and suggest timing related to specific days of the week or events from the user's past upload history. Furthermore, the reception unit can also suggest the optimal upload timing based on the user's life rhythm. This allows the reception unit to suggest the optimal upload timing based on the user's past history.

[0106] The reception unit can suggest related content based on the user's current field of interest when uploading an image. For example, the reception unit can analyze the user's search history and browsing history to suggest related cat information and articles. The reception unit can also suggest related videos and images based on the type and health condition of cats in which the user is interested. Furthermore, the reception unit can also suggest related advice and care methods based on the emotions of cats in which the user is interested. This allows the reception unit to suggest related content based on the user's interests.

[0107] The process flow of the second embodiment will be briefly described below.

[0108] Step 1: The reception unit receives a user's image of a cat. For example, the user can upload a cat image using a smartphone or computer. The reception unit inputs the uploaded image into the generation AI. Step 2: The estimation unit uses the generation AI to infer the cat's species, emotions, and signs of illness based on the uploaded image. For example, the generation AI analyzes the image of a cat and infers the species, such as Persian or Siamese. The generation AI also analyzes the cat's facial expressions and behavior to infer emotions, such as joy, sadness, and anger. Furthermore, the generation AI analyzes the condition of the cat's eyes and skin to detect signs of illness. Step 3: The display unit displays the information inferred by the estimation unit. For example, the display unit displays the cat's type, emotions, and signs of illness to the user. The display unit includes an input unit for the user to label the cat. The user can use the input unit to label the cat's name and age. For example, if the user inputs the name "Mike", the name is displayed. Also, if the user inputs the cat's age as "3 years old", the age is also displayed. The display unit displays information to promote communication between users. For example, the display unit provides a function for the user to share cat images and information uploaded by the user with other users. The user can comment on or like other users' cat images. This promotes communication between users.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0110] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0111] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements including the above-mentioned reception unit, estimation unit, display unit, and input unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit detects the user's facial expression and voice using the camera 42 and microphone 38B of the smart device 14, and estimates the emotion by the control unit 46A. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and estimates the cat's type, emotion, and signs of illness using a generation AI. The display unit displays the estimation result by, for example, the display 40A of the smart device 14, and displays information labeled by the user. The input unit allows the user to input the name and age of the cat using, for example, the touch panel 38A of the smart device 14. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] 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.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0120] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0121] 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.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0123] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements including the above-mentioned reception unit, estimation unit, display unit, and input unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit detects the user's facial expression and voice using the camera 42 and microphone 238 of the smart glasses 214, and estimates the emotion by the control unit 46A. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and estimates the cat's type, emotion, and signs of illness using a generation AI. The display unit displays the estimation result, for example, by the display of the smart glasses 214, and displays information labeled by the user. The input unit allows the user to input the name and age of the cat using, for example, the voice input function of the smart glasses 214. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 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.

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] 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.

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0136] 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.

[0137] 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.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0139] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0140] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements including the above-mentioned reception unit, estimation unit, display unit, and input unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit detects the user's facial expression and voice using the camera 42 and microphone 238 of the headset type terminal 314, and estimates the emotion by the control unit 46A. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and estimates the cat's type, emotion, and signs of illness using a generation AI. The display unit displays the estimation result by, for example, the display 343 of the headset type terminal 314, and displays information labeled by the user. The input unit allows the user to input the name and age of the cat using, for example, the voice input function of the headset type terminal 314. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0148] 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.

[0149] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0151] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0152] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0153] 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.

[0154] 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.

[0155] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0156] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0157] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0159] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0161] Each of the multiple elements including the above-mentioned reception unit, estimation unit, display unit, and input unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit detects the user's facial expression and voice using the camera 42 and microphone 238 of the robot 414, and estimates the emotion by the control unit 46A. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and estimates the cat's type, emotion, and signs of illness using a generation AI. The display unit displays the estimation result, for example, by the display of the robot 414, and displays information labeled by the user. The input unit allows the user to input the name and age of the cat using, for example, the voice input function of the robot 414. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0162] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0163] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0164] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0165] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0166] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0167] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0169] 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.

[0170] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0171] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0172] 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.

[0173] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0174] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0175] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0176] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0177] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0178] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0179] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0180] (Appendix 1) A reception area where you can upload photos of your cat, an estimation unit that estimates the type of cat and symptoms of illness based on the image uploaded by the reception unit; A display unit that displays the information estimated by the estimation unit. A system characterized by: (Appendix 2) The estimation unit is Guess the cat's breed 2. The system of claim 1, (Appendix 3) The estimation unit is Guess your cat's emotions 2. The system of claim 1, (Appendix 4) The estimation unit is Detecting signs of illness in cats 2. The system of claim 1, (Appendix 5) It has an input section for the user to label the cat. 2. The system of claim 1, (Appendix 6) The display unit is Show cat name and age 2. The system of claim 1, (Appendix 7) The display unit is Display information to promote communication between users 2. The system of claim 1, (Appendix 8) The reception unit is Estimate user emotions and adjust the timing of image uploads based on the estimated user emotions. 2. The system of claim 1, (Appendix 9) The reception unit is Analyze the user's past image upload history and select the optimal upload method 2. The system of claim 1, (Appendix 10) The reception unit is Filtering images as they are uploaded based on the user's current interests 2. The system of claim 1, (Appendix 11) The reception unit is Estimate user emotions and prioritize images to upload based on the estimated user emotions 2. The system of claim 1, (Appendix 12) The reception unit is When uploading images, it considers the user's geographic location and prioritizes uploading more relevant images. 2. The system of claim 1, (Appendix 13) The reception unit is When uploading an image, analyze the user's social media activity and upload relevant images. 2. The system of claim 1, (Appendix 14) The estimation unit is Inferring a user's emotions and adjusting the way the inference results are expressed based on the inferred user's emotions 2. The system of claim 1, (Appendix 15) The estimation unit is During estimation, the level of detail is adjusted based on the importance of the cat image. 2. The system of claim 1, (Appendix 16) The estimation unit is When making an estimation, different estimation algorithms are applied depending on the type and condition of the cat. 2. The system of claim 1, (Appendix 17) The estimation unit is Inferring a user's emotions and adjusting the display order of the inferred results based on the inferred user's emotions 2. The system of claim 1, (Appendix 18) The estimation unit is During estimation, we prioritize estimations based on when the cat image was taken. 2. The system of claim 1, (Appendix 19) The estimation unit is When making a prediction, the accuracy of the prediction is improved by referring to related information about the cat. 2. The system of claim 1, (Appendix 20) The display unit is Estimate the user's emotions and adjust the display content based on the estimated user emotions 2. The system of claim 1, (Appendix 21) The display unit is When displaying, the optimal display method is selected by referring to the user's past browsing history. 2. The system of claim 1, (Appendix 22) The display unit is When displaying, apply different display formats depending on the type and status of the cat 2. The system of claim 1, (Appendix 23) The display unit is Estimate the user's emotions and determine the display priority based on the estimated user's emotions 2. The system of claim 1, (Appendix 24) The display unit is When displaying, the optimal display method is selected taking into account the user's device information. 2. The system of claim 1, (Appendix 25) The display unit is When viewed, analyze the user's social media activity and display relevant information 2. The system of claim 1, (Appendix 26) The input unit includes: Estimate user's emotions and adjust input method based on the estimated user's emotions 2. The system of claim 1, (Appendix 27) The input unit includes: When inputting data, the system selects the optimal input method by referring to the user's past input history. 2. The system of claim 1, (Appendix 28) The input unit includes: Estimate the user's emotions and prioritize inputs based on the estimated user emotions. 2. The system of claim 1, (Appendix 29) The input unit includes: When inputting, the optimal input method is selected taking into account the user's device information. 2. The system of claim 1, [Explanation of symbols]

[0181] 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 area where you can upload photos of your cat, an estimation unit that estimates the type of cat and symptoms of illness based on the image uploaded by the reception unit; A display unit that displays the information estimated by the estimation unit. A system characterized by:

2. The estimation unit is Guess your cat's emotions 2. The system of claim 1 .

3. It has an input section for the user to label the cat.

2. The system of claim 1 .

4. The display unit is Show cat name and age 2. The system of claim 1 .

5. The display unit is Display information to promote communication between users 2. The system of claim 1 .

6. The reception unit is Estimate user emotions and adjust the timing of image uploads based on the estimated user emotions.

2. The system of claim 1 .

Images