System

Abstract

An object of the system according to the embodiment is to allow a user to enjoy designing and customizing a mobile phone according to the user's preference.SOLUTION: A system includes a preference input unit, a generation unit, and a provision unit. The preference input unit inputs a user's preference or image. The generation unit generates an original wallpaper, a ringtone, and an icon based on the preference or the image input by the preference input unit. The providing unit provides the original wallpaper, ringtone, and icon generated by the generating unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that it is difficult for users to design and customize their mobile phones to suit their preferences.

[0005] The system according to the embodiment aims to allow users to enjoy designing and customizing their mobile phones to suit their preferences. [Means for solving the problem]

[0006] The system according to the embodiment includes a preference input unit, a generation unit, and a provision unit. The preference input unit inputs a user's preferences and images. The generation unit generates original wallpaper, ringtones, and icons based on the preferences and images input by the preference input unit. The provision unit provides the original wallpaper, ringtones, and icons generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to enjoy designing and customizing their mobile phones to suit their preferences. [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) The customization service according to an embodiment of the present invention is a service in which a user inputs their preferences and image, and a generation AI generates and provides original wallpapers, ringtones, and icons. This allows the customization service to generate and provide original content based on the user's preferences and image.

[0029] A customization service according to an embodiment includes a preference input unit, a generation unit, and a provision unit. The preference input unit inputs a user's preferences and images. For example, a user can input a prompt including specific instructions such as "I like scenery of blue oceans and white sandy beaches" or "I like classical music." The generation unit generates original wallpaper, ringtones, and icons based on the preferences and images input by the preference input unit. For example, the generation AI generates a wallpaper depicting a beautiful ocean and sandy beach scene in response to the prompt "I like scenery of blue oceans and white sandy beaches." The generation AI also generates a ringtone incorporating elements of classical music in response to the prompt "I like classical music." The generation AI also generates an icon with a simple and modern design in response to the prompt "I like simple and modern designs." The provision unit provides the original wallpaper, ringtone, and icon generated by the generation unit. For example, the generated wallpaper is applied to the user's mobile phone, the generated ringtone is set on the mobile phone, and the generated icon is applied to an app on the mobile phone. This allows the customization service according to an embodiment to generate and provide original content based on the user's preferences and images.

[0030] The preference input unit can analyze the user's past customization history and learn how preferences have changed to improve the accuracy of prompts. For example, the preference input unit stores the user's past customization history in a database and analyzes the data to learn how the user's preferences have changed. For example, the next prompt is suggested based on trends in wallpapers and ringtones selected in the past. The preference input unit also analyzes the user's customization history and develops an algorithm that learns how preferences have changed. For example, it analyzes how preferences change with the seasons and suggests prompts that correspond to the seasons. The preference input unit also builds a system that learns how the user's preferences have changed based on the past customization history and improves the accuracy of prompts. For example, it suggests prompts that match specific events or anniversaries. This makes it possible to improve the accuracy of prompts based on the user's past customization history.

[0031] The preference input unit can analyze a user's voice input and enable the user to input preferences and images in a natural conversational format. The preference input unit, for example, allows the user to input prompts by voice and uses voice recognition technology to analyze the preferences and images in a natural conversational format. For example, if the user says, "I like seascapes," a related prompt is generated. The preference input unit also analyzes the voice input and develops a system that allows the user to input preferences and images in a natural conversational format. For example, if the user says, "I want relaxing music," an appropriate prompt is suggested. The preference input unit also uses voice recognition technology to analyze the user's voice input and generate prompts in a natural conversational format. For example, if the user says, "I like simple designs," a prompt with a simple theme is suggested. In this way, the user's voice input can be analyzed and preferences and images can be input in a natural conversational format.

[0032] The preference input unit can also analyze visual information by uploading images or videos when inputting the user's preferences and images. For example, the preference input unit allows the user to upload images or videos and analyzes the visual information to understand the preferences and images. For example, based on a landscape image uploaded by the user, a related prompt is generated. The preference input unit also uses image recognition technology to develop a system that analyzes images and videos uploaded by the user and extracts preferences and images. For example, based on an artwork uploaded by the user, a prompt on an art theme is suggested. The preference input unit also builds a system that analyzes visual information uploaded by the user and understands preferences and images. For example, based on travel photos uploaded by the user, a prompt on a travel theme is suggested. In this way, the visual information uploaded by the user can be analyzed to understand the preferences and images.

[0033] The preference input unit makes suggestions based on popular prompts from other users, thereby expanding the user's choices. The preference input unit, for example, stores popular prompts from other users in a database and builds a system that makes suggestions to new users as reference. For example, it displays the most popular prompts in a ranking format. The preference input unit also analyzes popular prompts from other users and makes suggestions to expand the options for new users. For example, it suggests prompts from users who have the same hobbies or interests. The preference input unit also develops a system that makes useful suggestions to new users based on popular prompts. For example, it displays popular prompts based on a specific theme. This allows the user to expand their options by referring to popular prompts from other users.

[0034] The generation unit can increase the variety of content generated and provide multiple options to users. For example, the generation unit increases the variety of wallpapers, ringtones, and icons generated by the generation AI, and builds a system that provides multiple options to users. For example, it generates multiple different designs based on the same theme. The generation unit also improves the generation AI's algorithm to increase the variety of content and provide users with a wider range of options. For example, it generates designs with different styles and colors. The generation unit also increases the variety of content generated by the generation AI and develops a system that provides users with multiple options. For example, it proposes multiple designs based on a theme selected by the user. This increases the variety of content generated and provides users with multiple options.

[0035] The generation unit can modify and improve the generated content in real time based on user feedback. For example, the generation unit collects user feedback on generated wallpapers, ringtones, and icons in real time, and builds a system that modifies and improves the content based on the results. For example, if a user wants to change part of the design, the modification can be made immediately. The generation unit also adds a function to modify and improve the generated content in real time based on user feedback. For example, if a user wants to change the color or style, the modification can be made immediately. The generation unit also develops a system that analyzes user feedback on the generated content in real time and modifies and improves it. For example, if a user does not like part of the design, the modification can be made immediately. This allows the generated content to be modified and improved in real time based on user feedback.

[0036] The generation unit can make the generated content applicable to other devices. The generation unit builds a system that enables, for example, the application of generated wallpaper, ringtone, and icon to other devices. For example, the generation unit synchronizes the content generated by the user with a tablet or PC. The generation unit also develops an interface for applying the generated content to other devices. For example, the generation unit sets the wallpaper generated by the user as the desktop background of a PC. The generation unit also adds a data synchronization function between devices to enable the generated content to be applied to other devices. For example, the generation unit sets the ringtone generated by the user as the notification sound of a tablet. This allows the generated content to be applied to other devices.

[0037] The generation unit can share the generated content on a social networking site (SNS) and receive feedback from other users. The generation unit, for example, adds a function that allows the generated wallpaper, ringtone, or icon to be shared on a social networking site (SNS) and builds a system that receives feedback from other users. For example, a user posts the generated content on a social networking site and receives comments and ratings. The generation unit also develops an interface for sharing the generated content on a social networking site and collects feedback from other users. For example, a user shares the generated icon on a social networking site and receives opinions from other users. The generation unit also adds a function that allows the generated content to be shared on a social networking site and receive feedback from other users. For example, a user posts the generated ringtone on a social networking site and receives ratings from other users. This allows the generated content to be shared on a social networking site and receive feedback from other users.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] When a user inputs their preferences and impressions, the preference input unit analyzes the user's past search history and browsing history to suggest more accurate prompts. For example, it generates relevant prompts based on keywords the user has previously searched for and the content of web pages the user has viewed. The preference input unit also analyzes the user's past behavioral data to develop an algorithm that predicts preferences and impressions. For example, it suggests prompts based on themes of websites the user frequently visits. The preference input unit also builds a system that predicts preferences and impressions based on the user's past search history and browsing history to improve the accuracy of prompts. For example, it suggests prompts on themes related to keywords the user has previously searched for. This makes it possible to suggest more accurate prompts based on the user's past behavioral data.

[0040] The preference input unit can analyze the user's past customization history and learn how preferences have changed to improve the accuracy of prompts. For example, the user's past customization history is stored in a database, and the data is analyzed to learn how the user's preferences have changed. For example, the next prompt is suggested based on trends in wallpapers and ringtones selected in the past. The preference input unit also analyzes the user's customization history and develops an algorithm that learns how preferences have changed. For example, it analyzes seasonal changes in preferences and suggests prompts that correspond to the season. The preference input unit also builds a system that learns how the user's preferences have changed based on the past customization history and improves the accuracy of prompts. For example, it suggests prompts that match specific events or anniversaries. This makes it possible to improve the accuracy of prompts based on the user's past customization history.

[0041] The preference input unit can analyze a user's voice input and enable the user to input preferences and images in a natural conversational format. For example, the user can input prompts by voice, and speech recognition technology is used to analyze the preferences and images in a natural conversational format. For example, if the user says, "I like seascapes," a related prompt is generated. The preference input unit also analyzes the voice input and develops a system that allows the user to input preferences and images in a natural conversational format. For example, if the user says, "I want relaxing music," an appropriate prompt is suggested. The preference input unit also uses speech recognition technology to analyze the user's voice input and generate prompts in a natural conversational format. For example, if the user says, "I like simple designs," a prompt with a simple theme is suggested. This allows the user's voice input to be analyzed and preferences and images to be input in a natural conversational format.

[0042] When inputting a user's preferences and images, the preference input unit can also analyze visual information by uploading images or videos. For example, the system allows the user to upload images or videos, and analyzes the visual information to understand the user's preferences and images. For example, a related prompt is generated based on a landscape image uploaded by the user. The preference input unit also uses image recognition technology to develop a system that analyzes images and videos uploaded by the user and extracts preferences and images. For example, an art-themed prompt is suggested based on an artwork uploaded by the user. The preference input unit also builds a system that analyzes visual information uploaded by the user and understands the user's preferences and images. For example, a travel-themed prompt is suggested based on travel photos uploaded by the user. This allows the system to analyze the visual information uploaded by the user and understand the user's preferences and images.

[0043] The preference input unit can make suggestions based on popular prompts from other users, thereby expanding the user's choices. For example, a system can be constructed that stores popular prompts from other users in a database and suggests them to new users as reference. For example, the most popular prompts are displayed in a ranking format. The preference input unit can also analyze popular prompts from other users and make suggestions that will expand the options for new users. For example, it can suggest prompts from users who have the same hobbies or interests. The preference input unit can also develop a system that makes useful suggestions to new users based on popular prompts. For example, it can display popular prompts based on a specific theme. This allows the user to expand their options by referring to popular prompts from other users.

[0044] The generation unit can increase the variety of content generated and provide multiple options to users. For example, a system can be built that increases the variety of wallpapers, ringtones, and icons generated by the generation AI and provides multiple options to users. For example, multiple different designs based on the same theme can be generated. The generation unit can also improve the generation AI's algorithm to increase the variety of content and provide users with a wider range of options. For example, it can generate designs with different styles and colors. The generation unit can also increase the variety of content generated by the generation AI and develop a system that provides users with multiple options. For example, it can propose multiple designs based on a theme selected by the user. This increases the variety of content generated and provides users with multiple options.

[0045] The generation unit can modify and improve the generated content in real time based on user feedback. For example, a system can be built that collects user feedback on generated wallpapers, ringtones, and icons in real time and modifies and improves the content based on the results. For example, if a user wants to change part of the design, the modification can be made immediately. The generation unit also adds a function to modify and improve the generated content in real time based on user feedback. For example, if a user wants to change the color or style, the modification can be made immediately. The generation unit also develops a system that analyzes user feedback on the generated content in real time and modifies and improves it. For example, if a user does not like part of the design, the modification can be made immediately. This allows the generated content to be modified and improved in real time based on user feedback.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The preference input unit inputs the user's preferences and images. For example, the user can input a prompt including specific instructions such as "I like scenery with blue seas and white sandy beaches" or "I like classical music." Step 2: The generator generates original wallpapers, ringtones, and icons based on the preferences and images input by the preference input unit. For example, in response to the prompt, "I like blue ocean and white sandy beach scenery," the generator AI generates wallpaper depicting beautiful ocean and sandy beach scenery. In response to the prompt, "I like classical music," the generator AI generates a ringtone incorporating elements of classical music. In response to the prompt, "I like simple and modern designs," the generator AI generates an icon with a simple and modern design. Step 3: The providing unit provides the original wallpaper, ringtone, and icon generated by the generating unit. For example, the generated wallpaper is applied to the user's mobile phone, the generated ringtone is set to the mobile phone, and the generated icon is applied to an app on the mobile phone.

[0048] (Example 2) The customization service according to an embodiment of the present invention is a service in which a user inputs their preferences and image, and a generation AI generates and provides original wallpapers, ringtones, and icons. This allows the customization service to generate and provide original content based on the user's preferences and image.

[0049] A customization service according to an embodiment includes a preference input unit, a generation unit, and a provision unit. The preference input unit inputs a user's preferences and images. For example, a user can input a prompt including specific instructions such as "I like scenery of blue oceans and white sandy beaches" or "I like classical music." The generation unit generates original wallpaper, ringtones, and icons based on the preferences and images input by the preference input unit. For example, the generation AI generates a wallpaper depicting a beautiful ocean and sandy beach scene in response to the prompt "I like scenery of blue oceans and white sandy beaches." The generation AI also generates a ringtone incorporating elements of classical music in response to the prompt "I like classical music." The generation AI also generates an icon with a simple and modern design in response to the prompt "I like simple and modern designs." The provision unit provides the original wallpaper, ringtone, and icon generated by the generation unit. For example, the generated wallpaper is applied to the user's mobile phone, the generated ringtone is set on the mobile phone, and the generated icon is applied to an app on the mobile phone. This allows the customization service according to an embodiment to generate and provide original content based on the user's preferences and images.

[0050] The preference input unit can estimate the user's emotions in real time and suggest prompts based on the emotions. For example, when the user inputs a prompt, the preference input unit analyzes the user's emotions in real time using a camera or microphone and automatically generates prompts based on the emotions. For example, if the user is smiling, a prompt with a positive theme is suggested. The preference input unit also monitors the user's emotional state in real time and presents prompts according to the emotion. For example, if the user is feeling stressed, a prompt with a relaxing theme is suggested. The preference input unit also uses emotion estimation technology to suggest prompts based on the user's emotions. For example, if the user is excited, a prompt with an active theme is suggested. This makes it possible to suggest appropriate prompts based on the user's emotions.

[0051] The preference input unit can analyze the user's past customization history and learn how preferences have changed to improve the accuracy of prompts. For example, the preference input unit stores the user's past customization history in a database and analyzes the data to learn how the user's preferences have changed. For example, the next prompt is suggested based on trends in wallpapers and ringtones selected in the past. The preference input unit also analyzes the user's customization history and develops an algorithm that learns how preferences have changed. For example, it analyzes how preferences change with the seasons and suggests prompts that correspond to the seasons. The preference input unit also builds a system that learns how the user's preferences have changed based on the past customization history and improves the accuracy of prompts. For example, it suggests prompts that match specific events or anniversaries. This makes it possible to improve the accuracy of prompts based on the user's past customization history.

[0052] The preference input unit can analyze a user's voice input and enable the user to input preferences and images in a natural conversational format. The preference input unit, for example, allows the user to input prompts by voice and uses voice recognition technology to analyze the preferences and images in a natural conversational format. For example, if the user says, "I like seascapes," a related prompt is generated. The preference input unit also analyzes the voice input and develops a system that allows the user to input preferences and images in a natural conversational format. For example, if the user says, "I want relaxing music," an appropriate prompt is suggested. The preference input unit also uses voice recognition technology to analyze the user's voice input and generate prompts in a natural conversational format. For example, if the user says, "I like simple designs," a prompt with a simple theme is suggested. In this way, the user's voice input can be analyzed and preferences and images can be input in a natural conversational format.

[0053] The preference input unit can also analyze visual information by uploading images or videos when inputting the user's preferences and images. For example, the preference input unit allows the user to upload images or videos and analyzes the visual information to understand the preferences and images. For example, based on a landscape image uploaded by the user, a related prompt is generated. The preference input unit also uses image recognition technology to develop a system that analyzes images and videos uploaded by the user and extracts preferences and images. For example, based on an artwork uploaded by the user, a prompt on an art theme is suggested. The preference input unit also builds a system that analyzes visual information uploaded by the user and understands preferences and images. For example, based on travel photos uploaded by the user, a prompt on a travel theme is suggested. In this way, the visual information uploaded by the user can be analyzed to understand the preferences and images.

[0054] The preference input unit makes suggestions based on popular prompts from other users, thereby expanding the user's choices. The preference input unit, for example, stores popular prompts from other users in a database and builds a system that makes suggestions to new users as reference. For example, it displays the most popular prompts in a ranking format. The preference input unit also analyzes popular prompts from other users and makes suggestions to expand the options for new users. For example, it suggests prompts from users who have the same hobbies or interests. The preference input unit also develops a system that makes useful suggestions to new users based on popular prompts. For example, it displays popular prompts based on a specific theme. This allows the user to expand their options by referring to popular prompts from other users.

[0055] The preference input unit uses the emotion estimation function to provide feedback on the emotional response to the prompt entered by the user, thereby generating a more positive prompt. For example, the preference input unit uses the emotion estimation function to analyze the emotional response to the prompt entered by the user, thereby building a system that generates positive prompts. For example, if the user expresses negative emotions, a prompt with a positive theme is suggested. The preference input unit also develops a system that analyzes the user's emotional response in real time and provides feedback based on the emotion. For example, if the user is feeling stressed, a prompt that will help the user relax is suggested. The preference input unit also feeds back the emotional response to the prompt entered by the user based on the emotion estimation data, thereby generating a positive prompt. For example, if the user is excited, a prompt with an active theme is suggested. This allows feedback of the user's emotional response to generate a more positive prompt.

[0056] The generation unit can analyze the user's emotional response to the generated content and reflect it in the next generation. The generation unit builds a system that analyzes the user's emotional response to, for example, generated wallpaper, ringtone, or icon, and reflects the data in the next generation. For example, it prioritizes the generation of designs to which the user has a positive response. The generation unit also develops a system that analyzes the user's emotional response in real time and evaluates the generated content. For example, if the user has a negative response, it proposes a different design. The generation unit also analyzes the user's emotional response to the generated content based on the emotion estimation data and reflects it in the next generation. For example, if the user is excited, it generates an active design. This makes it possible to analyze the user's emotional response and reflect it in the next generation.

[0057] The generation unit can increase the variety of content generated and provide multiple options to users. For example, the generation unit increases the variety of wallpapers, ringtones, and icons generated by the generation AI, and builds a system that provides multiple options to users. For example, it generates multiple different designs based on the same theme. The generation unit also improves the generation AI's algorithm to increase the variety of content and provide users with a wider range of options. For example, it generates designs with different styles and colors. The generation unit also increases the variety of content generated by the generation AI and develops a system that provides users with multiple options. For example, it proposes multiple designs based on a theme selected by the user. This increases the variety of content generated and provides users with multiple options.

[0058] The generation unit can modify and improve the generated content in real time based on user feedback. For example, the generation unit collects user feedback on generated wallpapers, ringtones, and icons in real time, and builds a system that modifies and improves the content based on the results. For example, if a user wants to change part of the design, the modification can be made immediately. The generation unit also adds a function to modify and improve the generated content in real time based on user feedback. For example, if a user wants to change the color or style, the modification can be made immediately. The generation unit also develops a system that analyzes user feedback on the generated content in real time and modifies and improves it. For example, if a user does not like part of the design, the modification can be made immediately. This allows the generated content to be modified and improved in real time based on user feedback.

[0059] The generation unit can make the generated content applicable to other devices. The generation unit builds a system that enables, for example, the application of generated wallpaper, ringtone, and icon to other devices. For example, the generation unit synchronizes the content generated by the user with a tablet or PC. The generation unit also develops an interface for applying the generated content to other devices. For example, the generation unit sets the wallpaper generated by the user as the desktop background of a PC. The generation unit also adds a data synchronization function between devices to enable the generated content to be applied to other devices. For example, the generation unit sets the ringtone generated by the user as the notification sound of a tablet. This allows the generated content to be applied to other devices.

[0060] The generation unit can share the generated content on a social networking site (SNS) and receive feedback from other users. The generation unit, for example, adds a function that allows the generated wallpaper, ringtone, or icon to be shared on a social networking site (SNS) and builds a system that receives feedback from other users. For example, a user posts the generated content on a social networking site and receives comments and ratings. The generation unit also develops an interface for sharing the generated content on a social networking site and collects feedback from other users. For example, a user shares the generated icon on a social networking site and receives opinions from other users. The generation unit also adds a function that allows the generated content to be shared on a social networking site and receive feedback from other users. For example, a user posts the generated ringtone on a social networking site and receives ratings from other users. This allows the generated content to be shared on a social networking site and receive feedback from other users.

[0061] The generation unit uses the emotion estimation function to monitor the user's emotional response to the generated content in real time and can suggest optimal content. The generation unit, for example, uses the emotion estimation function to build a system that monitors the user's emotional response to the generated content in real time. For example, the generation unit analyzes the user's facial expressions and voice and calculates an emotion score. The generation unit also develops a system that evaluates the generated content based on the user's emotional response data and suggests optimal content. For example, it preferentially suggests designs to which the user has a positive response. The generation unit also builds a system that collects emotion estimation data in real time and uses it to evaluate the generated content. For example, if the user has a negative response, it suggests a different design. In this way, the user's emotional response to the generated content can be monitored in real time and optimal content can be suggested.

[0062] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0063] When a user inputs their preferences and impressions, the preference input unit analyzes the user's past search history and browsing history to suggest more accurate prompts. For example, it generates relevant prompts based on keywords the user has previously searched for and the content of web pages the user has viewed. The preference input unit also analyzes the user's past behavioral data to develop an algorithm that predicts preferences and impressions. For example, it suggests prompts based on themes of websites the user frequently visits. The preference input unit also builds a system that predicts preferences and impressions based on the user's past search history and browsing history to improve the accuracy of prompts. For example, it suggests prompts on themes related to keywords the user has previously searched for. This makes it possible to suggest more accurate prompts based on the user's past behavioral data.

[0064] The preference input unit can estimate the user's emotions in real time and suggest prompts based on the emotions. For example, when the user inputs a prompt, the camera or microphone is used to analyze the user's emotions in real time and automatically generate prompts based on the emotions. For example, if the user is smiling, a prompt with a positive theme is suggested. The preference input unit also monitors the user's emotional state in real time and presents prompts according to the emotion. For example, if the user is feeling stressed, a prompt with a relaxing theme is suggested. The preference input unit also uses emotion estimation technology to suggest prompts based on the user's emotions. For example, if the user is excited, a prompt with an active theme is suggested. This makes it possible to suggest appropriate prompts based on the user's emotions.

[0065] The preference input unit can analyze the user's past customization history and learn how preferences have changed to improve the accuracy of prompts. For example, the user's past customization history is stored in a database, and the data is analyzed to learn how the user's preferences have changed. For example, the next prompt is suggested based on trends in wallpapers and ringtones selected in the past. The preference input unit also analyzes the user's customization history and develops an algorithm that learns how preferences have changed. For example, it analyzes seasonal changes in preferences and suggests prompts that correspond to the season. The preference input unit also builds a system that learns how the user's preferences have changed based on the past customization history and improves the accuracy of prompts. For example, it suggests prompts that match specific events or anniversaries. This makes it possible to improve the accuracy of prompts based on the user's past customization history.

[0066] The preference input unit can analyze a user's voice input and enable the user to input preferences and images in a natural conversational format. For example, the user can input prompts by voice, and speech recognition technology is used to analyze the preferences and images in a natural conversational format. For example, if the user says, "I like seascapes," a related prompt is generated. The preference input unit also analyzes the voice input and develops a system that allows the user to input preferences and images in a natural conversational format. For example, if the user says, "I want relaxing music," an appropriate prompt is suggested. The preference input unit also uses speech recognition technology to analyze the user's voice input and generate prompts in a natural conversational format. For example, if the user says, "I like simple designs," a prompt with a simple theme is suggested. This allows the user's voice input to be analyzed and preferences and images to be input in a natural conversational format.

[0067] When inputting a user's preferences and images, the preference input unit can also analyze visual information by uploading images or videos. For example, the system allows the user to upload images or videos, and analyzes the visual information to understand the user's preferences and images. For example, a related prompt is generated based on a landscape image uploaded by the user. The preference input unit also uses image recognition technology to develop a system that analyzes images and videos uploaded by the user and extracts preferences and images. For example, an art-themed prompt is suggested based on an artwork uploaded by the user. The preference input unit also builds a system that analyzes visual information uploaded by the user and understands the user's preferences and images. For example, a travel-themed prompt is suggested based on travel photos uploaded by the user. This allows the system to analyze the visual information uploaded by the user and understand the user's preferences and images.

[0068] The preference input unit can make suggestions based on popular prompts from other users, thereby expanding the user's choices. For example, a system can be constructed that stores popular prompts from other users in a database and suggests them to new users as reference. For example, the most popular prompts are displayed in a ranking format. The preference input unit can also analyze popular prompts from other users and make suggestions that will expand the options for new users. For example, it can suggest prompts from users who have the same hobbies or interests. The preference input unit can also develop a system that makes useful suggestions to new users based on popular prompts. For example, it can display popular prompts based on a specific theme. This allows the user to expand their options by referring to popular prompts from other users.

[0069] The preference input unit uses the emotion estimation function to provide feedback on the emotional response to the prompt entered by the user, thereby generating a more positive prompt. For example, a system is constructed that uses the emotion estimation function to analyze the emotional response to the prompt entered by the user and generate a positive prompt. For example, if the user expresses negative emotions, a prompt with a positive theme is suggested. The preference input unit also analyzes the user's emotional response in real time and develops a system that provides feedback based on the emotion. For example, if the user is feeling stressed, a prompt that will help the user relax is suggested. The preference input unit also feeds back the emotional response to the prompt entered by the user based on the emotion estimation data, thereby generating a positive prompt. For example, if the user is excited, a prompt with an active theme is suggested. This allows feedback of the user's emotional response to generate a more positive prompt.

[0070] The generation unit can analyze the user's emotional response to the generated content and reflect it in the next generation. For example, a system is constructed that analyzes the user's emotional response to generated wallpaper, ringtones, and icons and reflects the data in the next generation. For example, designs to which the user has a positive response are preferentially generated. The generation unit also develops a system that analyzes the user's emotional response in real time and evaluates the generated content. For example, if the user has a negative response, a different design is proposed. The generation unit also analyzes the user's emotional response to the generated content based on the emotion estimation data and reflects it in the next generation. For example, if the user is excited, an active design is generated. This makes it possible to analyze the user's emotional response and reflect it in the next generation.

[0071] The generation unit can increase the variety of content generated and provide multiple options to users. For example, a system can be built that increases the variety of wallpapers, ringtones, and icons generated by the generation AI and provides multiple options to users. For example, multiple different designs based on the same theme can be generated. The generation unit can also improve the generation AI's algorithm to increase the variety of content and provide users with a wider range of options. For example, it can generate designs with different styles and colors. The generation unit can also increase the variety of content generated by the generation AI and develop a system that provides users with multiple options. For example, it can propose multiple designs based on a theme selected by the user. This increases the variety of content generated and provides users with multiple options.

[0072] The generation unit can modify and improve the generated content in real time based on user feedback. For example, a system can be built that collects user feedback on generated wallpapers, ringtones, and icons in real time and modifies and improves the content based on the results. For example, if a user wants to change part of the design, the modification can be made immediately. The generation unit also adds a function to modify and improve the generated content in real time based on user feedback. For example, if a user wants to change the color or style, the modification can be made immediately. The generation unit also develops a system that analyzes user feedback on the generated content in real time and modifies and improves it. For example, if a user does not like part of the design, the modification can be made immediately. This allows the generated content to be modified and improved in real time based on user feedback.

[0073] The processing flow of the second embodiment will be briefly explained below.

[0074] Step 1: The preference input unit inputs the user's preferences and images. For example, the user can input a prompt including specific instructions such as "I like scenery with blue seas and white sandy beaches" or "I like classical music." Step 2: The generator generates original wallpapers, ringtones, and icons based on the preferences and images input by the preference input unit. For example, in response to the prompt, "I like blue ocean and white sandy beach scenery," the generator AI generates wallpaper depicting beautiful ocean and sandy beach scenery. In response to the prompt, "I like classical music," the generator AI generates a ringtone incorporating elements of classical music. In response to the prompt, "I like simple and modern designs," the generator AI generates an icon with a simple and modern design. Step 3: The providing unit provides the original wallpaper, ringtone, and icon generated by the generating unit. For example, the generated wallpaper is applied to the user's mobile phone, the generated ringtone is set to the mobile phone, and the generated icon is applied to an app on the mobile phone.

[0075] 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.

[0076] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0077] 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.

[0078] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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).

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0092] 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.

[0093] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0094] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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).

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0104] 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.

[0105] 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.

[0106] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] 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.

[0108] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0109] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0120] 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.

[0121] 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.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0129] 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."

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0142] 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 preference input unit for inputting user preferences and images; a generation unit that generates original wallpaper, ringtones, and icons based on the preferences and images input by the preference input unit; a providing unit that provides the original wallpaper, ringtone, and icon generated by the generating unit. A system characterized by:

2. The preference input unit Estimating the user's emotions in real time and suggesting prompts based on the emotions 2. The system of claim 1.

3. The preference input unit Analyzing the user's past customization history and learning the changes in the user's preferences to improve the accuracy of the prompts 2. The system of claim 1.

4. The preference input unit Analyze the user's voice input and allow them to input their preferences and impressions in a natural conversational format.

2. The system of claim 1.

5. The preference input unit When entering the user's preferences and image, images and videos can be uploaded and visual information analyzed.

2. The system of claim 1.

Images