system

Abstract

The system according to this embodiment aims to provide appropriate support and feedback to works submitted by users. [Solution] The system according to the embodiment comprises a reception unit, a support unit, a feedback unit, and a learning unit. The reception unit receives submissions of works from users. The support unit supports the production of works received by the reception unit. The feedback unit provides feedback on works supported by the support unit. The learning unit learns from works that have been feedbacked on by the feedback unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to provide appropriate support and feedback for works posted by users.

[0005] The system according to the embodiment aims to provide appropriate support and feedback for works posted by users.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a support unit, a feedback unit, and a learning unit. The reception unit receives submissions of works from users. The support unit supports the production of works received by the reception unit. The feedback unit provides feedback on works supported by the support unit. The learning unit learns from works that have been feedbacked on by the feedback unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide appropriate support and feedback to works posted by users. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The creators' platform according to an embodiment of the present invention is a system that provides a mechanism for users to freely post works such as "text," "images," "music," and "videos." In this system, users post works, AI supports the production of the posted works, and the AI ​​further provides feedback on the works. This feedback mainly consists of praise in order to enhance the user's self-esteem. In addition, the posted works are learned by the AI, contributing to the improvement of the AI's capabilities. First, users post works such as "text," "images," "music," and "videos." At this time, users can freely express themselves. For example, users can post poems, draw pictures, compose music, or produce short films. Next, the AI ​​supports the production of the posted works. For example, when a user posts a poem, the AI ​​provides advice on the structure and expression of the poem. When drawing a picture, the AI ​​makes suggestions on color and composition. When composing music, the AI ​​provides ideas for melody and rhythm. When producing a short film, the AI ​​provides advice on the scenario and filming techniques. Furthermore, the AI ​​provides feedback on the posted works. This feedback primarily consists of praise to boost users' self-esteem. For example, the AI ​​might provide feedback such as, "That's a wonderful poem," "This painting is very moving," "This music is touching," or "This short film is very interesting." Furthermore, the submitted works are learned by the AI. This allows the AI ​​to learn from people's free-thinking ideas and improve the quality of future support and feedback. For instance, by learning from many poems, the AI ​​can provide advice on more advanced poetic structure and expression. Similarly, the AI ​​can improve its capabilities through learning for paintings, music, and short films. This system allows users to freely express themselves and boost their self-esteem through AI support and feedback. Additionally, the AI ​​can improve the quality of future support and feedback by learning from submitted works. This enables the creators' platform to unleash users' creativity and provide a space for self-expression.This allows the creators' platform to provide a consistent platform for users to submit their work, receive production support, get feedback, and learn.

[0029] The creators platform according to this embodiment comprises a reception unit, a support unit, a feedback unit, and a learning unit. The reception unit accepts submissions of works from users. Submissions from users include, but are not limited to, poems, paintings, music, and films. The reception unit accepts works, for example, through an online form. The reception unit can also accept works via email or file upload. Furthermore, the reception unit can estimate the user's emotions and adjust the timing of submission based on the estimated emotions. For example, if the reception unit is feeling stressed, it may encourage the user to submit during a time when they can relax. The reception unit can also simplify the interface to allow for immediate submission if the user is excited. Furthermore, if the reception unit is tired, it may set a reminder to submit later. The support unit supports the production of works accepted by the reception unit. The support unit may, for example, provide advice on the structure and expression of a poem. The support unit may also offer suggestions on the colors and composition of a painting. Furthermore, the support unit may provide ideas for the melody and rhythm of music. The support team can also provide advice on short film scripts and filming techniques. For example, the support team provides advice on the structure of poetry, considering elements such as poetic form, rhythm, and rhyme. For the color scheme of a painting, they offer suggestions considering elements such as color selection, color scheme, and the meaning of colors. For musical melodies, they provide ideas considering elements such as scale, rhythm, and melodic structure. For short film scripts, they provide advice considering elements such as plot, character development, and dialogue. The feedback team provides feedback on works supported by the support team. The feedback team provides positive feedback to boost the user's self-esteem. For example, the feedback team may provide feedback such as, "That's a wonderful poem," "This painting is very moving," "This music is touching," or "This short film is very interesting." The learning team learns from works that have been feedbacked on by the feedback team.The learning unit studies submitted works to improve the quality of future support and feedback. For example, by studying many poems, the learning unit can provide advice on more advanced poetic structure and expression. Similarly, the learning unit can improve its capabilities through learning for paintings, music, and short films. This allows the creators platform, according to the embodiment, to consistently handle user submissions, production support, feedback, and learning.

[0030] The reception desk accepts submissions of works from users. These submissions include, but are not limited to, poetry, paintings, music, and films. The reception desk accepts submissions, for example, through online forms. It can also accept submissions via email or file uploads. Furthermore, the reception desk can estimate the user's emotions and adjust the timing of submissions based on these estimations. For example, if the reception desk is stressed, it might encourage submissions during a more relaxed time. If the reception desk is excited, it might simplify the interface to allow for immediate submission. Additionally, if the reception desk is tired, it can set a reminder to submit later. Specifically, the online form has an intuitive interface and is designed to allow users to easily upload their works. The form includes input fields for the title, description, and category of the work, allowing users to submit their work by entering this information. For email submissions, a dedicated email address is provided, allowing users to send their works as attachments. The file upload function allows for easy file uploads via drag-and-drop and supports multiple file formats. Furthermore, the reception desk uses AI to estimate the user's emotions. For example, it estimates the user's current emotional state by analyzing the user's input, past posting history, and even the user's behavior patterns. The AI ​​uses natural language processing technology to analyze the user's text input and classify emotions. For example, if a user uses words like "tired" or "stressed," the AI ​​recognizes this as a negative emotion and takes appropriate action. To encourage users to post during times when they can relax, the AI ​​estimates the optimal time based on the user's past behavior data. For example, if a user is often relaxed at night, it sends a notification encouraging them to post at night. Also, if a user is excited, the AI ​​simplifies the interface to allow for quick posting. For example, it reduces the burden on the user by minimizing input fields and requiring only necessary information to be entered.Furthermore, if a user is feeling tired, the AI ​​sets a reminder to post later. The reminder is sent at an appropriate time according to the user's schedule, helping them remember to post their work. This allows the reception desk to respond flexibly to the user's emotional state and provide an environment where users can comfortably post their work.

[0031] The support department assists in the production of works submitted by the reception department. For example, the support department provides advice on the structure and expression of poetry. They can also offer suggestions regarding the colors and composition of illustrations. Furthermore, they can provide ideas for melodies and rhythms in music. They can also provide advice on short film scripts and filming techniques. For instance, regarding poetry structure, the support department provides advice considering elements such as poetic form, rhythm, and rhyme. Regarding the colors of illustrations, they offer suggestions considering elements such as color selection, color scheme, and color meaning. For musical melodies, they offer ideas considering elements such as scales, rhythm, and melodic structure. For short film scripts, they provide advice considering elements such as plot, character development, and dialogue. Specifically, the support department uses AI to analyze the user's work and provide appropriate advice. For example, regarding poetry structure, the AI ​​analyzes the poetic form, rhythm, and rhyme, and suggests improvements and new ideas to the user. The AI ​​provides specific advice by referring to a database of excellent past poems and comparing them to the user's poem. For paintings, the AI ​​analyzes color choices and color schemes, offering suggestions to the user about the meaning and effect of colors. For example, it provides advice on the psychological effects of specific colors and the visual impact of color combinations. For musical melodies, the AI ​​analyzes scales, rhythms, and melodic structure, providing users with new melody and rhythm ideas. Based on music theory, the AI ​​analyzes the user's music and suggests more appealing melodies and rhythms. For short film scripts, the AI ​​analyzes the plot, character settings, and dialogue, providing users with specific areas for improvement and new ideas. For example, it provides advice on plot development, character motivations, and the naturalness of dialogue. Furthermore, the support team provides real-time assistance for problems and questions the user may encounter during the creative process. For example, if a user is struggling with the rhythm of a poem, the AI ​​will immediately suggest ways to improve the rhythm, helping the user complete the work smoothly.This allows the support department to effectively assist users in their creative activities and promote the production of higher-quality works.

[0032] The Feedback Department provides feedback on works supported by the Support Department. The Feedback Department provides positive feedback to enhance the user's self-esteem. For example, the Feedback Department might provide feedback such as, "That's a wonderful poem," "This painting is very moving," "This music is touching," or "This short film is very interesting." Specifically, the Feedback Department uses AI to analyze the works and generate appropriate feedback. The AI ​​uses natural language processing technology to analyze the content and characteristics of the user's work and identify points to praise. For example, in the case of poetry, the AI ​​analyzes the rhythm, rhyme, and expression of the poem and generates feedback praising particularly outstanding parts. In the case of paintings, the AI ​​analyzes the colors, composition, and technical elements and provides feedback praising the moving or technically excellent parts. In the case of music, the AI ​​analyzes the melody, rhythm, and structure of the music and generates feedback praising the emotional or original parts. In the case of short films, the AI ​​analyzes the plot, character development, and dialogue and provides feedback praising the interesting or moving parts. Furthermore, the Feedback Department also provides constructive feedback to encourage the user's growth. For example, if a poem's rhythm is unstable, the AI ​​will suggest specific areas for improvement, supporting the user in creating better works in the future. If a painting's composition is unnatural, the AI ​​will suggest ways to improve it, advising the user to create more appealing pictures. If a piece of music has a monotonous melody, the AI ​​will offer new melody ideas, helping the user create richer music. If a short film's plot is uninspired, the AI ​​will suggest ways to improve the plot development and character motivations, advising the user to create more interesting films. In this way, the feedback system not only boosts the user's self-esteem but also encourages their growth and supports the creation of higher-quality works.

[0033] The learning unit learns from works that have been feedbacked on by the feedback unit. The learning unit learns from submitted works and improves the quality of future support and feedback. For example, by learning from many poems, the learning unit can provide advice on more advanced poetic structure and expression. Similarly, the learning unit can improve its capabilities through learning for paintings, music, and short films. Specifically, the learning unit uses AI to analyze large amounts of work data and extract patterns and trends. For example, in the case of poetry, the AI ​​analyzes many poems and learns the commonalities and characteristics of excellent poems. This allows the learning unit to provide users with more specific and effective advice. In the case of paintings, the AI ​​analyzes color, composition, and technical elements and learns the characteristics of excellent paintings. This allows the learning unit to provide users with more advanced suggestions for color and composition. In the case of music, the AI ​​analyzes melody, rhythm, and musical structure and learns the characteristics of excellent music. This allows the learning unit to provide users with richer ideas for melody and rhythm. In the case of short films, the AI ​​analyzes the plot, character development, and dialogue to learn the characteristics of good films. This allows the learning unit to provide users with advice on more interesting plots and character development. Furthermore, the learning unit learns from user feedback and continuously improves the quality of support and feedback. For example, it analyzes the feedback provided by users to learn what kind of advice was effective. This allows the learning unit to provide more effective support and feedback tailored to the user's needs. As a result, the learning unit can always provide high-quality support and feedback based on the latest information and technology, effectively assisting users in their creative activities.

[0034] The support unit can provide advice on the structure and expression of poetry. For example, the support unit provides advice considering elements such as poetic form, rhythm, and rhyme. The support unit can also provide advice on expressive methods such as metaphor, symbolism, and vocabulary selection. For example, the support unit can advise on the choice of poetic form, such as free verse or fixed form. Regarding rhythm, it can suggest rhythmic patterns to make the flow of the poem smooth. Regarding rhyme, it can advise on the placement of rhymes to enrich the sound of the poem. In this way, it can support the user's poetry writing by providing advice on the structure and expression of poetry. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the support unit can input a poem submitted by a user into a generative AI, and the generative AI can generate advice on the structure and expression of the poem.

[0035] The support unit can make suggestions regarding the colors and composition of a painting. For example, the support unit will make suggestions considering elements such as color selection, color scheme, and the meaning of colors. The support unit can also make suggestions regarding composition, such as viewpoint, balance, and focus. For example, regarding color selection, the support unit will suggest colors that match the theme of the painting. Regarding color schemes, it will advise on how to ensure that the color combinations are visually harmonious. Regarding the meaning of colors, it will make suggestions considering the psychological effects of colors. Regarding viewpoint, it will advise on how to guide the viewer's gaze within the painting. Regarding balance, it will suggest how to ensure that the elements in the painting are evenly distributed. Regarding focus, it will advise on how to clearly highlight the parts of the painting that the user wants to emphasize. In this way, by making suggestions regarding the colors and composition of a painting, the support unit can support the user's painting creation. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or it may be performed without using a generative AI. For example, the support unit can input a painting submitted by a user into a generative AI, and the generative AI can generate suggestions regarding the colors and composition.

[0036] The support unit can provide ideas for musical melodies and rhythms. The support unit provides ideas considering elements such as scales, rhythm, and melodic structure. It can also provide ideas for rhythm, including tempo, time signature, and rhythm patterns. For example, regarding scales, the support unit suggests scales that match the mood of the song. Regarding rhythm, it suggests rhythm patterns to make the song flow smoothly. Regarding melodic structure, it provides advice to make the melody more memorable. Regarding tempo, it evaluates whether the song's tempo is appropriate and suggests adjustments as needed. Regarding time signature, it evaluates whether the song's time signature matches the rhythm and suggests adjustments as needed. In this way, by providing ideas for musical melodies and rhythms, it can support the user's composition. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or without a generative AI. For example, the support unit can input music submitted by the user into a generative AI, which can then generate ideas for melodies and rhythms.

[0037] The support team can provide advice on short film scripts and filming techniques. For example, the support team considers elements such as plot, character development, and dialogue when providing advice. They can also provide advice on filming techniques such as camera angles, lighting, and editing. For instance, regarding the plot, the support team evaluates whether the story unfolds smoothly and suggests adjustments as needed. Regarding character development, they evaluate whether the characters are compelling and suggest adjustments as needed. Regarding dialogue, they evaluate whether the dialogue is natural and suggest adjustments as needed. Regarding camera angles, they suggest angles suitable for filming the scene. Regarding lighting, they suggest lighting techniques to enhance the atmosphere of the scene. Regarding editing techniques, they suggest editing techniques to ensure a smooth flow of the scene. In this way, by providing advice on short film scripts and filming techniques, the support team can support the user's film production. Some or all of the above processes in the support team may be performed using, for example, a generative AI, or without one. For example, the support team can input a short film script submitted by a user into a generative AI, which can then generate advice on the script and filming techniques.

[0038] The learning unit can learn from submitted works and improve the quality of future support and feedback. For example, the learning unit can learn from poems submitted by users and improve the quality of advice on the structure and expression of the poems. It can also learn from paintings submitted by users and improve the quality of suggestions on color and composition. Furthermore, the learning unit can learn from music submitted by users and improve the quality of ideas for melody and rhythm. It can also learn from short films submitted by users and improve the quality of advice on scenarios and filming techniques. In this way, the quality of AI support and feedback can be improved by learning from submitted works. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the learning unit can input user-submitted works into a generative AI, and the generative AI can learn from the works to improve the quality of future support and feedback.

[0039] The reception desk can analyze a user's past posting history and select the most suitable reception method. For example, the reception desk can analyze the times when a user frequently posted in the past and prompt them to post during those times. The reception desk can also prioritize suggesting posting formats (text, images, etc.) that the user has used in the past. Furthermore, the reception desk can analyze the content of a user's past posts and suggest relevant themes and topics. In this way, by analyzing past posting history, the reception desk can provide the user with the most suitable reception method. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input the user's past posting history data into a generative AI, which can then select the most suitable reception method.

[0040] The reception system can filter submissions based on the user's current projects and areas of interest. For example, it can prioritize submissions related to projects the user is currently working on. It can also filter and display relevant submissions based on the user's areas of interest. Furthermore, it can prompt the user to submit their work at an appropriate time, depending on the progress of their project. This allows for priority submission of highly relevant submissions by filtering based on the user's projects and areas of interest. Some or all of the above processing in the reception system may be performed using, for example, a generative AI, or not. For example, the reception system can input the user's project data into a generative AI, which can then filter related submissions.

[0041] The reception desk can prioritize accepting works that are highly relevant to the user, taking into account the user's geographical location information. For example, if the user is in a specific region, the reception desk will prioritize accepting works related to that region. The reception desk can also suggest works related to local events or topics based on the user's location information. Furthermore, if the user is traveling, the reception desk can prioritize accepting works related to their travel destination. In this way, by considering the user's geographical location information, highly relevant works can be prioritized. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without a generative AI. For example, the reception desk can input the user's geographical location information data into a generative AI, which can then select relevant works.

[0042] The reception department can analyze a user's social media activity when receiving submissions and accept relevant works. For example, the reception department can prioritize accepting relevant works based on content shared by the user on social media. The reception department can also determine the priority of works by considering the user's number of social media followers and engagement. Furthermore, the reception department can suggest works related to communities the user participates in on social media. This allows for the priority acceptance of relevant works by analyzing social media activity. Some or all of the above processing in the reception department may be performed using, for example, generative AI, or not. For example, the reception department can input the user's social media activity data into a generative AI, which can then select relevant works.

[0043] The support unit can adjust the level of detail provided based on the importance of the work when providing support. For example, the support unit can provide detailed advice for important works, and concise advice for simpler works. Furthermore, the support unit can provide individually customized support for works that the user particularly values. This allows for more appropriate support to be provided by adjusting the level of detail based on the importance of the work. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the support unit can input importance data of works submitted by the user into a generative AI, which can then adjust the level of detail of the support.

[0044] The support unit can apply different support algorithms depending on the category of the work during support. For example, for poetry support, the support unit can apply an algorithm specialized in the structure and expression of poetry. Similarly, for painting support, the support unit can apply an algorithm specialized in color and composition. Furthermore, for music support, the support unit can apply an algorithm specialized in melody and rhythm. This allows for more appropriate support by applying different support algorithms depending on the category of the work. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or without a generative AI. For example, the support unit can input category data of user-submitted works into a generative AI, which can then apply an appropriate support algorithm.

[0045] The support unit can determine the priority of support based on the submission date of the work. For example, the support unit will prioritize support for works with approaching deadlines. It can also postpone support for works with later submission dates. Furthermore, the support unit can provide support in advance for works that users plan to submit at a specific time. This allows support to be provided in a more appropriate order by determining the priority of support based on the submission date of the work. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the support unit can input submission date data of works posted by users into a generative AI, and the generative AI can determine the priority of support.

[0046] The support unit can adjust the order of support based on the relevance of the works during the support process. For example, the support unit can prioritize supporting works related to the project the user is currently working on. The support unit can also prioritize supporting works related to the user's areas of interest. Furthermore, the support unit can provide support at the appropriate time according to the progress of the user's project. By adjusting the order of support based on the relevance of the works, support can be provided in a more appropriate order. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the support unit can input relevance data of works posted by the user into a generative AI, and the generative AI can adjust the order of support.

[0047] The feedback unit can adjust the level of detail in the feedback based on the importance of the work. For example, the feedback unit can provide detailed feedback for important works. It can also provide concise feedback for simple works. Furthermore, the feedback unit can provide individually customized feedback for works that the user particularly values. This allows for more appropriate feedback to be provided by adjusting the level of detail based on the importance of the work. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the feedback unit can input importance data of user-submitted works into a generative AI, which can then adjust the level of detail in the feedback.

[0048] The feedback unit can apply different feedback algorithms depending on the category of the work when providing feedback. For example, for poetry feedback, the feedback unit can apply an algorithm specialized in the structure and expression of the poem. Similarly, for painting feedback, it can apply an algorithm specialized in color and composition. Furthermore, for music feedback, it can apply an algorithm specialized in melody and rhythm. This allows for more appropriate feedback by applying different feedback algorithms depending on the category of the work. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback unit can input category data of the user-submitted work into a generative AI, which can then apply an appropriate feedback algorithm.

[0049] The feedback unit can prioritize feedback based on the submission date of the work. For example, it can prioritize feedback for works with approaching deadlines. It can also postpone feedback for works with later submission dates. Furthermore, it can provide feedback in advance for works that the user plans to submit at a specific time. This allows for feedback to be provided in a more appropriate order by prioritizing feedback based on the submission date of the work. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the feedback unit can input submission date data of user-submitted works into a generative AI, which can then determine the priority of feedback.

[0050] The feedback unit can adjust the order of feedback based on the relevance of the works during the feedback process. For example, the feedback unit can prioritize feedback on works related to the project the user is currently working on. It can also prioritize feedback on works related to the user's areas of interest. Furthermore, the feedback unit can provide feedback at an appropriate time according to the user's project progress. By adjusting the order of feedback based on the relevance of the works, feedback can be provided in a more appropriate order. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the feedback unit can input relevance data of works posted by the user into a generative AI, and the generative AI can adjust the order of feedback.

[0051] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can analyze past learning data and select the most effective learning algorithm. The learning unit can also extract specific patterns from past learning data and optimize the algorithm. Furthermore, the learning unit can adjust the parameters of the learning algorithm based on past learning data. This allows the learning algorithm to be optimized by referring to past learning data. Some or all of the above processes in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input past learning data into a generative AI, which can then optimize the learning algorithm.

[0052] The learning unit can weight the training data based on the submission date of the works during training. For example, the learning unit can prioritize data from recently submitted works during training. It can also lighten the weight of data from older works during training. Furthermore, the learning unit can dynamically adjust the weight of the training data according to the submission date. This allows for more appropriate training by weighting the training data based on the submission date of the works. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input the submission date data of the works into a generative AI, and the generative AI can weight the training data.

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

[0054] The creators' platform can also include a rating section that accepts evaluations of user works from other users. This rating section collects evaluations from other users on user-submitted works and provides feedback based on this. For example, the rating section allows other users to "like" or comment on works. It can also aggregate evaluations from other users and display popular works in a ranking format. Furthermore, the rating section can suggest specific areas for improvement to creators based on other users' evaluations. This allows users to improve the quality of their work by receiving feedback from other users.

[0055] The creators' platform can also include an inspiration section to provide users with inspiration for their work. This section provides resources for users to draw inspiration from past works and the works of other users when creating new pieces. For example, when a user is writing poetry, the inspiration section can display classic poems and poems by other users. It can also provide images to help users with color and composition when they are drawing. Furthermore, it can provide samples of melodies and rhythms when users are composing music. This allows users to gain inspiration when creating new works.

[0056] A creators' platform can also include a marketing department to provide marketing support for users' works. This department helps users to raise awareness of their creations. For example, it can provide tools for sharing users' works on platforms such as social media and blogs. It can also offer advice on advertising users' works to specific target audiences. Furthermore, it can support users in selling their works on online marketplaces. This allows users to gain wider recognition for their work and generate revenue.

[0057] The creators' platform can also include a collaboration section that provides collaborative features for users' work. The collaboration section assists users in creating works together with other users. For example, it can provide tools that allow users to collaboratively edit with other users in real time. It can also provide a forum where users can share ideas and receive feedback. Furthermore, the collaboration section can provide project management tools for users to manage projects together with other users. This allows users to collaborate with others to create higher-quality works.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The reception desk accepts submissions from users. Submissions include poetry, paintings, music, films, and more. The reception desk accepts submissions via online forms, email, and file uploads. The reception desk also estimates the user's emotions and adjusts the timing of submissions based on those emotions. For example, if a user is feeling stressed, it encourages them to submit during a time when they can relax; if they are excited, it simplifies the interface to allow them to submit immediately; and if they are tired, it sets a reminder to submit later. Step 2: The support department assists in the production of works received by the reception department. The support department provides advice on the structure and expression of poetry, the colors and composition of illustrations, the melody and rhythm of music, and the script and filming techniques of short films. For example, they provide advice considering elements such as the form, rhythm, and rhyme of poetry, and for the colors of illustrations, they make suggestions considering color selection, color scheme, and the meaning of colors. For the melodies of music, they provide ideas considering scales, rhythm, and melodic structure, and for the scripts of short films, they provide advice considering the plot, character setting, and dialogue. Step 3: The Feedback team provides feedback on works supported by the Support team. The Feedback team provides positive feedback to boost the user's self-esteem. For example, they might say, "That's a wonderful poem," "This painting is very moving," "This music is touching," or "This short film is very interesting." Step 4: The Learning Department studies the works that have been feedbacked on by the Feedback Department. The Learning Department studies the submitted works to improve the quality of future support and feedback. For example, by studying many poems, they can provide advice on more advanced poetic structure and expression. Similarly, the Learning Department can improve its abilities through learning for paintings, music, and short films.

[0060] (Example of form 2) The creators' platform according to an embodiment of the present invention is a system that provides a mechanism for users to freely post works such as "text," "images," "music," and "videos." In this system, users post works, AI supports the production of the posted works, and the AI ​​further provides feedback on the works. This feedback mainly consists of praise in order to enhance the user's self-esteem. In addition, the posted works are learned by the AI, contributing to the improvement of the AI's capabilities. First, users post works such as "text," "images," "music," and "videos." At this time, users can freely express themselves. For example, users can post poems, draw pictures, compose music, or produce short films. Next, the AI ​​supports the production of the posted works. For example, when a user posts a poem, the AI ​​provides advice on the structure and expression of the poem. When drawing a picture, the AI ​​makes suggestions on color and composition. When composing music, the AI ​​provides ideas for melody and rhythm. When producing a short film, the AI ​​provides advice on the scenario and filming techniques. Furthermore, the AI ​​provides feedback on the posted works. This feedback primarily consists of praise to boost users' self-esteem. For example, the AI ​​might provide feedback such as, "That's a wonderful poem," "This painting is very moving," "This music is touching," or "This short film is very interesting." Furthermore, the submitted works are learned by the AI. This allows the AI ​​to learn from people's free-thinking ideas and improve the quality of future support and feedback. For instance, by learning from many poems, the AI ​​can provide advice on more advanced poetic structure and expression. Similarly, the AI ​​can improve its capabilities through learning for paintings, music, and short films. This system allows users to freely express themselves and boost their self-esteem through AI support and feedback. Additionally, the AI ​​can improve the quality of future support and feedback by learning from submitted works. This enables the creators' platform to unleash users' creativity and provide a space for self-expression.This allows the creators' platform to provide a consistent platform for users to submit their work, receive production support, get feedback, and learn.

[0061] The creators platform according to this embodiment comprises a reception unit, a support unit, a feedback unit, and a learning unit. The reception unit accepts submissions of works from users. Submissions from users include, but are not limited to, poems, paintings, music, and films. The reception unit accepts works, for example, through an online form. The reception unit can also accept works via email or file upload. Furthermore, the reception unit can estimate the user's emotions and adjust the timing of submission based on the estimated emotions. For example, if the reception unit is feeling stressed, it may encourage the user to submit during a time when they can relax. The reception unit can also simplify the interface to allow for immediate submission if the user is excited. Furthermore, if the reception unit is tired, it may set a reminder to submit later. The support unit supports the production of works accepted by the reception unit. The support unit may, for example, provide advice on the structure and expression of a poem. The support unit may also offer suggestions on the colors and composition of a painting. Furthermore, the support unit may provide ideas for the melody and rhythm of music. The support team can also provide advice on short film scripts and filming techniques. For example, the support team provides advice on the structure of poetry, considering elements such as poetic form, rhythm, and rhyme. For the color scheme of a painting, they offer suggestions considering elements such as color selection, color scheme, and the meaning of colors. For musical melodies, they provide ideas considering elements such as scale, rhythm, and melodic structure. For short film scripts, they provide advice considering elements such as plot, character development, and dialogue. The feedback team provides feedback on works supported by the support team. The feedback team provides positive feedback to boost the user's self-esteem. For example, the feedback team may provide feedback such as, "That's a wonderful poem," "This painting is very moving," "This music is touching," or "This short film is very interesting." The learning team learns from works that have been feedbacked on by the feedback team.The learning unit studies submitted works to improve the quality of future support and feedback. For example, by studying many poems, the learning unit can provide advice on more advanced poetic structure and expression. Similarly, the learning unit can improve its capabilities through learning for paintings, music, and short films. This allows the creators platform, according to the embodiment, to consistently handle user submissions, production support, feedback, and learning.

[0062] The reception desk accepts submissions of works from users. These submissions include, but are not limited to, poetry, paintings, music, and films. The reception desk accepts submissions, for example, through online forms. It can also accept submissions via email or file uploads. Furthermore, the reception desk can estimate the user's emotions and adjust the timing of submissions based on these estimations. For example, if the reception desk is stressed, it might encourage submissions during a more relaxed time. If the reception desk is excited, it might simplify the interface to allow for immediate submission. Additionally, if the reception desk is tired, it can set a reminder to submit later. Specifically, the online form has an intuitive interface and is designed to allow users to easily upload their works. The form includes input fields for the title, description, and category of the work, allowing users to submit their work by entering this information. For email submissions, a dedicated email address is provided, allowing users to send their works as attachments. The file upload function allows for easy file uploads via drag-and-drop and supports multiple file formats. Furthermore, the reception desk uses AI to estimate the user's emotions. For example, it estimates the user's current emotional state by analyzing the user's input, past posting history, and even the user's behavior patterns. The AI ​​uses natural language processing technology to analyze the user's text input and classify emotions. For example, if a user uses words like "tired" or "stressed," the AI ​​recognizes this as a negative emotion and takes appropriate action. To encourage users to post during times when they can relax, the AI ​​estimates the optimal time based on the user's past behavior data. For example, if a user is often relaxed at night, it sends a notification encouraging them to post at night. Also, if a user is excited, the AI ​​simplifies the interface to allow for quick posting. For example, it reduces the burden on the user by minimizing input fields and requiring only necessary information to be entered.Furthermore, if a user is feeling tired, the AI ​​sets a reminder to post later. The reminder is sent at an appropriate time according to the user's schedule, helping them remember to post their work. This allows the reception desk to respond flexibly to the user's emotional state and provide an environment where users can comfortably post their work.

[0063] The support department assists in the production of works submitted by the reception department. For example, the support department provides advice on the structure and expression of poetry. They can also offer suggestions regarding the colors and composition of illustrations. Furthermore, they can provide ideas for melodies and rhythms in music. They can also provide advice on short film scripts and filming techniques. For instance, regarding poetry structure, the support department provides advice considering elements such as poetic form, rhythm, and rhyme. Regarding the colors of illustrations, they offer suggestions considering elements such as color selection, color scheme, and color meaning. For musical melodies, they offer ideas considering elements such as scales, rhythm, and melodic structure. For short film scripts, they provide advice considering elements such as plot, character development, and dialogue. Specifically, the support department uses AI to analyze the user's work and provide appropriate advice. For example, regarding poetry structure, the AI ​​analyzes the poetic form, rhythm, and rhyme, and suggests improvements and new ideas to the user. The AI ​​provides specific advice by referring to a database of excellent past poems and comparing them to the user's poem. For paintings, the AI ​​analyzes color choices and color schemes, offering suggestions to the user about the meaning and effect of colors. For example, it provides advice on the psychological effects of specific colors and the visual impact of color combinations. For musical melodies, the AI ​​analyzes scales, rhythms, and melodic structure, providing users with new melody and rhythm ideas. Based on music theory, the AI ​​analyzes the user's music and suggests more appealing melodies and rhythms. For short film scripts, the AI ​​analyzes the plot, character settings, and dialogue, providing users with specific areas for improvement and new ideas. For example, it provides advice on plot development, character motivations, and the naturalness of dialogue. Furthermore, the support team provides real-time assistance for problems and questions the user may encounter during the creative process. For example, if a user is struggling with the rhythm of a poem, the AI ​​will immediately suggest ways to improve the rhythm, helping the user complete the work smoothly.This allows the support department to effectively assist users in their creative activities and promote the production of higher-quality works.

[0064] The Feedback Department provides feedback on works supported by the Support Department. The Feedback Department provides positive feedback to enhance the user's self-esteem. For example, the Feedback Department might provide feedback such as, "That's a wonderful poem," "This painting is very moving," "This music is touching," or "This short film is very interesting." Specifically, the Feedback Department uses AI to analyze the works and generate appropriate feedback. The AI ​​uses natural language processing technology to analyze the content and characteristics of the user's work and identify points to praise. For example, in the case of poetry, the AI ​​analyzes the rhythm, rhyme, and expression of the poem and generates feedback praising particularly outstanding parts. In the case of paintings, the AI ​​analyzes the colors, composition, and technical elements and provides feedback praising the moving or technically excellent parts. In the case of music, the AI ​​analyzes the melody, rhythm, and structure of the music and generates feedback praising the emotional or original parts. In the case of short films, the AI ​​analyzes the plot, character development, and dialogue and provides feedback praising the interesting or moving parts. Furthermore, the Feedback Department also provides constructive feedback to encourage the user's growth. For example, if a poem's rhythm is unstable, the AI ​​will suggest specific areas for improvement, supporting the user in creating better works in the future. If a painting's composition is unnatural, the AI ​​will suggest ways to improve it, advising the user to create more appealing pictures. If a piece of music has a monotonous melody, the AI ​​will offer new melody ideas, helping the user create richer music. If a short film's plot is uninspired, the AI ​​will suggest ways to improve the plot development and character motivations, advising the user to create more interesting films. In this way, the feedback system not only boosts the user's self-esteem but also encourages their growth and supports the creation of higher-quality works.

[0065] The learning unit learns from works that have been feedbacked on by the feedback unit. The learning unit learns from submitted works and improves the quality of future support and feedback. For example, by learning from many poems, the learning unit can provide advice on more advanced poetic structure and expression. Similarly, the learning unit can improve its capabilities through learning for paintings, music, and short films. Specifically, the learning unit uses AI to analyze large amounts of work data and extract patterns and trends. For example, in the case of poetry, the AI ​​analyzes many poems and learns the commonalities and characteristics of excellent poems. This allows the learning unit to provide users with more specific and effective advice. In the case of paintings, the AI ​​analyzes color, composition, and technical elements and learns the characteristics of excellent paintings. This allows the learning unit to provide users with more advanced suggestions for color and composition. In the case of music, the AI ​​analyzes melody, rhythm, and musical structure and learns the characteristics of excellent music. This allows the learning unit to provide users with richer ideas for melody and rhythm. In the case of short films, the AI ​​analyzes the plot, character development, and dialogue to learn the characteristics of good films. This allows the learning unit to provide users with advice on more interesting plots and character development. Furthermore, the learning unit learns from user feedback and continuously improves the quality of support and feedback. For example, it analyzes the feedback provided by users to learn what kind of advice was effective. This allows the learning unit to provide more effective support and feedback tailored to the user's needs. As a result, the learning unit can always provide high-quality support and feedback based on the latest information and technology, effectively assisting users in their creative activities.

[0066] The support unit can provide advice on the structure and expression of poetry. For example, the support unit provides advice considering elements such as poetic form, rhythm, and rhyme. The support unit can also provide advice on expressive methods such as metaphor, symbolism, and vocabulary selection. For example, the support unit can advise on the choice of poetic form, such as free verse or fixed form. Regarding rhythm, it can suggest rhythmic patterns to make the flow of the poem smooth. Regarding rhyme, it can advise on the placement of rhymes to enrich the sound of the poem. In this way, it can support the user's poetry writing by providing advice on the structure and expression of poetry. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the support unit can input a poem submitted by a user into a generative AI, and the generative AI can generate advice on the structure and expression of the poem.

[0067] The support unit can make suggestions regarding the colors and composition of a painting. For example, the support unit will make suggestions considering elements such as color selection, color scheme, and the meaning of colors. The support unit can also make suggestions regarding composition, such as viewpoint, balance, and focus. For example, regarding color selection, the support unit will suggest colors that match the theme of the painting. Regarding color schemes, it will advise on how to ensure that the color combinations are visually harmonious. Regarding the meaning of colors, it will make suggestions considering the psychological effects of colors. Regarding viewpoint, it will advise on how to guide the viewer's gaze within the painting. Regarding balance, it will suggest how to ensure that the elements in the painting are evenly distributed. Regarding focus, it will advise on how to clearly highlight the parts of the painting that the user wants to emphasize. In this way, by making suggestions regarding the colors and composition of a painting, the support unit can support the user's painting creation. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or it may be performed without using a generative AI. For example, the support unit can input a painting submitted by a user into a generative AI, and the generative AI can generate suggestions regarding the colors and composition.

[0068] The support unit can provide ideas for musical melodies and rhythms. The support unit provides ideas considering elements such as scales, rhythm, and melodic structure. It can also provide ideas for rhythm, including tempo, time signature, and rhythm patterns. For example, regarding scales, the support unit suggests scales that match the mood of the song. Regarding rhythm, it suggests rhythm patterns to make the song flow smoothly. Regarding melodic structure, it provides advice to make the melody more memorable. Regarding tempo, it evaluates whether the song's tempo is appropriate and suggests adjustments as needed. Regarding time signature, it evaluates whether the song's time signature matches the rhythm and suggests adjustments as needed. In this way, by providing ideas for musical melodies and rhythms, it can support the user's composition. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or without a generative AI. For example, the support unit can input music submitted by the user into a generative AI, which can then generate ideas for melodies and rhythms.

[0069] The support team can provide advice on short film scripts and filming techniques. For example, the support team considers elements such as plot, character development, and dialogue when providing advice. They can also provide advice on filming techniques such as camera angles, lighting, and editing. For instance, regarding the plot, the support team evaluates whether the story unfolds smoothly and suggests adjustments as needed. Regarding character development, they evaluate whether the characters are compelling and suggest adjustments as needed. Regarding dialogue, they evaluate whether the dialogue is natural and suggest adjustments as needed. Regarding camera angles, they suggest angles suitable for filming the scene. Regarding lighting, they suggest lighting techniques to enhance the atmosphere of the scene. Regarding editing techniques, they suggest editing techniques to ensure a smooth flow of the scene. In this way, by providing advice on short film scripts and filming techniques, the support team can support the user's film production. Some or all of the above processes in the support team may be performed using, for example, a generative AI, or without one. For example, the support team can input a short film script submitted by a user into a generative AI, which can then generate advice on the script and filming techniques.

[0070] The feedback unit can provide positive feedback to enhance the user's self-esteem. For example, it might respond to a user's submitted poem with "That's a wonderful poem." It could also respond to a user's submitted painting with "This painting is very moving." Furthermore, it could respond to a user's submitted music with "This music is very touching." It could also respond to a user's submitted short film with "This short film is very interesting." By providing positive feedback to enhance the user's self-esteem, the feedback unit can improve the user's motivation. Some or all of the above processing in the feedback unit may be performed using, for example, an emotion engine or generative AI, or without using such an engine. For example, the feedback unit can input a user's submitted work into an emotion engine or generative AI, which can then generate positive feedback.

[0071] The learning unit can learn from submitted works and improve the quality of future support and feedback. For example, the learning unit can learn from poems submitted by users and improve the quality of advice on the structure and expression of the poems. It can also learn from paintings submitted by users and improve the quality of suggestions on color and composition. Furthermore, the learning unit can learn from music submitted by users and improve the quality of ideas for melody and rhythm. It can also learn from short films submitted by users and improve the quality of advice on scenarios and filming techniques. In this way, the quality of AI support and feedback can be improved by learning from submitted works. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the learning unit can input user-submitted works into a generative AI, and the generative AI can learn from the works to improve the quality of future support and feedback.

[0072] The reception desk can estimate the user's emotions and adjust the timing of submission based on those emotions. For example, if the user is feeling stressed, the reception desk may encourage them to submit during a time when they can relax. The reception desk can also simplify the interface to allow immediate submission if the user is excited. Furthermore, if the user is tired, the reception desk can set a reminder to submit later. This allows for submission at a more appropriate time by adjusting the timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using, for example, an emotion engine or generative AI, or without using such an emotion engine or generative AI. For example, the reception desk can input user facial expression data into a generative AI, which can estimate the user's emotions and adjust the submission timing.

[0073] The reception desk can analyze a user's past posting history and select the most suitable reception method. For example, the reception desk can analyze the times when a user frequently posted in the past and prompt them to post during those times. The reception desk can also prioritize suggesting posting formats (text, images, etc.) that the user has used in the past. Furthermore, the reception desk can analyze the content of a user's past posts and suggest relevant themes and topics. In this way, by analyzing past posting history, the reception desk can provide the user with the most suitable reception method. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input the user's past posting history data into a generative AI, which can then select the most suitable reception method.

[0074] The reception system can filter submissions based on the user's current projects and areas of interest. For example, it can prioritize submissions related to projects the user is currently working on. It can also filter and display relevant submissions based on the user's areas of interest. Furthermore, it can prompt the user to submit their work at an appropriate time, depending on the progress of their project. This allows for priority submission of highly relevant submissions by filtering based on the user's projects and areas of interest. Some or all of the above processing in the reception system may be performed using, for example, a generative AI, or not. For example, the reception system can input the user's project data into a generative AI, which can then filter related submissions.

[0075] The reception unit can estimate the user's emotions and determine the priority of submissions based on the estimated emotions. For example, if the user is excited, the reception unit will prioritize submissions that require immediate feedback. If the user is relaxed, the reception unit can also prioritize submissions that will take time to complete. Furthermore, if the user is stressed, the reception unit can prioritize submissions that can be completed easily. This allows for submissions to be received in a more appropriate order by prioritizing them based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using, for example, an emotion engine or generative AI, or without using such an emotion engine or generative AI. For example, the reception unit can input user facial expression data into a generative AI, which can estimate the user's emotions and determine the priority of submissions.

[0076] The reception desk can prioritize accepting works that are highly relevant to the user, taking into account the user's geographical location information. For example, if the user is in a specific region, the reception desk will prioritize accepting works related to that region. The reception desk can also suggest works related to local events or topics based on the user's location information. Furthermore, if the user is traveling, the reception desk can prioritize accepting works related to their travel destination. In this way, by considering the user's geographical location information, highly relevant works can be prioritized. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without a generative AI. For example, the reception desk can input the user's geographical location information data into a generative AI, which can then select relevant works.

[0077] The reception department can analyze a user's social media activity when receiving submissions and accept relevant works. For example, the reception department can prioritize accepting relevant works based on content shared by the user on social media. The reception department can also determine the priority of works by considering the user's number of social media followers and engagement. Furthermore, the reception department can suggest works related to communities the user participates in on social media. This allows for the priority acceptance of relevant works by analyzing social media activity. Some or all of the above processing in the reception department may be performed using, for example, generative AI, or not. For example, the reception department can input the user's social media activity data into a generative AI, which can then select relevant works.

[0078] The support unit can estimate the user's emotions and adjust the way it expresses support based on the estimated emotions. For example, if the user is nervous, the support unit can provide support using gentle words. If the user is relaxed, the support unit can also provide detailed advice. Furthermore, if the user is in a hurry, the support unit can provide concise and quick support. This allows for more appropriate support to be provided by adjusting the way support is expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the support unit may be performed using, for example, an emotion engine or generative AI, or without using an emotion engine or generative AI. For example, the support unit can input user facial expression data into a generative AI, which can estimate the user's emotions and adjust the way support is expressed.

[0079] The support unit can adjust the level of detail provided based on the importance of the work when providing support. For example, the support unit can provide detailed advice for important works, and concise advice for simpler works. Furthermore, the support unit can provide individually customized support for works that the user particularly values. This allows for more appropriate support to be provided by adjusting the level of detail based on the importance of the work. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the support unit can input importance data of works submitted by the user into a generative AI, which can then adjust the level of detail of the support.

[0080] The support unit can apply different support algorithms depending on the category of the work during support. For example, for poetry support, the support unit can apply an algorithm specialized in the structure and expression of poetry. Similarly, for painting support, the support unit can apply an algorithm specialized in color and composition. Furthermore, for music support, the support unit can apply an algorithm specialized in melody and rhythm. This allows for more appropriate support by applying different support algorithms depending on the category of the work. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or without a generative AI. For example, the support unit can input category data of user-submitted works into a generative AI, which can then apply an appropriate support algorithm.

[0081] The support unit can estimate the user's emotions and adjust the length of the support based on the estimated emotions. For example, if the user is in a hurry, the support unit can provide short, concise support. If the user is relaxed, the support unit can also provide longer support with detailed explanations. Furthermore, if the user is excited, the support unit can provide support with visually stimulating effects. By adjusting the length of the support based on the user's emotions, more appropriate support can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using, for example, an emotion engine or generative AI, or without using an emotion engine or generative AI. For example, the support unit can input user facial expression data into the generative AI, which can estimate the user's emotions and adjust the length of the support.

[0082] The support unit can determine the priority of support based on the submission date of the work. For example, the support unit will prioritize support for works with approaching deadlines. It can also postpone support for works with later submission dates. Furthermore, the support unit can provide support in advance for works that users plan to submit at a specific time. This allows support to be provided in a more appropriate order by determining the priority of support based on the submission date of the work. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the support unit can input submission date data of works posted by users into a generative AI, and the generative AI can determine the priority of support.

[0083] The support unit can adjust the order of support based on the relevance of the works during the support process. For example, the support unit can prioritize supporting works related to the project the user is currently working on. The support unit can also prioritize supporting works related to the user's areas of interest. Furthermore, the support unit can provide support at the appropriate time according to the progress of the user's project. By adjusting the order of support based on the relevance of the works, support can be provided in a more appropriate order. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the support unit can input relevance data of works posted by the user into a generative AI, and the generative AI can adjust the order of support.

[0084] The feedback unit can estimate the user's emotions and adjust the way it expresses the feedback based on the estimated emotions. For example, if the user is nervous, the feedback unit can provide feedback in gentle words. It can also provide detailed feedback if the user is relaxed. Furthermore, if the user is in a hurry, the feedback unit can provide concise and quick feedback. This allows for more appropriate feedback to be provided by adjusting the expression of feedback based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using, for example, an emotion engine or generative AI, or without using such an emotion engine or generative AI. For example, the feedback unit can input user facial expression data into the generative AI, which can estimate the user's emotions and adjust the way it expresses the feedback.

[0085] The feedback unit can adjust the level of detail in the feedback based on the importance of the work. For example, the feedback unit can provide detailed feedback for important works. It can also provide concise feedback for simple works. Furthermore, the feedback unit can provide individually customized feedback for works that the user particularly values. This allows for more appropriate feedback to be provided by adjusting the level of detail based on the importance of the work. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the feedback unit can input importance data of user-submitted works into a generative AI, which can then adjust the level of detail in the feedback.

[0086] The feedback unit can apply different feedback algorithms depending on the category of the work when providing feedback. For example, for poetry feedback, the feedback unit can apply an algorithm specialized in the structure and expression of the poem. Similarly, for painting feedback, it can apply an algorithm specialized in color and composition. Furthermore, for music feedback, it can apply an algorithm specialized in melody and rhythm. This allows for more appropriate feedback by applying different feedback algorithms depending on the category of the work. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback unit can input category data of the user-submitted work into a generative AI, which can then apply an appropriate feedback algorithm.

[0087] The feedback unit can estimate the user's emotions and adjust the length of the feedback based on the estimated emotions. For example, if the user is in a hurry, the feedback unit can provide short, concise feedback. If the user is relaxed, the feedback unit can also provide longer feedback with more detailed explanations. Furthermore, if the user is excited, the feedback unit can provide feedback with visually stimulating effects. By adjusting the length of the feedback based on the user's emotions, more appropriate feedback can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using, for example, an emotion engine or generative AI, or without using an emotion engine or generative AI. For example, the feedback unit can input user facial expression data into the generative AI, which can estimate the user's emotions and adjust the length of the feedback.

[0088] The feedback unit can prioritize feedback based on the submission date of the work. For example, it can prioritize feedback for works with approaching deadlines. It can also postpone feedback for works with later submission dates. Furthermore, it can provide feedback in advance for works that the user plans to submit at a specific time. This allows for feedback to be provided in a more appropriate order by prioritizing feedback based on the submission date of the work. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the feedback unit can input submission date data of user-submitted works into a generative AI, which can then determine the priority of feedback.

[0089] The feedback unit can adjust the order of feedback based on the relevance of the works during the feedback process. For example, the feedback unit can prioritize feedback on works related to the project the user is currently working on. It can also prioritize feedback on works related to the user's areas of interest. Furthermore, the feedback unit can provide feedback at an appropriate time according to the user's project progress. By adjusting the order of feedback based on the relevance of the works, feedback can be provided in a more appropriate order. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the feedback unit can input relevance data of works posted by the user into a generative AI, and the generative AI can adjust the order of feedback.

[0090] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is relaxed, the learning unit will prioritize selecting training data with positive content. It can also select training data that promotes relaxation if the user is stressed. Furthermore, if the user is excited, the learning unit can select training data with stimulating content. This allows for the selection of more appropriate training data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the learning unit may be performed using, for example, an emotion engine or generative AI, or without using such an emotion engine or generative AI. For example, the learning unit can input user facial expression data into a generative AI, which can then estimate the user's emotions and select training data.

[0091] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can analyze past learning data and select the most effective learning algorithm. The learning unit can also extract specific patterns from past learning data and optimize the algorithm. Furthermore, the learning unit can adjust the parameters of the learning algorithm based on past learning data. This allows the learning algorithm to be optimized by referring to past learning data. Some or all of the above processes in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input past learning data into a generative AI, which can then optimize the learning algorithm.

[0092] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, the learning unit will learn more frequently if the user is relaxed. It can also reduce the learning frequency if the user is stressed. Furthermore, it can increase the learning frequency if the user is excited. By adjusting the learning frequency based on the user's emotions, learning can be performed at a more appropriate frequency. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using, for example, an emotion engine or generative AI, or without using an emotion engine or generative AI. For example, the learning unit can input user facial expression data into the generative AI, which can estimate the user's emotions and adjust the learning frequency.

[0093] The learning unit can weight the training data based on the submission date of the works during training. For example, the learning unit can prioritize data from recently submitted works during training. It can also lighten the weight of data from older works during training. Furthermore, the learning unit can dynamically adjust the weight of the training data according to the submission date. This allows for more appropriate training by weighting the training data based on the submission date of the works. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input the submission date data of the works into a generative AI, and the generative AI can weight the training data.

[0094] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0095] The creators' platform can also include a rating section that accepts evaluations of user works from other users. This rating section collects evaluations from other users on user-submitted works and provides feedback based on this. For example, the rating section allows other users to "like" or comment on works. It can also aggregate evaluations from other users and display popular works in a ranking format. Furthermore, the rating section can suggest specific areas for improvement to creators based on other users' evaluations. This allows users to improve the quality of their work by receiving feedback from other users.

[0096] The creators' platform can also include an inspiration section to provide users with inspiration for their work. This section provides resources for users to draw inspiration from past works and the works of other users when creating new pieces. For example, when a user is writing poetry, the inspiration section can display classic poems and poems by other users. It can also provide images to help users with color and composition when they are drawing. Furthermore, it can provide samples of melodies and rhythms when users are composing music. This allows users to gain inspiration when creating new works.

[0097] A creators' platform can also include a marketing department to provide marketing support for users' works. This department helps users to raise awareness of their creations. For example, it can provide tools for sharing users' works on platforms such as social media and blogs. It can also offer advice on advertising users' works to specific target audiences. Furthermore, it can support users in selling their works on online marketplaces. This allows users to gain wider recognition for their work and generate revenue.

[0098] The creators' platform can also include a collaboration section that provides collaborative features for users' work. The collaboration section assists users in creating works together with other users. For example, it can provide tools that allow users to collaboratively edit with other users in real time. It can also provide a forum where users can share ideas and receive feedback. Furthermore, the collaboration section can provide project management tools for users to manage projects together with other users. This allows users to collaborate with others to create higher-quality works.

[0099] The creators' platform can also include a sentiment analysis unit that performs sentiment analysis on users' works. This unit analyzes how other users feel about a user's submitted work and provides feedback based on this analysis. For example, it analyzes how other users feel about a work and provides feedback to the creator based on the results. The sentiment analysis unit can also suggest areas for improvement based on the feelings of other users. Furthermore, it can evaluate the popularity of a work based on the feelings of other users. This allows users to understand the feelings of others and improve the quality of their work.

[0100] The creators' platform can also be equipped with an emotion estimation unit that estimates the emotions associated with users' works. The emotion estimation unit uses AI to estimate the emotions of user-submitted works and provides feedback based on the results. For example, the emotion estimation unit can estimate emotions such as emotion or joy in a user-submitted poem and provide feedback based on those emotions. The emotion estimation unit can also estimate emotions from the colors and composition of a user-submitted painting and provide feedback based on those emotions. Furthermore, the emotion estimation unit can estimate emotions from the melody and rhythm of a user-submitted music and provide feedback based on those emotions. This allows users to improve the quality of their works based on the emotion estimations made by the AI.

[0101] The creators' platform can also include an emotional feedback section that provides emotional feedback on users' works. This section collects emotional feedback from other users on user-submitted works and provides feedback based on the results. For example, the emotional feedback section allows other users to provide feedback on works expressing emotions such as "moved," "fun," or "sad." The emotional feedback section can also aggregate the emotional feedback from other users and provide feedback to the creator based on the results. Furthermore, the emotional feedback section can suggest areas for improvement in the work based on the emotional feedback from other users. This allows users to improve the quality of their work based on the emotional feedback from other users.

[0102] The creators' platform can also include an emotion monitoring unit that monitors the user's emotions towards their work. This unit monitors the user's emotions during the creative process and provides support based on the results. For example, it can monitor emotional changes when a user is writing poetry and provide advice at the appropriate time. It can also monitor emotional changes when a user is painting and offer suggestions regarding color and composition. Furthermore, it can monitor emotional changes when a user is composing music and provide ideas for melody and rhythm. This allows users to improve the quality of their work based on their emotional changes.

[0103] The creators' platform can also include an emotion tracking unit that tracks the user's emotions towards their work. This unit tracks the user's emotions during the creative process and provides feedback based on the results. For example, when a user is writing poetry, the emotion tracking unit tracks their emotional changes and provides feedback at the appropriate time. It can also track emotional changes when a user is drawing and offer suggestions regarding color and composition. Furthermore, when a user is composing music, the emotion tracking unit can track their emotional changes and offer ideas regarding melody and rhythm. This allows users to improve the quality of their work based on their emotional changes.

[0104] The creators' platform can also include an emotion prediction unit that predicts the emotions users feel towards their work. This unit predicts the emotions users experience during the creative process and provides support based on the results. For example, when a user is writing poetry, the emotion prediction unit can predict emotional changes and provide advice at the appropriate time. It can also predict emotional changes when a user is painting and offer suggestions regarding color and composition. Furthermore, when a user is composing music, the emotion prediction unit can predict emotional changes and offer ideas regarding melody and rhythm. This allows users to improve the quality of their work based on their emotional changes.

[0105] The following briefly describes the processing flow for example form 2.

[0106] Step 1: The reception desk accepts submissions from users. Submissions include poetry, paintings, music, films, and more. The reception desk accepts submissions via online forms, email, and file uploads. The reception desk also estimates the user's emotions and adjusts the timing of submissions based on those emotions. For example, if a user is feeling stressed, it encourages them to submit during a time when they can relax; if they are excited, it simplifies the interface to allow them to submit immediately; and if they are tired, it sets a reminder to submit later. Step 2: The support department assists in the production of works received by the reception department. The support department provides advice on the structure and expression of poetry, the colors and composition of illustrations, the melody and rhythm of music, and the script and filming techniques of short films. For example, they provide advice considering elements such as the form, rhythm, and rhyme of poetry, and for the colors of illustrations, they make suggestions considering color selection, color scheme, and the meaning of colors. For the melodies of music, they provide ideas considering scales, rhythm, and melodic structure, and for the scripts of short films, they provide advice considering the plot, character setting, and dialogue. Step 3: The Feedback team provides feedback on works supported by the Support team. The Feedback team provides positive feedback to boost the user's self-esteem. For example, they might say, "That's a wonderful poem," "This painting is very moving," "This music is touching," or "This short film is very interesting." Step 4: The Learning Department studies the works that have been feedbacked on by the Feedback Department. The Learning Department studies the submitted works to improve the quality of future support and feedback. For example, by studying many poems, they can provide advice on more advanced poetic structure and expression. Similarly, the Learning Department can improve its abilities through learning for paintings, music, and short films.

[0107] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0108] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0109] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0110] Each of the multiple elements described above, including the reception unit, support unit, feedback unit, and learning unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and accepts submissions of works from users. The support unit is implemented by the specific processing unit 290 of the data processing unit 12 and supports the creation of submitted works. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides feedback to enhance the user's self-esteem. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns from submitted works to improve the quality of future support and feedback. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0111] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0112] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0113] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0114] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0115] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0117] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0118] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0119] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0120] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0121] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0122] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0123] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0125] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the reception unit, support unit, feedback unit, and learning unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and accepts submissions of works from users. The support unit is implemented by the specific processing unit 290 of the data processing unit 12 and supports the creation of submitted works. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides feedback to enhance the user's self-esteem. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns from submitted works to improve the quality of future support and feedback. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0127] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0128] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the reception unit, support unit, feedback unit, and learning unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and accepts submissions of works from users. The support unit is implemented by the specific processing unit 290 of the data processing unit 12 and supports the creation of submitted works. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides feedback to enhance the user's self-esteem. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns from submitted works to improve the quality of future support and feedback. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0143] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0144] As shown in Figure 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.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0151] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0152] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements described above, including the reception unit, support unit, feedback unit, and learning unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and accepts submissions of works from users. The support unit is implemented by the specific processing unit 290 of the data processing unit 12 and supports the production of submitted works. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides feedback to enhance the user's self-esteem. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns from submitted works to improve the quality of future support and feedback. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0160] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0161] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0162] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0163] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0164] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0165] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0166] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0167] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0168] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0169] 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.

[0170] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0171] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0172] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0173] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0174] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0175] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0176] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0177] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0178] (Note 1) A reception desk that accepts submissions of works from users, A support department that supports the production of works received by the aforementioned reception department, A feedback unit provides feedback on works supported by the aforementioned support unit, The system comprises a learning unit that learns from the works that have been fed back by the aforementioned feedback unit. A system characterized by the following features. (Note 2) The aforementioned support unit is We provide advice on the structure and expression of poetry. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned support unit is I will make suggestions regarding the colors and composition of the painting. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned support unit is Provides ideas for musical melodies and rhythms. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned support unit is I provide advice on short film scripts and filming techniques. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned feedback unit is Provide positive feedback to boost users' self-esteem. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned learning unit, We learn from submitted works to improve the quality of future support and feedback. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of submissions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is Analyze the user's past posting history and select the most suitable submission method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When submitting artwork, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is The system estimates the user's emotions and determines the priority of the works to be accepted based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When accepting submissions, the system prioritizes accepting submissions that are highly relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When accepting submissions, we analyze the user's social media activity and accept submissions related to that activity. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned support unit is It estimates the user's emotions and adjusts the way support is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned support unit is When providing support, adjust the level of detail based on the importance of the work. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned support unit is When providing support, different support algorithms are applied depending on the category of the work. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned support unit is It estimates the user's emotions and adjusts the length of support based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned support unit is When providing support, we prioritize support based on the submission date of the work. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned support unit is When providing support, we adjust the order of support based on the relevance of the works. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned feedback unit is It estimates the user's emotions and adjusts how feedback is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned feedback unit is When providing feedback, adjust the level of detail based on the importance of the work. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned feedback unit is When providing feedback, different feedback algorithms are applied depending on the category of the artwork. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned feedback unit is It estimates the user's emotions and adjusts the length of the feedback based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned feedback unit is When providing feedback, we prioritize feedback based on when the work was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned feedback unit is When providing feedback, we adjust the order of feedback based on the relevance of the work. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned learning unit, During training, the training data is weighted based on the submission date of the work. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0179] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts submissions of works from users, A support department that supports the production of works received by the aforementioned reception department, A feedback unit provides feedback on works supported by the aforementioned support unit, The system comprises a learning unit that learns from the works that have been fed back by the aforementioned feedback unit. A system characterized by the following features.

2. The aforementioned support unit is We provide advice on the structure and expression of poetry. The system according to feature 1.

3. The aforementioned support unit is I will make suggestions regarding the colors and composition of the painting. The system according to feature 1.

4. The aforementioned support unit is Provides ideas for musical melodies and rhythms. The system according to feature 1.

5. The aforementioned support unit is I provide advice on short film scripts and filming techniques. The system according to feature 1.

6. The aforementioned feedback unit is Provide positive feedback to boost users' self-esteem. The system according to feature 1.

7. The aforementioned learning unit, We learn from submitted works to improve the quality of future support and feedback. The system according to feature 1.

8. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of submissions based on those estimated emotions. The system according to feature 1.

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