System

Abstract

An object of the system according to the embodiment is to reproduce a feature and a gesture of a pet in a virtual space and to maintain a memory of an owner with the pet.SOLUTION: A system includes a reception unit, a generation unit, a display unit, and a cooperation unit. The reception part inputs an image and a moving image file of a pet. The generation unit analyzes the file input by the reception unit and generates an avatar of the pet. The display unit displays the avatar generated by the generation unit. The cooperation part cooperates with a vector database storing the characteristics of the pet, and the avatar pet performs a specific operation according to the event.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to recreate the characteristics and behavior of deceased pets in a virtual space, leaving owners with limited options for preserving their memories of their pets.

[0005] The system according to the embodiment aims to reproduce the characteristics and behavior of pets in a virtual space, allowing owners to maintain memories of their pets. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a display unit, and a linking unit. The reception unit inputs images and video files of the pet. The generation unit analyzes the files input by the reception unit and generates an avatar of the pet. The display unit displays the avatar generated by the generation unit. The linking unit links with a vector database that stores the characteristics of the pet, and causes the avatar pet to perform specific actions in response to events. [Effects of the Invention]

[0007] The system according to the embodiment reproduces the characteristics and behavior of pets in a virtual space, allowing owners to maintain memories of their pets. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention uses image generation AI to recreate an avatar of a pet that the user has kept (or would like to keep) in a virtual space. In this system, a user inputs image or video files of the pet, and a multimodal AI analyzes these files to generate a pet avatar. The generated avatar reproduces the pet's movements and sounds, allowing the user to live with the pet as if it were alive within a smartphone app or dedicated tablet. The system also works with a vector database that stores the pet's characteristics, allowing the avatar pet to behave in a way that captures the pet's characteristics in response to events. For example, a user inputs image or video files of the pet. The file contains detailed information about the pet, such as its posture, movements, and sounds. The multimodal AI then analyzes the input files and generates a pet avatar. The generated avatar is displayed within a smartphone app or dedicated tablet, allowing the user to live with the avatar. Furthermore, the system works with a vector database that stores the pet's characteristics, allowing the avatar pet to behave in a way that captures the pet's characteristics in response to events. This allows the user to feel as if their pet is still alive. The system allows the user to feel as if their pet is still alive, allowing them to recreate their memories with their pet in a virtual space. For example, by reproducing the movements and sounds of a pet when it performs a specific action, the user can get the feeling that the pet is still alive. Also, by linking with a vector database that stores the pet's characteristics, the avatar pet can behave in a way that captures the characteristics according to the event. This gives the user the feeling that the pet is still alive.

[0029] The pet avatar generation system according to the embodiment includes a reception unit, a generation unit, a display unit, and a linking unit. The reception unit receives input of images and video files of a pet by a user. The pet image and video files received by the user may be in formats such as, but not limited to, JPEG, PNG, and MP4. The reception unit provides an interface through which the user uploads images of the pet. The reception unit also allows the user to input video files of the pet by dragging and dropping. The reception unit also allows the user to send images and video files of the pet by email. The generation unit uses multimodal AI to analyze the files received by the reception unit and generate an avatar of the pet. The generation unit may analyze the posture and movements of the pet using, for example, an image recognition algorithm. The generation unit may also analyze the sounds of the pet using audio analysis technology. The generation unit may also combine and analyze image data and audio data to reproduce the posture and movements of the pet. For example, the generation unit receives image data as input and reproduces the posture of the pet. The generation unit may also receive audio data as input and reproduce the sounds of the pet. The display unit displays the avatar generated by the generation unit. The display unit, for example, displays the avatar in a smartphone app. The display unit can also display the avatar in a dedicated tablet. The display unit also provides an interface that allows the user to live with the avatar. For example, the display unit provides an interactive function that allows the avatar to move according to the user's instructions. The display unit can also cause the avatar to reproduce a cry in response to the user's voice. The linking unit links with a vector database that stores the pet's characteristics, and causes the avatar pet to perform a specific action in response to an event. For example, when a user sets a specific event, the linking unit causes the avatar pet to perform a specific action based on the event. The linking unit can also access the vector database that stores the pet's characteristics and enable editing as needed. For example, the linking unit provides an interface that allows the user to edit the pet's characteristics. As a result, the pet avatar generation system according to the embodiment allows the user to experience the sensation of living with a pet in a virtual space.Some or all of the above-described processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit may input characteristic data of the pet stored in a vector database to the generation AI, and cause the generation AI to generate actions according to events.

[0030] The reception unit may include an interface that allows the user to input the pet's name, personality, and specific behaviors in text. Examples of the interface include, but are not limited to, a GUI (Graphical User Interface) and a voice input interface. For example, the reception unit may provide a text box in which the user inputs the pet's name. The reception unit may also provide a drop-down menu in which the user selects the pet's personality. The reception unit may also provide a text area in which the user inputs the pet's specific behaviors in text. For example, the reception unit may provide an auto-complete function when the user inputs the pet's name. The reception unit may also display multiple options when the user selects the pet's personality. This allows the user to input detailed information about the pet in text, thereby generating a more accurate avatar. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit may input the text data entered by the user into a generation AI and have the generation AI analyze the detailed information about the pet.

[0031] The generation unit can combine and analyze image data and audio data to reproduce the pet's posture and movements. The generation unit, for example, uses multimodal analysis technology to integrate and analyze image data and audio data. For example, the generation unit receives image data as input and reproduces the pet's posture. The generation unit can also receive audio data as input and reproduce the pet's barks. The generation unit can also combine and analyze image data and audio data to reproduce the pet's movements. For example, the generation unit integrates and analyzes image data and audio data to reproduce the movements and barks of the pet when performing a specific gesture. The generation unit can also combine and analyze image data and audio data to reproduce the movements and barks of the pet when playing. In this way, by combining and analyzing image data and audio data, the pet's posture and movements can be reproduced more realistically. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input image data and audio data to a generation AI and cause the generation AI to reproduce the pet's posture and movements.

[0032] The display unit displays the avatar in a smartphone app or dedicated tablet, allowing the user to live with the avatar. Examples of smartphone apps or dedicated tablets include, but are not limited to, operating systems such as iOS and Android. The display unit, for example, displays the avatar in a smartphone app. The display unit can also display the avatar in a dedicated tablet. The display unit also provides an interface that allows the user to live with the avatar. For example, the display unit provides an interactive function that allows the avatar to act according to the user's instructions. The display unit can also reproduce the avatar's sounds in response to the user's voice. This allows the user to feel as if they are living with the avatar by displaying the avatar in a smartphone app or dedicated tablet. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without AI. For example, the display unit may display an avatar generated by a generation AI on a smartphone app or dedicated tablet.

[0033] When a user sets a specific event, the linking unit can cause the avatar pet to perform a specific action based on the event. Examples of events include, but are not limited to, birthdays and mealtimes. For example, when a user sets a pet's birthday, the linking unit can cause the avatar pet to perform an action corresponding to the birthday. Furthermore, when a user sets a pet's mealtime, the linking unit can cause the avatar pet to perform an action corresponding to the mealtime. Furthermore, when a user sets a specific event, the linking unit can cause the avatar pet to perform a specific action based on the event. For example, when a user sets a playtime for the pet, the linking unit can cause the avatar pet to perform an action corresponding to the playtime. Furthermore, when a user sets a walktime for the pet, the linking unit can cause the avatar pet to perform an action corresponding to the walktime. In this way, when a user sets a specific event, the avatar pet can perform an action corresponding to the event. Some or all of the above-described processing in the linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the linking unit can input event data set by the user to a generation AI and cause the generation AI to generate an action corresponding to the event.

[0034] The linking unit can access a vector database that stores the pet's features and make it editable. The vector database includes, for example, the format of the feature vector and the type of database, but is not limited to these examples. For example, the linking unit accesses the vector database that stores the pet's features and provides an interface for editing the feature vector. The linking unit can also provide an interface for the user to add the pet's features. The linking unit can also provide an interface for the user to delete the pet's features. For example, the linking unit displays the format of the feature vector when the user edits the pet's features. The linking unit can also provide an input field for the feature vector when the user adds the pet's features. This allows the user to customize the behavior and characteristics of the avatar pet by accessing and editing the vector database that stores the pet's features. Some or all of the above-described processing in the linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the linking unit can input the pet's feature data stored in the vector database to the generation AI and cause the generation AI to edit the feature data.

[0035] The reception unit can analyze the user's past submission history of pet images and video files and select the optimal input method. The reception unit, for example, analyzes the formats of pet images and video files submitted by the user in the past. For example, the reception unit prioritizes and suggests input methods (such as voice, text, and image) that the user has frequently used in the past. The reception unit can also analyze the formats of pet images and video files submitted by the user in the past and suggest the optimal input format. Furthermore, the reception unit can suggest the optimal input method for a specific time period based on the user's past submission history. For example, the reception unit selects the optimal input method based on the formats of pet images and video files submitted by the user in the past. The reception unit can also analyze the user's past submission history and suggest the optimal input method for a specific time period. In this way, the analysis of the past submission history can suggest the optimal input method for the user. Some or all of the above-described processing by 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 past submission history data into a generation AI and cause the generation AI to select the optimal input method.

[0036] The reception unit can filter pet image and video files based on the user's current living situation or area of ​​interest when inputting them. The reception unit performs filtering based on, for example, the user's living situation. For example, if the user is busy, the reception unit can suggest an easy input method. Furthermore, if the user has a specific area of ​​interest related to pets, the reception unit can also suggest an input method related to that area. Furthermore, the reception unit can suggest the optimal input method depending on the user's living situation (e.g., at work, on vacation). For example, if the user is at work, the reception unit can suggest an easy input method. Furthermore, if the user is on vacation, the reception unit can encourage input at a time when the user is able to relax. This makes it possible to suggest the optimal input method depending on the user's living situation and area of ​​interest. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's living situation data into a generation AI and have the generation AI perform filtering to find the optimal input method.

[0037] When inputting an image or video file of a pet, the reception unit can select the optimal input means according to the user's input method. For example, if the user prefers voice input, the reception unit can preferentially suggest voice input. For example, if the user prefers voice input, the reception unit can preferentially suggest voice input. Furthermore, if the user prefers text input, the reception unit can preferentially suggest text input. Furthermore, if the user prefers image input, the reception unit can preferentially suggest image input. For example, if the user prefers voice input, the reception unit can preferentially suggest voice input. Furthermore, if the user prefers text input, the reception unit can preferentially suggest text input. This improves input convenience by selecting the optimal input means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input method data to the generation AI and cause the generation AI to select the optimal input means.

[0038] When inputting pet images and video files, the reception unit can prioritize inputting highly relevant files taking into account the user's geographical location information. The reception unit, for example, prioritizes inputting highly relevant files based on the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes inputting pet images and video files related to that area. Furthermore, when the user is traveling, the reception unit can also prioritize inputting pet images and video files related to the travel destination. Furthermore, when the user is at home, the reception unit can prioritize inputting pet images and video files taken at home. For example, when the user is in a specific area, the reception unit prioritizes inputting pet images and video files related to that area. Furthermore, when the user is traveling, the reception unit can also prioritize inputting pet images and video files related to the travel destination. In this way, by preferentially inputting highly relevant files based on the user's geographical location information, more appropriate files can be input. Some or all of the above-described processing by the reception unit may be performed, for example, using AI or without AI. For example, the reception unit can input the user's geographical location information data into the generation AI and have the generation AI select highly relevant files.

[0039] When inputting pet images or video files, the reception unit can analyze the user's online activity and input related files. The reception unit, for example, analyzes the user's social media activity and inputs related files. For example, the reception unit prioritizes inputting pet images and video files shared by the user on social media. The reception unit can also analyze the user's social media posts and input related pet images and video files. The reception unit can also input related pet images and video files based on the activity of the user's social media friends. For example, the reception unit prioritizes inputting pet images and video files shared by the user on social media. The reception unit can also analyze the user's social media posts and input related pet images and video files. This allows more appropriate files to be input by inputting related files based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit may input the user's social media activity data to the generation AI and cause the generation AI to select related files.

[0040] The reception unit can customize the input method by reflecting the user's feedback when inputting an image or video file of a pet. The reception unit, for example, prioritizes suggesting input methods that the user has previously preferred. For example, the reception unit prioritizes suggesting input methods that the user has previously preferred. The reception unit can also improve the input method based on the user's past feedback. The reception unit can also suggest input methods that the user has previously dissatisfied with, to avoid them. For example, the reception unit prioritizes suggesting input methods that the user has previously preferred. The reception unit can also improve the input method based on the user's past feedback. In this way, by customizing the input method by reflecting the user's past feedback, a more appropriate input method can be provided. Some or all of the above-described processing by 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 past feedback data into the generation AI and cause the generation AI to customize the input method.

[0041] The generation unit can adjust the level of detail of the avatar based on the pet's characteristics during generation. The generation unit can, for example, analyze image data in detail to reproduce the pet's characteristic patterns and colors. For example, the generation unit can analyze image data in detail to reproduce the pet's characteristic patterns and colors. The generation unit can also analyze video data in detail to reproduce the pet's specific movements and behaviors. The generation unit can also analyze audio data in detail to reproduce the pet's cries. For example, the generation unit can analyze image data in detail to reproduce the pet's characteristic patterns and colors. The generation unit can also analyze video data in detail to reproduce the pet's specific movements and behaviors. This allows for the generation of a more realistic avatar by adjusting the level of detail of the avatar based on the pet's important characteristics. Some or all of the above-described processing in the generation unit can be performed using, or without, AI. For example, the generation unit can input image data and audio data into a generation AI and cause the generation AI to reproduce the pet's characteristics.

[0042] The generation unit can apply different generation algorithms based on the type of pet during generation. For example, when generating a dog avatar, the generation unit applies an algorithm that reproduces dog-specific movements and sounds. For example, when generating a dog avatar, the generation unit applies an algorithm that reproduces dog-specific movements and sounds. Furthermore, when generating a cat avatar, the generation unit can apply an algorithm that reproduces cat-specific movements and sounds. Furthermore, when generating a bird avatar, the generation unit can apply an algorithm that reproduces bird-specific movements and sounds. For example, when generating a dog avatar, the generation unit applies an algorithm that reproduces dog-specific movements and sounds. Furthermore, when generating a cat avatar, the generation unit can apply an algorithm that reproduces cat-specific movements and sounds. In this way, by applying different generation algorithms depending on the type of pet, a more appropriate avatar can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input pet type data into the generation AI and cause the generation AI to apply a generation algorithm according to the type.

[0043] The generation unit can improve the accuracy of the avatar during generation by referring to the user's past generation results. For example, the generation unit improves the accuracy of the current avatar based on data of avatars previously generated by the user. For example, the generation unit improves the accuracy of the current avatar based on data of avatars previously generated by the user. The generation unit can also improve the avatar's movements and facial expressions based on the user's past feedback. Furthermore, the generation unit can analyze data of avatars previously generated by the user and apply an optimal generation algorithm. For example, the generation unit improves the accuracy of the current avatar based on data of avatars previously generated by the user. The generation unit can also improve the avatar's movements and facial expressions based on the user's past feedback. In this way, the accuracy of the avatar can be improved by referring to the user's past generation results. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past generation result data into the generation AI and cause the generation AI to improve the avatar's accuracy.

[0044] During generation, the generation unit can determine a generation priority based on the time of submission of pet images and video files. The generation unit, for example, prioritizes analysis of pet images and video files recently submitted by the user. For example, the generation unit prioritizes analysis of pet images and video files recently submitted by the user. The generation unit can also prioritize analysis of pet images and video files related to a specific event by the user. Furthermore, the generation unit can determine an optimal generation order based on pet images and video files previously submitted by the user. For example, the generation unit prioritizes analysis of pet images and video files recently submitted by the user. The generation unit can also prioritize analysis of pet images and video files related to a specific event by the user. In this way, by determining the generation priority based on the time of submission of pet images and video files, a more appropriate avatar can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input user submission time data into the generation AI and have the generation AI execute the generation priority.

[0045] The generation unit can adjust the generation order based on pet-related information during generation. For example, the generation unit prioritizes analyzing images and video files related to a specific pet that the user has. For example, the generation unit prioritizes analyzing images and video files related to a specific pet that the user has. Furthermore, if the user has multiple pets, the generation unit can prioritize analyzing images and video files of pets that are highly related to the user. Furthermore, the generation unit can prioritize analyzing images and video files of pets that are related to a specific event that the user has. For example, the generation unit prioritizes analyzing images and video files related to a specific pet that the user has. Furthermore, if the user has multiple pets, the generation unit can prioritize analyzing images and video files of pets that are highly related to the user. In this way, by adjusting the generation order based on pet-related information, a more appropriate avatar can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input pet-related information data to the generation AI and have the generation AI execute the generation order.

[0046] The generation unit can adjust the use of technical terminology during generation based on the user's level of expertise. For example, if the user has specialized knowledge, the generation unit uses detailed technical terminology to describe the generated result. For example, if the user has specialized knowledge, the generation unit can use detailed technical terminology to describe the generated result. Also, if the user does not have specialized knowledge, the generation unit can explain the generated result in simple terms. Furthermore, the generation unit can adjust the use of optimal technical terminology based on the user's past feedback. For example, if the user has specialized knowledge, the generation unit can use detailed technical terminology to describe the generated result. Also, if the user does not have specialized knowledge, the generation unit can explain the generated result in simple terms. In this way, by adjusting the use of technical terminology during generation according to the user's level of expertise, a more appropriate avatar can be generated. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the user's expertise level data into the generation AI and cause the generation AI to use technical terminology.

[0047] The display unit can select the optimal display method by referring to the user's operation history when displaying. The display unit, for example, prioritizes suggesting display methods that the user has previously preferred. For example, the display unit prioritizes suggesting display methods that the user has previously preferred. The display unit can also suggest the optimal display method based on the user's past operation history. The display unit can also suggest a display method that the user has previously dissatisfied with, so as to avoid such display methods. For example, the display unit prioritizes suggesting display methods that the user has previously preferred. The display unit can also suggest the optimal display method based on the user's past operation history. In this way, the optimal display method can be selected by referring to the user's past operation history. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's operation history data to a generation AI and cause the generation AI to select the optimal display method.

[0048] The display unit can customize the display content based on the user's task when displaying the display. For example, when the user is working, the display unit may reduce the display of the avatar and display only notifications. For example, when the user is working, the display unit may reduce the display of the avatar and display only notifications. Furthermore, when the user is relaxing, the display unit may increase the display of the avatar and add interactive actions. Furthermore, when the user is performing a specific task, the display unit can customize the display content related to the task. For example, when the user is working, the display unit may reduce the display of the avatar and display only notifications. Furthermore, when the user is relaxing, the display unit may increase the display of the avatar and add interactive actions. In this way, customizing the display content according to the user's current task allows for a more appropriate display. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without AI. For example, the display unit may input the user's task data into a generation AI and cause the generation AI to customize the display content.

[0049] The display unit can improve the display method based on user feedback during display. For example, the display unit improves the display method based on feedback previously provided by the user. For example, the display unit improves the display method based on feedback previously provided by the user. The display unit can also reflect user feedback in real time and adjust the display method. Furthermore, if the user is dissatisfied with a particular display method, the display unit can improve the display method. For example, the display unit improves the display method based on feedback previously provided by the user. The display unit can also reflect user feedback in real time and adjust the display method. In this way, by reflecting user feedback and improving the display method, a more appropriate display can be achieved. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input user feedback data into a generation AI and cause the generation AI to improve the display method.

[0050] The display unit can select the optimal display method by taking into account the user's device information when displaying. For example, if the user is using a smartphone, the display unit provides a display method tailored to the screen size. For example, if the user is using a smartphone, the display unit provides a display method tailored to the screen size. Furthermore, if the user is using a tablet, the display unit can provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the display unit can provide a simple and highly visible display method. For example, if the user is using a smartphone, the display unit provides a display method tailored to the screen size. Furthermore, if the user is using a tablet, the display unit can provide a display method optimized for a large screen. This allows for more appropriate display by selecting the optimal display method based on the user's device information. Some or all of the above-described processing in the display unit may be performed using, for example, 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.

[0051] The display unit can make the display content multilingual based on the user's language setting when displaying. The display unit automatically sets the display content based on, for example, the language setting of the user's device. For example, the display unit automatically sets the display content based on the language setting of the user's device. The display unit can also provide a language switching function when the user uses multiple languages. Furthermore, the display unit can provide the display content in a specific language when the user selects that language. For example, the display unit automatically sets the display content based on the language setting of the user's device. The display unit can also provide a language switching function when the user uses multiple languages. This allows the display content to be multilingual according to the user's language setting, resulting in a more appropriate display. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without AI. For example, the display unit can input the user's language setting data to a generation AI and cause the generation AI to execute multilingual display content.

[0052] The display unit can customize the display method by reflecting the user's past feedback when displaying. For example, the display unit prioritizes suggesting display methods that the user has previously preferred. For example, the display unit prioritizes suggesting display methods that the user has previously preferred. The display unit can also improve the display method based on the user's past feedback. Furthermore, the display unit can suggest avoiding display methods that the user has previously dissatisfied with. For example, the display unit prioritizes suggesting display methods that the user has previously preferred. The display unit can also improve the display method based on the user's past feedback. In this way, customizing the display method by reflecting the user's past feedback allows for a more appropriate display. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's past feedback data into a generation AI and cause the generation AI to customize the display method.

[0053] The linking unit can set the update frequency of the vector database that stores the pet's features during linking. For example, if the user frequently updates the pet's features, the linking unit increases the update frequency of the vector database. For example, if the user frequently updates the pet's features, the linking unit increases the update frequency of the vector database. Also, if the user does not update the pet's features frequently, the linking unit can decrease the update frequency of the vector database. Furthermore, the linking unit can set an optimal update frequency based on user feedback. For example, if the user frequently updates the pet's features, the linking unit increases the update frequency of the vector database. Also, if the user does not update the pet's features frequently, the linking unit can decrease the update frequency of the vector database. This allows for more appropriate linking by adjusting the update frequency of the vector database that stores the pet's features. Some or all of the above-described processing in the linking unit may be performed using AI, for example, or may be performed without using AI. For example, the linking unit can input vector database update frequency data to the generation AI and cause the generation AI to set the update frequency.

[0054] During linking, the linking unit can select the optimal linking method by referring to the user's event setting history. The linking unit, for example, proposes the optimal linking method based on events previously set by the user. For example, the linking unit proposes the optimal linking method based on events previously set by the user. The linking unit can also analyze the user's past event setting history and propose the most effective linking method. Furthermore, the linking unit can prioritize linking events previously preferred by the user. For example, the linking unit proposes the optimal linking method based on events previously set by the user. The linking unit can also analyze the user's past event setting history and propose the most effective linking method. In this way, the optimal linking method can be selected by referring to the user's past event setting history. Some or all of the above-described processing in the linking unit may be performed using, or without, AI. For example, the linking unit can input the user's event setting history data into the generation AI and cause the generation AI to select the optimal linking method.

[0055] The collaboration unit can improve the collaboration method based on user feedback during collaboration. For example, the collaboration unit improves the collaboration method based on feedback provided by the user in the past. For example, the collaboration unit improves the collaboration method based on feedback provided by the user in the past. The collaboration unit can also reflect user feedback in real time and adjust the collaboration method. Furthermore, if the user is dissatisfied with a particular collaboration method, the collaboration unit can improve the collaboration method. For example, the collaboration unit improves the collaboration method based on feedback provided by the user in the past. The collaboration unit can also reflect user feedback in real time and adjust the collaboration method. In this way, by reflecting user feedback and improving the collaboration method, more appropriate collaboration can be achieved. Some or all of the above-described processing in the collaboration unit may be performed using AI, for example, or may be performed without using AI. For example, the collaboration unit can input user feedback data into the generation AI and cause the generation AI to improve the collaboration method.

[0056] During linking, the linking unit can perform linking based on the geographical distribution of the vector database storing the pet's features. For example, when the user is in a specific area, the linking unit prioritizes linking pet's features related to that area. For example, when the user is in a specific area, the linking unit prioritizes linking pet's features related to that area. Furthermore, when the user is traveling, the linking unit can prioritize linking pet's features related to the travel destination. Furthermore, when the user is at home, the linking unit can prioritize linking pet's features photographed at home. For example, when the user is in a specific area, the linking unit prioritizes linking pet's features related to that area. Furthermore, when the user is traveling, the linking unit can prioritize linking pet's features related to the travel destination. This allows for more appropriate linking by taking into account the geographical distribution of the vector database storing the pet's features. Some or all of the above-described processing in the linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the linking unit can input geographical distribution data of the vector database into the generation AI and cause the generation AI to execute linking.

[0057] The linking unit can improve the accuracy of linking by referring to pet-related literature during linking. The linking unit, for example, proposes an optimal linking method based on pet-related literature. For example, the linking unit proposes an optimal linking method based on pet-related literature. The linking unit can also improve the accuracy of linking by referring to pet-related literature. Furthermore, the linking unit can propose an optimal event based on pet-related literature. For example, the linking unit proposes an optimal linking method based on pet-related literature. The linking unit can also improve the accuracy of linking by referring to pet-related literature. In this way, the accuracy of linking can be improved by referring to pet-related literature. Some or all of the above-mentioned processing in the linking unit may be performed using AI, for example, or may be performed without using AI. For example, the linking unit can input pet-related literature data to the generation AI and cause the generation AI to improve the accuracy of linking.

[0058] The collaboration unit can perform collaboration based on the market value of the pet at the time of collaboration. The collaboration unit, for example, proposes an optimal collaboration method based on the market value of the pet. For example, the collaboration unit proposes an optimal collaboration method based on the market value of the pet. The collaboration unit can also determine collaboration priorities taking into account the market value of the pet. Furthermore, the collaboration unit can propose optimal events based on the market value of the pet. For example, the collaboration unit proposes an optimal collaboration method based on the market value of the pet. The collaboration unit can also determine collaboration priorities taking into account the market value of the pet. This allows for more appropriate collaboration by taking into account the market value of the pet. Some or all of the above-described processing in the collaboration unit may be performed using AI, for example, or may be performed without using AI. For example, the collaboration unit can input pet market value data to the generation AI and have the generation AI execute the collaboration.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] When a user inputs an image or video file of a pet, the reception unit can suggest the optimal input method based on the user's past input history. For example, if the user has used many JPEG format images in the past, the reception unit can preferentially suggest JPEG format images. Also, if the user has used many video files in the past, the reception unit can preferentially suggest the input of video files. Furthermore, if the user has previously input data during a specific time period, the reception unit can also suggest the optimal input method for that time period. This improves input convenience by suggesting the optimal input method based on the user's past input history.

[0061] The display unit can select the optimal display method based on the user's device information. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. Also, if the user is using a tablet, the display unit can provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the display unit can provide a simple and highly visible display method. This allows for more appropriate display by selecting the optimal display method based on the user's device information.

[0062] The reception unit can prioritize input of pet images and video files that are highly relevant based on the user's geographical location information. For example, if the user is in a specific area, it can prioritize input of pet images and video files related to that area. Also, if the user is traveling, it can prioritize input of pet images and video files related to the travel destination. Furthermore, if the user is at home, it can prioritize input of pet images and video files taken at home. In this way, by prioritize input of highly relevant files based on the user's geographical location information, more appropriate files can be input.

[0063] When generating a pet avatar, the generation unit can improve the accuracy of the avatar by referring to the user's past generation results. For example, the accuracy of the current avatar can be improved based on data of avatars previously generated by the user. The avatar's movements and facial expressions can also be improved based on the user's past feedback. Furthermore, the data of avatars previously generated by the user can be analyzed and an optimal generation algorithm can be applied. In this way, the accuracy of the avatar can be improved by referring to the user's past generation results.

[0064] The linking unit can perform linking based on the geographical distribution of the vector database that stores the pet's features. For example, if the user is in a specific area, the linking unit can prioritize pet features related to that area. Also, if the user is traveling, the linking unit can prioritize pet features related to the travel destination. Furthermore, if the user is at home, the linking unit can prioritize pet features photographed at home. This allows for more appropriate linking by taking into account the geographical distribution of the vector database that stores the pet's features.

[0065] The reception unit can analyze the user's online activity and prioritize input of related pet images and video files. For example, it can prioritize input of pet images and video files shared by the user on social media. It can also analyze the content of the user's social media posts and input of related pet images and video files. It can also input related pet images and video files with reference to the activities of the user's friends on social media. This allows more appropriate files to be input by inputting related files based on the user's online activity.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The reception unit receives input of pet images and video files by the user. The pet images and video files input by the user may be in formats such as, but not limited to, JPEG, PNG, and MP4. The reception unit provides, for example, an interface for the user to upload pet images. The reception unit also allows the user to input pet video files by drag and drop. The reception unit also allows the user to send pet images and video files by email. Step 2: The generation unit uses multimodal AI to analyze the file input by the reception unit and generate a pet avatar. The generation unit, for example, uses an image recognition algorithm to analyze the pet's posture and movements. The generation unit can also analyze the pet's cries using audio analysis technology. The generation unit can also combine and analyze image data and audio data to reproduce the pet's posture and movements. For example, the generation unit receives image data as input and reproduces the pet's posture. The generation unit also receives audio data as input and reproduces the pet's cries. Step 3: The display unit displays the avatar generated by the generation unit. The display unit, for example, displays the avatar in a smartphone app. The display unit can also display the avatar in a dedicated tablet. The display unit also provides an interface that allows the user to live with the avatar. For example, the display unit provides an interactive function that allows the avatar to act according to the user's instructions. The display unit can also cause the avatar to reproduce sounds in response to the user's voice. Step 4: The collaboration unit collaborates with a vector database that stores the pet's features, and causes the avatar pet to perform a specific action in response to an event. For example, when a user sets a specific event, the collaboration unit causes the avatar pet to perform a specific action based on the event. The collaboration unit can also access the vector database that stores the pet's features and enable editing as needed. For example, the collaboration unit provides an interface that allows the user to edit the pet's features. This allows the pet avatar generation system according to the embodiment to give the user the feeling of living in a virtual space with their pet. Some or all of the above-described processing in the collaboration unit may be performed using, or without, AI, for example. For example, the collaboration unit can input the pet's feature data stored in the vector database into the generation AI and cause the generation AI to generate an action in response to an event.

[0068] (Example 2) A system according to an embodiment of the present invention uses image generation AI to recreate an avatar of a pet that the user has kept (or would like to keep) in a virtual space. In this system, a user inputs image or video files of the pet, and a multimodal AI analyzes these files to generate a pet avatar. The generated avatar reproduces the pet's movements and sounds, allowing the user to live with the pet as if it were alive within a smartphone app or dedicated tablet. The system also works with a vector database that stores the pet's characteristics, allowing the avatar pet to behave in a way that captures the pet's characteristics in response to events. For example, a user inputs image or video files of the pet. The file contains detailed information about the pet, such as its posture, movements, and sounds. The multimodal AI then analyzes the input files and generates a pet avatar. The generated avatar is displayed within a smartphone app or dedicated tablet, allowing the user to live with the avatar. Furthermore, the system works with a vector database that stores the pet's characteristics, allowing the avatar pet to behave in a way that captures the pet's characteristics in response to events. This allows the user to feel as if their pet is still alive. The system allows the user to feel as if their pet is still alive, allowing them to recreate their memories with their pet in a virtual space. For example, by reproducing the movements and sounds of a pet when it performs a specific action, the user can get the feeling that the pet is still alive. Also, by linking with a vector database that stores the pet's characteristics, the avatar pet can behave in a way that captures the characteristics according to the event. This gives the user the feeling that the pet is still alive.

[0069] The pet avatar generation system according to the embodiment includes a reception unit, a generation unit, a display unit, and a linking unit. The reception unit receives input of images and video files of a pet by a user. The pet image and video files received by the user may be in formats such as, but not limited to, JPEG, PNG, and MP4. The reception unit provides an interface through which the user uploads images of the pet. The reception unit also allows the user to input video files of the pet by dragging and dropping. The reception unit also allows the user to send images and video files of the pet by email. The generation unit uses multimodal AI to analyze the files received by the reception unit and generate an avatar of the pet. The generation unit may analyze the posture and movements of the pet using, for example, an image recognition algorithm. The generation unit may also analyze the sounds of the pet using audio analysis technology. The generation unit may also combine and analyze image data and audio data to reproduce the posture and movements of the pet. For example, the generation unit receives image data as input and reproduces the posture of the pet. The generation unit may also receive audio data as input and reproduce the sounds of the pet. The display unit displays the avatar generated by the generation unit. The display unit, for example, displays the avatar in a smartphone app. The display unit can also display the avatar in a dedicated tablet. The display unit also provides an interface that allows the user to live with the avatar. For example, the display unit provides an interactive function that allows the avatar to move according to the user's instructions. The display unit can also cause the avatar to reproduce a cry in response to the user's voice. The linking unit links with a vector database that stores the pet's characteristics, and causes the avatar pet to perform a specific action in response to an event. For example, when a user sets a specific event, the linking unit causes the avatar pet to perform a specific action based on the event. The linking unit can also access the vector database that stores the pet's characteristics and enable editing as needed. For example, the linking unit provides an interface that allows the user to edit the pet's characteristics. As a result, the pet avatar generation system according to the embodiment allows the user to experience the sensation of living with a pet in a virtual space.Some or all of the above-described processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit may input characteristic data of the pet stored in a vector database to the generation AI, and cause the generation AI to generate actions according to events.

[0070] The reception unit may include an interface that allows the user to input the pet's name, personality, and specific behaviors in text. Examples of the interface include, but are not limited to, a GUI (Graphical User Interface) and a voice input interface. For example, the reception unit may provide a text box in which the user inputs the pet's name. The reception unit may also provide a drop-down menu in which the user selects the pet's personality. The reception unit may also provide a text area in which the user inputs the pet's specific behaviors in text. For example, the reception unit may provide an auto-complete function when the user inputs the pet's name. The reception unit may also display multiple options when the user selects the pet's personality. This allows the user to input detailed information about the pet in text, thereby generating a more accurate avatar. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit may input the text data entered by the user into a generation AI and have the generation AI analyze the detailed information about the pet.

[0071] The generation unit can combine and analyze image data and audio data to reproduce the pet's posture and movements. The generation unit, for example, uses multimodal analysis technology to integrate and analyze image data and audio data. For example, the generation unit receives image data as input and reproduces the pet's posture. The generation unit can also receive audio data as input and reproduce the pet's barks. The generation unit can also combine and analyze image data and audio data to reproduce the pet's movements. For example, the generation unit integrates and analyzes image data and audio data to reproduce the movements and barks of the pet when performing a specific gesture. The generation unit can also combine and analyze image data and audio data to reproduce the movements and barks of the pet when playing. In this way, by combining and analyzing image data and audio data, the pet's posture and movements can be reproduced more realistically. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input image data and audio data to a generation AI and cause the generation AI to reproduce the pet's posture and movements.

[0072] The display unit displays the avatar in a smartphone app or dedicated tablet, allowing the user to live with the avatar. Examples of smartphone apps or dedicated tablets include, but are not limited to, operating systems such as iOS and Android. The display unit, for example, displays the avatar in a smartphone app. The display unit can also display the avatar in a dedicated tablet. The display unit also provides an interface that allows the user to live with the avatar. For example, the display unit provides an interactive function that allows the avatar to act according to the user's instructions. The display unit can also reproduce the avatar's sounds in response to the user's voice. This allows the user to feel as if they are living with the avatar by displaying the avatar in a smartphone app or dedicated tablet. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without AI. For example, the display unit may display an avatar generated by a generation AI on a smartphone app or dedicated tablet.

[0073] When a user sets a specific event, the linking unit can cause the avatar pet to perform a specific action based on the event. Examples of events include, but are not limited to, birthdays and mealtimes. For example, when a user sets a pet's birthday, the linking unit can cause the avatar pet to perform an action corresponding to the birthday. Furthermore, when a user sets a pet's mealtime, the linking unit can cause the avatar pet to perform an action corresponding to the mealtime. Furthermore, when a user sets a specific event, the linking unit can cause the avatar pet to perform a specific action based on the event. For example, when a user sets a playtime for the pet, the linking unit can cause the avatar pet to perform an action corresponding to the playtime. Furthermore, when a user sets a walktime for the pet, the linking unit can cause the avatar pet to perform an action corresponding to the walktime. In this way, when a user sets a specific event, the avatar pet can perform an action corresponding to the event. Some or all of the above-described processing in the linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the linking unit can input event data set by the user to a generation AI and cause the generation AI to generate an action corresponding to the event.

[0074] The linking unit can access a vector database that stores the pet's features and make it editable. The vector database includes, for example, the format of the feature vector and the type of database, but is not limited to these examples. For example, the linking unit accesses the vector database that stores the pet's features and provides an interface for editing the feature vector. The linking unit can also provide an interface for the user to add the pet's features. The linking unit can also provide an interface for the user to delete the pet's features. For example, the linking unit displays the format of the feature vector when the user edits the pet's features. The linking unit can also provide an input field for the feature vector when the user adds the pet's features. This allows the user to customize the behavior and characteristics of the avatar pet by accessing and editing the vector database that stores the pet's features. Some or all of the above-described processing in the linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the linking unit can input the pet's feature data stored in the vector database to the generation AI and cause the generation AI to edit the feature data.

[0075] The reception unit can estimate the user's emotions and adjust the timing of inputting pet images and video files based on the estimated user emotions. The reception unit, for example, captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on changes in facial expressions. The reception unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the reception unit analyzes the tone and speed of the voice and calculates an emotion score. The reception unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on heart rate fluctuations. This allows the input timing to be adjusted according to the user's emotions, thereby enabling the pet images and video files to be input at a more appropriate timing. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0076] The reception unit can analyze the user's past submission history of pet images and video files and select the optimal input method. The reception unit, for example, analyzes the formats of pet images and video files submitted by the user in the past. For example, the reception unit prioritizes and suggests input methods (such as voice, text, and image) that the user has frequently used in the past. The reception unit can also analyze the formats of pet images and video files submitted by the user in the past and suggest the optimal input format. Furthermore, the reception unit can suggest the optimal input method for a specific time period based on the user's past submission history. For example, the reception unit selects the optimal input method based on the formats of pet images and video files submitted by the user in the past. The reception unit can also analyze the user's past submission history and suggest the optimal input method for a specific time period. In this way, the analysis of the past submission history can suggest the optimal input method for the user. Some or all of the above-described processing by 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 past submission history data into a generation AI and cause the generation AI to select the optimal input method.

[0077] The reception unit can filter pet image and video files based on the user's current living situation or area of ​​interest when inputting them. The reception unit performs filtering based on, for example, the user's living situation. For example, if the user is busy, the reception unit can suggest an easy input method. Furthermore, if the user has a specific area of ​​interest related to pets, the reception unit can also suggest an input method related to that area. Furthermore, the reception unit can suggest the optimal input method depending on the user's living situation (e.g., at work, on vacation). For example, if the user is at work, the reception unit can suggest an easy input method. Furthermore, if the user is on vacation, the reception unit can encourage input at a time when the user is able to relax. This makes it possible to suggest the optimal input method depending on the user's living situation and area of ​​interest. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's living situation data into a generation AI and have the generation AI perform filtering to find the optimal input method.

[0078] When inputting an image or video file of a pet, the reception unit can select the optimal input means according to the user's input method. For example, if the user prefers voice input, the reception unit can preferentially suggest voice input. For example, if the user prefers voice input, the reception unit can preferentially suggest voice input. Furthermore, if the user prefers text input, the reception unit can preferentially suggest text input. Furthermore, if the user prefers image input, the reception unit can preferentially suggest image input. For example, if the user prefers voice input, the reception unit can preferentially suggest voice input. Furthermore, if the user prefers text input, the reception unit can preferentially suggest text input. This improves input convenience by selecting the optimal input means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input method data to the generation AI and cause the generation AI to select the optimal input means.

[0079] The reception unit can estimate the user's emotions and prioritize the pet image and video files to be input based on the estimated user emotions. For example, the reception unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on changes in facial expressions. The reception unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the reception unit analyzes the tone and speed of the voice and calculates an emotion score. The reception unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on heart rate fluctuations. This allows the user to prioritize the input files based on the user's emotions, thereby allowing more appropriate files to be input preferentially. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0080] When inputting pet images and video files, the reception unit can prioritize inputting highly relevant files taking into account the user's geographical location information. The reception unit, for example, prioritizes inputting highly relevant files based on the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes inputting pet images and video files related to that area. Furthermore, when the user is traveling, the reception unit can also prioritize inputting pet images and video files related to the travel destination. Furthermore, when the user is at home, the reception unit can prioritize inputting pet images and video files taken at home. For example, when the user is in a specific area, the reception unit prioritizes inputting pet images and video files related to that area. Furthermore, when the user is traveling, the reception unit can also prioritize inputting pet images and video files related to the travel destination. In this way, by preferentially inputting highly relevant files based on the user's geographical location information, more appropriate files can be input. Some or all of the above-described processing by the reception unit may be performed, for example, using AI or without AI. For example, the reception unit can input the user's geographical location information data into the generation AI and have the generation AI select highly relevant files.

[0081] When inputting pet images or video files, the reception unit can analyze the user's online activity and input related files. The reception unit, for example, analyzes the user's social media activity and inputs related files. For example, the reception unit prioritizes inputting pet images and video files shared by the user on social media. The reception unit can also analyze the user's social media posts and input related pet images and video files. The reception unit can also input related pet images and video files based on the activity of the user's social media friends. For example, the reception unit prioritizes inputting pet images and video files shared by the user on social media. The reception unit can also analyze the user's social media posts and input related pet images and video files. This allows more appropriate files to be input by inputting related files based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit may input the user's social media activity data to the generation AI and cause the generation AI to select related files.

[0082] The reception unit can customize the input method by reflecting the user's feedback when inputting an image or video file of a pet. The reception unit, for example, prioritizes suggesting input methods that the user has previously preferred. For example, the reception unit prioritizes suggesting input methods that the user has previously preferred. The reception unit can also improve the input method based on the user's past feedback. The reception unit can also suggest input methods that the user has previously dissatisfied with, to avoid them. For example, the reception unit prioritizes suggesting input methods that the user has previously preferred. The reception unit can also improve the input method based on the user's past feedback. In this way, by customizing the input method by reflecting the user's past feedback, a more appropriate input method can be provided. Some or all of the above-described processing by 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 past feedback data into the generation AI and cause the generation AI to customize the input method.

[0083] The generation unit can estimate the user's emotion and adjust the avatar's expression based on the estimated user emotion. For example, the generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on changes in facial expression. The generation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the generation unit analyzes the tone and speed of the voice and calculates an emotion score. The generation unit can also 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 generation unit calculates an emotion score based on heart rate fluctuations. This allows the generation of a more appropriate avatar by adjusting the avatar's expression based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0084] The generation unit can adjust the level of detail of the avatar based on the pet's characteristics during generation. The generation unit can, for example, analyze image data in detail to reproduce the pet's characteristic patterns and colors. For example, the generation unit can analyze image data in detail to reproduce the pet's characteristic patterns and colors. The generation unit can also analyze video data in detail to reproduce the pet's specific movements and behaviors. The generation unit can also analyze audio data in detail to reproduce the pet's cries. For example, the generation unit can analyze image data in detail to reproduce the pet's characteristic patterns and colors. The generation unit can also analyze video data in detail to reproduce the pet's specific movements and behaviors. This allows for the generation of a more realistic avatar by adjusting the level of detail of the avatar based on the pet's important characteristics. Some or all of the above-described processing in the generation unit can be performed using, or without, AI. For example, the generation unit can input image data and audio data into a generation AI and cause the generation AI to reproduce the pet's characteristics.

[0085] The generation unit can apply different generation algorithms based on the type of pet during generation. For example, when generating a dog avatar, the generation unit applies an algorithm that reproduces dog-specific movements and sounds. For example, when generating a dog avatar, the generation unit applies an algorithm that reproduces dog-specific movements and sounds. Furthermore, when generating a cat avatar, the generation unit can apply an algorithm that reproduces cat-specific movements and sounds. Furthermore, when generating a bird avatar, the generation unit can apply an algorithm that reproduces bird-specific movements and sounds. For example, when generating a dog avatar, the generation unit applies an algorithm that reproduces dog-specific movements and sounds. Furthermore, when generating a cat avatar, the generation unit can apply an algorithm that reproduces cat-specific movements and sounds. In this way, by applying different generation algorithms depending on the type of pet, a more appropriate avatar can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input pet type data into the generation AI and cause the generation AI to apply a generation algorithm according to the type.

[0086] The generation unit can improve the accuracy of the avatar during generation by referring to the user's past generation results. For example, the generation unit improves the accuracy of the current avatar based on data of avatars previously generated by the user. For example, the generation unit improves the accuracy of the current avatar based on data of avatars previously generated by the user. The generation unit can also improve the avatar's movements and facial expressions based on the user's past feedback. Furthermore, the generation unit can analyze data of avatars previously generated by the user and apply an optimal generation algorithm. For example, the generation unit improves the accuracy of the current avatar based on data of avatars previously generated by the user. The generation unit can also improve the avatar's movements and facial expressions based on the user's past feedback. In this way, the accuracy of the avatar can be improved by referring to the user's past generation results. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past generation result data into the generation AI and cause the generation AI to improve the avatar's accuracy.

[0087] The generation unit can estimate the user's emotion and adjust the length of the avatar's movements based on the estimated user's emotion. For example, the generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on changes in facial expression. The generation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the generation unit analyzes the tone and speed of the voice and calculates an emotion score. The generation unit can also 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 generation unit calculates an emotion score based on heart rate fluctuations. This allows the generation of a more appropriate avatar by adjusting the length of the avatar's movements according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0088] During generation, the generation unit can determine a generation priority based on the time of submission of pet images and video files. The generation unit, for example, prioritizes analysis of pet images and video files recently submitted by the user. For example, the generation unit prioritizes analysis of pet images and video files recently submitted by the user. The generation unit can also prioritize analysis of pet images and video files related to a specific event by the user. Furthermore, the generation unit can determine an optimal generation order based on pet images and video files previously submitted by the user. For example, the generation unit prioritizes analysis of pet images and video files recently submitted by the user. The generation unit can also prioritize analysis of pet images and video files related to a specific event by the user. In this way, by determining the generation priority based on the time of submission of pet images and video files, a more appropriate avatar can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input user submission time data into the generation AI and have the generation AI execute the generation priority.

[0089] The generation unit can adjust the generation order based on pet-related information during generation. For example, the generation unit prioritizes analyzing images and video files related to a specific pet that the user has. For example, the generation unit prioritizes analyzing images and video files related to a specific pet that the user has. Furthermore, if the user has multiple pets, the generation unit can prioritize analyzing images and video files of pets that are highly related to the user. Furthermore, the generation unit can prioritize analyzing images and video files of pets that are related to a specific event that the user has. For example, the generation unit prioritizes analyzing images and video files related to a specific pet that the user has. Furthermore, if the user has multiple pets, the generation unit can prioritize analyzing images and video files of pets that are highly related to the user. In this way, by adjusting the generation order based on pet-related information, a more appropriate avatar can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input pet-related information data to the generation AI and have the generation AI execute the generation order.

[0090] The generation unit can adjust the use of technical terminology during generation based on the user's level of expertise. For example, if the user has specialized knowledge, the generation unit uses detailed technical terminology to describe the generated result. For example, if the user has specialized knowledge, the generation unit can use detailed technical terminology to describe the generated result. Also, if the user does not have specialized knowledge, the generation unit can explain the generated result in simple terms. Furthermore, the generation unit can adjust the use of optimal technical terminology based on the user's past feedback. For example, if the user has specialized knowledge, the generation unit can use detailed technical terminology to describe the generated result. Also, if the user does not have specialized knowledge, the generation unit can explain the generated result in simple terms. In this way, by adjusting the use of technical terminology during generation according to the user's level of expertise, a more appropriate avatar can be generated. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the user's expertise level data into the generation AI and cause the generation AI to use technical terminology.

[0091] The display unit can estimate the user's emotion and adjust the display method of the avatar 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. 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 calculates an emotion score. The display unit can also 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 heart rate fluctuations. This allows the display unit to adjust the display method of the avatar according to the user's emotion, thereby displaying a more appropriate avatar. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit may input image data of a user taken by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0092] The display unit can select the optimal display method by referring to the user's operation history when displaying. The display unit, for example, prioritizes suggesting display methods that the user has previously preferred. For example, the display unit prioritizes suggesting display methods that the user has previously preferred. The display unit can also suggest the optimal display method based on the user's past operation history. The display unit can also suggest a display method that the user has previously dissatisfied with, so as to avoid such display methods. For example, the display unit prioritizes suggesting display methods that the user has previously preferred. The display unit can also suggest the optimal display method based on the user's past operation history. In this way, the optimal display method can be selected by referring to the user's past operation history. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's operation history data to a generation AI and cause the generation AI to select the optimal display method.

[0093] The display unit can customize the display content based on the user's task when displaying the display. For example, when the user is working, the display unit may reduce the display of the avatar and display only notifications. For example, when the user is working, the display unit may reduce the display of the avatar and display only notifications. Furthermore, when the user is relaxing, the display unit may increase the display of the avatar and add interactive actions. Furthermore, when the user is performing a specific task, the display unit can customize the display content related to the task. For example, when the user is working, the display unit may reduce the display of the avatar and display only notifications. Furthermore, when the user is relaxing, the display unit may increase the display of the avatar and add interactive actions. In this way, customizing the display content according to the user's current task allows for a more appropriate display. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without AI. For example, the display unit may input the user's task data into a generation AI and cause the generation AI to customize the display content.

[0094] The display unit can improve the display method based on user feedback during display. For example, the display unit improves the display method based on feedback previously provided by the user. For example, the display unit improves the display method based on feedback previously provided by the user. The display unit can also reflect user feedback in real time and adjust the display method. Furthermore, if the user is dissatisfied with a particular display method, the display unit can improve the display method. For example, the display unit improves the display method based on feedback previously provided by the user. The display unit can also reflect user feedback in real time and adjust the display method. In this way, by reflecting user feedback and improving the display method, a more appropriate display can be achieved. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input user feedback data into a generation AI and cause the generation AI to improve the display method.

[0095] The display unit can estimate the user's emotions and adjust the display order of avatars based on the estimated user emotions. For example, the display unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the display unit calculates an emotion score based on changes in facial expressions. The display unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the display unit analyzes the tone and speed of the voice and calculates an emotion score. The display unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the display unit calculates an emotion score based on heart rate fluctuations. This allows the display unit to adjust the display order of avatars according to the user's emotions, thereby displaying a more appropriate avatar. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit may input image data of a user taken by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0096] The display unit can select the optimal display method by taking into account the user's device information when displaying. For example, if the user is using a smartphone, the display unit provides a display method tailored to the screen size. For example, if the user is using a smartphone, the display unit provides a display method tailored to the screen size. Furthermore, if the user is using a tablet, the display unit can provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the display unit can provide a simple and highly visible display method. For example, if the user is using a smartphone, the display unit provides a display method tailored to the screen size. Furthermore, if the user is using a tablet, the display unit can provide a display method optimized for a large screen. This allows for more appropriate display by selecting the optimal display method based on the user's device information. Some or all of the above-described processing in the display unit may be performed using, for example, 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.

[0097] The display unit can make the display content multilingual based on the user's language setting when displaying. The display unit automatically sets the display content based on, for example, the language setting of the user's device. For example, the display unit automatically sets the display content based on the language setting of the user's device. The display unit can also provide a language switching function when the user uses multiple languages. Furthermore, the display unit can provide the display content in a specific language when the user selects that language. For example, the display unit automatically sets the display content based on the language setting of the user's device. The display unit can also provide a language switching function when the user uses multiple languages. This allows the display content to be multilingual according to the user's language setting, resulting in a more appropriate display. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without AI. For example, the display unit can input the user's language setting data to a generation AI and cause the generation AI to execute multilingual display content.

[0098] The display unit can customize the display method by reflecting the user's past feedback when displaying. For example, the display unit prioritizes suggesting display methods that the user has previously preferred. For example, the display unit prioritizes suggesting display methods that the user has previously preferred. The display unit can also improve the display method based on the user's past feedback. Furthermore, the display unit can suggest avoiding display methods that the user has previously dissatisfied with. For example, the display unit prioritizes suggesting display methods that the user has previously preferred. The display unit can also improve the display method based on the user's past feedback. In this way, customizing the display method by reflecting the user's past feedback allows for a more appropriate display. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's past feedback data into a generation AI and cause the generation AI to customize the display method.

[0099] The linking unit can estimate the user's emotions and determine the priority of events to be linked based on the estimated user emotions. For example, the linking unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the linking unit calculates an emotion score based on changes in facial expression. The linking unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the linking unit analyzes the tone and speed of the voice and calculates an emotion score. The linking unit can also 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 linking unit calculates an emotion score based on heart rate fluctuations. This allows the priority of events to be linked based on the user's emotions, thereby enabling more appropriate events to be linked. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0100] The linking unit can set the update frequency of the vector database that stores the pet's features during linking. For example, if the user frequently updates the pet's features, the linking unit increases the update frequency of the vector database. For example, if the user frequently updates the pet's features, the linking unit increases the update frequency of the vector database. Also, if the user does not update the pet's features frequently, the linking unit can decrease the update frequency of the vector database. Furthermore, the linking unit can set an optimal update frequency based on user feedback. For example, if the user frequently updates the pet's features, the linking unit increases the update frequency of the vector database. Also, if the user does not update the pet's features frequently, the linking unit can decrease the update frequency of the vector database. This allows for more appropriate linking by adjusting the update frequency of the vector database that stores the pet's features. Some or all of the above-described processing in the linking unit may be performed using AI, for example, or may be performed without using AI. For example, the linking unit can input vector database update frequency data to the generation AI and cause the generation AI to set the update frequency.

[0101] During linking, the linking unit can select the optimal linking method by referring to the user's event setting history. The linking unit, for example, proposes the optimal linking method based on events previously set by the user. For example, the linking unit proposes the optimal linking method based on events previously set by the user. The linking unit can also analyze the user's past event setting history and propose the most effective linking method. Furthermore, the linking unit can prioritize linking events previously preferred by the user. For example, the linking unit proposes the optimal linking method based on events previously set by the user. The linking unit can also analyze the user's past event setting history and propose the most effective linking method. In this way, the optimal linking method can be selected by referring to the user's past event setting history. Some or all of the above-described processing in the linking unit may be performed using, or without, AI. For example, the linking unit can input the user's event setting history data into the generation AI and cause the generation AI to select the optimal linking method.

[0102] The collaboration unit can improve the collaboration method based on user feedback during collaboration. For example, the collaboration unit improves the collaboration method based on feedback provided by the user in the past. For example, the collaboration unit improves the collaboration method based on feedback provided by the user in the past. The collaboration unit can also reflect user feedback in real time and adjust the collaboration method. Furthermore, if the user is dissatisfied with a particular collaboration method, the collaboration unit can improve the collaboration method. For example, the collaboration unit improves the collaboration method based on feedback provided by the user in the past. The collaboration unit can also reflect user feedback in real time and adjust the collaboration method. In this way, by reflecting user feedback and improving the collaboration method, more appropriate collaboration can be achieved. Some or all of the above-described processing in the collaboration unit may be performed using AI, for example, or may be performed without using AI. For example, the collaboration unit can input user feedback data into the generation AI and cause the generation AI to improve the collaboration method.

[0103] The linking unit can estimate the user's emotion and adjust the display method of the linked event based on the estimated user emotion. For example, the linking unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the linking unit calculates an emotion score based on changes in facial expression. The linking unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the linking unit analyzes the tone and speed of the voice and calculates an emotion score. The linking unit can also 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 linking unit calculates an emotion score based on heart rate fluctuations. This allows the display method of the linked event to be adjusted according to the user's emotion, thereby displaying more appropriate events. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0104] During linking, the linking unit can perform linking based on the geographical distribution of the vector database storing the pet's features. For example, when the user is in a specific area, the linking unit prioritizes linking pet's features related to that area. For example, when the user is in a specific area, the linking unit prioritizes linking pet's features related to that area. Furthermore, when the user is traveling, the linking unit can prioritize linking pet's features related to the travel destination. Furthermore, when the user is at home, the linking unit can prioritize linking pet's features photographed at home. For example, when the user is in a specific area, the linking unit prioritizes linking pet's features related to that area. Furthermore, when the user is traveling, the linking unit can prioritize linking pet's features related to the travel destination. This allows for more appropriate linking by taking into account the geographical distribution of the vector database storing the pet's features. Some or all of the above-described processing in the linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the linking unit can input geographical distribution data of the vector database into the generation AI and cause the generation AI to execute linking.

[0105] The linking unit can improve the accuracy of linking by referring to pet-related literature during linking. The linking unit, for example, proposes an optimal linking method based on pet-related literature. For example, the linking unit proposes an optimal linking method based on pet-related literature. The linking unit can also improve the accuracy of linking by referring to pet-related literature. Furthermore, the linking unit can propose an optimal event based on pet-related literature. For example, the linking unit proposes an optimal linking method based on pet-related literature. The linking unit can also improve the accuracy of linking by referring to pet-related literature. In this way, the accuracy of linking can be improved by referring to pet-related literature. Some or all of the above-mentioned processing in the linking unit may be performed using AI, for example, or may be performed without using AI. For example, the linking unit can input pet-related literature data to the generation AI and cause the generation AI to improve the accuracy of linking.

[0106] The collaboration unit can perform collaboration based on the market value of the pet at the time of collaboration. The collaboration unit, for example, proposes an optimal collaboration method based on the market value of the pet. For example, the collaboration unit proposes an optimal collaboration method based on the market value of the pet. The collaboration unit can also determine collaboration priorities taking into account the market value of the pet. Furthermore, the collaboration unit can propose optimal events based on the market value of the pet. For example, the collaboration unit proposes an optimal collaboration method based on the market value of the pet. The collaboration unit can also determine collaboration priorities taking into account the market value of the pet. This allows for more appropriate collaboration by taking into account the market value of the pet. Some or all of the above-described processing in the collaboration unit may be performed using AI, for example, or may be performed without using AI. For example, the collaboration unit can input pet market value data to the generation AI and have the generation AI execute the collaboration. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, display unit, and linking 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 allows a user to input images or video files of a pet using the reception device 38 of the smart device 14. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an avatar of the pet using multimodal AI. The display unit displays the generated avatar using the output device 40 of the smart device 14. The linking unit is realized by the specific processing unit 290 of the data processing device 12 and links with the vector database 24 to cause the avatar pet to perform an action according to an event. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, display unit, and linking 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 allows the user to input an image or video file of the pet using the microphone 238 of the smart glasses 214. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an avatar of the pet using multimodal AI. The display unit displays the generated avatar using the speaker 240 of the smart glasses 214. The linking unit is realized by the specific processing unit 290 of the data processing device 12 and links with the vector database 24 to cause the avatar pet to perform an action according to an event. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, display unit, and linking 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 allows the user to input an image or video file of the pet using the microphone 238 of the headset-type terminal 314. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates an avatar of the pet using multimodal AI. The display unit displays the generated avatar using the display 343 of the headset-type terminal 314. The linking unit is realized by the specific processing unit 290 of the data processing device 12, and links with the vector database 24 to cause the avatar pet to perform an action according to an event. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, display unit, and collaboration unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit allows the user to input an image or video file of the pet using the microphone 238 of the robot 414. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates an avatar of the pet using multimodal AI. The display unit displays the generated avatar using the speaker 240 of the robot 414. The collaboration unit is realized by the specific processing unit 290 of the data processing device 12, and collaborates with the vector database 24 to cause the avatar pet to perform an action according to an event.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] When a user inputs an image or video file of a pet, the reception unit can suggest the optimal input method based on the user's past input history. For example, if the user has used many JPEG format images in the past, the reception unit can preferentially suggest JPEG format images. Also, if the user has used many video files in the past, the reception unit can preferentially suggest the input of video files. Furthermore, if the user has previously input data during a specific time period, the reception unit can also suggest the optimal input method for that time period. This improves input convenience by suggesting the optimal input method based on the user's past input history.

[0109] When generating a pet avatar, the generation unit can estimate the user's emotions and adjust the avatar's facial expressions and movements based on the estimated emotions. For example, if the user is sad, the generation unit can add a gentle facial expression and movements to the avatar. If the user is happy, the generation unit can also add a lively facial expression and movements to the avatar. Furthermore, if the user is relaxed, the generation unit can also add a gentle facial expression and movements to the avatar. In this way, by adjusting the avatar's facial expressions and movements according to the user's emotions, a more friendly avatar can be generated.

[0110] The display unit can select the optimal display method based on the user's device information. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. Also, if the user is using a tablet, the display unit can provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the display unit can provide a simple and highly visible display method. This allows for more appropriate display by selecting the optimal display method based on the user's device information.

[0111] The linking unit can estimate the user's emotions and determine the priority of events to be linked based on the estimated emotions. For example, if the user is feeling stressed, the linking unit can prioritize linking events that are relaxing. Also, if the user is excited, the linking unit can prioritize linking active events. Furthermore, if the user is sad, the linking unit can prioritize linking events that are comforting. In this way, by determining the priority of events to be linked according to the user's emotions, more appropriate events can be linked.

[0112] The reception unit can prioritize input of pet images and video files that are highly relevant based on the user's geographical location information. For example, if the user is in a specific area, it can prioritize input of pet images and video files related to that area. Also, if the user is traveling, it can prioritize input of pet images and video files related to the travel destination. Furthermore, if the user is at home, it can prioritize input of pet images and video files taken at home. In this way, by prioritize input of highly relevant files based on the user's geographical location information, more appropriate files can be input.

[0113] When generating a pet avatar, the generation unit can improve the accuracy of the avatar by referring to the user's past generation results. For example, the accuracy of the current avatar can be improved based on data of avatars previously generated by the user. The avatar's movements and facial expressions can also be improved based on the user's past feedback. Furthermore, the data of avatars previously generated by the user can be analyzed and an optimal generation algorithm can be applied. In this way, the accuracy of the avatar can be improved by referring to the user's past generation results.

[0114] The display unit can estimate the user's emotions and adjust the display method of the avatar based on the estimated emotions. For example, if the user is tired, the display unit can reduce the display of the avatar and only display notifications. If the user is relaxed, the display unit can increase the display of the avatar and add interactive actions. Furthermore, if the user is excited, the display unit can increase the display of the avatar and add active actions. In this way, by adjusting the display method of the avatar according to the user's emotions, a more appropriate display can be achieved.

[0115] The linking unit can perform linking based on the geographical distribution of the vector database that stores the pet's features. For example, if the user is in a specific area, the linking unit can prioritize pet features related to that area. Also, if the user is traveling, the linking unit can prioritize pet features related to the travel destination. Furthermore, if the user is at home, the linking unit can prioritize pet features photographed at home. This allows for more appropriate linking by taking into account the geographical distribution of the vector database that stores the pet's features.

[0116] The reception unit can analyze the user's online activity and prioritize input of related pet images and video files. For example, it can prioritize input of pet images and video files shared by the user on social media. It can also analyze the content of the user's social media posts and input of related pet images and video files. It can also input related pet images and video files with reference to the activities of the user's friends on social media. This allows more appropriate files to be input by inputting related files based on the user's online activity.

[0117] The generation unit can estimate the user's emotion and adjust the length of the avatar's movement based on the estimated emotion. For example, if the user is relaxed, the generation unit can adjust the avatar's movement to be slower. Also, if the user is excited, the generation unit can speed up the avatar's movement. Furthermore, if the user is sad, the generation unit can make the avatar's movement gentler. In this way, by adjusting the length of the avatar's movement according to the user's emotion, a more appropriate avatar can be generated.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The reception unit receives input of pet images and video files by the user. The pet images and video files input by the user may be in formats such as, but not limited to, JPEG, PNG, and MP4. The reception unit provides, for example, an interface for the user to upload pet images. The reception unit also allows the user to input pet video files by drag and drop. The reception unit also allows the user to send pet images and video files by email. Step 2: The generation unit uses multimodal AI to analyze the file input by the reception unit and generate a pet avatar. The generation unit, for example, uses an image recognition algorithm to analyze the pet's posture and movements. The generation unit can also analyze the pet's cries using audio analysis technology. The generation unit can also combine and analyze image data and audio data to reproduce the pet's posture and movements. For example, the generation unit receives image data as input and reproduces the pet's posture. The generation unit also receives audio data as input and reproduces the pet's cries. Step 3: The display unit displays the avatar generated by the generation unit. The display unit, for example, displays the avatar in a smartphone app. The display unit can also display the avatar in a dedicated tablet. The display unit also provides an interface that allows the user to live with the avatar. For example, the display unit provides an interactive function that allows the avatar to act according to the user's instructions. The display unit can also cause the avatar to reproduce sounds in response to the user's voice. Step 4: The collaboration unit collaborates with a vector database that stores the pet's features, and causes the avatar pet to perform a specific action in response to an event. For example, when a user sets a specific event, the collaboration unit causes the avatar pet to perform a specific action based on the event. The collaboration unit can also access the vector database that stores the pet's features and enable editing as needed. For example, the collaboration unit provides an interface that allows the user to edit the pet's features. This allows the pet avatar generation system according to the embodiment to give the user the feeling of living in a virtual space with their pet. Some or all of the above-described processing in the collaboration unit may be performed using, or without, AI, for example. For example, the collaboration unit can input the pet's feature data stored in the vector database into the generation AI and cause the generation AI to generate an action in response to an event.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 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.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] 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.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] 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.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0142] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0143] 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.

[0144] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0147] 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.

[0148] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 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.

[0158] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0159] 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.

[0160] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0161] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0162] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0163] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0164] 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.

[0165] 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.

[0166] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0167] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0168] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0169] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0174] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0175] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0176] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0177] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0178] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0179] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0180] 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.

[0181] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0182] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0183] 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.

[0184] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0185] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0186] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0187] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0188] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0189] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0190] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0191] [Explanation of symbols]

[0192] 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 input pet images and video files, a generation unit that analyzes the file input by the reception unit and generates a pet avatar; a display unit that displays the avatar generated by the generation unit; and a linking unit that links with a vector database that stores the characteristics of the pet and causes the avatar pet to perform a specific action in response to an event. A system characterized by:

2. The reception unit It has an interface that allows users to input their pet's name, personality, and specific behaviors in text.

2. The system of claim 1.

3. The generation unit Image and audio data are combined and analyzed to reproduce the pet's posture and movements.

2. The system of claim 1.

4. The display unit The avatar is displayed on a smartphone app or a dedicated tablet, allowing users to live with the avatar.

2. The system of claim 1.

5. The linking unit is When the user sets a specific event, the avatar pet will perform a specific action based on that event.

2. The system of claim 1.

6. The linking unit is Access and edit vector databases that store pet characteristics 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the timing of inputting pet images and video files based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past pet image and video file submission history and select the optimal input method.

2. The system of claim 1.

Images