System

Abstract

An object of the system according to the embodiment is to properly evaluate the effort of prompt creation and realize transparent reward distribution.SOLUTION: A system according to an embodiment includes a prompt registration unit, a prompt search unit, and a reward allocation unit. The prompt registration unit registers the prompt created by the creator. The prompt search unit searches for the prompt registered by the prompt registration unit. The reward distribution unit distributes a reward through a usage fee or a point based on the AI content generated using the prompt retrieved by the prompt retrieval 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] Previous technology had issues such as the effort put into creating prompts in AI content generation not being properly evaluated and the lack of transparency in reward distribution.

[0005] The system according to the embodiment aims to fairly evaluate the effort put into creating prompts and realize transparent reward distribution. [Means for solving the problem]

[0006] The system according to the embodiment includes a prompt registration unit, a prompt search unit, and a reward distribution unit. The prompt registration unit registers prompts created by creators. The prompt search unit searches for prompts registered by the prompt registration unit. The reward distribution unit distributes rewards in the form of usage fees or points based on AI content generated using the prompts searched for by the prompt search unit. [Effects of the Invention]

[0007] The system according to the embodiment can properly evaluate the effort put into creating prompts and realize transparent reward distribution. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 AI ​​content generation platform according to an embodiment of the present invention is a system that allows users to utilize AI based on prompts created by creators. This system keeps the contents of the prompts confidential and distributes rewards through usage fees and points. This allows the AI ​​content generation platform to protect the value of creators' intellectual labor and realize transparent reward distribution.

[0029] An AI content generation platform according to an embodiment includes a prompt registration unit, a prompt search unit, and a reward distribution unit. The prompt registration unit registers prompts created by creators. For example, creators can upload and register their own prompts to the platform. The prompt registration unit also has a function for keeping the contents of prompts private. For example, details of the prompt are not displayed to users, and only summaries and usage examples are displayed. The prompt search unit searches for prompts registered by the prompt registration unit. For example, users can search for prompts by entering specific keywords or categories. The prompt search unit also has a function for recommending prompts based on the user's past usage history and preferences. For example, prompts related to prompts used by the user in the past are preferentially displayed. The reward distribution unit distributes rewards through usage fees or points based on AI content generated using the prompts searched by the prompt search unit. For example, usage fees or points are paid to creators for content generated by users using the prompts. The reward distribution unit also has a function for notifying creators of the reward distribution status. For example, creators can check how much their prompts have been used and how much reward they have earned. As a result, the AI ​​content generation platform according to the embodiment can protect the value of creators' intellectual labor and realize transparent reward distribution. For example, creators can check how their prompts are used and how much reward they will receive. This increases creators' motivation and encourages them to create high-quality prompts.

[0030] The prompt customization unit can customize prompts and generate derivative work prompts. For example, the prompt customization unit can add customizations to prompts selected by the user. For example, it is possible to adjust the prompt to match a particular theme or style. The prompt customization unit also allows the user to register a newly created prompt and receive new rewards when other users use it. For example, a user can register a newly created prompt on the platform and receive rewards as a derivative work prompt when other users use it. This allows users to customize prompts and generate original content.

[0031] The prompt evaluation unit can use the generation AI to evaluate the quality of the prompt and provide feedback on the results to the creator. The prompt evaluation unit, for example, uses the generation AI to analyze the content of the prompt and provide quality evaluation. For example, the generation AI uses a text generation AI (e.g., LLM) to evaluate the clarity and specificity of the prompt. The generation AI can also use a multimodal generation AI to evaluate the quality of the prompt. For example, the generation AI evaluates the quality of the generated content based on the content of the prompt. The prompt evaluation unit also provides feedback on the evaluation results to the creator. For example, it can provide a detailed report of the evaluation results to the creator and suggest areas for improvement. This makes it possible to evaluate the quality of the prompt and provide feedback to the creator.

[0032] The prompt recommendation unit can recommend optimal prompts based on the user's past usage history and preferences. For example, the prompt recommendation unit analyzes the user's past usage history and recommends optimal prompts based on the user's preferences. For example, prompts related to a specific genre or theme are preferentially displayed. The prompt recommendation unit also learns the user's preferences and makes individually customized recommendations. For example, it recommends new prompts based on the trends in prompts the user has used in the past. This makes it possible to recommend optimal prompts based on the user's preferences.

[0033] The prompt search unit can search for prompts using voice input or image input. For example, the prompt search unit develops a prompt search function using voice input to allow users to search for prompts by voice. For example, it uses voice recognition technology to convert the user's voice instructions into text and perform a search. The prompt search unit also develops a prompt search function using image input to allow users to search for prompts by image. For example, it uses image recognition technology to analyze images uploaded by the user and search for related prompts. This allows users to search for prompts in a variety of ways.

[0034] The collaboration unit can provide a function that allows creators from different fields to jointly create prompts. For example, the collaboration unit develops a collaboration function that allows creators from different fields to jointly create prompts. For example, it provides an interface that allows real-time collaborative editing. The collaboration unit also provides communication tools that allow creators to exchange opinions with each other and improve the quality of the prompts. For example, creators can exchange opinions with each other and improve the prompts through a chat function or comment function. This can promote ideas that combine different fields.

[0035] The prompt monitoring unit can use the generation AI to monitor the usage of prompts in real time and provide detailed usage reports to creators. The prompt monitoring unit, for example, uses the generation AI to monitor the usage of prompts in real time. For example, it analyzes the number of times and usage time of prompts and provides a report to creators. The prompt monitoring unit also provides detailed reports of usage to creators. For example, it provides detailed reports including user feedback and ratings to creators and suggests areas for improving prompts. This makes it possible to monitor the usage of prompts in real time and provide detailed usage reports to creators.

[0036] The compensation adjustment unit can dynamically adjust the creator's compensation based on the prompt usage history. The compensation adjustment unit, for example, analyzes the prompt usage history and develops an algorithm to dynamically adjust the creator's compensation. For example, the compensation is calculated based on the number of uses and the evaluation score. The compensation adjustment unit also adjusts the compensation in real time according to the usage status. For example, the compensation is increased for prompts that are used frequently, and decreased for prompts that are used infrequently. This makes it possible to dynamically adjust the compensation based on the prompt usage history.

[0037] The feedback exchange unit can provide a function that allows creators to exchange feedback anonymously with each other. The feedback exchange unit, for example, develops a function that allows creators to exchange feedback anonymously with each other. For example, it provides anonymous chat and comment functions to share improvements to prompts. The feedback exchange unit also promotes the exchange of opinions between creators through anonymous feedback exchange. For example, anonymous feedback exchange allows creators to freely express their opinions. This allows creators to exchange feedback anonymously with each other.

[0038] The ranking generation unit generates rankings of creators based on the usage of Prompt, and can provide special rewards and benefits to top creators. The ranking generation unit, for example, analyzes the usage of Prompt and builds a system for generating rankings of creators. For example, it creates rankings based on the number of uses and evaluation scores. The ranking generation unit also provides special rewards and benefits to top creators. For example, it provides bonus points and special access rights to the top 10% of creators. This makes it possible to generate rankings of creators and provide special rewards and benefits to top creators.

[0039] The prompt adjustment unit can use generation AI to provide an interface that allows users to easily adjust prompts. For example, the prompt adjustment unit uses generation AI to develop an interface that allows users to easily adjust prompts. For example, it can provide a function that allows users to add and delete prompt elements by drag and drop. The prompt adjustment unit also provides guidelines for users when customizing prompts. For example, it can provide guidelines that show how to adjust prompts and best practices. This makes it possible to provide an interface that allows users to easily adjust prompts.

[0040] The customization suggestion unit can make optimal customization suggestions to the user based on the usage history of the customized prompt. The customization suggestion unit, for example, analyzes the usage history of the customized prompt and builds a system that makes optimal customization suggestions to the user. For example, suggestions are made based on usage frequency and evaluation scores. The customization suggestion unit also makes customization suggestions based on the user's preferences. For example, the optimal customization method is suggested based on the past usage history and the user's preferences. This makes it possible to make optimal customization suggestions to the user.

[0041] The customization option unit can provide customization options that support different languages ​​or cultures. For example, the customization option unit develops customization options that support different languages ​​or cultures and builds a system that supports global users. For example, it provides a multilingual interface. The customization option unit also provides customization options that take cultural backgrounds into consideration. For example, it suggests a customization method for prompts that is suitable for a specific culture. This makes it possible to provide customization options that support different languages ​​and cultures.

[0042] The sharing unit can provide a function that allows customized prompts to be shared with other users. For example, the sharing unit develops a function that allows customized prompts to be shared with other users and builds a system that promotes the exchange of ideas within a community. For example, the sharing unit provides a rating and comment function for shared prompts. The sharing unit also monitors the usage status of shared prompts and provides feedback. For example, the sharing unit analyzes the number of times a shared prompt is used and its rating, and provides feedback to the creator. This allows customized prompts to be shared with other users.

[0043] The creation process recording unit can use a generation AI to automatically record the prompt creation process and evaluate the effort involved. The creation process recording unit, for example, uses a generation AI to build a system that automatically records the prompt creation process. For example, it records the creation time and number of edits and evaluates the effort based on that data. The creation process recording unit also evaluates the creator's effort based on the recorded data and distributes rewards. For example, it provides appropriate rewards to the creator based on the data on the creation process. This makes it possible to automatically record the prompt creation process and evaluate the effort involved.

[0044] The reward adjustment unit can dynamically adjust the creator's reward based on the usage status and evaluation of the prompt. The reward adjustment unit, for example, analyzes the usage status and evaluation of the prompt and develops an algorithm to dynamically adjust the creator's reward. For example, it calculates the reward based on the number of times it is used and the evaluation score. The reward adjustment unit also adjusts the reward in real time according to the usage status. For example, it increases the reward for prompts that are used frequently and decreases the reward for prompts that are used infrequently. This makes it possible to dynamically adjust the reward based on the usage status and evaluation of the prompt.

[0045] The feedback reflection unit reflects feedback from users in real time, enabling transparent reward distribution. The feedback reflection unit, for example, builds a system that collects feedback from users in real time and reflects it in reward distribution. For example, the unit adjusts rewards based on the content of the feedback and evaluation scores. The feedback reflection unit also notifies the creator of the content of the feedback. For example, the unit provides feedback from users to the creator and suggests improvements to prompts. This allows feedback from users to be reflected in real time, enabling transparent reward distribution.

[0046] The evaluation criteria setting unit sets evaluation criteria for prompts from multiple angles and can comprehensively evaluate technical feasibility, economic effectiveness, social impact, etc. The evaluation criteria setting unit, for example, sets evaluation criteria for prompts from multiple angles and builds a system that comprehensively evaluates technical feasibility, economic effectiveness, social impact, etc. For example, it calculates a score for each criterion and performs an overall evaluation. The evaluation criteria setting unit also feeds back the evaluation results to the creator. For example, it provides a detailed report of the evaluation results to the creator and suggests areas for improvement. This makes it possible to set evaluation criteria for prompts from multiple angles and perform a comprehensive evaluation.

[0047] The content evaluation unit can use the generation AI to automatically evaluate the quality of content generated based on a prompt selected by a user and provide feedback. The content evaluation unit, for example, uses the generation AI to build a system that automatically evaluates the quality of content generated based on a prompt selected by a user. For example, it evaluates the clarity and specificity of the content and provides feedback on the results to the user. The content evaluation unit also suggests improvements to the content based on the evaluation results. For example, it suggests specific improvements to the user based on the evaluation results of the generated content. In this way, the quality of the generated content can be automatically evaluated and feedback can be provided.

[0048] The usage method suggestion unit can suggest the optimal usage method to the user based on the usage examples of the prompt. The usage method suggestion unit, for example, analyzes the usage examples of the prompt and builds a system that suggests the optimal usage method to the user. For example, suggestions are made based on usage frequency and evaluation scores. The usage method suggestion unit also suggests usage methods based on the user's preferences. For example, the optimal usage method is suggested based on past usage history and user preferences. This makes it possible to suggest the optimal usage method based on the usage examples of the prompt.

[0049] The usage example providing unit can provide usage examples of prompts in a form that corresponds to different languages ​​or cultures. For example, the usage example providing unit automatically translates usage examples of prompts into different languages ​​to build a system that corresponds to global users. For example, it translates into multiple languages ​​such as English, French, and Chinese. The usage example providing unit also provides usage examples that take cultural backgrounds into consideration. For example, it suggests how to use the prompt that is suitable for a specific culture. This makes it possible to provide usage examples of prompts in a form that corresponds to different languages ​​and cultures.

[0050] The sharing unit can provide a function that allows prompt use cases to be shared with other users. For example, the sharing unit develops a function that allows prompt use cases to be shared with other users and builds a system that promotes the exchange of ideas within the community. For example, the sharing unit provides a rating and comment function for shared use cases. The sharing unit also monitors the usage status of shared use cases and provides feedback. For example, the sharing unit analyzes the number of times a shared use case is used and the rating, and provides feedback to the creator. This allows prompt use cases to be shared with other users.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The prompt evaluation unit can use the generation AI to evaluate the quality of the prompt and provide feedback to the creator. For example, the generation AI can use a text generation AI (e.g., LLM) to evaluate the clarity and specificity of the prompt. The generation AI can also use a multimodal generation AI to evaluate the quality of the prompt. For example, the generation AI can evaluate the quality of the generated content based on the content of the prompt. The prompt evaluation unit also provides feedback on the evaluation results to the creator. For example, the prompt evaluation unit can provide a detailed report of the evaluation results to the creator and suggest areas for improvement. This allows the quality of the prompt to be evaluated and feedback to be provided to the creator.

[0053] The prompt search unit can search for prompts using voice input or image input. For example, a prompt search function using voice input can be developed to allow users to search for prompts by voice. For example, speech recognition technology can be used to convert the user's voice instructions into text and perform a search. The prompt search unit can also develop a prompt search function using image input to allow users to search for prompts by image. For example, image recognition technology can be used to analyze images uploaded by the user and search for related prompts. This allows users to search for prompts in a variety of ways.

[0054] The collaboration department can provide a function that allows creators from different fields to jointly create prompts. For example, it can develop a collaboration function that allows creators from different fields to jointly create prompts. For example, it can provide an interface that allows real-time collaborative editing. The collaboration department can also provide communication tools that allow creators to exchange opinions with each other and improve the quality of prompts. For example, creators can exchange opinions with each other and improve prompts through chat and comment functions. This can promote ideas that combine different fields.

[0055] The prompt monitoring unit can use the generation AI to monitor the usage of prompts in real time and provide detailed usage reports to creators. For example, the generation AI can be used to monitor the usage of prompts in real time. For example, the number of times and usage time of prompts can be analyzed and a report can be provided to creators. The prompt monitoring unit also provides detailed reports of usage to creators. For example, the prompt monitoring unit can provide detailed reports including user feedback and ratings to creators and suggest improvements to prompts. This makes it possible to monitor the usage of prompts in real time and provide detailed usage reports to creators.

[0056] The feedback exchange unit can provide a function that allows creators to exchange feedback anonymously with each other. For example, a function that allows creators to exchange feedback anonymously with each other may be developed. For example, an anonymous chat or comment function may be provided to share improvements to prompts. The feedback exchange unit also promotes the exchange of opinions between creators through anonymous feedback exchange. For example, anonymous feedback exchange allows creators to freely express their opinions. This allows creators to exchange feedback anonymously with each other.

[0057] The prompt adjustment unit can use generation AI to provide an interface that allows users to easily adjust prompts. For example, generation AI can be used to develop an interface that allows users to easily adjust prompts. For example, a function can be provided that allows users to add and delete prompt elements by drag and drop. The prompt adjustment unit can also provide guidelines for users when customizing prompts. For example, guidelines can be provided that show how to adjust prompts and best practices. This makes it possible to provide an interface that allows users to easily adjust prompts.

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

[0059] Step 1: The prompt registration unit registers prompts created by creators. For example, creators can upload their own prompts to the platform and register them. The prompt registration unit also has a function to keep the contents of prompts private. For example, the details of the prompt will not be displayed to users, and only an overview and usage examples will be displayed. Step 2: The prompt search unit searches for prompts registered by the prompt registration unit. For example, a user can search for prompts by entering specific keywords or categories. The prompt search unit also has the function of recommending prompts based on the user's past usage history and preferences. For example, it preferentially displays prompts related to prompts that the user has used in the past. Step 3: The reward distribution unit distributes rewards in the form of royalties or points based on the AI ​​content generated using the prompts found by the prompt search unit. For example, royalties or points are paid to creators for content generated by users using prompts. The reward distribution unit also has the function of notifying creators of the reward distribution status. For example, creators can check how much their prompts have been used and how much reward they have earned.

[0060] (Example 2) The AI ​​content generation platform according to an embodiment of the present invention is a system that allows users to utilize AI based on prompts created by creators. This system keeps the contents of the prompts confidential and distributes rewards through usage fees and points. This allows the AI ​​content generation platform to protect the value of creators' intellectual labor and realize transparent reward distribution.

[0061] An AI content generation platform according to an embodiment includes a prompt registration unit, a prompt search unit, and a reward distribution unit. The prompt registration unit registers prompts created by creators. For example, creators can upload and register their own prompts to the platform. The prompt registration unit also has a function for keeping the contents of prompts private. For example, details of the prompt are not displayed to users, and only summaries and usage examples are displayed. The prompt search unit searches for prompts registered by the prompt registration unit. For example, users can search for prompts by entering specific keywords or categories. The prompt search unit also has a function for recommending prompts based on the user's past usage history and preferences. For example, prompts related to prompts used by the user in the past are preferentially displayed. The reward distribution unit distributes rewards through usage fees or points based on AI content generated using the prompts searched by the prompt search unit. For example, usage fees or points are paid to creators for content generated by users using the prompts. The reward distribution unit also has a function for notifying creators of the reward distribution status. For example, creators can check how much their prompts have been used and how much reward they have earned. As a result, the AI ​​content generation platform according to the embodiment can protect the value of creators' intellectual labor and realize transparent reward distribution. For example, creators can check how their prompts are used and how much reward they will receive. This increases creators' motivation and encourages them to create high-quality prompts.

[0062] The prompt customization unit can customize prompts and generate derivative work prompts. For example, the prompt customization unit can add customizations to prompts selected by the user. For example, it is possible to adjust the prompt to match a particular theme or style. The prompt customization unit also allows the user to register a newly created prompt and receive new rewards when other users use it. For example, a user can register a newly created prompt on the platform and receive rewards as a derivative work prompt when other users use it. This allows users to customize prompts and generate original content.

[0063] The prompt evaluation unit can use the generation AI to evaluate the quality of the prompt and provide feedback on the results to the creator. The prompt evaluation unit, for example, uses the generation AI to analyze the content of the prompt and provide quality evaluation. For example, the generation AI uses a text generation AI (e.g., LLM) to evaluate the clarity and specificity of the prompt. The generation AI can also use a multimodal generation AI to evaluate the quality of the prompt. For example, the generation AI evaluates the quality of the generated content based on the content of the prompt. The prompt evaluation unit also provides feedback on the evaluation results to the creator. For example, it can provide a detailed report of the evaluation results to the creator and suggest areas for improvement. This makes it possible to evaluate the quality of the prompt and provide feedback to the creator.

[0064] The prompt recommendation unit can recommend optimal prompts based on the user's past usage history and preferences. For example, the prompt recommendation unit analyzes the user's past usage history and recommends optimal prompts based on the user's preferences. For example, prompts related to a specific genre or theme are preferentially displayed. The prompt recommendation unit also learns the user's preferences and makes individually customized recommendations. For example, it recommends new prompts based on the trends in prompts the user has used in the past. This makes it possible to recommend optimal prompts based on the user's preferences.

[0065] The emotion estimation unit can estimate the user's emotion and preferentially display prompts that elicit positive emotions. The emotion estimation unit, for example, uses the emotion estimation function to analyze the emotion of the user when selecting a prompt in real time. For example, the emotion estimation unit analyzes the user's facial expression and voice and calculates an emotion score. The emotion estimation unit also preferentially displays prompts based on the user's emotion. For example, preferentially displaying prompts that elicit positive emotions improves user satisfaction. This makes it possible to preferentially display prompts based on the user's emotion.

[0066] The prompt search unit can search for prompts using voice input or image input. For example, the prompt search unit develops a prompt search function using voice input to allow users to search for prompts by voice. For example, it uses voice recognition technology to convert the user's voice instructions into text and perform a search. The prompt search unit also develops a prompt search function using image input to allow users to search for prompts by image. For example, it uses image recognition technology to analyze images uploaded by the user and search for related prompts. This allows users to search for prompts in a variety of ways.

[0067] The collaboration unit can provide a function that allows creators from different fields to jointly create prompts. For example, the collaboration unit develops a collaboration function that allows creators from different fields to jointly create prompts. For example, it provides an interface that allows real-time collaborative editing. The collaboration unit also provides communication tools that allow creators to exchange opinions with each other and improve the quality of the prompts. For example, creators can exchange opinions with each other and improve the prompts through a chat function or comment function. This can promote ideas that combine different fields.

[0068] The emotion estimation unit can analyze the user's emotion in real time and customize search results based on the emotion. The emotion estimation unit, for example, uses the emotion estimation function to analyze the emotion of the user when searching for a prompt in real time. For example, the emotion estimation unit analyzes the user's facial expression and voice and calculates an emotion score. The emotion estimation unit also customizes search results based on the user's emotion. For example, by preferentially displaying prompts that elicit positive emotions, user satisfaction can be improved. This makes it possible to customize search results based on the user's emotion.

[0069] The prompt monitoring unit can use the generation AI to monitor the usage of prompts in real time and provide detailed usage reports to creators. The prompt monitoring unit, for example, uses the generation AI to monitor the usage of prompts in real time. For example, it analyzes the number of times and usage time of prompts and provides a report to creators. The prompt monitoring unit also provides detailed reports of usage to creators. For example, it provides detailed reports including user feedback and ratings to creators and suggests areas for improving prompts. This makes it possible to monitor the usage of prompts in real time and provide detailed usage reports to creators.

[0070] The compensation adjustment unit can dynamically adjust the creator's compensation based on the prompt usage history. The compensation adjustment unit, for example, analyzes the prompt usage history and develops an algorithm to dynamically adjust the creator's compensation. For example, the compensation is calculated based on the number of uses and the evaluation score. The compensation adjustment unit also adjusts the compensation in real time according to the usage status. For example, the compensation is increased for prompts that are used frequently, and decreased for prompts that are used infrequently. This makes it possible to dynamically adjust the compensation based on the prompt usage history.

[0071] The emotion estimation unit can analyze the user's emotions and provide an additional reward for prompts that elicit positive emotions. The emotion estimation unit, for example, uses an emotion estimation function to analyze the emotions of the user when using a prompt in real time. For example, the emotion estimation unit analyzes the user's facial expressions and voice to calculate an emotion score. The emotion estimation unit also provides an additional reward for prompts that elicit positive emotions. For example, bonus points or a special reward are provided for prompts with a high emotion score. This makes it possible to provide an additional reward for prompts that elicit positive emotions.

[0072] The feedback exchange unit can provide a function that allows creators to exchange feedback anonymously with each other. The feedback exchange unit, for example, develops a function that allows creators to exchange feedback anonymously with each other. For example, it provides anonymous chat and comment functions to share improvements to prompts. The feedback exchange unit also promotes the exchange of opinions between creators through anonymous feedback exchange. For example, anonymous feedback exchange allows creators to freely express their opinions. This allows creators to exchange feedback anonymously with each other.

[0073] The ranking generation unit generates rankings of creators based on the usage of Prompt, and can provide special rewards and benefits to top creators. The ranking generation unit, for example, analyzes the usage of Prompt and builds a system for generating rankings of creators. For example, it creates rankings based on the number of uses and evaluation scores. The ranking generation unit also provides special rewards and benefits to top creators. For example, it provides bonus points and special access rights to the top 10% of creators. This makes it possible to generate rankings of creators and provide special rewards and benefits to top creators.

[0074] The emotion estimation unit can analyze the user's emotion in real time and distribute rewards based on the emotion. The emotion estimation unit, for example, uses an emotion estimation function to analyze the emotion of the user when using a prompt in real time. For example, the emotion estimation unit analyzes the user's facial expression and voice and calculates an emotion score. The emotion estimation unit also distributes rewards based on the emotion. For example, the reward is increased for a prompt that elicits positive emotions. This allows reward distribution based on the user's emotion.

[0075] The prompt adjustment unit can use generation AI to provide an interface that allows users to easily adjust prompts. For example, the prompt adjustment unit uses generation AI to develop an interface that allows users to easily adjust prompts. For example, it can provide a function that allows users to add and delete prompt elements by drag and drop. The prompt adjustment unit also provides guidelines for users when customizing prompts. For example, it can provide guidelines that show how to adjust prompts and best practices. This makes it possible to provide an interface that allows users to easily adjust prompts.

[0076] The customization suggestion unit can make optimal customization suggestions to the user based on the usage history of the customized prompt. The customization suggestion unit, for example, analyzes the usage history of the customized prompt and builds a system that makes optimal customization suggestions to the user. For example, suggestions are made based on usage frequency and evaluation scores. The customization suggestion unit also makes customization suggestions based on the user's preferences. For example, the optimal customization method is suggested based on the past usage history and the user's preferences. This makes it possible to make optimal customization suggestions to the user.

[0077] The emotion estimation unit can analyze the user's emotions and make customization suggestions that elicit positive emotions. The emotion estimation unit, for example, uses the emotion estimation function to analyze the user's emotions in real time when customizing a prompt. For example, the emotion estimation unit analyzes the user's facial expressions and voice and calculates an emotion score. The emotion estimation unit also makes customization suggestions that elicit positive emotions. For example, the emotion estimation unit suggests a customization method with a high emotion score. This makes it possible to make customization suggestions based on the user's emotions.

[0078] The customization option unit can provide customization options that support different languages ​​or cultures. For example, the customization option unit develops customization options that support different languages ​​or cultures and builds a system that supports global users. For example, it provides a multilingual interface. The customization option unit also provides customization options that take cultural backgrounds into consideration. For example, it suggests a customization method for prompts that is suitable for a specific culture. This makes it possible to provide customization options that support different languages ​​and cultures.

[0079] The sharing unit can provide a function that allows customized prompts to be shared with other users. For example, the sharing unit develops a function that allows customized prompts to be shared with other users and builds a system that promotes the exchange of ideas within a community. For example, the sharing unit provides a rating and comment function for shared prompts. The sharing unit also monitors the usage status of shared prompts and provides feedback. For example, the sharing unit analyzes the number of times a shared prompt is used and its rating, and provides feedback to the creator. This allows customized prompts to be shared with other users.

[0080] The emotion estimation unit can collect other users' emotional reactions to prompts customized by the user and propose an optimal customization method. The emotion estimation unit, for example, uses the emotion estimation function to build a system that collects other users' emotional reactions to prompts customized by the user in real time. For example, the emotion estimation unit analyzes the facial expressions and voices of other users and calculates an emotion score. The emotion estimation unit also proposes an optimal customization method based on the collected emotional reactions. For example, the emotion estimation unit proposes a customization method with a high emotion score. This makes it possible to collect other users' emotional reactions to prompts customized by the user and propose an optimal customization method.

[0081] The creation process recording unit can use a generation AI to automatically record the prompt creation process and evaluate the effort involved. The creation process recording unit, for example, uses a generation AI to build a system that automatically records the prompt creation process. For example, it records the creation time and number of edits and evaluates the effort based on that data. The creation process recording unit also evaluates the creator's effort based on the recorded data and distributes rewards. For example, it provides appropriate rewards to the creator based on the data on the creation process. This makes it possible to automatically record the prompt creation process and evaluate the effort involved.

[0082] The reward adjustment unit can dynamically adjust the creator's reward based on the usage status and evaluation of the prompt. The reward adjustment unit, for example, analyzes the usage status and evaluation of the prompt and develops an algorithm to dynamically adjust the creator's reward. For example, it calculates the reward based on the number of times it is used and the evaluation score. The reward adjustment unit also adjusts the reward in real time according to the usage status. For example, it increases the reward for prompts that are used frequently and decreases the reward for prompts that are used infrequently. This makes it possible to dynamically adjust the reward based on the usage status and evaluation of the prompt.

[0083] The emotion estimation unit can analyze the user's emotions and provide an additional reward for prompts that elicit positive emotions. The emotion estimation unit, for example, uses an emotion estimation function to analyze the emotions of the user when using a prompt in real time. For example, the emotion estimation unit analyzes the user's facial expressions and voice to calculate an emotion score. The emotion estimation unit also provides an additional reward for prompts that elicit positive emotions. For example, bonus points or a special reward are provided for prompts with a high emotion score. This makes it possible to provide an additional reward for prompts that elicit positive emotions.

[0084] The feedback reflection unit reflects feedback from users in real time, enabling transparent reward distribution. The feedback reflection unit, for example, builds a system that collects feedback from users in real time and reflects it in reward distribution. For example, the unit adjusts rewards based on the content of the feedback and evaluation scores. The feedback reflection unit also notifies the creator of the content of the feedback. For example, the unit provides feedback from users to the creator and suggests improvements to prompts. This allows feedback from users to be reflected in real time, enabling transparent reward distribution.

[0085] The evaluation criteria setting unit sets evaluation criteria for prompts from multiple angles and can comprehensively evaluate technical feasibility, economic effectiveness, social impact, etc. The evaluation criteria setting unit, for example, sets evaluation criteria for prompts from multiple angles and builds a system that comprehensively evaluates technical feasibility, economic effectiveness, social impact, etc. For example, it calculates a score for each criterion and performs an overall evaluation. The evaluation criteria setting unit also feeds back the evaluation results to the creator. For example, it provides a detailed report of the evaluation results to the creator and suggests areas for improvement. This makes it possible to set evaluation criteria for prompts from multiple angles and perform a comprehensive evaluation.

[0086] The emotion deduction unit can identify the user's interests and update the information in real time. For example, the emotion deduction unit uses the emotion deduction function to identify the user's most interesting interests. For example, themes with high emotion scores are preferentially displayed. The emotion deduction unit also updates information related to the interests in real time. For example, the latest information related to the user's interests is provided. This makes it possible to identify the user's interests and update the information in real time.

[0087] The content evaluation unit can use the generation AI to automatically evaluate the quality of content generated based on a prompt selected by a user and provide feedback. The content evaluation unit, for example, uses the generation AI to build a system that automatically evaluates the quality of content generated based on a prompt selected by a user. For example, it evaluates the clarity and specificity of the content and provides feedback on the results to the user. The content evaluation unit also suggests improvements to the content based on the evaluation results. For example, it suggests specific improvements to the user based on the evaluation results of the generated content. In this way, the quality of the generated content can be automatically evaluated and feedback can be provided.

[0088] The usage method suggestion unit can suggest the optimal usage method to the user based on the usage examples of the prompt. The usage method suggestion unit, for example, analyzes the usage examples of the prompt and builds a system that suggests the optimal usage method to the user. For example, suggestions are made based on usage frequency and evaluation scores. The usage method suggestion unit also suggests usage methods based on the user's preferences. For example, the optimal usage method is suggested based on past usage history and user preferences. This makes it possible to suggest the optimal usage method based on the usage examples of the prompt.

[0089] The emotion estimation unit can analyze emotional reactions to content generated by users and propose use cases that elicit positive emotions. The emotion estimation unit, for example, uses the emotion estimation function to build a system that analyzes emotional reactions to content generated by users in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The emotion estimation unit also proposes optimal use cases based on the collected emotional reactions. For example, it proposes use cases with high emotion scores. This makes it possible to analyze emotional reactions to content generated by users and propose use cases that elicit positive emotions.

[0090] The usage example providing unit can provide usage examples of prompts in a form that corresponds to different languages ​​or cultures. For example, the usage example providing unit automatically translates usage examples of prompts into different languages ​​to build a system that corresponds to global users. For example, it translates into multiple languages ​​such as English, French, and Chinese. The usage example providing unit also provides usage examples that take cultural backgrounds into consideration. For example, it suggests how to use the prompt that is suitable for a specific culture. This makes it possible to provide usage examples of prompts in a form that corresponds to different languages ​​and cultures.

[0091] The sharing unit can provide a function that allows prompt use cases to be shared with other users. For example, the sharing unit develops a function that allows prompt use cases to be shared with other users and builds a system that promotes the exchange of ideas within the community. For example, the sharing unit provides a rating and comment function for shared use cases. The sharing unit also monitors the usage status of shared use cases and provides feedback. For example, the sharing unit analyzes the number of times a shared use case is used and the rating, and provides feedback to the creator. This allows prompt use cases to be shared with other users.

[0092] The emotion estimation unit can collect other users' emotional reactions to content generated by the user and propose optimal use cases. The emotion estimation unit, for example, uses the emotion estimation function to build a system that collects other users' emotional reactions to content generated by the user in real time. For example, the emotion estimation unit analyzes the facial expressions and voices of other users and calculates an emotion score. The emotion estimation unit also proposes optimal use cases based on the collected emotional reactions. For example, it proposes use cases with high emotion scores. This makes it possible to collect other users' emotional reactions to content generated by the user and propose optimal use cases.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The prompt evaluation unit can use the generation AI to evaluate the quality of the prompt and provide feedback to the creator. For example, the generation AI can use a text generation AI (e.g., LLM) to evaluate the clarity and specificity of the prompt. The generation AI can also use a multimodal generation AI to evaluate the quality of the prompt. For example, the generation AI can evaluate the quality of the generated content based on the content of the prompt. The prompt evaluation unit also provides feedback on the evaluation results to the creator. For example, the prompt evaluation unit can provide a detailed report of the evaluation results to the creator and suggest areas for improvement. This allows the quality of the prompt to be evaluated and feedback to be provided to the creator.

[0095] The emotion estimation unit can estimate the user's emotion and preferentially display prompts that elicit positive emotions. For example, the emotion estimation function is used to analyze the emotion of the user when selecting a prompt in real time. For example, the emotion estimation unit analyzes the user's facial expression and voice and calculates an emotion score. The emotion estimation unit also preferentially displays prompts based on the user's emotion. For example, preferentially displaying prompts that elicit positive emotions improves user satisfaction. This makes it possible to preferentially display prompts based on the user's emotion.

[0096] The prompt search unit can search for prompts using voice input or image input. For example, a prompt search function using voice input can be developed to allow users to search for prompts by voice. For example, speech recognition technology can be used to convert the user's voice instructions into text and perform a search. The prompt search unit can also develop a prompt search function using image input to allow users to search for prompts by image. For example, image recognition technology can be used to analyze images uploaded by the user and search for related prompts. This allows users to search for prompts in a variety of ways.

[0097] The collaboration department can provide a function that allows creators from different fields to jointly create prompts. For example, it can develop a collaboration function that allows creators from different fields to jointly create prompts. For example, it can provide an interface that allows real-time collaborative editing. The collaboration department can also provide communication tools that allow creators to exchange opinions with each other and improve the quality of prompts. For example, creators can exchange opinions with each other and improve prompts through chat and comment functions. This can promote ideas that combine different fields.

[0098] The emotion estimation unit can analyze the user's emotions in real time and customize search results based on the emotions. For example, the emotion estimation function is used to analyze the emotions of the user when searching for prompts in real time. For example, the emotion estimation unit analyzes the user's facial expressions and voice and calculates an emotion score. The emotion estimation unit also customizes search results based on the user's emotions. For example, by preferentially displaying prompts that elicit positive emotions, user satisfaction can be improved. This makes it possible to customize search results based on the user's emotions.

[0099] The prompt monitoring unit can use the generation AI to monitor the usage of prompts in real time and provide detailed usage reports to creators. For example, the generation AI can be used to monitor the usage of prompts in real time. For example, the number of times and usage time of prompts can be analyzed and a report can be provided to creators. The prompt monitoring unit also provides detailed reports of usage to creators. For example, the prompt monitoring unit can provide detailed reports including user feedback and ratings to creators and suggest improvements to prompts. This makes it possible to monitor the usage of prompts in real time and provide detailed usage reports to creators.

[0100] The emotion estimation unit can analyze the user's emotions and provide additional rewards for prompts that elicit positive emotions. For example, the emotion estimation function is used to analyze the emotions of the user when using the prompts in real time. For example, the emotion estimation unit may analyze the user's facial expressions and voice to calculate an emotion score. The emotion estimation unit also provides additional rewards for prompts that elicit positive emotions. For example, bonus points or special rewards are provided for prompts with high emotion scores. This makes it possible to provide additional rewards for prompts that elicit positive emotions.

[0101] The feedback exchange unit can provide a function that allows creators to exchange feedback anonymously with each other. For example, a function that allows creators to exchange feedback anonymously with each other may be developed. For example, an anonymous chat or comment function may be provided to share improvements to prompts. The feedback exchange unit also promotes the exchange of opinions between creators through anonymous feedback exchange. For example, anonymous feedback exchange allows creators to freely express their opinions. This allows creators to exchange feedback anonymously with each other.

[0102] The emotion estimation unit can analyze the user's emotion in real time and distribute rewards based on the emotion. For example, the emotion estimation function is used to analyze the emotion of the user when using a prompt in real time. For example, the emotion estimation unit analyzes the user's facial expression and voice to calculate an emotion score. The emotion estimation unit also distributes rewards based on the emotion. For example, the reward is increased for prompts that elicit positive emotions. This allows reward distribution based on the user's emotion.

[0103] The prompt adjustment unit can use generation AI to provide an interface that allows users to easily adjust prompts. For example, generation AI can be used to develop an interface that allows users to easily adjust prompts. For example, a function can be provided that allows users to add and delete prompt elements by drag and drop. The prompt adjustment unit can also provide guidelines for users when customizing prompts. For example, guidelines can be provided that show how to adjust prompts and best practices. This makes it possible to provide an interface that allows users to easily adjust prompts.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The prompt registration unit registers prompts created by creators. For example, creators can upload their own prompts to the platform and register them. The prompt registration unit also has a function to keep the contents of prompts private. For example, the details of the prompt will not be displayed to users, and only an overview and usage examples will be displayed. Step 2: The prompt search unit searches for prompts registered by the prompt registration unit. For example, a user can search for prompts by entering specific keywords or categories. The prompt search unit also has the function of recommending prompts based on the user's past usage history and preferences. For example, it preferentially displays prompts related to prompts that the user has used in the past. Step 3: The reward distribution unit distributes rewards in the form of royalties or points based on the AI ​​content generated using the prompts found by the prompt search unit. For example, royalties or points are paid to creators for content generated by users using prompts. The reward distribution unit also has the function of notifying creators of the reward distribution status. For example, creators can check how much their prompts have been used and how much reward they have earned.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0119] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 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.

[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 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.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 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.

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

[0127] The 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.

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

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

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

[0131] Fig. 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.

[0132] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0134] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0136] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0138] 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.

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

[0140] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

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

[0146] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0147] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0150] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] The 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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."

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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]

[0173] 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 prompt registration unit for registering prompts created by creators; a prompt search unit that searches for the prompt registered by the prompt registration unit; and a reward distribution unit that distributes rewards through usage fees or points based on AI content generated using the prompts searched by the prompt search unit. A system characterized by:

2. The emotion estimation unit The system estimates the user's emotions and prioritizes displaying prompts that elicit positive emotions.

2. The system of claim 1.

3. The prompt search unit Retrieving the prompt using voice or image input 2. The system of claim 1.

4. The prompt monitoring section The generating AI is used to monitor usage of the prompt in real time and provide the creator with a detailed usage report.

2. The system of claim 1.

5. The prompt adjustment unit The generating AI is used to provide an interface that allows the user to easily adjust the prompt.

2. The system of claim 1.

6. The creation process record section is The generation AI is used to automatically record the prompt creation process and evaluate the effort.

2. The system of claim 1.

7. The content evaluation section Using the generation AI, automatically evaluate the quality of the content generated based on the prompt selected by the user and provide feedback.

2. The system of claim 1.

8. The emotion estimation unit Collecting other users' emotional reactions to the content generated by the user and suggesting optimal use cases 2. The system of claim 1.

Images