System

Abstract

An object of the system according to the embodiment is to integrate creation and publication of a work using a generative AI so that a user can recognize and utilize a possibility of the generative AI.SOLUTION: A system according to an embodiment includes a generation unit, a publication unit, an operation unit, an evaluation unit, a ranking unit, a technology management unit, a copyright management unit, and an instruction unit. The generation unit generates a work using the generated AI. The publication unit publishes the work generated by the generation unit. The operation unit is for the user to operate the generated AI. The evaluation unit evaluates the work generated by the generation unit. The ranking unit ranks the works evaluated by the evaluation unit. The technology management unit manages details of the generation unit. The copyright management unit protects the copyright of the work generated by the generation unit. The instruction unit is for the user to issue an instruction to the generated AI.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the creation and publication of works using generative AI were not integrated, making it difficult for users to fully recognize and utilize the potential of generative AI.

[0005] The system of the embodiment aims to integrate the creation and publication of works using generative AI, allowing users to recognize and utilize the potential of generative AI. [Means for solving the problem]

[0006] The system according to the embodiment comprises a generation unit, a publication unit, an operation unit, an evaluation unit, a ranking unit, a technical management unit, a copyright management unit, and an instruction unit. The generation unit creates works using a generation AI. The publication unit publishes works generated by the generation unit. The operation unit allows the user to operate the generation AI. The evaluation unit evaluates works generated by the generation unit. The ranking unit ranks the works evaluated by the evaluation unit. The technical management unit manages details of the generation unit. The copyright management unit protects the copyright of works generated by the generation unit. The instruction unit allows the user to give instructions to the generation AI. [Effects of the Invention]

[0007] The system according to the embodiment integrates the creation and publication of works using generative AI, enabling users to recognize and utilize the potential of generative AI. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A generative AI work submission system according to an embodiment of the present invention is a system that can publish, evaluate, rank, technically manage, copyright, and provide instructions for works created using generative AI. The generative AI work submission system can create works using generative AI and publish them on a platform. For example, a user can create a video using generative AI and publish the video on the platform. It can also create music using generative AI and publish the music on the platform. The generative AI work submission system is provided as a platform specialized in Japanese culture and markets, aiming to create new IP and cultivate a group of works that can be sold overseas. For example, by creating a work themed around traditional Japanese culture using generative AI and publishing it on the platform, overseas users can become familiar with Japanese culture. This allows the generative AI work submission system to widely recognize the potential of generative AI and provide a platform where many people can thrive as creators. This allows the generative AI work submission system to widely publish, evaluate, rank, technically manage, and protect copyright for works created using generative AI. For example, the generative AI work submission system allows users to quickly and accurately publish works created using generative AI and receive reviews from other users. In addition, the generative AI work submission system protects the copyright of works created using generative AI, preventing legal trouble.

[0029] A generative AI work posting system according to an embodiment includes a generation unit, a publication unit, an operation unit, an evaluation unit, a ranking unit, a technology management unit, a copyright management unit, and an instruction unit. The generation unit creates works using a generative AI. The generation unit generates images using, for example, a deep learning model. The generation unit can also generate text using a natural language generation model. The generation unit can also generate music using a music generation model. The publication unit publishes the generated works on a platform. The publication unit publishes the works, for example, through a website. The publication unit can also publish the works through a mobile app. The publication unit can also publish the works through social media. The operation unit provides an interface for a user to operate the generative AI. The operation unit provides, for example, a graphical user interface (GUI). The operation unit can also provide a voice interface. The operation unit can also provide a touch interface. The evaluation unit provides a function for evaluating the generated works. The evaluation unit performs, for example, user evaluation. The evaluation unit can also perform evaluation using an algorithm. The evaluation unit can also perform evaluation by an expert. The ranking unit ranks the works based on the evaluation. The ranking unit ranks the works based on the evaluation, for example. The ranking unit ranks the works based on the evaluation score. The ranking unit can also rank the works based on popularity. The ranking unit can also rank the works based on expert evaluation. The technical management unit manages technical details of the generation AI. The technical management unit performs version management, for example. The technical management unit can also perform performance monitoring. The technical management unit can also perform security management. The copyright management unit protects the copyright of the generated works. The copyright management unit performs digital rights management (DRM), for example. The copyright management unit can also perform license management. The copyright management unit can also monitor copyright infringement. The instruction unit instructs the generation AI on what kind of work to create by the user. The instruction unit gives instructions using, for example, text input. The instruction unit can also give instructions using voice commands.The instruction unit can also use gestures to give instructions, which allows the AI-generated work submission system according to the embodiment to publish, evaluate, rank, manage technology, protect copyrights, and give instructions to works created using the AI-generated work.

[0030] The generation unit can create a work using a generative AI. The generation unit can generate images using, for example, a deep learning model. The generation unit can also generate text using, for example, a natural language generation model. The generation unit can also generate music using, for example, a music generation model. This makes the creation of works more efficient by using generative AI. Some or all of the above-mentioned processing in the generation unit can be performed using generative AI (for example, text generation AI or multimodal generation AI), or can be performed without using generative AI. For example, the generation unit receives user instructions as input, and the generative AI generates a work based on those instructions.

[0031] The publishing unit can publish the generated work on the platform. For example, the publishing unit publishes the work through a website. For example, the publishing unit can also publish the work through a mobile app. For example, the publishing unit can also publish the work through social media. In this way, by publishing the generated work on the platform, it can be widely shared. Some or all of the above-mentioned processing in the publishing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the publishing unit can input the generated work into the generation AI and have the generation AI select the optimal publication method.

[0032] The operation unit can provide an interface for the user to operate the generation AI. The operation unit can provide, for example, a graphical user interface (GUI). The operation unit can also provide, for example, a voice interface. The operation unit can also provide, for example, a touch interface. This makes it easier for the user to operate the generation AI. Some or all of the above-mentioned processing in the operation unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the operation unit can input the user's operation history into the generation AI and have the generation AI select the optimal operation interface.

[0033] The evaluation unit can provide a function for evaluating the generated work. The evaluation unit, for example, performs user evaluation. The evaluation unit can also perform evaluation using an algorithm, for example. The evaluation unit can also perform evaluation by an expert, for example. This enables evaluation of the generated work. Some or all of the above-mentioned processing in the evaluation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the evaluation unit can input the generated work into the generation AI and have the generation AI perform the evaluation.

[0034] The ranking unit can rank works based on the evaluation. The ranking unit performs ranking based on, for example, the evaluation score. The ranking unit can also perform ranking based on, for example, popularity. The ranking unit can also perform ranking based on, for example, expert evaluation. In this way, by ranking works based on the evaluation, the quality of works can be compared. Some or all of the above-described processing in the ranking unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the ranking unit can input the evaluation results into the generation AI and have the generation AI perform the ranking.

[0035] The technical management department can manage the technical details of the generation AI. The technical management department, for example, performs version management. The technical management department can also perform performance monitoring, for example. The technical management department can also perform security management, for example. By managing the technical details of the generation AI, the stability of the system is improved. Some or all of the above-mentioned processing in the technical management department may be performed using the generation AI, or may be performed without using the generation AI. For example, the technical management department can input the technical data of the generation AI into the generation AI and have the generation AI perform technical management.

[0036] The copyright management unit can protect the copyright of the generated work. The copyright management unit, for example, performs digital rights management (DRM). The copyright management unit can also perform license management, for example. The copyright management unit can also monitor copyright infringement, for example. This protects the copyright of the generated work, thereby preventing legal trouble. Some or all of the above-mentioned processing in the copyright management unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the copyright management unit can input copyright data of the generated work into the generation AI and have the generation AI perform copyright management.

[0037] The instruction unit allows the user to instruct the generation AI on what kind of work to create. The instruction unit gives instructions using, for example, text input. The instruction unit can also give instructions using, for example, voice commands. The instruction unit can also give instructions using, for example, gestures. This allows the user to give specific instructions to the generation AI. Some or all of the above-mentioned processing in the instruction unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the instruction unit can input the user's instructions to the generation AI and cause the generation AI to execute the instructions.

[0038] During generation, the generation unit can optimize the generation algorithm by referring to the user's past work history. For example, the generation unit analyzes the theme and style of works created by the user in the past, and the generation AI generates a new work based on that. For example, the generation unit can also incorporate elements of works that the user has previously highly rated, and the generation AI can generate a new work. For example, the generation unit can analyze the failure points of works created by the user in the past, and the generation AI can generate a work that improves on those failures. In this way, the generation algorithm is optimized by referring to the user's past work history. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's past work history data into the generation AI and optimize the generation algorithm based on the generation AI.

[0039] The generation unit can generate works that reflect the user's current interests and trends at the time of generation. For example, the generation unit uses the generation AI to analyze topics that the user has recently been interested in and generate works based on that. For example, the generation unit can also generate works that suit the user by incorporating current social trends and fashions. For example, the generation unit can also generate works on related themes based on keywords recently searched by the user. This provides more interesting works by generating works that reflect the user's current interests and trends. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's current interests and trend data into the generation AI and generate works based on the generation AI.

[0040] The generation unit can improve the generation algorithm by reflecting user feedback during generation. For example, the generation unit adjusts the algorithm based on feedback provided by the user and reflects the feedback in generating the next work. For example, the generation unit can analyze the good and bad points of the work evaluated by the user, and the generation AI can optimize the algorithm based on that. For example, if the user points out specific areas for improvement, the generation unit can generate a new work by reflecting the suggestions. In this way, the generation algorithm is improved by reflecting user feedback. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input user feedback data into the generation AI and improve the generation algorithm based on the generation AI.

[0041] During generation, the generation unit can incorporate region-specific elements by taking into account the user's geographical location information. For example, if the user is in Japan, the generation AI can generate a work themed on Japanese traditional culture and scenery. For example, if the user is in the United States, the generation AI can generate a work themed on American pop culture and scenery. For example, if the user is in Europe, the generation AI can generate a work themed on European history and scenery. In this way, a work incorporating region-specific elements is generated by taking the user's geographical location information into account. Some or all of the above-described processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's geographical location information data into the generation AI and generate a work incorporating region-specific elements based on the generation AI.

[0042] The generation unit can analyze the user's social media activity and incorporate related themes during generation. For example, the generation unit uses a generation AI to analyze themes that the user frequently posts on social media and generate a work based on that analysis. For example, the generation unit can also generate a work by using the generation AI to incorporate topics from accounts the user follows on social media. For example, the generation unit can also generate a work on a related theme based on the content of posts that the user has "liked" on social media. This generates a work that reflects the user's social media activity. Some or all of the above-described processing in the generation unit may be performed using or without the generation AI. For example, the generation unit can input the user's social media activity data into the generation AI and generate a work on a related theme based on the generation AI.

[0043] The generation unit can customize the generation method by reflecting the user's past feedback during generation. For example, the generation unit adjusts the algorithm based on feedback provided by the user in the past and reflects this in the next work generation. For example, the generation unit can analyze the good and bad points of the work evaluated by the user, and the generation AI can optimize the algorithm based on that. For example, if the user points out specific areas for improvement, the generation unit can generate a new work by reflecting the suggestions. In this way, the generation method is customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's past feedback data into the generation AI and customize the generation method based on the generation AI.

[0044] At the time of publication, the publication unit can analyze the viewing history of the work and select the optimal publication method. For example, the publication unit analyzes the trends of works that the user has viewed in the past, and the generation AI selects the optimal publication method based on that. For example, the publication unit can also have the generation AI select the publication method for a new work by referring to the publication methods of works that the user has highly rated. For example, the publication unit can analyze the viewing time periods of works viewed by the user, and the generation AI can select the optimal publication time. In this way, the optimal publication method is selected by analyzing the viewing history. Some or all of the above-mentioned processing in the publication unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the publication unit can input the viewing history data of the work into the generation AI and select the optimal publication method based on the generation AI.

[0045] The publishing unit can determine the publishing priority based on the user's current activity status at the time of publishing. For example, the publishing unit allows the generation AI to prioritize publishing works during times when the user is active. For example, if the user is engaged in another activity, the publishing unit can also publish the work when the generation AI finishes that activity. For example, if the user is taking a break, the publishing unit can also publish the work during times when the generation AI can relax. This enables more effective publishing by determining the publishing priority based on the user's current activity status. Some or all of the above-mentioned processing in the publishing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the publishing unit can input the user's activity status data into the generation AI and determine the publishing priority based on the generation AI.

[0046] The publishing unit can improve the publishing method by reflecting user feedback at the time of publishing. For example, the publishing unit can have the generation AI adjust the publishing method based on feedback provided by the user and reflect this in the next publication. For example, the publishing unit can analyze the good and bad points of the publishing method evaluated by the user, and the generation AI can optimize the publishing method based on that. For example, if the user points out specific areas for improvement, the publishing unit can have the generation AI reflect those suggestions and adopt a new publishing method. In this way, the publishing method is improved by reflecting user feedback. Some or all of the above-mentioned processing in the publishing unit can be performed using or without the generation AI. For example, the publishing unit can input user feedback data into the generation AI and improve the publishing method based on the generation AI.

[0047] At the time of publication, the publication unit can select a region-specific publication method by taking into account the user's geographical location information. For example, if the user is in Japan, the generation AI can prioritize publishing works related to Japanese culture. For example, if the user is in the United States, the generation AI can prioritize publishing works related to American pop culture. For example, if the user is in Europe, the generation AI can prioritize publishing works related to European history. In this way, a region-specific publication method is selected by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the publication unit may be performed using or without the generation AI. For example, the publication unit can input the user's geographical location information data into the generation AI and select a region-specific publication method based on the generation AI.

[0048] At the time of publication, the publication unit can analyze the user's social media activity and select a relevant publication method. For example, the publication unit uses a generation AI to analyze the themes that the user frequently posts on social media and selects a publication method based on that analysis. For example, the publication unit can also select a publication method by using the generation AI to incorporate the topics of accounts the user follows on social media. For example, the publication unit can also select a relevant publication method based on the content of posts that the user "likes" on social media. In this way, a publication method that reflects the user's social media activity is selected. Some or all of the above-mentioned processing in the publication unit may be performed using or without the generation AI. For example, the publication unit can input the user's social media activity data into the generation AI and select a relevant publication method based on the generation AI.

[0049] The publishing unit can customize the publishing method by reflecting the user's past feedback at the time of publishing. For example, the publishing unit has the generation AI adjust the publishing method based on feedback provided by the user in the past and reflect this in the next publication. For example, the publishing unit can analyze the good and bad points of the publishing method evaluated by the user, and the generation AI can optimize the publishing method based on that. For example, if the user points out specific areas for improvement, the publishing unit can have the generation AI reflect those suggestions and adopt a new publishing method. In this way, the publishing method is customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the publishing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the publishing unit can input the user's past feedback data into the generation AI and customize the publishing method based on the generation AI.

[0050] The operation unit can provide an optimal operation method by referring to the user's past operation history during operation. For example, the operation unit preferentially suggests operation methods (voice, text, etc.) that the user has used in the past. For example, the operation unit can also preferentially display functions that the user has used frequently in the past. For example, the operation unit can predict and suggest functions to be used in a specific time period based on the user's past operation history. In this way, the optimal operation method is provided by referring to the user's past operation history. Some or all of the above-mentioned processing in the operation unit may be performed using or without the generation AI. For example, the operation unit can input the user's past operation history data into the generation AI and provide an optimal operation method based on the generation AI.

[0051] The operation unit can customize the operation content according to the user's current task when operated. For example, if the user is creating a video, the generation AI can provide an operation method specialized for video creation. For example, if the user is creating music, the operation unit can also provide an operation method specialized for music creation. For example, if the user is creating a manga, the generation AI can also provide an operation method specialized for manga creation. This provides operation content according to the user's current task. Some or all of the above-mentioned processing in the operation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the operation unit can input the user's current task data into the generation AI and customize the operation content based on the generation AI.

[0052] The operation unit can improve the operation interface by reflecting user feedback during operation. For example, the operation unit allows the generation AI to adjust the operation interface based on feedback provided by the user and reflect the adjustment in the next operation. For example, the operation unit can analyze the good and bad points of the operation interface evaluated by the user, and the generation AI can optimize the interface based on that. For example, when the user points out specific areas for improvement, the operation unit can also provide a new operation interface by reflecting the suggestions. In this way, the operation interface is improved by reflecting user feedback. Some or all of the above-mentioned processing in the operation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the operation unit can input user feedback data into the generation AI and improve the operation interface based on the generation AI.

[0053] The operation unit can provide an optimal operation method by taking into account the user's device information during operation. For example, if the user is using a smartphone, the operation unit can provide an operation method that matches the screen size. For example, if the user is using a tablet, the operation unit can also provide an operation method optimized for a large screen. For example, if the user is using a desktop, the operation unit can also provide an operation method optimized for a keyboard and mouse. In this way, the optimal operation method is provided by taking into account the user's device information. Some or all of the above-described processing in the operation unit may be performed using or without the generation AI. For example, the operation unit can input the user's device information into the generation AI and provide an optimal operation method based on the generation AI.

[0054] The operation unit can make the operation content multilingual according to the user's language setting during operation. The operation unit automatically sets the language of the operation interface based on, for example, the language setting of the user's device. The operation unit can also provide a language switching function when, for example, the user uses multiple languages. For example, when the user selects a specific language, the operation unit can provide the operation interface in that language. This provides multilingual operation content according to the user's language setting. Some or all of the above-mentioned processing in the operation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the operation unit can input the user's language setting data into the generation AI and make the operation content multilingual based on the generation AI.

[0055] The operation unit can customize the operation method by reflecting the user's past feedback during operation. For example, the operation unit allows the generation AI to adjust the operation method based on feedback provided by the user in the past and reflect the adjustment in the next operation. For example, the operation unit can analyze the good and bad points of the operation method evaluated by the user, and the generation AI can optimize the operation method based on the analysis. For example, when the user points out specific areas for improvement, the operation unit can provide a new operation method by reflecting the user's past feedback. In this way, the operation method is customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the operation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the operation unit can input the user's past feedback data into the generation AI and customize the operation method based on the generation AI.

[0056] During evaluation, the evaluation unit can optimize the evaluation algorithm by referring to past evaluation data. For example, the evaluation unit causes the generation AI to adjust the evaluation algorithm based on data on works previously rated by users. For example, the evaluation unit can also cause the generation AI to optimize the evaluation algorithm by incorporating elements of works that users have rated highly. For example, the evaluation unit can also analyze elements of works that users have rated poorly, and provide an evaluation algorithm that improves on those elements. In this way, the evaluation algorithm is optimized by referring to past evaluation data. Some or all of the above-mentioned processing in the evaluation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the evaluation unit can input past evaluation data into the generation AI and optimize the evaluation algorithm based on the generation AI.

[0057] The evaluation unit can make an evaluation taking into account the user's attribute information. In the evaluation unit, the generation AI adjusts the evaluation criteria taking into account, for example, the user's age and gender. In the evaluation unit, the generation AI can also adjust the evaluation criteria taking into account, for example, the user's occupation and hobbies. In the evaluation unit, the generation AI can also adjust the evaluation criteria taking into account, for example, the user's place of residence and cultural background. In this way, a more appropriate evaluation is made by taking into account the user's attribute information. Some or all of the above-mentioned processing in the evaluation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the evaluation unit can input the user's attribute information into the generation AI and make an evaluation based on the generation AI.

[0058] The evaluation unit can improve the evaluation method by reflecting user feedback during evaluation. For example, the evaluation unit allows the generation AI to adjust the evaluation method based on feedback provided by the user and reflect the adjustment in the next evaluation. For example, the evaluation unit can analyze the good and bad points of the evaluation method evaluated by the user, and the generation AI can optimize the evaluation method based on that. For example, if the user points out specific areas for improvement, the evaluation unit can provide a new evaluation method by reflecting the suggestions. In this way, the evaluation method is improved by reflecting user feedback. Some or all of the above-mentioned processing in the evaluation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the evaluation unit can input user feedback data into the generation AI and improve the evaluation method based on the generation AI.

[0059] The evaluation unit can take the user's geographical location information into account when making the evaluation. For example, if the user is in Japan, the generation AI can provide evaluation criteria related to Japanese culture. For example, if the user is in the United States, the generation AI can provide evaluation criteria related to American pop culture. For example, if the user is in Europe, the generation AI can provide evaluation criteria related to European history. This allows for a more appropriate evaluation by taking the user's geographical location information into account. Some or all of the above-mentioned processing in the evaluation unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the evaluation unit can input the user's geographical location information into the generation AI and make an evaluation based on the generation AI.

[0060] During evaluation, the evaluation unit can analyze the user's social media activity to improve the accuracy of the evaluation. For example, the evaluation unit uses the generation AI to analyze the themes that the user frequently posts on social media and makes an evaluation based on that. For example, the evaluation unit can also use the generation AI to incorporate topics of accounts the user follows on social media to make an evaluation. For example, the evaluation unit can also use the generation AI to make a related evaluation based on the content of posts that the user "likes" on social media. This allows an evaluation that reflects the user's social media activity. Some or all of the above-mentioned processing in the evaluation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the evaluation unit can input the user's social media activity data into the generation AI and improve the accuracy of the evaluation based on the generation AI.

[0061] The evaluation unit can customize the evaluation method by reflecting the user's past feedback during evaluation. For example, the evaluation unit allows the generation AI to adjust the evaluation method based on feedback provided by the user in the past and reflect this in the next evaluation. For example, the evaluation unit can analyze the good and bad points of the evaluation method evaluated by the user, and the generation AI can optimize the evaluation method based on that. For example, if the user points out specific areas for improvement, the evaluation unit can provide a new evaluation method by reflecting the user's past feedback. In this way, the evaluation method is customized by reflecting the user's past feedback. Some or all of the above-described processing in the evaluation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the evaluation unit can input the user's past feedback data into the generation AI and customize the evaluation method based on the generation AI.

[0062] The ranking unit can optimize the ranking algorithm by referring to past ranking data when ranking. For example, the ranking unit adjusts the ranking algorithm using the generation AI based on data on works that users have previously ranked. For example, the ranking unit can also optimize the ranking algorithm using the generation AI by incorporating elements of works that users have highly rated. For example, the ranking unit can analyze elements of works that users have low rated, and provide an improved ranking algorithm using the generation AI. In this way, the ranking algorithm is optimized by referring to past ranking data. Some or all of the above-mentioned processing in the ranking unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the ranking unit can input past ranking data into the generation AI and optimize the ranking algorithm based on the generation AI.

[0063] The ranking unit can perform ranking taking into account user attribute information. In the ranking unit, the generation AI adjusts the ranking criteria taking into account, for example, the user's age and gender. In the ranking unit, the generation AI can also adjust the ranking criteria taking into account, for example, the user's occupation and hobbies. In the ranking unit, the generation AI can also adjust the ranking criteria taking into account, for example, the user's place of residence and cultural background. In this way, more appropriate ranking is performed by taking into account the user's attribute information. Some or all of the above-described processing in the ranking unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the ranking unit can input the user's attribute information into the generation AI and perform ranking based on the generation AI.

[0064] The ranking unit can improve the ranking method by reflecting user feedback during ranking. For example, the ranking unit adjusts the ranking method using the generation AI based on feedback provided by the user and reflects this in the next ranking. For example, the ranking unit can analyze the good and bad points of the ranking method evaluated by the user, and the generation AI can optimize the ranking method based on that. For example, if the user points out specific areas for improvement, the ranking unit can provide a new ranking method by reflecting the suggestions. In this way, the ranking method is improved by reflecting user feedback. Some or all of the above-mentioned processing in the ranking unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the ranking unit can input user feedback data into the generation AI and improve the ranking method based on the generation AI.

[0065] The ranking unit can perform ranking taking into account the user's geographical location information. For example, if the user is in Japan, the generating AI can provide ranking criteria related to Japanese culture. For example, if the user is in the United States, the generating AI can provide ranking criteria related to American pop culture. For example, if the user is in Europe, the generating AI can provide ranking criteria related to European history. This allows for more appropriate ranking by taking the user's geographical location information into consideration. Some or all of the above-described processing in the ranking unit can be performed using the generating AI, or can be performed without using the generating AI. For example, the ranking unit can input the user's geographical location information into the generating AI and perform ranking based on the generating AI.

[0066] The ranking unit can analyze the user's social media activity during ranking to improve the accuracy of the ranking. For example, the ranking unit uses a generation AI to analyze themes that the user frequently posts on social media and ranks based on the results. For example, the ranking unit can also use the generation AI to incorporate topics of accounts the user follows on social media to perform rankings. For example, the ranking unit can also perform rankings based on the content of posts that the user "likes" on social media. This allows rankings that reflect the user's social media activity. Some or all of the above-mentioned processing in the ranking unit can be performed using or without the generation AI. For example, the ranking unit can input the user's social media activity data into the generation AI and improve the accuracy of the ranking based on the generation AI.

[0067] The ranking unit can customize the ranking method by reflecting the user's past feedback when ranking. For example, the ranking unit adjusts the ranking method using the generation AI based on feedback provided by the user in the past and reflects this in the next ranking. For example, the ranking unit can analyze the advantages and disadvantages of the ranking method evaluated by the user, and the generation AI can optimize the ranking method based on this. For example, if the user points out specific areas for improvement, the ranking unit can provide a new ranking method by reflecting the user's past feedback. In this way, the ranking method is customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the ranking unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the ranking unit can input the user's past feedback data into the generation AI and customize the ranking method based on the generation AI.

[0068] During technology management, the technology management department can optimize the technology management algorithm by referring to past technology data. For example, the technology management department has the generation AI adjust the technology management algorithm based on technology data previously managed by the user. For example, the technology management department can also optimize the technology management algorithm by incorporating elements of a technology management method that the user has rated highly. For example, the technology management department can analyze elements of a technology management method that the user has rated poorly, and provide an improved technology management algorithm by the generation AI. In this way, the technology management algorithm is optimized by referring to past technology data. Some or all of the above-described processing in the technology management department may be performed using the generation AI, or may be performed without using the generation AI. For example, the technology management department can input past technology data into the generation AI and optimize the technology management algorithm based on the generation AI.

[0069] The technology management unit can perform technology management by taking into account the user's attribute information. In the technology management unit, the generation AI adjusts the technology management standards by taking into account, for example, the user's age and gender. In the technology management unit, the generation AI can also adjust the technology management standards by taking into account, for example, the user's occupation and hobbies. In the technology management unit, the generation AI can also adjust the technology management standards by taking into account, for example, the user's place of residence and cultural background. In this way, more appropriate technology management is performed by taking into account the user's attribute information. Some or all of the above-mentioned processing in the technology management unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the technology management unit can input the user's attribute information into the generation AI and perform technology management based on the generation AI.

[0070] During technology management, the technology management department can improve the technology management method by reflecting user feedback. For example, the technology management department can have the generation AI adjust the technology management method based on feedback provided by the user and reflect the adjustment in the next technology management. For example, the technology management department can analyze the advantages and disadvantages of the technology management method evaluated by the user, and the generation AI can optimize the technology management method based on that. For example, if the user points out specific areas for improvement, the technology management department can have the generation AI reflect those suggestions and provide a new technology management method. This improves the technology management method by reflecting user feedback. Some or all of the above-described processing in the technology management department can be performed using or without the generation AI. For example, the technology management department can input user feedback data into the generation AI and improve the technology management method based on the generation AI.

[0071] The technology management department can perform technology management by taking into account the user's geographic location information. For example, if the user is in Japan, the generation AI can provide management standards related to Japanese technology. For example, if the user is in the United States, the generation AI can provide management standards related to American technology. For example, if the user is in Europe, the generation AI can provide management standards related to European technology. This allows for more appropriate technology management by taking the user's geographic location information into consideration. Some or all of the above-mentioned processing in the technology management department may be performed using the generation AI, or may be performed without using the generation AI. For example, the technology management department can input the user's geographic location information into the generation AI and perform technology management based on the generation AI.

[0072] During technology management, the technology management unit can analyze the user's social media activities to improve the accuracy of technology management. For example, the technology management unit uses a generation AI to analyze technology-related topics that the user frequently posts on social media and performs technology management based on the results. For example, the technology management unit can also perform technology management by using the generation AI to incorporate topics from technology-related accounts that the user follows on social media. For example, the technology management unit can also perform related technology management based on the content of technology-related posts that the user "likes" on social media. This allows technology management that reflects the user's social media activities. Some or all of the above-mentioned processing in the technology management unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the technology management unit can input the user's social media activity data into the generation AI and improve the accuracy of technology management based on the generation AI.

[0073] During technology management, the technology management department can customize the technology management method by reflecting the user's past feedback. For example, the technology management department can have the generation AI adjust the technology management method based on feedback provided by the user in the past and reflect the adjustment in the next technology management. For example, the technology management department can analyze the advantages and disadvantages of the technology management method evaluated by the user, and the generation AI can optimize the technology management method based on that. For example, if the user points out specific areas for improvement, the generation AI can reflect those suggestions and provide a new technology management method. In this way, the technology management method is customized by reflecting the user's past feedback. Some or all of the above-described processing in the technology management department may be performed using the generation AI, or may be performed without using the generation AI. For example, the technology management department can input the user's past feedback data into the generation AI and customize the technology management method based on the generation AI.

[0074] During copyright management, the copyright management unit can optimize the copyright management algorithm by referring to past copyright data. For example, the copyright management unit uses a generation AI to adjust the copyright management algorithm based on copyright data previously managed by the user. For example, the copyright management unit can also optimize the copyright management algorithm by incorporating elements of copyright management methods that users have rated highly. For example, the copyright management unit can analyze elements of copyright management methods that users have rated poorly and provide an improved copyright management algorithm. This optimizes the copyright management algorithm by referring to past copyright data. Some or all of the above-described processing in the copyright management unit may be performed using or without the generation AI. For example, the copyright management unit can input past copyright data into the generation AI and optimize the copyright management algorithm based on the generation AI.

[0075] The copyright management unit can perform copyright management by taking into account the user's attribute information. For example, the generation AI adjusts the copyright management standards by taking into account the user's age and gender. For example, the copyright management unit can also adjust the copyright management standards by taking into account the user's occupation and hobbies. For example, the copyright management unit can also adjust the copyright management standards by taking into account the user's place of residence and cultural background. In this way, more appropriate copyright management is performed by taking into account the user's attribute information. Some or all of the above-mentioned processing in the copyright management unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the copyright management unit can input the user's attribute information into the generation AI and perform copyright management based on the generation AI.

[0076] The copyright management unit can improve the copyright management method by reflecting user feedback during copyright management. For example, the copyright management unit can have the generation AI adjust the copyright management method based on feedback provided by the user and reflect this in the next copyright management. For example, the copyright management unit can analyze the good and bad points of the copyright management method evaluated by the user, and the generation AI can optimize the copyright management method based on that. For example, if the user points out specific areas for improvement, the copyright management unit can have the generation AI reflect those suggestions and provide a new copyright management method. In this way, the copyright management method is improved by reflecting user feedback. Some or all of the above-mentioned processing in the copyright management unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the copyright management unit can input user feedback data into the generation AI and improve the copyright management method based on the generation AI.

[0077] The copyright management unit can perform copyright management by taking into account the user's geographic location information. For example, if the user is in Japan, the generating AI can provide management standards related to Japanese copyright law. For example, if the user is in the United States, the generating AI can provide management standards related to American copyright law. For example, if the user is in Europe, the generating AI can provide management standards related to European copyright law. This allows for more appropriate copyright management by taking the user's geographic location information into account. Some or all of the above-mentioned processing in the copyright management unit can be performed using the generating AI, or can be performed without using the generating AI. For example, the copyright management unit can input the user's geographic location information into the generating AI and perform copyright management based on the generating AI.

[0078] During copyright management, the copyright management unit can analyze the user's social media activities to improve the accuracy of copyright management. For example, the copyright management unit performs copyright management based on an analysis of copyright-related topics frequently posted on social media by the generation AI. For example, the copyright management unit can also perform copyright management by incorporating topics from copyright-related accounts that the user follows on social media. For example, the copyright management unit can also perform copyright management based on the content of copyright-related posts that the user "likes" on social media. This allows copyright management that reflects the user's social media activities. Some or all of the above-described processing in the copyright management unit may be performed using or without the generation AI. For example, the copyright management unit can input the user's social media activity data into the generation AI and improve the accuracy of copyright management based on the generation AI.

[0079] The copyright management unit can customize the copyright management method by reflecting the user's past feedback during copyright management. For example, the copyright management unit can have the generation AI adjust the copyright management method based on feedback provided by the user in the past and reflect this in the next copyright management. For example, the copyright management unit can analyze the advantages and disadvantages of the copyright management method evaluated by the user, and the generation AI can optimize the copyright management method based on this. For example, if the user points out specific areas for improvement, the copyright management unit can have the generation AI reflect these suggestions and provide a new copyright management method. In this way, the copyright management method is customized by reflecting the user's past feedback. Some or all of the above-described processing in the copyright management unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the copyright management unit can input the user's past feedback data into the generation AI and customize the copyright management method based on the generation AI.

[0080] When issuing an instruction, the instruction unit can optimize the instruction algorithm by referring to past instruction data. In the instruction unit, for example, the generation AI adjusts the instruction algorithm based on data of instructions issued by the user in the past. In the instruction unit, for example, the generation AI can also optimize the instruction algorithm by incorporating elements of instruction content that the user has given a high rating. In the instruction unit, for example, the generation AI can analyze elements of instruction content that the user has given a low rating, and provide an instruction algorithm that has improved on those elements. In this way, the instruction algorithm is optimized by referring to past instruction data. Some or all of the above-mentioned processing in the instruction unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the instruction unit can input past instruction data into the generation AI and optimize the instruction algorithm based on the generation AI.

[0081] When giving an instruction, the instruction unit can give an instruction taking into account the user's attribute information. In the instruction unit, the generation AI can adjust the instruction content taking into account, for example, the user's age and gender. In the instruction unit, the generation AI can also adjust the instruction content taking into account, for example, the user's occupation and hobbies. In the instruction unit, the generation AI can also adjust the instruction content taking into account, for example, the user's place of residence and cultural background. In this way, more appropriate instructions are given by taking into account the user's attribute information. Some or all of the above-mentioned processing in the instruction unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the instruction unit can input the user's attribute information into the generation AI and give instructions based on the generation AI.

[0082] The instruction unit can improve the instruction method by reflecting user feedback when giving an instruction. For example, the instruction unit allows the generation AI to adjust the instruction method based on feedback provided by the user and reflect the adjustment in the next instruction. For example, the instruction unit can analyze the good and bad points of the instruction method evaluated by the user, and the generation AI can optimize the instruction method based on the analysis. For example, when the user points out specific areas for improvement, the instruction unit can provide a new instruction method by reflecting the suggestions. In this way, the instruction method is improved by reflecting the user's feedback. Some or all of the above-mentioned processing in the instruction unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the instruction unit can input user feedback data into the generation AI and improve the instruction method based on the generation AI.

[0083] When giving instructions, the instruction unit can take into account the user's geographical location information. For example, if the user is in Japan, the generation AI can provide instructions related to Japanese culture. For example, if the user is in America, the generation AI can provide instructions related to American pop culture. For example, if the user is in Europe, the generation AI can provide instructions related to European history. This allows more appropriate instructions to be given by taking the user's geographical location information into account. Some or all of the above-mentioned processing in the instruction unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the instruction unit can input the user's geographical location information into the generation AI and give instructions based on the generation AI.

[0084] When giving instructions, the instruction unit can analyze the user's social media activity to improve the accuracy of the instructions. For example, the instruction unit causes the generation AI to analyze themes that the user frequently posts on social media and give instructions based on that. For example, the instruction unit can also cause the generation AI to incorporate topics of accounts the user follows on social media and give instructions. For example, the instruction unit can also cause the generation AI to give related instructions based on the content of posts that the user has "liked" on social media. In this way, instructions that reflect the user's social media activity are given. Some or all of the above-mentioned processing in the instruction unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the instruction unit can input the user's social media activity data into the generation AI and improve the accuracy of the instructions based on the generation AI.

[0085] The instruction unit can customize the instruction method by reflecting the user's past feedback when giving an instruction. For example, the instruction unit has the generation AI adjust the instruction method based on feedback provided by the user in the past and reflect this in the next instruction. For example, the instruction unit can analyze the good and bad points of the instruction method evaluated by the user, and the generation AI can optimize the instruction method based on that. For example, if the user points out specific areas for improvement, the instruction unit can have the generation AI reflect those points and provide a new instruction method. In this way, the instruction method is customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the instruction unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the instruction unit can input the user's past feedback data into the generation AI and customize the instruction method based on the generation AI.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The generation unit can optimize the generation algorithm by referring to the user's past work history. For example, the generation AI analyzes the theme and style of works created by the user in the past, and generates a new work based on that. For example, the generation unit can also incorporate elements of works that the user has previously received high praise from, and generate a new work by the generation AI. For example, the generation unit can analyze the failure points of works created by the user in the past, and generate a work that improves on those failures. In this way, the generation algorithm is optimized by referring to the user's past work history.

[0088] The generation unit can generate works that reflect the user's current interests and trends at the time of generation. For example, the generation AI analyzes topics that the user has recently been interested in and generates works based on that. The generation unit can also generate works that suit the user, for example, by having the generation AI incorporate current social trends and fashions. For example, the generation unit can generate works on related themes based on keywords that the user has recently searched. This allows the generation unit to provide more interesting works by generating works that reflect the user's current interests and trends.

[0089] The generation unit can improve the generation algorithm by reflecting user feedback during generation. For example, the generation AI can adjust the algorithm based on feedback provided by the user and reflect this in the next work generation. The generation unit can also analyze the good and bad points of the work evaluated by the user, and the generation AI can optimize the algorithm based on that. For example, if the user points out specific areas for improvement, the generation unit can have the generation AI reflect those suggestions to generate a new work. In this way, the generation algorithm is improved by reflecting user feedback.

[0090] During generation, the generation unit can incorporate region-specific elements by taking into account the user's geographical location information. For example, if the user is in Japan, the generation AI can generate works themed around Japanese traditional culture and scenery. For example, if the user is in America, the generation unit can also generate works themed around American pop culture and scenery. For example, if the user is in Europe, the generation unit can also generate works themed around European history and scenery. In this way, by taking into account the user's geographical location information, works that incorporate region-specific elements are generated.

[0091] At the time of publication, the publishing unit can analyze the viewing history of a work and select the optimal publication method. For example, the tendency of works that a user has viewed in the past is analyzed, and the generation AI selects the optimal publication method based on that. For example, the publishing unit can also select the publication method for a new work by referring to the publication method of works that users have highly rated. For example, the publishing unit can analyze the viewing time periods of works that users have viewed, and the generation AI can select the optimal publication time. In this way, the optimal publication method is selected by analyzing the viewing history.

[0092] The publishing unit can determine the publishing priority based on the user's current activity status at the time of publishing. For example, the generation AI will prioritize publishing works during times when the user is active. For example, if the user is engaged in another activity, the publishing unit can also publish the work when the generation AI finishes that activity. For example, if the user is on a break, the publishing unit can also publish the work during times when the generation AI can relax. This allows for more effective publishing by determining the publishing priority based on the user's current activity status.

[0093] The operation unit can provide the optimal operation method by referring to the user's past operation history when operating the device. For example, it can preferentially suggest operation methods (voice, text, etc.) that the user has used in the past. For example, the operation unit can also preferentially display functions that the user has frequently used in the past. For example, the operation unit can predict and suggest functions that will be used in a specific time period based on the user's past operation history. In this way, the optimal operation method can be provided by referring to the user's past operation history.

[0094] The processing flow of the first embodiment will be briefly explained below.

[0095] Step 1: The generator creates the work using generative AI. The generator can generate images using a deep learning model, generate text using a natural language generation model, or generate music using a music generation model. Step 2: The publishing department publishes the created work on the platform. The publishing department can publish the work through a website, mobile app, or social media. Step 3: The operation unit provides an interface for the user to operate the generated AI. The operation unit can provide a graphical user interface (GUI), a voice interface, or a touch interface. Step 4: The evaluation unit provides a function for evaluating the generated work. The evaluation unit can perform user evaluation, algorithmic evaluation, and expert evaluation. Step 5: The ranking unit ranks the works based on the ratings. The ranking unit can rank based on rating scores, popularity, and expert ratings. Step 6: The technical management team manages the technical details of the generative AI. This team can handle version control, performance monitoring, and security management. Step 7: The copyright management unit protects the copyright of the generated work. The copyright management unit can perform digital rights management (DRM), license management, and piracy monitoring. Step 8: The instruction unit instructs the AI ​​what kind of work the user wants to create. The instruction unit can use text input, voice commands, or gestures to give instructions.

[0096] (Example 2) A generative AI work submission system according to an embodiment of the present invention is a system that can publish, evaluate, rank, technically manage, copyright, and provide instructions for works created using generative AI. The generative AI work submission system can create works using generative AI and publish them on a platform. For example, a user can create a video using generative AI and publish the video on the platform. It can also create music using generative AI and publish the music on the platform. The generative AI work submission system is provided as a platform specialized in Japanese culture and markets, aiming to create new IP and cultivate a group of works that can be sold overseas. For example, by creating a work themed around traditional Japanese culture using generative AI and publishing it on the platform, overseas users can become familiar with Japanese culture. This allows the generative AI work submission system to widely recognize the potential of generative AI and provide a platform where many people can thrive as creators. This allows the generative AI work submission system to widely publish, evaluate, rank, technically manage, and protect copyright for works created using generative AI. For example, the generative AI work submission system allows users to quickly and accurately publish works created using generative AI and receive reviews from other users. In addition, the generative AI work submission system protects the copyright of works created using generative AI, preventing legal trouble.

[0097] A generative AI work posting system according to an embodiment includes a generation unit, a publication unit, an operation unit, an evaluation unit, a ranking unit, a technology management unit, a copyright management unit, and an instruction unit. The generation unit creates works using a generative AI. The generation unit generates images using, for example, a deep learning model. The generation unit can also generate text using a natural language generation model. The generation unit can also generate music using a music generation model. The publication unit publishes the generated works on a platform. The publication unit publishes the works, for example, through a website. The publication unit can also publish the works through a mobile app. The publication unit can also publish the works through social media. The operation unit provides an interface for a user to operate the generative AI. The operation unit provides, for example, a graphical user interface (GUI). The operation unit can also provide a voice interface. The operation unit can also provide a touch interface. The evaluation unit provides a function for evaluating the generated works. The evaluation unit performs, for example, user evaluation. The evaluation unit can also perform evaluation using an algorithm. The evaluation unit can also perform evaluation by an expert. The ranking unit ranks the works based on the evaluation. The ranking unit ranks the works based on the evaluation, for example. The ranking unit ranks the works based on the evaluation score. The ranking unit can also rank the works based on popularity. The ranking unit can also rank the works based on expert evaluation. The technical management unit manages technical details of the generation AI. The technical management unit performs version management, for example. The technical management unit can also perform performance monitoring. The technical management unit can also perform security management. The copyright management unit protects the copyright of the generated works. The copyright management unit performs digital rights management (DRM), for example. The copyright management unit can also perform license management. The copyright management unit can also monitor copyright infringement. The instruction unit instructs the generation AI on what kind of work to create by the user. The instruction unit gives instructions using, for example, text input. The instruction unit can also give instructions using voice commands.The instruction unit can also use gestures to give instructions, which allows the AI-generated work submission system according to the embodiment to publish, evaluate, rank, manage technology, protect copyrights, and give instructions to works created using the AI-generated work.

[0098] The generation unit can create a work using a generative AI. The generation unit can generate images using, for example, a deep learning model. The generation unit can also generate text using, for example, a natural language generation model. The generation unit can also generate music using, for example, a music generation model. This makes the creation of works more efficient by using generative AI. Some or all of the above-mentioned processing in the generation unit can be performed using generative AI (for example, text generation AI or multimodal generation AI), or can be performed without using generative AI. For example, the generation unit receives user instructions as input, and the generative AI generates a work based on those instructions.

[0099] The publishing unit can publish the generated work on the platform. For example, the publishing unit publishes the work through a website. For example, the publishing unit can also publish the work through a mobile app. For example, the publishing unit can also publish the work through social media. In this way, by publishing the generated work on the platform, it can be widely shared. Some or all of the above-mentioned processing in the publishing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the publishing unit can input the generated work into the generation AI and have the generation AI select the optimal publication method.

[0100] The operation unit can provide an interface for the user to operate the generation AI. The operation unit can provide, for example, a graphical user interface (GUI). The operation unit can also provide, for example, a voice interface. The operation unit can also provide, for example, a touch interface. This makes it easier for the user to operate the generation AI. Some or all of the above-mentioned processing in the operation unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the operation unit can input the user's operation history into the generation AI and have the generation AI select the optimal operation interface.

[0101] The evaluation unit can provide a function for evaluating the generated work. The evaluation unit, for example, performs user evaluation. The evaluation unit can also perform evaluation using an algorithm, for example. The evaluation unit can also perform evaluation by an expert, for example. This enables evaluation of the generated work. Some or all of the above-mentioned processing in the evaluation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the evaluation unit can input the generated work into the generation AI and have the generation AI perform the evaluation.

[0102] The ranking unit can rank works based on the evaluation. The ranking unit performs ranking based on, for example, the evaluation score. The ranking unit can also perform ranking based on, for example, popularity. The ranking unit can also perform ranking based on, for example, expert evaluation. In this way, by ranking works based on the evaluation, the quality of works can be compared. Some or all of the above-described processing in the ranking unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the ranking unit can input the evaluation results into the generation AI and have the generation AI perform the ranking.

[0103] The technical management department can manage the technical details of the generation AI. The technical management department, for example, performs version management. The technical management department can also perform performance monitoring, for example. The technical management department can also perform security management, for example. By managing the technical details of the generation AI, the stability of the system is improved. Some or all of the above-mentioned processing in the technical management department may be performed using the generation AI, or may be performed without using the generation AI. For example, the technical management department can input the technical data of the generation AI into the generation AI and have the generation AI perform technical management.

[0104] The copyright management unit can protect the copyright of the generated work. The copyright management unit, for example, performs digital rights management (DRM). The copyright management unit can also perform license management, for example. The copyright management unit can also monitor copyright infringement, for example. This protects the copyright of the generated work, thereby preventing legal trouble. Some or all of the above-mentioned processing in the copyright management unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the copyright management unit can input copyright data of the generated work into the generation AI and have the generation AI perform copyright management.

[0105] The instruction unit allows the user to instruct the generation AI on what kind of work to create. The instruction unit gives instructions using, for example, text input. The instruction unit can also give instructions using, for example, voice commands. The instruction unit can also give instructions using, for example, gestures. This allows the user to give specific instructions to the generation AI. Some or all of the above-mentioned processing in the instruction unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the instruction unit can input the user's instructions to the generation AI and cause the generation AI to execute the instructions.

[0106] The generation unit can estimate the user's emotions and adjust the theme and style of the generated artwork based on the estimated user emotions. For example, if the user is relaxed, the generation AI can generate a video with calm music or a landscape theme. For example, if the user is excited, the generation AI can generate an action-packed video or energetic music. For example, if the user is sad, the generation AI can generate an inspiring story or soothing music. This allows for the creation of artworks based on the user's emotions, resulting in a more personalized artwork. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using the generation AI, or can be performed without the generation AI. For example, the generation unit can input the user's emotion data into the generation AI and adjust the theme and style of the artwork based on the generation AI.

[0107] During generation, the generation unit can optimize the generation algorithm by referring to the user's past work history. For example, the generation unit analyzes the theme and style of works created by the user in the past, and the generation AI generates a new work based on that. For example, the generation unit can also incorporate elements of works that the user has previously highly rated, and the generation AI can generate a new work. For example, the generation unit can analyze the failure points of works created by the user in the past, and the generation AI can generate a work that improves on those failures. In this way, the generation algorithm is optimized by referring to the user's past work history. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's past work history data into the generation AI and optimize the generation algorithm based on the generation AI.

[0108] The generation unit can generate works that reflect the user's current interests and trends at the time of generation. For example, the generation unit uses the generation AI to analyze topics that the user has recently been interested in and generate works based on that. For example, the generation unit can also generate works that suit the user by incorporating current social trends and fashions. For example, the generation unit can also generate works on related themes based on keywords recently searched by the user. This provides more interesting works by generating works that reflect the user's current interests and trends. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's current interests and trend data into the generation AI and generate works based on the generation AI.

[0109] The generation unit can improve the generation algorithm by reflecting user feedback during generation. For example, the generation unit adjusts the algorithm based on feedback provided by the user and reflects the feedback in generating the next work. For example, the generation unit can analyze the good and bad points of the work evaluated by the user, and the generation AI can optimize the algorithm based on that. For example, if the user points out specific areas for improvement, the generation unit can generate a new work by reflecting the suggestions. In this way, the generation algorithm is improved by reflecting user feedback. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input user feedback data into the generation AI and improve the generation algorithm based on the generation AI.

[0110] The generation unit can estimate the user's emotions and determine the genre of the work to be generated based on the estimated user emotions. For example, if the user is relaxed, the generation AI can generate relaxing music or a landscape video. For example, if the user is excited, the generation AI can generate an action movie or energetic music. For example, if the user is sad, the generation AI can generate an inspiring story or soothing music. This allows for the generation of works in a genre that corresponds to the user's emotions, providing a more personalized work. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the generation unit can input the user's emotion data into the generation AI and determine the genre of the work based on the generation AI.

[0111] During generation, the generation unit can incorporate region-specific elements by taking into account the user's geographical location information. For example, if the user is in Japan, the generation AI can generate a work themed on Japanese traditional culture and scenery. For example, if the user is in the United States, the generation AI can generate a work themed on American pop culture and scenery. For example, if the user is in Europe, the generation AI can generate a work themed on European history and scenery. In this way, a work incorporating region-specific elements is generated by taking the user's geographical location information into account. Some or all of the above-described processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's geographical location information data into the generation AI and generate a work incorporating region-specific elements based on the generation AI.

[0112] The generation unit can analyze the user's social media activity and incorporate related themes during generation. For example, the generation unit uses a generation AI to analyze themes that the user frequently posts on social media and generate a work based on that analysis. For example, the generation unit can also generate a work by using the generation AI to incorporate topics from accounts the user follows on social media. For example, the generation unit can also generate a work on a related theme based on the content of posts that the user has "liked" on social media. This generates a work that reflects the user's social media activity. Some or all of the above-described processing in the generation unit may be performed using or without the generation AI. For example, the generation unit can input the user's social media activity data into the generation AI and generate a work on a related theme based on the generation AI.

[0113] The generation unit can customize the generation method by reflecting the user's past feedback during generation. For example, the generation unit adjusts the algorithm based on feedback provided by the user in the past and reflects this in the next work generation. For example, the generation unit can analyze the good and bad points of the work evaluated by the user, and the generation AI can optimize the algorithm based on that. For example, if the user points out specific areas for improvement, the generation unit can generate a new work by reflecting the suggestions. In this way, the generation method is customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's past feedback data into the generation AI and customize the generation method based on the generation AI.

[0114] The publishing unit can estimate the user's emotions and determine the timing of publishing based on the estimated user emotions. For example, if the user is relaxed, the generation AI can publish the work during a calm time. For example, if the user is excited, the publishing unit can also publish the work during a time when the user is most active. For example, if the user is sad, the generation AI can also publish the work during a time when the user's emotions are stable. This enables more effective publishing by publishing the work at a time that corresponds to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the publishing unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the publishing unit can input the user's emotion data into the generation AI and determine the timing of publishing based on the generation AI.

[0115] At the time of publication, the publication unit can analyze the viewing history of the work and select the optimal publication method. For example, the publication unit analyzes the trends of works that the user has viewed in the past, and the generation AI selects the optimal publication method based on that. For example, the publication unit can also have the generation AI select the publication method for a new work by referring to the publication methods of works that the user has highly rated. For example, the publication unit can analyze the viewing time periods of works viewed by the user, and the generation AI can select the optimal publication time. In this way, the optimal publication method is selected by analyzing the viewing history. Some or all of the above-mentioned processing in the publication unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the publication unit can input the viewing history data of the work into the generation AI and select the optimal publication method based on the generation AI.

[0116] The publishing unit can determine the publishing priority based on the user's current activity status at the time of publishing. For example, the publishing unit allows the generation AI to prioritize publishing works during times when the user is active. For example, if the user is engaged in another activity, the publishing unit can also publish the work when the generation AI finishes that activity. For example, if the user is taking a break, the publishing unit can also publish the work during times when the generation AI can relax. This enables more effective publishing by determining the publishing priority based on the user's current activity status. Some or all of the above-mentioned processing in the publishing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the publishing unit can input the user's activity status data into the generation AI and determine the publishing priority based on the generation AI.

[0117] The publishing unit can improve the publishing method by reflecting user feedback at the time of publishing. For example, the publishing unit can have the generation AI adjust the publishing method based on feedback provided by the user and reflect this in the next publication. For example, the publishing unit can analyze the good and bad points of the publishing method evaluated by the user, and the generation AI can optimize the publishing method based on that. For example, if the user points out specific areas for improvement, the publishing unit can have the generation AI reflect those suggestions and adopt a new publishing method. In this way, the publishing method is improved by reflecting user feedback. Some or all of the above-mentioned processing in the publishing unit can be performed using or without the generation AI. For example, the publishing unit can input user feedback data into the generation AI and improve the publishing method based on the generation AI.

[0118] The publishing unit can estimate the user's emotions and determine the order of works to be published based on the estimated user emotions. For example, if the user is relaxed, the generation AI can publish calm works first. For example, if the user is excited, the generation AI can publish energetic works first. For example, if the user is sad, the generation AI can publish moving works first. This enables more effective publishing by publishing works in an order according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the publishing unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the publishing unit can input the user's emotion data into the generation AI and determine the order of works to be published based on the generation AI.

[0119] At the time of publication, the publication unit can select a region-specific publication method by taking into account the user's geographical location information. For example, if the user is in Japan, the generation AI can prioritize publishing works related to Japanese culture. For example, if the user is in the United States, the generation AI can prioritize publishing works related to American pop culture. For example, if the user is in Europe, the generation AI can prioritize publishing works related to European history. In this way, a region-specific publication method is selected by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the publication unit may be performed using or without the generation AI. For example, the publication unit can input the user's geographical location information data into the generation AI and select a region-specific publication method based on the generation AI.

[0120] At the time of publication, the publication unit can analyze the user's social media activity and select a relevant publication method. For example, the publication unit uses a generation AI to analyze the themes that the user frequently posts on social media and selects a publication method based on that analysis. For example, the publication unit can also select a publication method by using the generation AI to incorporate the topics of accounts the user follows on social media. For example, the publication unit can also select a relevant publication method based on the content of posts that the user "likes" on social media. In this way, a publication method that reflects the user's social media activity is selected. Some or all of the above-mentioned processing in the publication unit may be performed using or without the generation AI. For example, the publication unit can input the user's social media activity data into the generation AI and select a relevant publication method based on the generation AI.

[0121] The publishing unit can customize the publishing method by reflecting the user's past feedback at the time of publishing. For example, the publishing unit has the generation AI adjust the publishing method based on feedback provided by the user in the past and reflect this in the next publication. For example, the publishing unit can analyze the good and bad points of the publishing method evaluated by the user, and the generation AI can optimize the publishing method based on that. For example, if the user points out specific areas for improvement, the publishing unit can have the generation AI reflect those suggestions and adopt a new publishing method. In this way, the publishing method is customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the publishing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the publishing unit can input the user's past feedback data into the generation AI and customize the publishing method based on the generation AI.

[0122] The operation unit can estimate the user's emotions and determine how to display the operation interface based on the estimated user emotions. For example, when the user is nervous, the operation unit provides a simple, highly visible interface. For example, when the user is relaxed, the operation unit can also provide an interface containing detailed information. For example, when the user is in a hurry, the operation unit can also provide an interface that focuses on the main points. This provides an operation interface that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the operation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the operation unit can input user emotion data into the generation AI and determine how to display the operation interface based on the generation AI.

[0123] The operation unit can provide an optimal operation method by referring to the user's past operation history during operation. For example, the operation unit preferentially suggests operation methods (voice, text, etc.) that the user has used in the past. For example, the operation unit can also preferentially display functions that the user has used frequently in the past. For example, the operation unit can predict and suggest functions to be used in a specific time period based on the user's past operation history. In this way, the optimal operation method is provided by referring to the user's past operation history. Some or all of the above-mentioned processing in the operation unit may be performed using or without the generation AI. For example, the operation unit can input the user's past operation history data into the generation AI and provide an optimal operation method based on the generation AI.

[0124] The operation unit can customize the operation content according to the user's current task when operated. For example, if the user is creating a video, the generation AI can provide an operation method specialized for video creation. For example, if the user is creating music, the operation unit can also provide an operation method specialized for music creation. For example, if the user is creating a manga, the generation AI can also provide an operation method specialized for manga creation. This provides operation content according to the user's current task. Some or all of the above-mentioned processing in the operation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the operation unit can input the user's current task data into the generation AI and customize the operation content based on the generation AI.

[0125] The operation unit can improve the operation interface by reflecting user feedback during operation. For example, the operation unit allows the generation AI to adjust the operation interface based on feedback provided by the user and reflect the adjustment in the next operation. For example, the operation unit can analyze the good and bad points of the operation interface evaluated by the user, and the generation AI can optimize the interface based on that. For example, when the user points out specific areas for improvement, the operation unit can also provide a new operation interface by reflecting the suggestions. In this way, the operation interface is improved by reflecting user feedback. Some or all of the above-mentioned processing in the operation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the operation unit can input user feedback data into the generation AI and improve the operation interface based on the generation AI.

[0126] The operation unit can estimate the user's emotions and determine operation procedures based on the estimated user emotions. For example, when the user is nervous, the operation unit provides simple and intuitive operation procedures. For example, when the user is relaxed, the operation unit can also provide detailed operation procedures. For example, when the user is in a hurry, the operation unit can also provide procedures that can be performed quickly. This allows operation procedures to be provided according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the operation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the operation unit can input the user's emotion data into the generation AI and determine operation procedures based on the generation AI.

[0127] The operation unit can provide an optimal operation method by taking into account the user's device information during operation. For example, if the user is using a smartphone, the operation unit can provide an operation method that matches the screen size. For example, if the user is using a tablet, the operation unit can also provide an operation method optimized for a large screen. For example, if the user is using a desktop, the operation unit can also provide an operation method optimized for a keyboard and mouse. In this way, the optimal operation method is provided by taking into account the user's device information. Some or all of the above-described processing in the operation unit may be performed using or without the generation AI. For example, the operation unit can input the user's device information into the generation AI and provide an optimal operation method based on the generation AI.

[0128] The operation unit can make the operation content multilingual according to the user's language setting during operation. The operation unit automatically sets the language of the operation interface based on, for example, the language setting of the user's device. The operation unit can also provide a language switching function when, for example, the user uses multiple languages. For example, when the user selects a specific language, the operation unit can provide the operation interface in that language. This provides multilingual operation content according to the user's language setting. Some or all of the above-mentioned processing in the operation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the operation unit can input the user's language setting data into the generation AI and make the operation content multilingual based on the generation AI.

[0129] The operation unit can customize the operation method by reflecting the user's past feedback during operation. For example, the operation unit allows the generation AI to adjust the operation method based on feedback provided by the user in the past and reflect the adjustment in the next operation. For example, the operation unit can analyze the good and bad points of the operation method evaluated by the user, and the generation AI can optimize the operation method based on the analysis. For example, when the user points out specific areas for improvement, the operation unit can provide a new operation method by reflecting the user's past feedback. In this way, the operation method is customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the operation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the operation unit can input the user's past feedback data into the generation AI and customize the operation method based on the generation AI.

[0130] The evaluation unit can estimate the user's emotions and determine evaluation criteria based on the estimated user's emotions. For example, if the user is relaxed, the generation AI can provide gentle evaluation criteria. For example, if the user is excited, the evaluation unit can also provide strict evaluation criteria. For example, if the user is sad, the generation AI can provide evaluation criteria that take emotions into consideration. This allows evaluation criteria to be provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the evaluation unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the evaluation unit can input the user's emotion data into the generation AI and determine the evaluation criteria based on the generation AI.

[0131] During evaluation, the evaluation unit can optimize the evaluation algorithm by referring to past evaluation data. For example, the evaluation unit causes the generation AI to adjust the evaluation algorithm based on data on works previously rated by users. For example, the evaluation unit can also cause the generation AI to optimize the evaluation algorithm by incorporating elements of works that users have rated highly. For example, the evaluation unit can also analyze elements of works that users have rated poorly, and provide an evaluation algorithm that improves on those elements. In this way, the evaluation algorithm is optimized by referring to past evaluation data. Some or all of the above-mentioned processing in the evaluation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the evaluation unit can input past evaluation data into the generation AI and optimize the evaluation algorithm based on the generation AI.

[0132] The evaluation unit can make an evaluation taking into account the user's attribute information. In the evaluation unit, the generation AI adjusts the evaluation criteria taking into account, for example, the user's age and gender. In the evaluation unit, the generation AI can also adjust the evaluation criteria taking into account, for example, the user's occupation and hobbies. In the evaluation unit, the generation AI can also adjust the evaluation criteria taking into account, for example, the user's place of residence and cultural background. In this way, a more appropriate evaluation is made by taking into account the user's attribute information. Some or all of the above-mentioned processing in the evaluation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the evaluation unit can input the user's attribute information into the generation AI and make an evaluation based on the generation AI.

[0133] The evaluation unit can improve the evaluation method by reflecting user feedback during evaluation. For example, the evaluation unit allows the generation AI to adjust the evaluation method based on feedback provided by the user and reflect the adjustment in the next evaluation. For example, the evaluation unit can analyze the good and bad points of the evaluation method evaluated by the user, and the generation AI can optimize the evaluation method based on that. For example, if the user points out specific areas for improvement, the evaluation unit can provide a new evaluation method by reflecting the suggestions. In this way, the evaluation method is improved by reflecting user feedback. Some or all of the above-mentioned processing in the evaluation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the evaluation unit can input user feedback data into the generation AI and improve the evaluation method based on the generation AI.

[0134] The evaluation unit can estimate the user's emotions and determine a display method for the evaluation results based on the estimated user emotions. For example, if the user is nervous, the evaluation unit can provide a simple, highly visible evaluation result. For example, if the user is relaxed, the evaluation unit can also provide a detailed evaluation result. For example, if the user is in a hurry, the evaluation unit can also provide a summary evaluation result. This provides a display method for the evaluation results according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the evaluation unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the evaluation unit can input the user's emotion data into the generation AI and determine a display method for the evaluation results based on the generation AI.

[0135] The evaluation unit can take the user's geographical location information into account when making the evaluation. For example, if the user is in Japan, the generation AI can provide evaluation criteria related to Japanese culture. For example, if the user is in the United States, the generation AI can provide evaluation criteria related to American pop culture. For example, if the user is in Europe, the generation AI can provide evaluation criteria related to European history. This allows for a more appropriate evaluation by taking the user's geographical location information into account. Some or all of the above-mentioned processing in the evaluation unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the evaluation unit can input the user's geographical location information into the generation AI and make an evaluation based on the generation AI.

[0136] During evaluation, the evaluation unit can analyze the user's social media activity to improve the accuracy of the evaluation. For example, the evaluation unit uses the generation AI to analyze the themes that the user frequently posts on social media and makes an evaluation based on that. For example, the evaluation unit can also use the generation AI to incorporate topics of accounts the user follows on social media to make an evaluation. For example, the evaluation unit can also use the generation AI to make a related evaluation based on the content of posts that the user "likes" on social media. This allows an evaluation that reflects the user's social media activity. Some or all of the above-mentioned processing in the evaluation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the evaluation unit can input the user's social media activity data into the generation AI and improve the accuracy of the evaluation based on the generation AI.

[0137] The evaluation unit can customize the evaluation method by reflecting the user's past feedback during evaluation. For example, the evaluation unit allows the generation AI to adjust the evaluation method based on feedback provided by the user in the past and reflect this in the next evaluation. For example, the evaluation unit can analyze the good and bad points of the evaluation method evaluated by the user, and the generation AI can optimize the evaluation method based on that. For example, if the user points out specific areas for improvement, the evaluation unit can provide a new evaluation method by reflecting the user's past feedback. In this way, the evaluation method is customized by reflecting the user's past feedback. Some or all of the above-described processing in the evaluation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the evaluation unit can input the user's past feedback data into the generation AI and customize the evaluation method based on the generation AI.

[0138] The ranking unit can estimate the user's emotions and determine ranking criteria based on the estimated user's emotions. For example, if the user is relaxed, the generation AI can provide gentle ranking criteria. For example, if the user is excited, the generation AI can provide strict ranking criteria. For example, if the user is sad, the generation AI can provide ranking criteria that take emotions into consideration. This provides ranking criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the ranking unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the ranking unit can input the user's emotion data into the generation AI and determine ranking criteria based on the generation AI.

[0139] The ranking unit can optimize the ranking algorithm by referring to past ranking data when ranking. For example, the ranking unit adjusts the ranking algorithm using the generation AI based on data on works that users have previously ranked. For example, the ranking unit can also optimize the ranking algorithm using the generation AI by incorporating elements of works that users have highly rated. For example, the ranking unit can analyze elements of works that users have low rated, and provide an improved ranking algorithm using the generation AI. In this way, the ranking algorithm is optimized by referring to past ranking data. Some or all of the above-mentioned processing in the ranking unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the ranking unit can input past ranking data into the generation AI and optimize the ranking algorithm based on the generation AI.

[0140] The ranking unit can perform ranking taking into account user attribute information. In the ranking unit, the generation AI adjusts the ranking criteria taking into account, for example, the user's age and gender. In the ranking unit, the generation AI can also adjust the ranking criteria taking into account, for example, the user's occupation and hobbies. In the ranking unit, the generation AI can also adjust the ranking criteria taking into account, for example, the user's place of residence and cultural background. In this way, more appropriate ranking is performed by taking into account the user's attribute information. Some or all of the above-described processing in the ranking unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the ranking unit can input the user's attribute information into the generation AI and perform ranking based on the generation AI.

[0141] The ranking unit can improve the ranking method by reflecting user feedback during ranking. For example, the ranking unit adjusts the ranking method using the generation AI based on feedback provided by the user and reflects this in the next ranking. For example, the ranking unit can analyze the good and bad points of the ranking method evaluated by the user, and the generation AI can optimize the ranking method based on that. For example, if the user points out specific areas for improvement, the ranking unit can provide a new ranking method by reflecting the suggestions. In this way, the ranking method is improved by reflecting user feedback. Some or all of the above-mentioned processing in the ranking unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the ranking unit can input user feedback data into the generation AI and improve the ranking method based on the generation AI.

[0142] The ranking unit can estimate the user's emotions and determine a display method for the ranking results based on the estimated user emotions. For example, when the user is nervous, the ranking unit provides a simple, highly visible ranking result. For example, when the user is relaxed, the ranking unit can also provide a detailed ranking result. For example, when the user is in a hurry, the ranking unit can also provide a ranking result that focuses on the main points. This provides a display method for the ranking results according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the ranking unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the ranking unit can input the user's emotion data into the generation AI and determine a display method for the ranking results based on the generation AI.

[0143] The ranking unit can perform ranking taking into account the user's geographical location information. For example, if the user is in Japan, the generating AI can provide ranking criteria related to Japanese culture. For example, if the user is in the United States, the generating AI can provide ranking criteria related to American pop culture. For example, if the user is in Europe, the generating AI can provide ranking criteria related to European history. This allows for more appropriate ranking by taking the user's geographical location information into consideration. Some or all of the above-described processing in the ranking unit can be performed using the generating AI, or can be performed without using the generating AI. For example, the ranking unit can input the user's geographical location information into the generating AI and perform ranking based on the generating AI.

[0144] The ranking unit can analyze the user's social media activity during ranking to improve the accuracy of the ranking. For example, the ranking unit uses a generation AI to analyze themes that the user frequently posts on social media and ranks based on the results. For example, the ranking unit can also use the generation AI to incorporate topics of accounts the user follows on social media to perform rankings. For example, the ranking unit can also perform rankings based on the content of posts that the user "likes" on social media. This allows rankings that reflect the user's social media activity. Some or all of the above-mentioned processing in the ranking unit can be performed using or without the generation AI. For example, the ranking unit can input the user's social media activity data into the generation AI and improve the accuracy of the ranking based on the generation AI.

[0145] The ranking unit can customize the ranking method by reflecting the user's past feedback when ranking. For example, the ranking unit adjusts the ranking method using the generation AI based on feedback provided by the user in the past and reflects this in the next ranking. For example, the ranking unit can analyze the advantages and disadvantages of the ranking method evaluated by the user, and the generation AI can optimize the ranking method based on this. For example, if the user points out specific areas for improvement, the ranking unit can provide a new ranking method by reflecting the user's past feedback. In this way, the ranking method is customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the ranking unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the ranking unit can input the user's past feedback data into the generation AI and customize the ranking method based on the generation AI.

[0146] The technology management unit can estimate the user's emotions and determine a technology management method based on the estimated user's emotions. For example, if the user is relaxed, the generation AI can provide a gentle technology management method. For example, if the user is excited, the generation AI can provide a strict technology management method. For example, if the user is sad, the generation AI can provide a technology management method that takes emotions into consideration. This provides a technology management method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the technology management unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the technology management unit can input the user's emotion data into the generation AI and determine a technology management method based on the generation AI.

[0147] During technology management, the technology management department can optimize the technology management algorithm by referring to past technology data. For example, the technology management department has the generation AI adjust the technology management algorithm based on technology data previously managed by the user. For example, the technology management department can also optimize the technology management algorithm by incorporating elements of a technology management method that the user has rated highly. For example, the technology management department can analyze elements of a technology management method that the user has rated poorly, and provide an improved technology management algorithm by the generation AI. In this way, the technology management algorithm is optimized by referring to past technology data. Some or all of the above-described processing in the technology management department may be performed using the generation AI, or may be performed without using the generation AI. For example, the technology management department can input past technology data into the generation AI and optimize the technology management algorithm based on the generation AI.

[0148] The technology management unit can perform technology management by taking into account the user's attribute information. In the technology management unit, the generation AI adjusts the technology management standards by taking into account, for example, the user's age and gender. In the technology management unit, the generation AI can also adjust the technology management standards by taking into account, for example, the user's occupation and hobbies. In the technology management unit, the generation AI can also adjust the technology management standards by taking into account, for example, the user's place of residence and cultural background. In this way, more appropriate technology management is performed by taking into account the user's attribute information. Some or all of the above-mentioned processing in the technology management unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the technology management unit can input the user's attribute information into the generation AI and perform technology management based on the generation AI.

[0149] During technology management, the technology management department can improve the technology management method by reflecting user feedback. For example, the technology management department can have the generation AI adjust the technology management method based on feedback provided by the user and reflect the adjustment in the next technology management. For example, the technology management department can analyze the advantages and disadvantages of the technology management method evaluated by the user, and the generation AI can optimize the technology management method based on that. For example, if the user points out specific areas for improvement, the technology management department can have the generation AI reflect those suggestions and provide a new technology management method. This improves the technology management method by reflecting user feedback. Some or all of the above-described processing in the technology management department can be performed using or without the generation AI. For example, the technology management department can input user feedback data into the generation AI and improve the technology management method based on the generation AI.

[0150] The technology management unit can estimate the user's emotions and determine how to display the technology management results based on the estimated user emotions. For example, if the user is nervous, the technology management unit can provide simple, highly visible technology management results. For example, if the user is relaxed, the technology management unit can provide detailed technology management results. For example, if the user is in a hurry, the technology management unit can provide technology management results that focus on the main points. This provides a display method for the technology management results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the technology management unit can be performed using or without the generation AI. For example, the technology management unit can input user emotion data into the generation AI and determine how to display the technology management results based on the generation AI.

[0151] The technology management department can perform technology management by taking into account the user's geographic location information. For example, if the user is in Japan, the generation AI can provide management standards related to Japanese technology. For example, if the user is in the United States, the generation AI can provide management standards related to American technology. For example, if the user is in Europe, the generation AI can provide management standards related to European technology. This allows for more appropriate technology management by taking the user's geographic location information into consideration. Some or all of the above-mentioned processing in the technology management department may be performed using the generation AI, or may be performed without using the generation AI. For example, the technology management department can input the user's geographic location information into the generation AI and perform technology management based on the generation AI.

[0152] During technology management, the technology management unit can analyze the user's social media activities to improve the accuracy of technology management. For example, the technology management unit uses a generation AI to analyze technology-related topics that the user frequently posts on social media and performs technology management based on the results. For example, the technology management unit can also perform technology management by using the generation AI to incorporate topics from technology-related accounts that the user follows on social media. For example, the technology management unit can also perform related technology management based on the content of technology-related posts that the user "likes" on social media. This allows technology management that reflects the user's social media activities. Some or all of the above-mentioned processing in the technology management unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the technology management unit can input the user's social media activity data into the generation AI and improve the accuracy of technology management based on the generation AI.

[0153] During technology management, the technology management department can customize the technology management method by reflecting the user's past feedback. For example, the technology management department can have the generation AI adjust the technology management method based on feedback provided by the user in the past and reflect the adjustment in the next technology management. For example, the technology management department can analyze the advantages and disadvantages of the technology management method evaluated by the user, and the generation AI can optimize the technology management method based on that. For example, if the user points out specific areas for improvement, the generation AI can reflect those suggestions and provide a new technology management method. In this way, the technology management method is customized by reflecting the user's past feedback. Some or all of the above-described processing in the technology management department may be performed using the generation AI, or may be performed without using the generation AI. For example, the technology management department can input the user's past feedback data into the generation AI and customize the technology management method based on the generation AI.

[0154] The copyright management unit can estimate the user's emotions and determine a copyright management method based on the estimated user's emotions. For example, if the user is relaxed, the generation AI can provide a gentle copyright management method. For example, if the user is excited, the copyright management unit can provide a strict copyright management method. For example, if the user is sad, the generation AI can provide a copyright management method that takes emotions into consideration. This provides a copyright management method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the copyright management unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the copyright management unit can input the user's emotion data into the generation AI and determine the copyright management method based on the generation AI.

[0155] During copyright management, the copyright management unit can optimize the copyright management algorithm by referring to past copyright data. For example, the copyright management unit uses a generation AI to adjust the copyright management algorithm based on copyright data previously managed by the user. For example, the copyright management unit can also optimize the copyright management algorithm by incorporating elements of copyright management methods that users have rated highly. For example, the copyright management unit can analyze elements of copyright management methods that users have rated poorly and provide an improved copyright management algorithm. This optimizes the copyright management algorithm by referring to past copyright data. Some or all of the above-described processing in the copyright management unit may be performed using or without the generation AI. For example, the copyright management unit can input past copyright data into the generation AI and optimize the copyright management algorithm based on the generation AI.

[0156] The copyright management unit can perform copyright management by taking into account the user's attribute information. For example, the generation AI adjusts the copyright management standards by taking into account the user's age and gender. For example, the copyright management unit can also adjust the copyright management standards by taking into account the user's occupation and hobbies. For example, the copyright management unit can also adjust the copyright management standards by taking into account the user's place of residence and cultural background. In this way, more appropriate copyright management is performed by taking into account the user's attribute information. Some or all of the above-mentioned processing in the copyright management unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the copyright management unit can input the user's attribute information into the generation AI and perform copyright management based on the generation AI.

[0157] The copyright management unit can improve the copyright management method by reflecting user feedback during copyright management. For example, the copyright management unit can have the generation AI adjust the copyright management method based on feedback provided by the user and reflect this in the next copyright management. For example, the copyright management unit can analyze the good and bad points of the copyright management method evaluated by the user, and the generation AI can optimize the copyright management method based on that. For example, if the user points out specific areas for improvement, the copyright management unit can have the generation AI reflect those suggestions and provide a new copyright management method. In this way, the copyright management method is improved by reflecting user feedback. Some or all of the above-mentioned processing in the copyright management unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the copyright management unit can input user feedback data into the generation AI and improve the copyright management method based on the generation AI.

[0158] The copyright management unit can estimate the user's emotions and determine how to display the copyright management results based on the estimated user emotions. For example, if the user is nervous, the copyright management unit can provide simple, highly visible copyright management results. For example, if the user is relaxed, the copyright management unit can provide detailed copyright management results. For example, if the user is in a hurry, the copyright management unit can provide copyright management results that are concise. This provides a display method for the copyright management results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the copyright management unit can be performed using or without the generation AI. For example, the copyright management unit can input user emotion data into the generation AI and determine how to display the copyright management results based on the generation AI.

[0159] The copyright management unit can perform copyright management by taking into account the user's geographic location information. For example, if the user is in Japan, the generating AI can provide management standards related to Japanese copyright law. For example, if the user is in the United States, the generating AI can provide management standards related to American copyright law. For example, if the user is in Europe, the generating AI can provide management standards related to European copyright law. This allows for more appropriate copyright management by taking the user's geographic location information into account. Some or all of the above-mentioned processing in the copyright management unit can be performed using the generating AI, or can be performed without using the generating AI. For example, the copyright management unit can input the user's geographic location information into the generating AI and perform copyright management based on the generating AI.

[0160] During copyright management, the copyright management unit can analyze the user's social media activities to improve the accuracy of copyright management. For example, the copyright management unit performs copyright management based on an analysis of copyright-related topics frequently posted on social media by the generation AI. For example, the copyright management unit can also perform copyright management by incorporating topics from copyright-related accounts that the user follows on social media. For example, the copyright management unit can also perform copyright management based on the content of copyright-related posts that the user "likes" on social media. This allows copyright management that reflects the user's social media activities. Some or all of the above-described processing in the copyright management unit may be performed using or without the generation AI. For example, the copyright management unit can input the user's social media activity data into the generation AI and improve the accuracy of copyright management based on the generation AI.

[0161] The copyright management unit can customize the copyright management method by reflecting the user's past feedback during copyright management. For example, the copyright management unit can have the generation AI adjust the copyright management method based on feedback provided by the user in the past and reflect this in the next copyright management. For example, the copyright management unit can analyze the advantages and disadvantages of the copyright management method evaluated by the user, and the generation AI can optimize the copyright management method based on this. For example, if the user points out specific areas for improvement, the copyright management unit can have the generation AI reflect these suggestions and provide a new copyright management method. In this way, the copyright management method is customized by reflecting the user's past feedback. Some or all of the above-described processing in the copyright management unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the copyright management unit can input the user's past feedback data into the generation AI and customize the copyright management method based on the generation AI.

[0162] The instruction unit can estimate the user's emotions and determine the instruction content based on the estimated user's emotions. For example, if the user is relaxed, the generation AI can provide gentle instruction content. For example, if the user is excited, the generation AI can provide energetic instruction content. For example, if the user is sad, the generation AI can provide instruction content that takes the user's emotions into consideration. This allows the instruction content to be provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the instruction unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the instruction unit can input the user's emotion data into the generation AI and determine the instruction content based on the generation AI.

[0163] When issuing an instruction, the instruction unit can optimize the instruction algorithm by referring to past instruction data. In the instruction unit, for example, the generation AI adjusts the instruction algorithm based on data of instructions issued by the user in the past. In the instruction unit, for example, the generation AI can also optimize the instruction algorithm by incorporating elements of instruction content that the user has given a high rating. In the instruction unit, for example, the generation AI can analyze elements of instruction content that the user has given a low rating, and provide an instruction algorithm that has improved on those elements. In this way, the instruction algorithm is optimized by referring to past instruction data. Some or all of the above-mentioned processing in the instruction unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the instruction unit can input past instruction data into the generation AI and optimize the instruction algorithm based on the generation AI.

[0164] When giving an instruction, the instruction unit can give an instruction taking into account the user's attribute information. In the instruction unit, the generation AI can adjust the instruction content taking into account, for example, the user's age and gender. In the instruction unit, the generation AI can also adjust the instruction content taking into account, for example, the user's occupation and hobbies. In the instruction unit, the generation AI can also adjust the instruction content taking into account, for example, the user's place of residence and cultural background. In this way, more appropriate instructions are given by taking into account the user's attribute information. Some or all of the above-mentioned processing in the instruction unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the instruction unit can input the user's attribute information into the generation AI and give instructions based on the generation AI.

[0165] The instruction unit can improve the instruction method by reflecting user feedback when giving an instruction. For example, the instruction unit allows the generation AI to adjust the instruction method based on feedback provided by the user and reflect the adjustment in the next instruction. For example, the instruction unit can analyze the good and bad points of the instruction method evaluated by the user, and the generation AI can optimize the instruction method based on the analysis. For example, when the user points out specific areas for improvement, the instruction unit can provide a new instruction method by reflecting the suggestions. In this way, the instruction method is improved by reflecting the user's feedback. Some or all of the above-mentioned processing in the instruction unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the instruction unit can input user feedback data into the generation AI and improve the instruction method based on the generation AI.

[0166] The instruction unit can estimate the user's emotions and determine a display method for the instruction result based on the estimated user's emotions. For example, if the user is nervous, the instruction unit can provide a simple, highly visible instruction result. For example, if the user is relaxed, the instruction unit can provide a detailed instruction result. For example, if the user is in a hurry, the instruction unit can provide a brief instruction result. This provides a display method for the instruction result according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the instruction unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the instruction unit can input the user's emotion data into the generation AI and determine a display method for the instruction result based on the generation AI.

[0167] When giving instructions, the instruction unit can take into account the user's geographical location information. For example, if the user is in Japan, the generation AI can provide instructions related to Japanese culture. For example, if the user is in America, the generation AI can provide instructions related to American pop culture. For example, if the user is in Europe, the generation AI can provide instructions related to European history. This allows more appropriate instructions to be given by taking the user's geographical location information into account. Some or all of the above-mentioned processing in the instruction unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the instruction unit can input the user's geographical location information into the generation AI and give instructions based on the generation AI.

[0168] When giving instructions, the instruction unit can analyze the user's social media activity to improve the accuracy of the instructions. For example, the instruction unit causes the generation AI to analyze themes that the user frequently posts on social media and give instructions based on that. For example, the instruction unit can also cause the generation AI to incorporate topics of accounts the user follows on social media and give instructions. For example, the instruction unit can also cause the generation AI to give related instructions based on the content of posts that the user has "liked" on social media. In this way, instructions that reflect the user's social media activity are given. Some or all of the above-mentioned processing in the instruction unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the instruction unit can input the user's social media activity data into the generation AI and improve the accuracy of the instructions based on the generation AI.

[0169] The instruction unit can customize the instruction method by reflecting the user's past feedback when giving an instruction. For example, the instruction unit has the generation AI adjust the instruction method based on feedback provided by the user in the past and reflect this in the next instruction. For example, the instruction unit can analyze the good and bad points of the instruction method evaluated by the user, and the generation AI can optimize the instruction method based on that. For example, if the user points out specific areas for improvement, the instruction unit can have the generation AI reflect those points and provide a new instruction method. In this way, the instruction method is customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the instruction unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the instruction unit can input the user's past feedback data into the generation AI and customize the instruction method based on the generation AI. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned generation unit, publication unit, operation unit, evaluation unit, ranking unit, technical management unit, copyright management unit, and instruction unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the generation unit is realized by the processor 46 of the smart device 14 or the processor 28 of the data processing device 12. The publication unit is realized, for example, by the output device 40 of the smart device 14 or the communication I / F 26 of the data processing device 12. The operation unit is realized, for example, by the reception device 38 of the smart device 14 or the communication I / F 26 of the data processing device 12. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The ranking unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The technical management unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The copyright management unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The instruction unit is realized by, for example, the reception device 38 of the smart device 14 or the communication I / F 26 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned generation unit, publication unit, operation unit, evaluation unit, ranking unit, technical management unit, copyright management unit, and instruction unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the generation unit is realized by the processor 46 of the smart glasses 214 or the processor 28 of the data processing device 12. The publication unit is realized, for example, by the speaker 240 of the smart glasses 214 or the communication I / F 26 of the data processing device 12. The operation unit is realized, for example, by the microphone 238 of the smart glasses 214 or the communication I / F 26 of the data processing device 12. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The ranking unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The technical management unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The copyright management unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The instruction unit is realized by, for example, the microphone 238 of the smart glasses 214 or the communication I / F 26 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned generation unit, publication unit, operation unit, evaluation unit, ranking unit, technical management unit, copyright management unit, and instruction unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the generation unit is realized by the processor 46 of the headset type terminal 314 or the processor 28 of the data processing device 12. The publication unit is realized, for example, by the display 343 of the headset type terminal 314 or the communication I / F 26 of the data processing device 12. The operation unit is realized, for example, by the microphone 238 of the headset type terminal 314 or the communication I / F 26 of the data processing device 12. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The ranking unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The technical management unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The copyright management unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The instruction unit is realized by, for example, the microphone 238 of the headset terminal 314 or the communication I / F 26 of the data processing device 12 . === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned generation unit, publication unit, operation unit, evaluation unit, ranking unit, technical management unit, copyright management unit, and instruction unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the generation unit is realized by the processor 46 of the robot 414 or the processor 28 of the data processing device 12. The publication unit is realized, for example, by the speaker 240 of the robot 414 or the communication I / F 26 of the data processing device 12. The operation unit is realized, for example, by the microphone 238 of the robot 414 or the communication I / F 26 of the data processing device 12. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The ranking unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The technical management unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The copyright management unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The instruction unit is realized by, for example, the microphone 238 of the robot 414 or the communication I / F 26 of the data processing device 12 .

[0170] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0171] The generation unit can optimize the generation algorithm by referring to the user's past work history. For example, the generation AI analyzes the theme and style of works created by the user in the past, and generates a new work based on that. For example, the generation unit can also incorporate elements of works that the user has previously received high praise from, and generate a new work by the generation AI. For example, the generation unit can analyze the failure points of works created by the user in the past, and generate a work that improves on those failures. In this way, the generation algorithm is optimized by referring to the user's past work history.

[0172] The generation unit can generate works that reflect the user's current interests and trends at the time of generation. For example, the generation AI analyzes topics that the user has recently been interested in and generates works based on that. The generation unit can also generate works that suit the user, for example, by having the generation AI incorporate current social trends and fashions. For example, the generation unit can generate works on related themes based on keywords that the user has recently searched. This allows the generation unit to provide more interesting works by generating works that reflect the user's current interests and trends.

[0173] The generation unit can improve the generation algorithm by reflecting user feedback during generation. For example, the generation AI can adjust the algorithm based on feedback provided by the user and reflect this in the next work generation. The generation unit can also analyze the good and bad points of the work evaluated by the user, and the generation AI can optimize the algorithm based on that. For example, if the user points out specific areas for improvement, the generation unit can have the generation AI reflect those suggestions to generate a new work. In this way, the generation algorithm is improved by reflecting user feedback.

[0174] The generation unit can estimate the user's emotions and determine the genre of the work to be generated based on the estimated user emotions. For example, if the user is relaxed, the generation AI can generate relaxing music or landscape videos. For example, if the user is excited, the generation unit can also generate action movies or energetic music. For example, if the user is sad, the generation unit can also generate moving stories or soothing music. This allows for the generation of works in a genre that corresponds to the user's emotions, providing more personalized works.

[0175] During generation, the generation unit can incorporate region-specific elements by taking into account the user's geographical location information. For example, if the user is in Japan, the generation AI can generate works themed around Japanese traditional culture and scenery. For example, if the user is in America, the generation unit can also generate works themed around American pop culture and scenery. For example, if the user is in Europe, the generation unit can also generate works themed around European history and scenery. In this way, by taking into account the user's geographical location information, works that incorporate region-specific elements are generated.

[0176] The publishing unit can estimate the user's emotions and determine the timing of publication based on the estimated user emotions. For example, if the user is relaxed, the generation AI will publish the work during a calm time. For example, if the user is excited, the publishing unit can also publish the work during a time when the user is most active. For example, if the user is sad, the publishing unit can also publish the work during a time when the user's emotions are stable. This allows for more effective publication by publishing the work at a time that suits the user's emotions.

[0177] At the time of publication, the publishing unit can analyze the viewing history of a work and select the optimal publication method. For example, the tendency of works that a user has viewed in the past is analyzed, and the generation AI selects the optimal publication method based on that. For example, the publishing unit can also select the publication method for a new work by referring to the publication method of works that users have highly rated. For example, the publishing unit can analyze the viewing time periods of works that users have viewed, and the generation AI can select the optimal publication time. In this way, the optimal publication method is selected by analyzing the viewing history.

[0178] The publishing unit can determine the publishing priority based on the user's current activity status at the time of publishing. For example, the generation AI will prioritize publishing works during times when the user is active. For example, if the user is engaged in another activity, the publishing unit can also publish the work when the generation AI finishes that activity. For example, if the user is on a break, the publishing unit can also publish the work during times when the generation AI can relax. This allows for more effective publishing by determining the publishing priority based on the user's current activity status.

[0179] The operation unit can estimate the user's emotions and determine how to display the operation interface based on the estimated user's emotions. For example, if the user is nervous, the operation unit can provide a simple, highly visible interface. For example, if the user is relaxed, the operation unit can also provide an interface that includes detailed information. For example, if the user is in a hurry, the operation unit can also provide an interface that focuses on the main points. In this way, an operation interface is provided that corresponds to the user's emotions.

[0180] The operation unit can provide the optimal operation method by referring to the user's past operation history when operating the device. For example, it can preferentially suggest operation methods (voice, text, etc.) that the user has used in the past. For example, the operation unit can also preferentially display functions that the user has frequently used in the past. For example, the operation unit can predict and suggest functions that will be used in a specific time period based on the user's past operation history. In this way, the optimal operation method can be provided by referring to the user's past operation history.

[0181] The processing flow of the second embodiment will be briefly explained below.

[0182] Step 1: The generator creates the work using generative AI. The generator can generate images using a deep learning model, generate text using a natural language generation model, or generate music using a music generation model. Step 2: The publishing department publishes the created work on the platform. The publishing department can publish the work through a website, mobile app, or social media. Step 3: The operation unit provides an interface for the user to operate the generated AI. The operation unit can provide a graphical user interface (GUI), a voice interface, or a touch interface. Step 4: The evaluation unit provides a function for evaluating the generated work. The evaluation unit can perform user evaluation, algorithmic evaluation, and expert evaluation. Step 5: The ranking unit ranks the works based on the ratings. The ranking unit can rank based on rating scores, popularity, and expert ratings. Step 6: The technical management team manages the technical details of the generative AI. This team can handle version control, performance monitoring, and security management. Step 7: The copyright management unit protects the copyright of the generated work. The copyright management unit can perform digital rights management (DRM), license management, and piracy monitoring. Step 8: The instruction unit instructs the AI ​​what kind of work the user wants to create. The instruction unit can use text input, voice commands, or gestures to give instructions.

[0183] 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.

[0184] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0185] 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.

[0186] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0187] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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).

[0193] 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.

[0194] 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.

[0195] 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.

[0196] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0197] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0203] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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).

[0209] 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.

[0210] 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.

[0211] 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.

[0212] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0213] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0219] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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).

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0230] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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).

[0240] 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.

[0241] 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."

[0242] 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.

[0243] 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.

[0244] 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.

[0245] 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.

[0246] 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.

[0247] 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.

[0248] 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.

[0249] 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.

[0250] 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.

[0251] 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.

[0252] 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.

[0253] 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.

[0254] [Explanation of symbols]

[0255] 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 generation unit that creates a work using a generation AI; a publishing unit that publishes the work generated by the generating unit; an operation unit for a user to operate the generated AI; an evaluation unit that evaluates the work generated by the generation unit; a ranking unit that ranks the works evaluated by the evaluation unit; a technical management unit that manages details of the generation unit; a copyright management unit that protects the copyright of the work generated by the generation unit; An instruction unit for a user to give instructions to the generation AI; Equipped with A system characterized by:

2. The generation unit Creating artwork using generative AI 2. The system of claim 1.

3. The disclosure section Publishing the resulting work on the platform 2. The system of claim 1.

4. The operation unit includes: Provides an interface for users to operate the generated AI 2. The system of claim 1.

5. The evaluation unit Providing functionality for evaluating the generated work 2. The system of claim 1.

6. The ranking unit Rank works based on their ratings 2. The system of claim 1.

7. The technical management department Manage the technical details of generative AI 2. The system of claim 1.

8. The copyright management unit Protect the copyright of the generated work 2. The system of claim 1.

Images