System

Abstract

An object of the system according to the embodiment is for the right holder to develop and sell a generated AI using his / her own work.SOLUTION: A system according to an embodiment includes a literary work providing unit, a training AI creation unit, a generative data development unit, and a sales platform providing unit. The literary work providing unit provides a literary work of the right holder. The learning data creation unit creates learning data based on the work provided by the work providing unit. The generative AI development unit develops a generative AI based on the training data created by the training data creation unit. The sales platform providing unit provides a sales platform for selling the generated AI.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, there is a risk of copyright issues arising when using copyrighted material as training data for generative AI.

[0005] The system of the embodiment aims to enable rights holders to develop and sell generative AI using their own copyrighted works. [Means for solving the problem]

[0006] The system according to the embodiment includes a work providing unit, a training data creating unit, a generation AI development unit, and a sales platform providing unit. The work providing unit allows rights holders to provide their own works. The training data creating unit creates training data based on the works provided by the work providing unit. The generation AI development unit develops a generation AI based on the training data created by the training data creating unit. The sales platform providing unit provides a sales platform for selling the generation AI. [Effects of the Invention]

[0007] The system according to the embodiment allows rights holders to develop and sell generative AI using their own copyrighted works. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The generative AI system according to an embodiment of the present invention is a system in which a rights holder loads a large amount of their own copyrighted work as training data, develops their own unique generative AI, and sells that generative AI. This system solves the rights issues that arise when using copyrighted work as training data for the generative AI, and allows rights holders to build new business models by utilizing their own copyrighted work.

[0029] The generative AI system according to the embodiment includes a work providing unit, a training data creating unit, a generation AI development unit, and a sales platform providing unit. The work providing unit allows rights holders to provide their own works. For example, authors can provide their own novels or poems. The work providing unit can also receive works provided by rights holders as digital data. The training data creating unit creates training data based on the works provided by the work providing unit. For example, it organizes the provided novels or poems as text data and converts them into a format that is easy for the generation AI to learn. The training data creating unit can also analyze the content of the provided works, extract important information, and optimize the training data. The generation AI development unit develops a generation AI based on the training data created by the training data creating unit. For example, the generation AI uses a text generation AI (e.g., LLM) to learn the style and characteristics of the provided works and generate new content. The generation AI can also use a multimodal generation AI to handle multiple modalities, such as images and audio, in addition to text. The sales platform providing unit provides a sales platform for selling the generation AI. For example, an online marketplace can be created, allowing rights holders to register their own generation AIs and users to purchase and use them. The sales platform provider also has a function for efficiently selling the generation AIs. This allows the generation AI system according to the embodiment to resolve rights issues when using copyrighted works as training data for the generation AI, and allows rights holders to build new business models by utilizing their own copyrighted works. For example, a writer can obtain a new source of revenue by selling a generation AI that reflects their own writing style. Furthermore, generation AIs can be sold efficiently by using a sales platform provided by a specific company.

[0030] The learning data creation unit adds metadata such as creation date and time, genre, and theme to the work, allowing the generation AI to learn based on that information. For example, the learning data creation unit adds metadata such as creation date and time, genre, and theme to the work provided by the rights holder, allowing the generation AI to learn based on that information. For example, it generates content specialized for a specific historical background or genre. The learning data creation unit also uses the metadata to classify the data the generation AI learns from and generates content that fits a specific theme. For example, it optimizes the learning data for each genre, such as romance novels or mystery novels. The learning data creation unit also customizes the dataset that the generation AI learns from based on the metadata added to the provided work. For example, it prioritizes learning data related to a specific theme or topic. This allows the generation AI to perform more precise learning.

[0031] The training data creation unit can use the generation AI to automatically generate new variations based on existing works in order to increase the variety of works. For example, the training data creation unit uses the generation AI to automatically generate new variations based on novels or poems provided by the rights holder. For example, it generates different storylines using the same theme and characters. The training data creation unit also learns the style and characteristics of the provided works, and the generation AI generates new content that reflects them. For example, it generates new works that imitate the writing style of a particular author. The training data creation unit also uses the generation AI to generate new variations based on existing works, expanding the range of content provided by rights holders. For example, it generates new versions using different perspectives or settings. This allows the range of content provided by rights holders to be expanded.

[0032] The training data creation unit can add multimodal data, including audio or video data, to the copyrighted work, thereby expanding the learning scope of the generative AI. For example, the training data creation unit provides audio and video data in addition to novels and poems provided by the rights holder, and the generative AI learns from this multimodal data. For example, reading audio and movie scenes can be included in the training data. The training data creation unit also uses voice recognition technology to convert the provided audio data into text data, which the generative AI uses for learning. For example, reading audio is converted into text, and the generative AI learns from that content. The training data creation unit also uses video analysis technology to extract important scenes and elements from the provided video data, which the generative AI uses for learning. For example, a movie scene can be analyzed, and the generative AI can learn from that content. This expands the learning scope of the generative AI.

[0033] The training data creation unit allows different rights holders to jointly provide works, allowing the development of a generative AI that has learned multiple styles and characteristics. For example, multiple authors may jointly provide their own works to the training data creation unit, and the generative AI may learn those styles and characteristics. For example, a new piece of writing may be generated that combines the writing styles of different authors. The training data creation unit also allows different artists to jointly provide their own works, and the generative AI may learn those styles. For example, a new image may be generated that combines the characteristics of different artists. The training data creation unit also allows multiple rights holders to jointly provide works, and the generative AI may generate new content based on those works. For example, a new work may be generated that combines different genres and themes. This makes it possible to develop a generative AI that has learned multiple styles and characteristics.

[0034] The Generative AI Development Department introduces an algorithm that simulates the creative process of the rights holder into the Generative AI, allowing it to reflect the style and characteristics of the rights holder. For example, the Generative AI Development Department introduces an algorithm that simulates the creative process of the rights holder, allowing the Generative AI to learn that process. For example, it simulates the writing style and composition method of an author. The Generative AI Development Department also analyzes the creative process of the rights holder, and the Generative AI learns based on that data. For example, it has the AI ​​learn how an author develops an idea. The Generative AI Development Department also uses an algorithm that simulates the creative process, allowing the Generative AI to generate content that reflects the style and characteristics of the rights holder. For example, it generates sentences that imitate the writing style of a particular author. This makes it possible to generate content that reflects the style and characteristics of the rights holder.

[0035] The Generative AI Development Department can collect feedback from users to evaluate the quality of content generated by the Generative AI and reflect it in the Generative AI's learning data. For example, the Generative AI Development Department collects user feedback on generated content, and the Generative AI learns based on that data. For example, the generated content is optimized based on user evaluation scores. The Generative AI Development Department also collects user feedback in real time, and the Generative AI adjusts the generated content based on the results. For example, it reflects the user's preferred themes and styles. The Generative AI Development Department also evaluates the quality of content generated by the Generative AI based on the feedback data and reflects it in the learning data. For example, it can focus on learning parts that have a lot of positive feedback. This can improve the content quality of the Generative AI.

[0036] The Generative AI Development Department can apply the generative AI developed by rights holders to different industries and applications, thereby opening up new markets. For example, the Generative AI Development Department can apply the generative AI developed by rights holders to the education field to generate educational content. For example, it can generate teaching materials and workbooks based on specific learning themes. The Generative AI Development Department can also apply it to the entertainment field, where the generative AI generates new stories and characters. For example, it can automatically generate movie and game scripts. The Generative AI Development Department can also apply it to the advertising field, where the generative AI generates advertising content tailored to target users. For example, it can generate advertising copy based on the user's interests and concerns. This can broaden the scope of application of generative AI and open up new markets.

[0037] The Generative AI Development Department can use the Generative AI to generate interactive content based on the copyrighted work of the rights holder. For example, the Generative AI Development Department can use the Generative AI to generate interactive games based on the copyrighted work of the rights holder. For example, the Generative AI Development Department can develop an adventure game based on a story by a particular author. The Generative AI Development Department can also generate virtual reality (VR) content to allow users to immerse themselves in the world of the copyrighted work of the rights holder. For example, the Generative AI Development Department can recreate the setting of a novel in VR. The Generative AI Development Department can also generate interactive content to allow users to enjoy experiences based on the copyrighted work of the rights holder. For example, the Generative AI Development Department can generate a story in which the user progresses by selecting options. This allows the generation of interactive content based on the copyrighted work of the rights holder.

[0038] The sales platform providing unit can provide a demonstration of the generative AI on the sales platform, allowing users to actually experience the performance of the generative AI. For example, the sales platform providing unit can add a generative AI demonstration function to the sales platform, allowing users to actually experience the performance of the generative AI. For example, the sales platform providing unit can provide samples of content generated by the generative AI. The sales platform providing unit can also use the demonstration to help users understand how to use the generative AI and its functions. For example, the sales platform providing unit can provide interactive demos and tutorials. After users experience the performance of the generative AI, the sales platform providing unit can collect feedback and use it to improve the generative AI. For example, the sales platform providing unit can optimize the learning data of the generative AI based on the results of the demonstration. This allows users to experience the performance of the generative AI.

[0039] The sales platform providing unit introduces an evaluation system for the generative AI into the sales platform, and is able to evaluate the quality of the generative AI based on user feedback. For example, the sales platform providing unit introduces an evaluation system into the sales platform, allowing users to evaluate the quality of the generative AI. For example, it provides a star rating and comment function. The sales platform providing unit also collects feedback from users and builds a system to evaluate the quality of the generative AI based on that data. For example, it prioritizes displaying generative AIs with many positive ratings. The sales platform providing unit also uses the evaluation system to continuously improve the quality of the generative AI. For example, it updates the learning data for the generative AI based on user feedback. This allows the quality of the generative AI to be evaluated based on user feedback.

[0040] The sales platform providing unit can make the sales platform compatible with different languages ​​and regions, thereby promoting sales of the generative AI in the global market. For example, the sales platform providing unit can make the sales platform multilingual, allowing users who speak different languages ​​to purchase the generative AI. For example, it can support multiple languages ​​such as English, French, and Chinese. The sales platform providing unit can also develop marketing strategies tailored to the characteristics and needs of each region, promoting sales of the generative AI in the global market. For example, it can develop promotions and advertisements for each region. The sales platform providing unit can also collect feedback from users in different regions and optimize the sales platform based on that data. For example, it can provide functions and services tailored to the needs of each region. This can promote sales of the generative AI in the global market.

[0041] The sales platform providing unit can add a customization function for the generation AI to the sales platform, allowing users to adjust the generation AI to suit their needs. For example, the sales platform providing unit adds a customization function for the generation AI to the sales platform, allowing users to adjust the generation AI to suit their needs. For example, it provides customization to suit a specific style or theme. The sales platform providing unit also provides an interface that allows users to change the settings of the generation AI, increasing the degree of freedom in customization. For example, it adds a function that allows users to adjust the length and tone of the content to be generated. The sales platform providing unit also uses the customization function to allow users to optimize the generation AI to their projects or uses. For example, it provides customization options specialized for specific industries or uses. This allows users to adjust the generation AI to suit their needs.

[0042] The training data creation unit manages copyright information of copyrighted works using blockchain technology, thereby ensuring the transparency and reliability of the rights. The training data creation unit, for example, manages copyright information of copyrighted works provided by rights holders using blockchain technology, thereby ensuring the transparency and reliability of the rights. For example, the copyright information of the copyrighted works is recorded on a blockchain to prevent tampering. The training data creation unit also uses blockchain technology to track the usage history of the copyrighted works provided by the rights holders, thereby ensuring the transparency of the rights. For example, it records how the copyrighted works are used. The training data creation unit also improves the reliability between rights holders and users by recording the copyright information on a blockchain. For example, it makes the copyright information of the copyrighted works public to ensure transparency. This ensures the transparency and reliability of the rights.

[0043] The training data creation unit can introduce a system that tracks the usage history of copyrighted works and clarifies which copyrighted works the generation AI used and how. The training data creation unit, for example, introduces a system that tracks the usage history of copyrighted works provided by rights holders, and clarifies which copyrighted works the generation AI used and how. For example, the usage history of copyrighted works is recorded in a database. The training data creation unit also tracks the usage history to clarify which copyrighted works the generation AI used as training data. For example, it records how a specific copyrighted work was used as training data for the generation AI. The training data creation unit also understands the usage status of copyrighted works provided by rights holders based on the usage history and resolves rights issues. For example, it makes the usage history of copyrighted works public to ensure transparency. This makes it possible to clarify which copyrighted works the generation AI used and how.

[0044] The learning data creation unit converts copyrighted works into different formats, allowing them to be used in a variety of ways as learning data for the generative AI. For example, the learning data creation unit converts copyrighted works provided by copyright holders into different formats, such as text, images, and audio, and uses them in a variety of ways as learning data for the generative AI. For example, a novel is converted into audio data, which the generative AI learns from. The learning data creation unit also integrates data in different formats, which the generative AI uses to learn. For example, text data and image data can be combined for learning. The learning data creation unit also expands the range of learning data for the generative AI by converting copyrighted works provided by copyright holders into a variety of formats. For example, a poem can be converted into image data, which the generative AI can use to generate new content. This allows for a variety of uses as learning data for the generative AI.

[0045] The training data creation unit can combine works from other rights holders to create new training data and improve the performance of the generative AI. For example, the training data creation unit combines works provided by a rights holder with works from other rights holders to create new training data. For example, it creates a dataset that combines works from different authors. The training data creation unit also trains the generative AI based on the combined works to improve its performance. For example, it generates diverse content by training it on different styles and themes. The training data creation unit also broadens the range of training data for the generative AI by combining it with works from other rights holders. For example, it trains it by combining works from different genres and formats. This can improve the performance of the generative AI.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The generative AI system may further include a user interface unit. The user interface unit provides an interface for managing copyrighted works provided by rights holders and configuring the generative AI. For example, rights holders may upload their own copyrighted works and configure them for use as learning data. The user interface unit may also include a function for visually displaying the generative AI's learning progress and generation results. For example, it may be possible to check in real time what kind of content the generative AI is generating. The user interface unit may also provide a function for rights holders to adjust the parameters of the generative AI and customize the style and characteristics of the generated content. This allows rights holders to optimize the generative AI to suit their needs.

[0048] The training data creation unit identifies the target audience based on the content of the work, allowing the generation AI to generate content optimal for that audience. For example, when providing picture books or educational content for children, the generation AI learns language and themes appropriate for that age group. The training data creation unit can also generate content for a specific audience based on the genre and theme of the provided work. For example, when providing horror or mystery novels, the generation AI learns storytelling techniques specific to that genre. The training data creation unit can also collect feedback from the target audience, allowing the generation AI to learn based on that data. This allows the generation AI to generate content optimal for a specific audience.

[0049] The training data creation unit can analyze the content of copyrighted works and extract specific keywords and phrases to optimize the training data. For example, it can extract important keywords and phrases from provided novels or poems, and the generation AI can use them to generate new content. The training data creation unit can also use the extracted keywords and phrases to enable the generation AI to generate content specialized for specific themes or topics. For example, it can focus on learning keywords related to specific genres, such as romance novels or adventure novels. The training data creation unit can also use the extracted keywords and phrases to provide inspiration for the generation AI to generate new ideas and storylines. This allows the generation AI to generate more diverse content.

[0050] The learning data creation unit can analyze the content of the work and extract specific characters and settings to optimize the learning data. For example, it can extract key characters and settings from a provided novel or poem, and the generation AI can generate new content based on them. The learning data creation unit can also enable the generation AI to generate content specialized for a specific theme or topic based on the extracted characters and settings. For example, it can focus on learning characters and settings related to a specific genre, such as fantasy or science fiction novels. The learning data creation unit can also use the extracted characters and settings to provide inspiration for the generation AI to generate new storylines and scenarios. This allows the generation AI to generate more diverse content.

[0051] The training data creation unit can analyze the content of copyrighted works and extract specific cultural and historical backgrounds to optimize the training data. For example, it can extract elements related to a specific culture or history from a provided novel or poem, and the generation AI can generate new content based on these. The training data creation unit can also enable the generation AI to generate content specialized for a specific theme or topic based on the extracted cultural and historical backgrounds. For example, it can focus on learning backgrounds related to a specific genre, such as historical novels or cultural essays. The training data creation unit can also use the extracted cultural and historical backgrounds to provide inspiration for the generation AI to generate new storylines and scenarios. This allows the generation AI to generate more diverse content.

[0052] The sales platform providing unit can provide a demonstration of the generative AI to the sales platform, allowing users to actually experience the performance of the generative AI. For example, a generative AI demonstration function can be added to the sales platform, allowing users to actually experience the performance of the generative AI. For example, samples of content generated by the generative AI can be provided. The sales platform providing unit can also use the demonstration to help users understand how to use the generative AI and its functions. For example, it can provide interactive demos and tutorials. After users experience the performance of the generative AI, the sales platform providing unit can collect feedback and use it to improve the generative AI. For example, it can optimize the learning data of the generative AI based on the results of the demonstration. This allows users to experience the performance of the generative AI.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The copyright holder provides their own copyrighted work to the copyright providing unit. For example, a writer can provide a novel or poem they have written. The copyright providing unit can also receive the copyrighted work provided by the copyright holder as digital data. Step 2: The training data creation unit creates training data based on the works provided by the work provision unit. For example, it organizes the provided novels or poems as text data and converts them into a format that is easy for the generation AI to learn from. The training data creation unit can also analyze the content of the provided works, extract important information, and optimize the training data. Step 3: The Generative AI Development Department develops the Generative AI based on the training data created by the Training Data Creation Department. For example, the Generative AI uses a text generation AI (e.g., LLM) to learn the style and characteristics of the provided copyrighted work and generate new content. The Generative AI can also use a multimodal generation AI to handle multiple modalities, such as images and audio, in addition to text. Step 4: The sales platform provider provides a sales platform for selling the generation AI. For example, it builds an online marketplace where rights holders can register their own generation AI and users can purchase and use it. The sales platform provider also has functions for efficiently selling the generation AI.

[0055] (Example 2) The generative AI system according to an embodiment of the present invention is a system in which a rights holder loads a large amount of their own copyrighted work as training data, develops their own unique generative AI, and sells that generative AI. This system solves the rights issues that arise when using copyrighted work as training data for the generative AI, and allows rights holders to build new business models by utilizing their own copyrighted work.

[0056] The generative AI system according to the embodiment includes a work providing unit, a training data creating unit, a generation AI development unit, and a sales platform providing unit. The work providing unit allows rights holders to provide their own works. For example, authors can provide their own novels or poems. The work providing unit can also receive works provided by rights holders as digital data. The training data creating unit creates training data based on the works provided by the work providing unit. For example, it organizes the provided novels or poems as text data and converts them into a format that is easy for the generation AI to learn. The training data creating unit can also analyze the content of the provided works, extract important information, and optimize the training data. The generation AI development unit develops a generation AI based on the training data created by the training data creating unit. For example, the generation AI uses a text generation AI (e.g., LLM) to learn the style and characteristics of the provided works and generate new content. The generation AI can also use a multimodal generation AI to handle multiple modalities, such as images and audio, in addition to text. The sales platform providing unit provides a sales platform for selling the generation AI. For example, an online marketplace can be created, allowing rights holders to register their own generation AIs and users to purchase and use them. The sales platform provider also has a function for efficiently selling the generation AIs. This allows the generation AI system according to the embodiment to resolve rights issues when using copyrighted works as training data for the generation AI, and allows rights holders to build new business models by utilizing their own copyrighted works. For example, a writer can obtain a new source of revenue by selling a generation AI that reflects their own writing style. Furthermore, generation AIs can be sold efficiently by using a sales platform provided by a specific company.

[0057] The training data creation unit can perform sentiment analysis on copyrighted works and optimize the training data based on the intensity and type of emotion. For example, the training data creation unit can perform sentiment analysis on novels or poems provided by the rights holder and quantify the intensity and type of emotion. For example, emotions such as joy, sadness, and surprise can be quantified so that the generation AI can generate content that reflects those emotions. The training data creation unit also optimizes the dataset that the generation AI learns from based on the sentiment score of the provided copyrighted work. For example, by focusing training on parts with high emotional intensity, it can generate emotionally rich content. The training data creation unit also uses the results of the sentiment analysis to classify the data that the generation AI learns from and generate content specialized for specific emotions. For example, it can create a dataset containing many moving scenes and generate moving sentences. This allows the training data to be optimized for generating emotionally rich content.

[0058] The learning data creation unit adds metadata such as creation date and time, genre, and theme to the work, allowing the generation AI to learn based on that information. For example, the learning data creation unit adds metadata such as creation date and time, genre, and theme to the work provided by the rights holder, allowing the generation AI to learn based on that information. For example, it generates content specialized for a specific historical background or genre. The learning data creation unit also uses the metadata to classify the data the generation AI learns from and generates content that fits a specific theme. For example, it optimizes the learning data for each genre, such as romance novels or mystery novels. The learning data creation unit also customizes the dataset that the generation AI learns from based on the metadata added to the provided work. For example, it prioritizes learning data related to a specific theme or topic. This allows the generation AI to perform more precise learning.

[0059] The training data creation unit can use the generation AI to automatically generate new variations based on existing works in order to increase the variety of works. For example, the training data creation unit uses the generation AI to automatically generate new variations based on novels or poems provided by the rights holder. For example, it generates different storylines using the same theme and characters. The training data creation unit also learns the style and characteristics of the provided works, and the generation AI generates new content that reflects them. For example, it generates new works that imitate the writing style of a particular author. The training data creation unit also uses the generation AI to generate new variations based on existing works, expanding the range of content provided by rights holders. For example, it generates new versions using different perspectives or settings. This allows the range of content provided by rights holders to be expanded.

[0060] The training data creation unit can add multimodal data, including audio or video data, to the copyrighted work, thereby expanding the learning scope of the generative AI. For example, the training data creation unit provides audio and video data in addition to novels and poems provided by the rights holder, and the generative AI learns from this multimodal data. For example, reading audio and movie scenes can be included in the training data. The training data creation unit also uses voice recognition technology to convert the provided audio data into text data, which the generative AI uses for learning. For example, reading audio is converted into text, and the generative AI learns from that content. The training data creation unit also uses video analysis technology to extract important scenes and elements from the provided video data, which the generative AI uses for learning. For example, a movie scene can be analyzed, and the generative AI can learn from that content. This expands the learning scope of the generative AI.

[0061] The training data creation unit allows different rights holders to jointly provide works, allowing the development of a generative AI that has learned multiple styles and characteristics. For example, multiple authors may jointly provide their own works to the training data creation unit, and the generative AI may learn those styles and characteristics. For example, a new piece of writing may be generated that combines the writing styles of different authors. The training data creation unit also allows different artists to jointly provide their own works, and the generative AI may learn those styles. For example, a new image may be generated that combines the characteristics of different artists. The training data creation unit also allows multiple rights holders to jointly provide works, and the generative AI may generate new content based on those works. For example, a new work may be generated that combines different genres and themes. This makes it possible to develop a generative AI that has learned multiple styles and characteristics.

[0062] The training data creation unit can use the emotion estimation function to analyze the emotional elements of a work and optimize the training data based on emotions. The training data creation unit can analyze the emotional elements of, for example, novels or poems provided by a rights holder using the emotion estimation function, and the generation AI can then learn based on the results. For example, the training can focus on moving or tense scenes. The training data creation unit can also optimize the dataset that the generation AI learns from based on the emotion score of the provided work. For example, by prioritizing learning parts with high emotional intensity, it can generate emotionally rich content. The training data creation unit can also use the emotion estimation function to classify the emotional elements of works provided by the rights holder, and the generation AI can generate new content based on that. For example, it can create a dataset that includes many emotional scenes and generate emotionally rich sentences. This allows the training data to be optimized for generating emotionally rich content.

[0063] The Generative AI Development Department uses an emotion estimation function to provide real-time feedback on the user's emotional reactions to content generated by the Generative AI based on copyrighted works, thereby optimizing the generated content. For example, the Generative AI Development Department analyzes the user's emotional reactions to text and images generated by the Generative AI in real time, and optimizes the generated content based on the results. For example, it emphasizes scenes that move the user. The Generative AI Development Department also uses the emotion estimation function to collect the user's emotional scores for the generated content, and the Generative AI learns based on that data. For example, it focuses on parts that have a high number of positive emotional reactions. The Generative AI Development Department also provides real-time feedback on the user's emotional reactions, and the Generative AI adjusts the generated content based on the results. For example, it emphasizes themes that interest the user. This allows the generated content to be optimized based on the user's emotional reactions.

[0064] The Generative AI Development Department introduces an algorithm that simulates the creative process of the rights holder into the Generative AI, allowing it to reflect the style and characteristics of the rights holder. For example, the Generative AI Development Department introduces an algorithm that simulates the creative process of the rights holder, allowing the Generative AI to learn that process. For example, it simulates the writing style and composition method of an author. The Generative AI Development Department also analyzes the creative process of the rights holder, and the Generative AI learns based on that data. For example, it has the AI ​​learn how an author develops an idea. The Generative AI Development Department also uses an algorithm that simulates the creative process, allowing the Generative AI to generate content that reflects the style and characteristics of the rights holder. For example, it generates sentences that imitate the writing style of a particular author. This makes it possible to generate content that reflects the style and characteristics of the rights holder.

[0065] The Generative AI Development Department can collect feedback from users to evaluate the quality of content generated by the Generative AI and reflect it in the Generative AI's learning data. For example, the Generative AI Development Department collects user feedback on generated content, and the Generative AI learns based on that data. For example, the generated content is optimized based on user evaluation scores. The Generative AI Development Department also collects user feedback in real time, and the Generative AI adjusts the generated content based on the results. For example, it reflects the user's preferred themes and styles. The Generative AI Development Department also evaluates the quality of content generated by the Generative AI based on the feedback data and reflects it in the learning data. For example, it can focus on learning parts that have a lot of positive feedback. This can improve the content quality of the Generative AI.

[0066] The Generative AI Development Department can apply the generative AI developed by rights holders to different industries and applications, thereby opening up new markets. For example, the Generative AI Development Department can apply the generative AI developed by rights holders to the education field to generate educational content. For example, it can generate teaching materials and workbooks based on specific learning themes. The Generative AI Development Department can also apply it to the entertainment field, where the generative AI generates new stories and characters. For example, it can automatically generate movie and game scripts. The Generative AI Development Department can also apply it to the advertising field, where the generative AI generates advertising content tailored to target users. For example, it can generate advertising copy based on the user's interests and concerns. This can broaden the scope of application of generative AI and open up new markets.

[0067] The Generative AI Development Department can use the Generative AI to generate interactive content based on the copyrighted work of the rights holder. For example, the Generative AI Development Department can use the Generative AI to generate interactive games based on the copyrighted work of the rights holder. For example, the Generative AI Development Department can develop an adventure game based on a story by a particular author. The Generative AI Development Department can also generate virtual reality (VR) content to allow users to immerse themselves in the world of the copyrighted work of the rights holder. For example, the Generative AI Development Department can recreate the setting of a novel in VR. The Generative AI Development Department can also generate interactive content to allow users to enjoy experiences based on the copyrighted work of the rights holder. For example, the Generative AI Development Department can generate a story in which the user progresses by selecting options. This allows the generation of interactive content based on the copyrighted work of the rights holder.

[0068] The Generative AI Development Department uses the emotion estimation function to analyze the user's emotional response to content generated by the Generative AI, allowing it to provide personalized content based on emotions. For example, the Generative AI Development Department uses the emotion estimation function to analyze the user's emotional response to generated content in real time and provide personalized content based on the results. For example, it may emphasize scenes that moved the user. The Generative AI Development Department also uses the user's emotion score to generate personalized content. For example, it may prioritize themes with a high number of positive emotional responses. The Generative AI Development Department also builds a system that provides content tailored to the user's preferences and interests based on the emotion estimation data. For example, it may dynamically adjust content according to changes in the user's emotions. This allows it to provide personalized content based on the user's emotions.

[0069] The sales platform providing unit equips the sales platform with an emotion estimation function, analyzes the emotions of users when they purchase the generative AI, and can make suggestions to increase their willingness to purchase. For example, the sales platform providing unit equips the sales platform with an emotion estimation function and analyzes the emotions of users when they purchase the generative AI in real time. For example, it analyzes the user's facial expressions and voice and makes suggestions to increase their willingness to purchase. The sales platform providing unit also makes customized suggestions to increase the user's willingness to purchase based on the emotion estimation data. For example, it highlights features and benefits that interest the user. The sales platform providing unit also develops a marketing strategy to increase the user's willingness to purchase based on the user's emotional response. For example, it runs advertisements and promotions that elicit positive emotions. This makes it possible to make suggestions to increase the user's willingness to purchase based on the user's emotions.

[0070] The sales platform providing unit can provide a demonstration of the generative AI on the sales platform, allowing users to actually experience the performance of the generative AI. For example, the sales platform providing unit can add a generative AI demonstration function to the sales platform, allowing users to actually experience the performance of the generative AI. For example, the sales platform providing unit can provide samples of content generated by the generative AI. The sales platform providing unit can also use the demonstration to help users understand how to use the generative AI and its functions. For example, the sales platform providing unit can provide interactive demos and tutorials. After users experience the performance of the generative AI, the sales platform providing unit can collect feedback and use it to improve the generative AI. For example, the sales platform providing unit can optimize the learning data of the generative AI based on the results of the demonstration. This allows users to experience the performance of the generative AI.

[0071] The sales platform providing unit introduces an evaluation system for the generative AI into the sales platform, and is able to evaluate the quality of the generative AI based on user feedback. For example, the sales platform providing unit introduces an evaluation system into the sales platform, allowing users to evaluate the quality of the generative AI. For example, it provides a star rating and comment function. The sales platform providing unit also collects feedback from users and builds a system to evaluate the quality of the generative AI based on that data. For example, it prioritizes displaying generative AIs with many positive ratings. The sales platform providing unit also uses the evaluation system to continuously improve the quality of the generative AI. For example, it updates the learning data for the generative AI based on user feedback. This allows the quality of the generative AI to be evaluated based on user feedback.

[0072] The sales platform providing unit can make the sales platform compatible with different languages ​​and regions, thereby promoting sales of the generative AI in the global market. For example, the sales platform providing unit can make the sales platform multilingual, allowing users who speak different languages ​​to purchase the generative AI. For example, it can support multiple languages ​​such as English, French, and Chinese. The sales platform providing unit can also develop marketing strategies tailored to the characteristics and needs of each region, promoting sales of the generative AI in the global market. For example, it can develop promotions and advertisements for each region. The sales platform providing unit can also collect feedback from users in different regions and optimize the sales platform based on that data. For example, it can provide functions and services tailored to the needs of each region. This can promote sales of the generative AI in the global market.

[0073] The sales platform providing unit can add a customization function for the generation AI to the sales platform, allowing users to adjust the generation AI to suit their needs. For example, the sales platform providing unit adds a customization function for the generation AI to the sales platform, allowing users to adjust the generation AI to suit their needs. For example, it provides customization to suit a specific style or theme. The sales platform providing unit also provides an interface that allows users to change the settings of the generation AI, increasing the degree of freedom in customization. For example, it adds a function that allows users to adjust the length and tone of the content to be generated. The sales platform providing unit also uses the customization function to allow users to optimize the generation AI to their projects or uses. For example, it provides customization options specialized for specific industries or uses. This allows users to adjust the generation AI to suit their needs.

[0074] The sales platform providing unit can use the emotion estimation function to analyze the emotional responses of users on the sales platform and develop an emotion-based marketing strategy. The sales platform providing unit, for example, analyzes the emotional responses of users on the sales platform in real time and develops an emotion-based marketing strategy based on that data. For example, it highlights products that interest users. The sales platform providing unit also provides customized marketing messages to increase users' purchasing motivation based on the emotion estimation data. For example, it carries out advertisements and promotions that elicit positive emotions. The sales platform providing unit also optimizes the design and functions of the sales platform based on the user's emotional responses. For example, it provides an interface that does not cause users stress. This makes it possible to develop a marketing strategy based on users' emotions.

[0075] The training data creation unit uses an emotion estimation function to evaluate the emotional value of copyrighted works, and can prioritize the use of copyrighted works with high emotional value as training data. The training data creation unit, for example, uses the emotion estimation function to evaluate the emotional value of copyrighted works provided by rights holders, and prioritizes the use of copyrighted works with high emotional scores as training data. For example, it focuses on learning works with many moving scenes. The training data creation unit also selects copyrighted works with high emotional value based on the emotion estimation data, and the generation AI uses this as the basis for training. For example, it creates a dataset that includes many scenes that move the user. The training data creation unit also introduces an algorithm that evaluates emotional value, allowing the generation AI to generate emotionally rich content. For example, it focuses on learning parts with high emotional scores. This allows copyrighted works with high emotional value to be used preferentially as training data.

[0076] The training data creation unit manages copyright information of copyrighted works using blockchain technology, thereby ensuring the transparency and reliability of the rights. The training data creation unit, for example, manages copyright information of copyrighted works provided by rights holders using blockchain technology, thereby ensuring the transparency and reliability of the rights. For example, the copyright information of the copyrighted works is recorded on a blockchain to prevent tampering. The training data creation unit also uses blockchain technology to track the usage history of the copyrighted works provided by the rights holders, thereby ensuring the transparency of the rights. For example, it records how the copyrighted works are used. The training data creation unit also improves the reliability between rights holders and users by recording the copyright information on a blockchain. For example, it makes the copyright information of the copyrighted works public to ensure transparency. This ensures the transparency and reliability of the rights.

[0077] The training data creation unit can introduce a system that tracks the usage history of copyrighted works and clarifies which copyrighted works the generation AI used and how. The training data creation unit, for example, introduces a system that tracks the usage history of copyrighted works provided by rights holders, and clarifies which copyrighted works the generation AI used and how. For example, the usage history of copyrighted works is recorded in a database. The training data creation unit also tracks the usage history to clarify which copyrighted works the generation AI used as training data. For example, it records how a specific copyrighted work was used as training data for the generation AI. The training data creation unit also understands the usage status of copyrighted works provided by rights holders based on the usage history and resolves rights issues. For example, it makes the usage history of copyrighted works public to ensure transparency. This makes it possible to clarify which copyrighted works the generation AI used and how.

[0078] The learning data creation unit converts copyrighted works into different formats, allowing them to be used in a variety of ways as learning data for the generative AI. For example, the learning data creation unit converts copyrighted works provided by copyright holders into different formats, such as text, images, and audio, and uses them in a variety of ways as learning data for the generative AI. For example, a novel is converted into audio data, which the generative AI learns from. The learning data creation unit also integrates data in different formats, which the generative AI uses to learn. For example, text data and image data can be combined for learning. The learning data creation unit also expands the range of learning data for the generative AI by converting copyrighted works provided by copyright holders into a variety of formats. For example, a poem can be converted into image data, which the generative AI can use to generate new content. This allows for a variety of uses as learning data for the generative AI.

[0079] The training data creation unit can combine works from other rights holders to create new training data and improve the performance of the generative AI. For example, the training data creation unit combines works provided by a rights holder with works from other rights holders to create new training data. For example, it creates a dataset that combines works from different authors. The training data creation unit also trains the generative AI based on the combined works to improve its performance. For example, it generates diverse content by training it on different styles and themes. The training data creation unit also broadens the range of training data for the generative AI by combining it with works from other rights holders. For example, it trains it by combining works from different genres and formats. This can improve the performance of the generative AI.

[0080] The training data creation unit can use the emotion estimation function to evaluate the emotional value of a work and propose solutions to rights issues based on emotions. The training data creation unit, for example, uses the emotion estimation function to evaluate the emotional value of a work provided by a rights holder and propose solutions to rights issues based on the data. For example, it sets an appropriate remuneration for works with high emotional value. The training data creation unit also builds a system that evaluates the emotional value of a work provided by a rights holder based on the emotion estimation data and proposes solutions to rights issues based on emotions. For example, it prioritizes rights protection for works with high emotion scores. The training data creation unit also uses the emotion estimation function to evaluate the emotional value of a work provided by a rights holder and proposes solutions to rights issues based on the results. For example, it takes special protection measures for works with high emotional value. This makes it possible to propose solutions to rights issues based on emotions.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The generative AI system may further include a user interface unit. The user interface unit provides an interface for managing copyrighted works provided by rights holders and configuring the generative AI. For example, rights holders may upload their own copyrighted works and configure them for use as learning data. The user interface unit may also include a function for visually displaying the generative AI's learning progress and generation results. For example, it may be possible to check in real time what kind of content the generative AI is generating. The user interface unit may also provide a function for rights holders to adjust the parameters of the generative AI and customize the style and characteristics of the generated content. This allows rights holders to optimize the generative AI to suit their needs.

[0083] The training data creation unit identifies the target audience based on the content of the work, allowing the generation AI to generate content optimal for that audience. For example, when providing picture books or educational content for children, the generation AI learns language and themes appropriate for that age group. The training data creation unit can also generate content for a specific audience based on the genre and theme of the provided work. For example, when providing horror or mystery novels, the generation AI learns storytelling techniques specific to that genre. The training data creation unit can also collect feedback from the target audience, allowing the generation AI to learn based on that data. This allows the generation AI to generate content optimal for a specific audience.

[0084] The training data creation unit can analyze the content of copyrighted works and extract specific keywords and phrases to optimize the training data. For example, it can extract important keywords and phrases from provided novels or poems, and the generation AI can use them to generate new content. The training data creation unit can also use the extracted keywords and phrases to enable the generation AI to generate content specialized for specific themes or topics. For example, it can focus on learning keywords related to specific genres, such as romance novels or adventure novels. The training data creation unit can also use the extracted keywords and phrases to provide inspiration for the generation AI to generate new ideas and storylines. This allows the generation AI to generate more diverse content.

[0085] The learning data creation unit can analyze the content of the work and extract specific characters and settings to optimize the learning data. For example, it can extract key characters and settings from a provided novel or poem, and the generation AI can generate new content based on them. The learning data creation unit can also enable the generation AI to generate content specialized for a specific theme or topic based on the extracted characters and settings. For example, it can focus on learning characters and settings related to a specific genre, such as fantasy or science fiction novels. The learning data creation unit can also use the extracted characters and settings to provide inspiration for the generation AI to generate new storylines and scenarios. This allows the generation AI to generate more diverse content.

[0086] The training data creation unit can analyze the content of copyrighted works and extract specific cultural and historical backgrounds to optimize the training data. For example, it can extract elements related to a specific culture or history from a provided novel or poem, and the generation AI can generate new content based on these. The training data creation unit can also enable the generation AI to generate content specialized for a specific theme or topic based on the extracted cultural and historical backgrounds. For example, it can focus on learning backgrounds related to a specific genre, such as historical novels or cultural essays. The training data creation unit can also use the extracted cultural and historical backgrounds to provide inspiration for the generation AI to generate new storylines and scenarios. This allows the generation AI to generate more diverse content.

[0087] The training data creation unit can use the emotion estimation function to analyze the emotional elements of copyrighted works and optimize the training data based on emotions. For example, the emotion estimation function can be used to analyze the emotional elements of novels or poems provided by the rights holder, and the generation AI can learn based on the results. For example, the learning can focus on moving or tense scenes. The training data creation unit also optimizes the dataset that the generation AI learns from based on the emotion score of the provided copyrighted work. For example, by prioritizing learning parts with high emotional intensity, emotionally rich content can be generated. The training data creation unit also uses the emotion estimation function to classify the emotional elements of copyrighted works provided by the rights holder, and the generation AI can generate new content based on that. For example, a dataset containing many emotional scenes can be created to generate emotionally rich sentences. This allows the training data to be optimized for generating emotionally rich content.

[0088] The Generative AI Development Department uses the emotion estimation function to provide real-time feedback on the user's emotional reactions to the content generated by the Generative AI, allowing it to optimize the generated content. For example, it analyzes the user's emotional reactions to the text and images generated by the Generative AI in real time and optimizes the generated content based on the results. For example, it can emphasize scenes that moved the user. The Generative AI Development Department also uses the emotion estimation function to collect the user's emotional scores for the generated content, and the Generative AI learns based on that data. For example, it can focus on learning parts that have a high number of positive emotional reactions. The Generative AI Development Department also provides real-time feedback on the user's emotional reactions, allowing the Generative AI to adjust the generated content based on the results. For example, it can emphasize themes that interest the user. This allows it to optimize the generated content based on the user's emotional reactions.

[0089] The Generative AI Development Department uses the emotion estimation function to analyze the user's emotional response to content generated by the Generative AI, enabling it to provide personalized content based on emotions. For example, the emotion estimation function can be used to analyze the user's emotional response to generated content in real time and provide personalized content based on the results. For example, scenes that moved the user can be emphasized. The Generative AI Development Department also uses the user's emotion score to generate personalized content. For example, themes with a high number of positive emotional responses can be prioritized. The Generative AI Development Department also builds a system that provides content tailored to the user's preferences and interests based on the emotion estimation data. For example, the content can be dynamically adjusted according to changes in the user's emotions. This makes it possible to provide personalized content based on the user's emotions.

[0090] The sales platform providing unit equips the sales platform with an emotion estimation function, analyzes the emotions a user feels when purchasing a generative AI, and can make suggestions to increase their willingness to purchase. For example, the sales platform can be equipped with an emotion estimation function to analyze the emotions a user feels when purchasing a generative AI in real time. For example, the sales platform providing unit can analyze the user's facial expressions and voice to make suggestions to increase their willingness to purchase. The sales platform providing unit also makes customized suggestions to increase the user's willingness to purchase based on the emotion estimation data. For example, it can highlight features and benefits that interest the user. The sales platform providing unit also develops a marketing strategy to increase the user's willingness to purchase based on the user's emotional response. For example, it can run advertisements and promotions that elicit positive emotions. This makes it possible to make suggestions to increase the user's willingness to purchase based on the user's emotions.

[0091] The sales platform providing unit can provide a demonstration of the generative AI to the sales platform, allowing users to actually experience the performance of the generative AI. For example, a generative AI demonstration function can be added to the sales platform, allowing users to actually experience the performance of the generative AI. For example, samples of content generated by the generative AI can be provided. The sales platform providing unit can also use the demonstration to help users understand how to use the generative AI and its functions. For example, it can provide interactive demos and tutorials. After users experience the performance of the generative AI, the sales platform providing unit can collect feedback and use it to improve the generative AI. For example, it can optimize the learning data of the generative AI based on the results of the demonstration. This allows users to experience the performance of the generative AI.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The copyright holder provides their own copyrighted work to the copyright providing unit. For example, a writer can provide a novel or poem they have written. The copyright providing unit can also receive the copyrighted work provided by the copyright holder as digital data. Step 2: The training data creation unit creates training data based on the works provided by the work provision unit. For example, it organizes the provided novels or poems as text data and converts them into a format that is easy for the generation AI to learn from. The training data creation unit can also analyze the content of the provided works, extract important information, and optimize the training data. Step 3: The Generative AI Development Department develops the Generative AI based on the training data created by the Training Data Creation Department. For example, the Generative AI uses a text generation AI (e.g., LLM) to learn the style and characteristics of the provided copyrighted work and generate new content. The Generative AI can also use a multimodal generation AI to handle multiple modalities, such as images and audio, in addition to text. Step 4: The sales platform provider provides a sales platform for selling the generation AI. For example, it builds an online marketplace where rights holders can register their own generation AI and users can purchase and use it. The sales platform provider also has functions for efficiently selling the generation AI.

[0094] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0096] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0100] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0101] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0102] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0103] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0104] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0105] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0107] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0108] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0114] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0115] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0116] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0118] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0119] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0120] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0122] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0123] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0124] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 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.

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

[0130] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0135] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0136] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0138] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0139] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0140] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0143] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0144] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0145] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0146] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0147] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0148] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0149] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0150] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0151] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0152] 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.

[0153] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0154] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0155] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0156] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0157] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0158] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0159] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0160] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0161] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a copyrighted work providing section in which right holders provide their own copyrighted works; a training data creation unit that creates training data based on the copyrighted work provided by the copyrighted work provision unit; a generation AI development unit that develops a generation AI based on the learning data created by the learning data creation unit; A sales platform providing unit that provides a sales platform for selling the generated AI. A system characterized by:

2. The learning data creation unit Conducting sentiment analysis on the copyrighted work and optimizing the learning data based on the intensity and type of sentiment 2. The system of claim 1.

3. The learning data creation unit Add multimodal data, including audio or video data, to the work to broaden the learning scope of the generative AI.

2. The system of claim 1.

4. The Generative AI Development Department: The AI ​​generates content based on the copyrighted work, and the AI ​​provides real-time feedback on the user's emotional reactions to optimize the content.

2. The system of claim 1.

5. The sales platform providing unit: The sales platform is equipped with an emotion estimation function, which analyzes the emotions of users when they purchase the AI ​​and makes suggestions to increase their desire to purchase.

2. The system of claim 1.

6. The learning data creation unit Emotional value is evaluated for the copyrighted work, and copyrighted work with high emotional value is used preferentially as the training data.

2. The system of claim 1.

7. The learning data creation unit Analyzing the emotional elements of the work and optimizing the learning data based on emotions 2. The system of claim 1.

8. The sales platform providing unit: Analyzing users' emotional responses on the sales platform and developing emotion-based marketing strategies 2. The system of claim 1.

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