System

Abstract

An object of a system according to an embodiment is to improve the variety and creativity of a generation AI.SOLUTION: A system includes a generation AI, a cooperation part, a competition part, a cooperation part, and a participation part. The generation AI manages the cooperation between the generation AI. The cooperation unit manages cooperation between the generation AI. The competition unit manages competition between the generation AI. The association unit manages association of generated AI of different fields or genres. The participation unit manages participation of users.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] Existing technologies lack sufficient mechanisms for cooperation and competition to maximize the diversity and creativity of generative AI, leaving room for improvement.

[0005] The system according to the embodiment aims to improve the diversity and creativity of generative AI. [Means for solving the problem]

[0006] The system according to the embodiment comprises a generation AI, a cooperation unit, a competition unit, a collaboration unit, and a participation unit. The generation AI manages cooperation between generation AIs. The cooperation unit manages cooperation between generation AIs. The competition unit manages competition between generation AIs. The collaboration unit manages collaboration between generation AIs in different fields and genres. The participation unit manages user participation. [Effects of the Invention]

[0007] The system according to the embodiment can improve the diversity and creativity of generative AI. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The generative AI cooperation and competition system according to an embodiment of the present invention is a system in which generative AIs cooperate and compete with each other, improving the diversity and creativity of the generative AIs. As a result, the generative AI cooperation and competition system allows the generative AIs to stimulate and challenge each other, generating new ideas and expressions. Users can also participate and have fun together with the generative AIs.

[0029] A generative AI cooperation and competition system according to an embodiment includes a generative AI, a cooperation unit, a competition unit, a collaboration unit, and a participation unit. Generative AIs include, for example, text generation AIs (e.g., LLMs), image generation AIs, and music generation AIs. The collaboration unit manages cooperation between the generative AIs. For example, a music generation AI and a painting generation AI collaborate to create an artwork that combines music and visuals. The collaboration unit manages a collaborative project between the generative AIs and monitors task allocation and progress. The competition unit manages competition between the generative AIs. For example, a poetry generation AI and a story generation AI compete to see which can generate the most moving text. The competition unit sets competition rules and evaluation criteria for the generative AIs. The collaboration unit manages collaboration between generative AIs from different fields and genres. For example, a cooking recipe generation AI and a fashion design generation AI collaborate to propose a new lifestyle that combines cooking and fashion. The collaboration unit manages data exchange and protocols between the generative AIs. The participation unit manages user participation. For example, a user can specify a theme and conditions for the generating AI, and the generating AI can generate a work based on those conditions. The participation unit collects user feedback and uses it as learning data for the generating AI. This allows the generating AI cooperation and competition system according to the embodiment to improve the diversity and creativity of the generating AI. For example, collaboration between generating AIs from different fields and genres can lead to the creation of new ideas and expressions that have never been seen before. Furthermore, participation by users together with the generating AI can expand the possibilities of the generating AI.

[0030] The collaboration unit can monitor the progress of projects in which the generation AIs cooperate in real time and automatically redistribute tasks as necessary. For example, the collaboration unit monitors the progress of projects in which the generation AIs cooperate in real time and automatically redistributes tasks according to the progress of the tasks. For example, when a music generation AI completes part of a song, it assigns the next task to a painting generation AI. The collaboration unit also monitors the progress of the project and builds a system to evenly distribute the load between the generation AIs. For example, when a poetry generation AI and a story generation AI cooperate, it adjusts the load according to the progress of the task. The collaboration unit also uses real-time monitoring to maximize the efficiency of projects in which the generation AIs cooperate. For example, when a cooking recipe generation AI and a fashion design generation AI cooperate, it redistributes tasks according to progress. This allows the progress of projects in which the generation AIs cooperate to be monitored in real time and redistributed as necessary, thereby improving the efficiency of the project.

[0031] The Collaboration Department can introduce a dynamic role-sharing algorithm to maximize the areas of expertise of each generative AI. For example, the Collaboration Department can introduce a dynamic role-sharing algorithm to maximize the areas of expertise of each generative AI, thereby improving the efficiency of collaborative projects. For example, a music generation AI creates music, and a painting generation AI generates visuals that match the music. The Collaboration Department can also dynamically divide roles between generative AIs based on their areas of expertise to maximize the results of collaborative projects. For example, a poetry generation AI creates moving poetry, and a story generation AI generates a story based on the poem. The Collaboration Department can also use a dynamic role-sharing algorithm to increase the flexibility of collaborative projects between generative AIs. For example, a cooking recipe generation AI suggests dishes, and a fashion design generation AI suggests fashion that matches the dishes. By introducing a dynamic role-sharing algorithm to maximize the areas of expertise of each generative AI, the results of collaborative projects can be maximized.

[0032] The collaboration unit enables collaboration between generation AIs from different cultures and languages ​​to generate works that reflect cultural diversity. For example, generation AIs from different cultures and languages ​​collaborate to generate works that reflect cultural diversity. For example, a Japanese music generation AI and a French painting generation AI collaborate to create an international work of art. The collaboration unit also generates works that combine elements from different cultures through collaboration between generation AIs. For example, a Chinese poetry generation AI and an American story generation AI collaborate to create a story that combines Eastern and Western cultures. The collaboration unit also enables generation AIs from different languages ​​to collaborate to generate works that are multilingual. For example, a Spanish cooking recipe generation AI and an Italian fashion design generation AI collaborate to create a work that combines cooking and fashion. In this way, collaboration between generation AIs from different cultures and languages ​​to generate works that reflect cultural diversity can create new ideas from a global perspective.

[0033] The Collaboration Department can enable generative AIs to cooperate with each other in specific specialized fields, such as education or medicine, to provide new solutions that utilize their specialized knowledge. For example, the Collaboration Department can apply collaboration between generative AIs to the education field to provide new educational solutions that utilize their specialized knowledge. For example, a history generation AI and a science generation AI can cooperate to create educational content themed around historical scientific discoveries. The Collaboration Department can also utilize collaboration between generative AIs in the medical field to provide new medical solutions based on specialized knowledge. For example, a medical data generation AI and a diagnosis generation AI can cooperate to propose patient diagnoses and treatment plans. The Collaboration Department can also provide new solutions through collaboration between generative AIs in specific specialized fields. For example, an environment generation AI and an energy generation AI can cooperate to propose sustainable energy solutions. This allows generative AIs to cooperate with each other in specific specialized fields, such as education or medicine, to provide new solutions that utilize their specialized knowledge, thereby providing effective solutions to specialized challenges.

[0034] The competition unit can add a function that allows the generation AI to learn its own generation process during the process of competition between generation AIs and improve itself for the next competition. For example, the competition unit adds a function that allows the generation AI to learn its own generation process during the process of competition between generation AIs and improve itself for the next competition. For example, a poetry generation AI improves its generation process based on the results of the competition. The competition unit also introduces a function that allows the generation AI to self-learn during the competition and optimize its generation process for the next competition. For example, a music generation AI improves its music generation algorithm based on the results of the competition. The competition unit also builds a system that allows the generation AI to learn its own generation process through competition between generation AIs and improve itself for the next competition. For example, a story generation AI improves its story generation method based on the results of the competition. In this way, the generation AI can learn its own generation process during the process of competition between generation AIs and improve itself for the next competition, thereby improving the performance of the generation AI.

[0035] The competition department can introduce new evaluation criteria to evaluate the diversity of generated works in competitions between generative AIs. For example, the competition department introduces new evaluation criteria to evaluate the diversity of generated works in competitions between generative AIs. For example, when a poetry generation AI and a story generation AI compete, the originality and creativity of the works are added to the evaluation criteria. The competition department also sets new evaluation criteria to evaluate the diversity of generated works and reflects them in the results of competitions between generative AIs. For example, when a music generation AI and a painting generation AI compete, the diversity of the works is added to the evaluation criteria. The competition department also introduces new evaluation criteria to evaluate the diversity of generated works in competitions between generative AIs and evaluates the results of the competition from multiple perspectives. For example, when a cooking recipe generation AI and a fashion design generation AI compete, the diversity of the works is added to the evaluation criteria. In this way, by introducing new evaluation criteria to evaluate the diversity of generated works in competitions between generative AIs, the results of the competition can be evaluated from multiple perspectives.

[0036] The Competition Division allows generative AIs from different industries and fields to compete against each other to come up with new solutions to industry-specific challenges. For example, the Competition Division allows generative AIs from different industries and fields to compete against each other to come up with new solutions to industry-specific challenges. For example, a medical generative AI and an educational generative AI could compete to propose solutions to challenges in their respective fields. The Competition Division also builds a system in which generative AIs from different industries and fields compete against each other to come up with new solutions to industry-specific challenges. For example, an environmental generative AI could compete against an energy generative AI to propose sustainable solutions. The Competition Division also allows generative AIs from different fields to compete against each other to come up with new solutions to industry-specific challenges, thereby generating innovative ideas. For example, a fashion generative AI could compete against a technology generative AI to propose new fashion technologies. In this way, generative AIs from different industries and fields can compete against each other to come up with new solutions to industry-specific challenges, thereby generating innovative ideas.

[0037] The competition unit can hold competitions between generation AIs in the form of live events in which users participate, and reflect real-time feedback. For example, the competition unit can hold competitions between generation AIs in the form of live events, with users participating in real time and providing feedback. For example, an event can be held in which a poetry generation AI and a story generation AI compete, with users providing on-the-spot evaluations. The competition unit can also build a system in which generation AIs compete in the form of live events and reflect real-time user feedback. For example, an event can be held in which a music generation AI and a painting generation AI compete, with users providing on-the-spot evaluations. The competition unit can also hold competitions between generation AIs in the form of live events in which users participate, and reflect real-time feedback in the results of the competition. For example, an event can be held in which a cooking recipe generation AI and a fashion design generation AI compete, with users providing on-the-spot evaluations. In this way, generation AIs can compete in the form of live events in which users participate, and real-time feedback can be reflected in evaluations that reflect user opinions.

[0038] The Collaboration Department can develop protocols to streamline data exchange between generative AIs in projects where generative AIs from different fields or genres collaborate. For example, the Collaboration Department develops protocols to streamline data exchange between generative AIs in projects where generative AIs from different fields or genres collaborate. For example, when a music generation AI and a painting generation AI collaborate, the Collaboration Department optimizes the data exchange protocol. The Collaboration Department also develops protocols to streamline data exchange between generative AIs, facilitating collaboration between generative AIs from different fields or genres. For example, when a poetry generation AI and a story generation AI collaborate, the Collaboration Department optimizes the data exchange protocol. The Collaboration Department also develops protocols to streamline data exchange in projects where generative AIs from different fields or genres collaborate, maximizing the benefits of collaboration. For example, when a cooking recipe generation AI and a fashion design generation AI collaborate, the Collaboration Department optimizes the data exchange protocol. This allows the benefits of collaboration to be maximized by developing protocols to streamline data exchange between generative AIs in projects where generative AIs from different fields or genres collaborate.

[0039] The collaboration unit can introduce an algorithm that promotes mutual learning between generation AIs from different fields or genres and maximizes the effects of collaboration when generation AIs from different fields or genres collaborate. For example, the collaboration unit introduces an algorithm that promotes mutual learning between generation AIs and maximizes the effects of collaboration when generation AIs from different fields or genres collaborate. For example, when a music generation AI and a painting generation AI collaborate, an algorithm that promotes mutual learning is introduced. The collaboration unit also introduces an algorithm that promotes mutual learning between generation AIs and maximizes the effects of collaboration, thereby smoothly promoting collaboration between generation AIs from different fields or genres. For example, when a poetry generation AI and a story generation AI collaborate, an algorithm that promotes mutual learning is introduced. The collaboration unit also builds a system that introduces an algorithm that promotes mutual learning between generation AIs from different fields or genres and maximizes the effects of collaboration when generation AIs from different fields or genres collaborate. For example, when a cooking recipe generation AI and a fashion design generation AI collaborate, an algorithm that promotes mutual learning is introduced. This allows the performance of the generation AI to be improved by introducing an algorithm that promotes mutual learning between generation AIs and maximizes the effects of collaboration when generation AIs from different fields or genres collaborate.

[0040] The collaboration unit can customize collaboration between generation AIs of different fields and genres for users of different age groups and hobbies and preferences, thereby catering to a wide range of users. For example, the collaboration unit customizes collaboration between generation AIs of different fields and genres for users of different age groups and hobbies and preferences, thereby catering to a wide range of users. For example, when a music generation AI and a painting generation AI collaborate, they customize it to suit the user's age group and hobbies and preferences. The collaboration unit also customizes collaboration between generation AIs for users of different age groups and hobbies and preferences, thereby building a system that caters to a wide range of users. For example, when a poetry generation AI and a story generation AI collaborate, they customize it to suit the user's age group and hobbies and preferences. The collaboration unit also customizes collaboration between generation AIs of different fields and genres for the user's age group and hobbies and preferences, thereby catering to a wide range of users. For example, when a cooking recipe generation AI and a fashion design generation AI collaborate, they customize it to suit the user's age group and hobbies and preferences. This allows the collaboration of generation AIs from different fields and genres to be customized for users of different age groups and with different hobbies and preferences, thereby catering to a wide range of users and improving user satisfaction.

[0041] The collaboration unit can apply the collaboration of generation AIs from different fields and genres to a company's marketing strategy to generate content tailored to the target market. For example, the collaboration unit applies the collaboration of generation AIs from different fields and genres to a company's marketing strategy to generate content tailored to the target market. For example, a music generation AI and a painting generation AI collaborate to create art works for a specific market. The collaboration unit also applies the collaboration of generation AIs to a company's marketing strategy to build a system that generates content tailored to the target market. For example, a poetry generation AI and a story generation AI collaborate to create stories for a specific market. The collaboration unit also applies the collaboration of generation AIs from different fields and genres to a company's marketing strategy to generate content tailored to the target market. For example, a cooking recipe generation AI and a fashion design generation AI collaborate to make lifestyle suggestions for a specific market. In this way, the company's marketing effectiveness can be improved by applying the collaboration of generation AIs from different fields and genres to a company's marketing strategy to generate content tailored to the target market.

[0042] The participation unit can generate prompts that allow the generation AI to automatically optimize themes and conditions provided by the user to the generation AI and generate better works. The participation unit, for example, allows the generation AI to automatically optimize themes and conditions provided by the user to the generation AI and generate prompts that allow the generation AI to generate better works. For example, it automatically generates optimal prompts based on user input. The participation unit also builds a system that allows the generation AI to automatically optimize themes and conditions provided by the user and generate prompts that allow the generation AI to generate better works. For example, it analyzes user input and generates optimal prompts. The participation unit also improves the performance of the generation AI by automatically optimizing themes and conditions provided by the user to the generation AI and generating prompts that allow the generation AI to generate better works. For example, it generates optimal prompts based on user input. In this way, the performance of the generation AI can be improved by allowing the generation AI to automatically optimize themes and conditions provided by the user to the generation AI and generating prompts that allow the generation AI to generate better works.

[0043] The participation unit can add a function that allows the generation AI to learn the user's past works and preferences and make suggestions when the user collaborates with the generation AI to generate a work. For example, the participation unit adds a function that allows the generation AI to learn the user's past works and preferences and make suggestions when the user collaborates with the generation AI to generate a work. For example, the participation unit analyzes the user's past works and suggests similar styles. The participation unit also builds a system in which the generation AI learns the user's past works and preferences and makes optimal suggestions when collaboratively generating a work. For example, the participation unit suggests themes and styles that match the user's preferences. The participation unit also improves user satisfaction by adding a function that learns the user's past works and preferences and makes suggestions when the generation AI collaboratively generates a work. For example, the participation unit makes customization suggestions based on the user's preferences. As a result, when the user collaborates with the generation AI to generate a work, the generation AI learns the user's past works and preferences and makes suggestions, thereby improving user satisfaction.

[0044] The participation unit can link user participation with a social media platform to widely share the generated work and collect feedback. For example, the participation unit links user participation with a social media platform to widely share the generated work and collect feedback. For example, the participation unit posts the generated work on a social media platform and collects comments and ratings from users. The participation unit also links with a social media platform to widely share the generated work, thereby building a system to collect user feedback. For example, the participation unit shares the generated work on a social media platform and analyzes user responses. The participation unit also links user participation with a social media platform to widely share the generated work and collect feedback, thereby improving the performance of the generative AI. For example, the participation unit improves the generative AI's algorithm based on user responses on the social media platform. In this way, the performance of the generative AI can be improved by linking user participation with a social media platform to widely share the generated work and collect feedback.

[0045] The participation department can provide users with opportunities to learn how to utilize generative AI by having them participate in educational programs or workshops. For example, the participation department can provide users with opportunities to learn how to utilize generative AI by having them participate in educational programs or workshops. For example, the participation department can hold a creative workshop using generative AI, where users actually create works using generative AI. The participation department can also build a system that encourages users to participate in educational programs or workshops and provides opportunities to learn how to utilize generative AI. For example, the participation department can provide an online course that teaches the basic usage and application methods of generative AI. The participation department can also improve users' skills by having users participate in educational programs or workshops and providing opportunities to learn how to utilize generative AI. For example, the participation department can provide a project-based learning program using generative AI. This can improve users' skills by having users participate in educational programs or workshops and providing opportunities to learn how to utilize generative AI.

[0046] The collaboration unit can introduce an algorithm that refers to past data and trends and predicts future trends when the generative AI creates new ideas and expressions. For example, the collaboration unit introduces an algorithm that refers to past data and trends and predicts future trends when the generative AI creates new ideas and expressions. For example, predicting future trends based on past success stories. The collaboration unit also builds a system that refers to past data and trends and introduces an algorithm that predicts future trends when the generative AI creates new ideas and expressions. For example, analyzing past data and predicting future trends. The collaboration unit also improves the performance of the generative AI by introducing an algorithm that refers to past data and trends and predicts future trends when the generative AI creates new ideas and expressions. For example, predicting future trends based on past data. In this way, the performance of the generative AI can be improved by introducing an algorithm that refers to past data and trends and predicts future trends when the generative AI creates new ideas and expressions.

[0047] The collaboration unit can add functions that promote knowledge sharing between different generative AIs and enable them to learn from each other when they create new ideas and expressions. For example, the collaboration unit promotes knowledge sharing between different generative AIs and enables them to learn from each other when they create new ideas and expressions. For example, a music generation AI and a painting generation AI share knowledge and learn from each other. The collaboration unit also builds a system that promotes knowledge sharing between different generative AIs and adds functions that enable them to learn from each other when they create new ideas and expressions. For example, a poetry generation AI and a story generation AI share knowledge and learn from each other. The collaboration unit also improves the performance of generative AIs by adding functions that promote knowledge sharing between different generative AIs and enable them to learn from each other when they create new ideas and expressions. For example, a cooking recipe generation AI and a fashion design generation AI share knowledge and learn from each other. This can improve the performance of generative AIs by adding functions that promote knowledge sharing between different generative AIs and enable them to learn from each other when they create new ideas and expressions.

[0048] The collaboration unit creates new ideas and expressions between AIs from different cultures and regions, generating new ideas from a global perspective. For example, a Japanese music generation AI and a French painting generation AI could collaborate to create an internationally renowned artwork. The collaboration unit also creates new ideas and expressions between AIs from different cultures and regions, building a system that generates new ideas from a global perspective. For example, a Chinese poetry generation AI and an American story generation AI could collaborate to create a story that blends Eastern and Western cultures. The collaboration unit also creates new ideas and expressions between AIs from different cultures and regions, generating new ideas from a global perspective, improving the performance of the AIs. For example, a Spanish cooking recipe generation AI and an Italian fashion design generation AI could collaborate to create a work that combines cooking and fashion. This allows the creation of new ideas and expressions between AIs from different cultures and regions, generating new ideas from a global perspective, improving the performance of the AIs.

[0049] The Collaboration Department can apply the creation of new ideas and expressions to a company's product development and service improvements, thereby benefiting actual business. For example, the Collaboration Department can apply the creation of new ideas and expressions to a company's product development and service improvements, thereby benefiting actual business. For example, a music generation AI and a painting generation AI can work together to create artwork for a company's marketing campaign. The Collaboration Department can also build a system that applies the new ideas and expressions created by the generative AI to a company's product development and service improvements, thereby benefiting actual business. For example, a poetry generation AI and a story generation AI can work together to create a company's brand story. The Collaboration Department can also improve the performance of the generative AI by applying the creation of new ideas and expressions to a company's product development and service improvements, thereby benefiting actual business. For example, a cooking recipe generation AI and a fashion design generation AI can work together to propose new products and services for a company. In this way, the creation of new ideas and expressions can be applied to a company's product development and service improvements, thereby benefiting actual business, thereby improving the performance of the generative AI.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: There are various types of generative AI, such as text generation AI (e.g., LLM), image generation AI, and music generation AI. Step 2: The Collaboration Department manages collaboration between the generative AIs. For example, a music generation AI and a painting generation AI collaborate to create an artwork that combines music and visuals. The Collaboration Department manages collaborative projects between the generative AIs, and monitors the allocation of tasks and progress. Step 3: The competition department manages the competition between the generation AIs. For example, a poetry generation AI and a story generation AI compete to see which can generate the most moving text. The competition department sets the competition rules for the generation AIs and establishes evaluation criteria. Step 4: The collaboration unit manages collaboration between generation AIs from different fields and genres. For example, a cooking recipe generation AI and a fashion design generation AI can collaborate to propose a new lifestyle that combines cooking and fashion. The collaboration unit manages data exchange and protocols between the generation AIs. Step 5: The participation department manages user participation. For example, the user specifies a theme and conditions for the generation AI, and the generation AI generates a work based on those conditions. The participation department collects user feedback and uses it as learning data for the generation AI.

[0053] (Example 2) The generative AI cooperation and competition system according to an embodiment of the present invention is a system in which generative AIs cooperate and compete with each other, improving the diversity and creativity of the generative AIs. As a result, the generative AI cooperation and competition system allows the generative AIs to stimulate and challenge each other, generating new ideas and expressions. Users can also participate and have fun together with the generative AIs.

[0054] A generative AI cooperation and competition system according to an embodiment includes a generative AI, a cooperation unit, a competition unit, a collaboration unit, and a participation unit. Generative AIs include, for example, text generation AIs (e.g., LLMs), image generation AIs, and music generation AIs. The collaboration unit manages cooperation between the generative AIs. For example, a music generation AI and a painting generation AI collaborate to create an artwork that combines music and visuals. The collaboration unit manages a collaborative project between the generative AIs and monitors task allocation and progress. The competition unit manages competition between the generative AIs. For example, a poetry generation AI and a story generation AI compete to see which can generate the most moving text. The competition unit sets competition rules and evaluation criteria for the generative AIs. The collaboration unit manages collaboration between generative AIs from different fields and genres. For example, a cooking recipe generation AI and a fashion design generation AI collaborate to propose a new lifestyle that combines cooking and fashion. The collaboration unit manages data exchange and protocols between the generative AIs. The participation unit manages user participation. For example, a user can specify a theme and conditions for the generating AI, and the generating AI can generate a work based on those conditions. The participation unit collects user feedback and uses it as learning data for the generating AI. This allows the generating AI cooperation and competition system according to the embodiment to improve the diversity and creativity of the generating AI. For example, collaboration between generating AIs from different fields and genres can lead to the creation of new ideas and expressions that have never been seen before. Furthermore, participation by users together with the generating AI can expand the possibilities of the generating AI.

[0055] When generation AIs cooperate with each other, the collaboration unit can use the emotion estimation function to analyze the emotional interactions that occur during the collaboration process and propose optimal collaboration methods. For example, when generation AIs cooperate with each other, the collaboration unit uses the emotion estimation function to analyze the emotional reactions to each generation AI's output in real time. For example, a music generation AI analyzes the emotional reactions of a painting generation AI to a song created by the music generation AI and generates optimal visuals. The collaboration unit also analyzes the emotional interactions that occur during the collaboration process and optimizes communication between the generation AIs. For example, when a poetry generation AI and a story generation AI cooperate, the collaboration unit adjusts the story development based on emotional agreement. The collaboration unit also uses the emotion estimation function to reduce emotional friction that occurs during collaboration between generation AIs. For example, when generation AIs of different genres cooperate, the collaboration unit detects emotional disagreements and adjusts the method of cooperation. This makes it possible to maximize the effectiveness of cooperation by analyzing the emotional interactions that occur during collaboration between generation AIs and proposing optimal collaboration methods.

[0056] The collaboration unit can monitor the progress of projects in which the generation AIs cooperate in real time and automatically redistribute tasks as necessary. For example, the collaboration unit monitors the progress of projects in which the generation AIs cooperate in real time and automatically redistributes tasks according to the progress of the tasks. For example, when a music generation AI completes part of a song, it assigns the next task to a painting generation AI. The collaboration unit also monitors the progress of the project and builds a system to evenly distribute the load between the generation AIs. For example, when a poetry generation AI and a story generation AI cooperate, it adjusts the load according to the progress of the task. The collaboration unit also uses real-time monitoring to maximize the efficiency of projects in which the generation AIs cooperate. For example, when a cooking recipe generation AI and a fashion design generation AI cooperate, it redistributes tasks according to progress. This allows the progress of projects in which the generation AIs cooperate to be monitored in real time and redistributed as necessary, thereby improving the efficiency of the project.

[0057] The Collaboration Department can introduce a dynamic role-sharing algorithm to maximize the areas of expertise of each generative AI. For example, the Collaboration Department can introduce a dynamic role-sharing algorithm to maximize the areas of expertise of each generative AI, thereby improving the efficiency of collaborative projects. For example, a music generation AI creates music, and a painting generation AI generates visuals that match the music. The Collaboration Department can also dynamically divide roles between generative AIs based on their areas of expertise to maximize the results of collaborative projects. For example, a poetry generation AI creates moving poetry, and a story generation AI generates a story based on the poem. The Collaboration Department can also use a dynamic role-sharing algorithm to increase the flexibility of collaborative projects between generative AIs. For example, a cooking recipe generation AI suggests dishes, and a fashion design generation AI suggests fashion that matches the dishes. By introducing a dynamic role-sharing algorithm to maximize the areas of expertise of each generative AI, the results of collaborative projects can be maximized.

[0058] The collaboration unit enables collaboration between generation AIs from different cultures and languages ​​to generate works that reflect cultural diversity. For example, generation AIs from different cultures and languages ​​collaborate to generate works that reflect cultural diversity. For example, a Japanese music generation AI and a French painting generation AI collaborate to create an international work of art. The collaboration unit also generates works that combine elements from different cultures through collaboration between generation AIs. For example, a Chinese poetry generation AI and an American story generation AI collaborate to create a story that combines Eastern and Western cultures. The collaboration unit also enables generation AIs from different languages ​​to collaborate to generate works that are multilingual. For example, a Spanish cooking recipe generation AI and an Italian fashion design generation AI collaborate to create a work that combines cooking and fashion. In this way, collaboration between generation AIs from different cultures and languages ​​to generate works that reflect cultural diversity can create new ideas from a global perspective.

[0059] The Collaboration Department can enable generative AIs to cooperate with each other in specific specialized fields, such as education or medicine, to provide new solutions that utilize their specialized knowledge. For example, the Collaboration Department can apply collaboration between generative AIs to the education field to provide new educational solutions that utilize their specialized knowledge. For example, a history generation AI and a science generation AI can cooperate to create educational content themed around historical scientific discoveries. The Collaboration Department can also utilize collaboration between generative AIs in the medical field to provide new medical solutions based on specialized knowledge. For example, a medical data generation AI and a diagnosis generation AI can cooperate to propose patient diagnoses and treatment plans. The Collaboration Department can also provide new solutions through collaboration between generative AIs in specific specialized fields. For example, an environment generation AI and an energy generation AI can cooperate to propose sustainable energy solutions. This allows generative AIs to cooperate with each other in specific specialized fields, such as education or medicine, to provide new solutions that utilize their specialized knowledge, thereby providing effective solutions to specialized challenges.

[0060] The collaboration unit uses the emotion estimation function to generate artwork that reflects the user's emotions and can provide content that is in line with the user's emotions. For example, the collaboration unit uses the emotion estimation function to generate artwork that reflects the user's emotions through cooperation between generation AIs. For example, a music generation AI and a painting generation AI cooperate to create an artwork that is in line with the user's emotions. The collaboration unit also provides content that is in line with the emotions through cooperation between generation AIs based on the user's emotion data. For example, a poetry generation AI and a story generation AI cooperate to create a moving story that matches the user's emotions. The collaboration unit also utilizes the emotion estimation function to generate artwork that is in line with the user's emotions through cooperation between generation AIs. For example, a cooking recipe generation AI and a fashion design generation AI cooperate to make lifestyle suggestions that match the user's emotions. In this way, the emotion estimation function can be used to generate artwork that reflects the user's emotions and provide content that is in line with the user's emotions, thereby improving user satisfaction.

[0061] The competition unit can use the emotion estimation function to analyze users' emotional responses in real time in a competition between generation AIs and reflect them in the results of the competition. For example, in a competition between generation AIs, the competition unit can use the emotion estimation function to analyze users' emotional responses in real time and reflect them in the results of the competition. For example, in a competition between a poetry generation AI and a story generation AI, the winner is determined based on the user's emotional responses. The competition unit also analyzes users' emotional responses in real time and reflects them in the evaluation criteria for the competition between generation AIs. For example, in a competition between a music generation AI and a painting generation AI, evaluation is based on the user's emotional score. The competition unit also utilizes the emotion estimation function to incorporate users' emotional responses into the evaluation criteria for the competition between generation AIs. For example, in a competition between a cooking recipe generation AI and a fashion design generation AI, the winner is determined based on the user's emotional responses. In this way, in a competition between generation AIs, users' emotional responses can be analyzed in real time and reflected in the results of the competition, allowing evaluation based on user emotions.

[0062] The competition unit can add a function that allows the generation AI to learn its own generation process during the process of competition between generation AIs and improve itself for the next competition. For example, the competition unit adds a function that allows the generation AI to learn its own generation process during the process of competition between generation AIs and improve itself for the next competition. For example, a poetry generation AI improves its generation process based on the results of the competition. The competition unit also introduces a function that allows the generation AI to self-learn during the competition and optimize its generation process for the next competition. For example, a music generation AI improves its music generation algorithm based on the results of the competition. The competition unit also builds a system that allows the generation AI to learn its own generation process through competition between generation AIs and improve itself for the next competition. For example, a story generation AI improves its story generation method based on the results of the competition. In this way, the generation AI can learn its own generation process during the process of competition between generation AIs and improve itself for the next competition, thereby improving the performance of the generation AI.

[0063] The competition department can introduce new evaluation criteria to evaluate the diversity of generated works in competitions between generative AIs. For example, the competition department introduces new evaluation criteria to evaluate the diversity of generated works in competitions between generative AIs. For example, when a poetry generation AI and a story generation AI compete, the originality and creativity of the works are added to the evaluation criteria. The competition department also sets new evaluation criteria to evaluate the diversity of generated works and reflects them in the results of competitions between generative AIs. For example, when a music generation AI and a painting generation AI compete, the diversity of the works is added to the evaluation criteria. The competition department also introduces new evaluation criteria to evaluate the diversity of generated works in competitions between generative AIs and evaluates the results of the competition from multiple perspectives. For example, when a cooking recipe generation AI and a fashion design generation AI compete, the diversity of the works is added to the evaluation criteria. In this way, by introducing new evaluation criteria to evaluate the diversity of generated works in competitions between generative AIs, the results of the competition can be evaluated from multiple perspectives.

[0064] The Competition Division allows generative AIs from different industries and fields to compete against each other to come up with new solutions to industry-specific challenges. For example, the Competition Division allows generative AIs from different industries and fields to compete against each other to come up with new solutions to industry-specific challenges. For example, a medical generative AI and an educational generative AI could compete to propose solutions to challenges in their respective fields. The Competition Division also builds a system in which generative AIs from different industries and fields compete against each other to come up with new solutions to industry-specific challenges. For example, an environmental generative AI could compete against an energy generative AI to propose sustainable solutions. The Competition Division also allows generative AIs from different fields to compete against each other to come up with new solutions to industry-specific challenges, thereby generating innovative ideas. For example, a fashion generative AI could compete against a technology generative AI to propose new fashion technologies. In this way, generative AIs from different industries and fields can compete against each other to come up with new solutions to industry-specific challenges, thereby generating innovative ideas.

[0065] The competition unit can hold competitions between generation AIs in the form of live events in which users participate, and reflect real-time feedback. For example, the competition unit can hold competitions between generation AIs in the form of live events, with users participating in real time and providing feedback. For example, an event can be held in which a poetry generation AI and a story generation AI compete, with users providing on-the-spot evaluations. The competition unit can also build a system in which generation AIs compete in the form of live events and reflect real-time user feedback. For example, an event can be held in which a music generation AI and a painting generation AI compete, with users providing on-the-spot evaluations. The competition unit can also hold competitions between generation AIs in the form of live events in which users participate, and reflect real-time feedback in the results of the competition. For example, an event can be held in which a cooking recipe generation AI and a fashion design generation AI compete, with users providing on-the-spot evaluations. In this way, generation AIs can compete in the form of live events in which users participate, and real-time feedback can be reflected in evaluations that reflect user opinions.

[0066] The competition unit can use the emotion estimation function to evaluate the competition between generation AIs based on the user's emotions and select works that resonate with the user emotionally. The competition unit, for example, uses the emotion estimation function to evaluate the competition between generation AIs based on the user's emotions. For example, when a poetry generation AI and a story generation AI compete, the evaluation is based on the user's emotional response. The competition unit also sets evaluation criteria for the competition between generation AIs based on the user's emotional data and selects works that resonate with the user emotionally. For example, when a music generation AI and a painting generation AI compete, the evaluation is based on the user's emotional score. The competition unit also utilizes the emotion estimation function to evaluate the competition between generation AIs based on the user's emotions and build a system that selects works that resonate with the user emotionally. For example, when a cooking recipe generation AI and a fashion design generation AI compete, the evaluation is based on the user's emotional response. This allows the emotion estimation function to evaluate the competition between generation AIs based on the user's emotions and select works that resonate with the user emotionally, thereby making it possible to perform evaluations that are in line with the user's emotions.

[0067] When generation AIs from different fields or genres collaborate, the collaboration unit can use the emotion estimation function to analyze the impact that the output of each generation AI has on the user's emotions and propose the optimal collaboration method. For example, when generation AIs from different fields or genres collaborate, the collaboration unit uses the emotion estimation function to analyze the impact that the output of each generation AI has on the user's emotions. For example, when a music generation AI and a painting generation AI collaborate, the collaboration unit proposes the optimal collaboration method based on the user's emotional response. The collaboration unit also uses the emotion estimation function to build a system that analyzes user emotions when generation AIs from different fields or genres collaborate and proposes the optimal collaboration method. For example, when a poetry generation AI and a story generation AI collaborate, the collaboration unit adjusts the collaboration method based on the user's emotional response. The collaboration unit also uses the emotion estimation function to analyze the impact that the output of each generation AI has on the user's emotions and proposes the optimal collaboration method when generation AIs from different fields or genres collaborate. For example, when a cooking recipe generation AI and a fashion design generation AI collaborate, the collaboration unit adjusts the collaboration method based on the user's emotional response. This means that when generative AIs from different fields or genres work together, the emotion estimation function can be used to analyze the impact that the output of each generative AI has on the user's emotions, and the optimal method of collaboration can be proposed, enabling collaboration that is in line with the user's emotions.

[0068] The Collaboration Department can develop protocols to streamline data exchange between generative AIs in projects where generative AIs from different fields or genres collaborate. For example, the Collaboration Department develops protocols to streamline data exchange between generative AIs in projects where generative AIs from different fields or genres collaborate. For example, when a music generation AI and a painting generation AI collaborate, the Collaboration Department optimizes the data exchange protocol. The Collaboration Department also develops protocols to streamline data exchange between generative AIs, facilitating collaboration between generative AIs from different fields or genres. For example, when a poetry generation AI and a story generation AI collaborate, the Collaboration Department optimizes the data exchange protocol. The Collaboration Department also develops protocols to streamline data exchange in projects where generative AIs from different fields or genres collaborate, maximizing the benefits of collaboration. For example, when a cooking recipe generation AI and a fashion design generation AI collaborate, the Collaboration Department optimizes the data exchange protocol. This allows the benefits of collaboration to be maximized by developing protocols to streamline data exchange between generative AIs in projects where generative AIs from different fields or genres collaborate.

[0069] The collaboration unit can introduce an algorithm that promotes mutual learning between generation AIs from different fields or genres and maximizes the effects of collaboration when generation AIs from different fields or genres collaborate. For example, the collaboration unit introduces an algorithm that promotes mutual learning between generation AIs and maximizes the effects of collaboration when generation AIs from different fields or genres collaborate. For example, when a music generation AI and a painting generation AI collaborate, an algorithm that promotes mutual learning is introduced. The collaboration unit also introduces an algorithm that promotes mutual learning between generation AIs and maximizes the effects of collaboration, thereby smoothly promoting collaboration between generation AIs from different fields or genres. For example, when a poetry generation AI and a story generation AI collaborate, an algorithm that promotes mutual learning is introduced. The collaboration unit also builds a system that introduces an algorithm that promotes mutual learning between generation AIs from different fields or genres and maximizes the effects of collaboration when generation AIs from different fields or genres collaborate. For example, when a cooking recipe generation AI and a fashion design generation AI collaborate, an algorithm that promotes mutual learning is introduced. This allows the performance of the generation AI to be improved by introducing an algorithm that promotes mutual learning between generation AIs and maximizes the effects of collaboration when generation AIs from different fields or genres collaborate.

[0070] The collaboration unit can customize collaboration between generation AIs of different fields and genres for users of different age groups and hobbies and preferences, thereby catering to a wide range of users. For example, the collaboration unit customizes collaboration between generation AIs of different fields and genres for users of different age groups and hobbies and preferences, thereby catering to a wide range of users. For example, when a music generation AI and a painting generation AI collaborate, they customize it to suit the user's age group and hobbies and preferences. The collaboration unit also customizes collaboration between generation AIs for users of different age groups and hobbies and preferences, thereby building a system that caters to a wide range of users. For example, when a poetry generation AI and a story generation AI collaborate, they customize it to suit the user's age group and hobbies and preferences. The collaboration unit also customizes collaboration between generation AIs of different fields and genres for the user's age group and hobbies and preferences, thereby catering to a wide range of users. For example, when a cooking recipe generation AI and a fashion design generation AI collaborate, they customize it to suit the user's age group and hobbies and preferences. This allows the collaboration of generation AIs from different fields and genres to be customized for users of different age groups and with different hobbies and preferences, thereby catering to a wide range of users and improving user satisfaction.

[0071] The collaboration unit can apply the collaboration of generation AIs from different fields and genres to a company's marketing strategy to generate content tailored to the target market. For example, the collaboration unit applies the collaboration of generation AIs from different fields and genres to a company's marketing strategy to generate content tailored to the target market. For example, a music generation AI and a painting generation AI collaborate to create art works for a specific market. The collaboration unit also applies the collaboration of generation AIs to a company's marketing strategy to build a system that generates content tailored to the target market. For example, a poetry generation AI and a story generation AI collaborate to create stories for a specific market. The collaboration unit also applies the collaboration of generation AIs from different fields and genres to a company's marketing strategy to generate content tailored to the target market. For example, a cooking recipe generation AI and a fashion design generation AI collaborate to make lifestyle suggestions for a specific market. In this way, the company's marketing effectiveness can be improved by applying the collaboration of generation AIs from different fields and genres to a company's marketing strategy to generate content tailored to the target market.

[0072] The collaboration unit can use the emotion estimation function to provide personalized content based on a user's emotions when collaboration between generation AIs of different fields and genres takes place. For example, the collaboration unit uses the emotion estimation function to provide personalized content based on a user's emotions when collaboration between generation AIs of different fields and genres takes place. For example, a music generation AI and a painting generation AI collaborate to create a work of art that matches the user's emotions. The collaboration unit also builds a system in which generation AIs of different fields and genres collaborate to provide personalized content based on the user's emotion data. For example, a poetry generation AI and a story generation AI collaborate to create a story that matches the user's emotions. The collaboration unit also utilizes the emotion estimation function to provide personalized content based on a user's emotions when collaboration between generation AIs of different fields and genres takes place. For example, a cooking recipe generation AI and a fashion design generation AI collaborate to make lifestyle suggestions that match the user's emotions. This allows the emotion estimation function to be used to provide personalized content based on a user's emotions when collaboration between generation AIs of different fields and genres takes place, thereby improving user satisfaction.

[0073] The participation unit can analyze feedback provided by users to the generation AI using an emotion estimation function and use it as learning data for the generation AI. For example, the participation unit analyzes feedback provided by users to the generation AI using the emotion estimation function and uses it as learning data for the generation AI. For example, the participation unit improves the generation AI's algorithm based on the user's emotional reaction. The participation unit also uses the emotion estimation function to build a system that analyzes user feedback and uses it as learning data for the generation AI. For example, the participation unit optimizes the generation process of the generation AI based on the user's emotional data. The participation unit also improves the performance of the generation AI by analyzing user feedback using the emotion estimation function and using it as learning data for the generation AI. For example, the participation unit improves the generation algorithm of the generation AI based on the user's emotional reaction. In this way, the performance of the generation AI can be improved by analyzing feedback provided by users to the generation AI using the emotion estimation function and using it as learning data for the generation AI.

[0074] The participation unit can generate prompts that allow the generation AI to automatically optimize themes and conditions provided by the user to the generation AI and generate better works. The participation unit, for example, allows the generation AI to automatically optimize themes and conditions provided by the user to the generation AI and generate prompts that allow the generation AI to generate better works. For example, it automatically generates optimal prompts based on user input. The participation unit also builds a system that allows the generation AI to automatically optimize themes and conditions provided by the user and generate prompts that allow the generation AI to generate better works. For example, it analyzes user input and generates optimal prompts. The participation unit also improves the performance of the generation AI by automatically optimizing themes and conditions provided by the user to the generation AI and generating prompts that allow the generation AI to generate better works. For example, it generates optimal prompts based on user input. In this way, the performance of the generation AI can be improved by allowing the generation AI to automatically optimize themes and conditions provided by the user to the generation AI and generating prompts that allow the generation AI to generate better works.

[0075] The participation unit can add a function that allows the generation AI to learn the user's past works and preferences and make suggestions when the user collaborates with the generation AI to generate a work. For example, the participation unit adds a function that allows the generation AI to learn the user's past works and preferences and make suggestions when the user collaborates with the generation AI to generate a work. For example, the participation unit analyzes the user's past works and suggests similar styles. The participation unit also builds a system in which the generation AI learns the user's past works and preferences and makes optimal suggestions when collaboratively generating a work. For example, the participation unit suggests themes and styles that match the user's preferences. The participation unit also improves user satisfaction by adding a function that learns the user's past works and preferences and makes suggestions when the generation AI collaboratively generates a work. For example, the participation unit makes customization suggestions based on the user's preferences. As a result, when the user collaborates with the generation AI to generate a work, the generation AI learns the user's past works and preferences and makes suggestions, thereby improving user satisfaction.

[0076] The participation unit can link user participation with a social media platform to widely share the generated work and collect feedback. For example, the participation unit links user participation with a social media platform to widely share the generated work and collect feedback. For example, the participation unit posts the generated work on a social media platform and collects comments and ratings from users. The participation unit also links with a social media platform to widely share the generated work, thereby building a system to collect user feedback. For example, the participation unit shares the generated work on a social media platform and analyzes user responses. The participation unit also links user participation with a social media platform to widely share the generated work and collect feedback, thereby improving the performance of the generative AI. For example, the participation unit improves the generative AI's algorithm based on user responses on the social media platform. In this way, the performance of the generative AI can be improved by linking user participation with a social media platform to widely share the generated work and collect feedback.

[0077] The participation department can provide users with opportunities to learn how to utilize generative AI by having them participate in educational programs or workshops. For example, the participation department can provide users with opportunities to learn how to utilize generative AI by having them participate in educational programs or workshops. For example, the participation department can hold a creative workshop using generative AI, where users actually create works using generative AI. The participation department can also build a system that encourages users to participate in educational programs or workshops and provides opportunities to learn how to utilize generative AI. For example, the participation department can provide an online course that teaches the basic usage and application methods of generative AI. The participation department can also improve users' skills by having users participate in educational programs or workshops and providing opportunities to learn how to utilize generative AI. For example, the participation department can provide a project-based learning program using generative AI. This can improve users' skills by having users participate in educational programs or workshops and providing opportunities to learn how to utilize generative AI.

[0078] The participation unit can use the emotion estimation function to provide an interactive experience based on the user's emotions. For example, in user participation, the participation unit uses the emotion estimation function to provide an interactive experience based on the user's emotions. For example, a generation AI adjusts a work in real time based on the user's emotional response. The participation unit also uses the emotion estimation function to build a system that provides an interactive experience based on the user's emotions. For example, a generation AI generates interactive content based on the user's emotional data. The participation unit also utilizes the emotion estimation function to provide an interactive experience based on the user's emotions. For example, a user's emotional response is analyzed in real time, and the generation AI adjusts a work on the spot. In this way, the emotion estimation function can be used to provide an interactive experience based on the user's emotions, thereby improving user satisfaction.

[0079] The collaboration unit can use the emotion estimation function to analyze the emotional impact that generated ideas and expressions have on a user when the generation AIs cooperate or compete with each other, and select the optimal idea. For example, the collaboration unit uses the emotion estimation function to analyze the emotional impact that generated ideas and expressions have on a user when the generation AIs cooperate or compete with each other. For example, it selects the optimal idea based on the user's emotional reaction. The collaboration unit also uses the emotion estimation function to analyze the emotional impact that generated ideas and expressions have on a user when the generation AIs cooperate or compete with each other, and builds a system that selects the optimal idea. For example, it selects the optimal idea based on user emotional data. The collaboration unit also uses the emotion estimation function to analyze the emotional impact that generated ideas and expressions have on a user when the generation AIs cooperate or compete with each other, and selects the optimal idea. For example, it selects the optimal idea based on the user's emotional reaction. As a result, it is possible to provide ideas that are in line with the user's emotions by using the emotion estimation function to analyze the emotional impact that generated ideas and expressions have on a user when the generation AIs cooperate or compete with each other, and selecting the optimal idea.

[0080] The collaboration unit can introduce an algorithm that refers to past data and trends and predicts future trends when the generative AI creates new ideas and expressions. For example, the collaboration unit introduces an algorithm that refers to past data and trends and predicts future trends when the generative AI creates new ideas and expressions. For example, predicting future trends based on past success stories. The collaboration unit also builds a system that refers to past data and trends and introduces an algorithm that predicts future trends when the generative AI creates new ideas and expressions. For example, analyzing past data and predicting future trends. The collaboration unit also improves the performance of the generative AI by introducing an algorithm that refers to past data and trends and predicts future trends when the generative AI creates new ideas and expressions. For example, predicting future trends based on past data. In this way, the performance of the generative AI can be improved by introducing an algorithm that refers to past data and trends and predicts future trends when the generative AI creates new ideas and expressions.

[0081] The collaboration unit can add functions that promote knowledge sharing between different generative AIs and enable them to learn from each other when they create new ideas and expressions. For example, the collaboration unit promotes knowledge sharing between different generative AIs and enables them to learn from each other when they create new ideas and expressions. For example, a music generation AI and a painting generation AI share knowledge and learn from each other. The collaboration unit also builds a system that promotes knowledge sharing between different generative AIs and adds functions that enable them to learn from each other when they create new ideas and expressions. For example, a poetry generation AI and a story generation AI share knowledge and learn from each other. The collaboration unit also improves the performance of generative AIs by adding functions that promote knowledge sharing between different generative AIs and enable them to learn from each other when they create new ideas and expressions. For example, a cooking recipe generation AI and a fashion design generation AI share knowledge and learn from each other. This can improve the performance of generative AIs by adding functions that promote knowledge sharing between different generative AIs and enable them to learn from each other when they create new ideas and expressions.

[0082] The collaboration unit creates new ideas and expressions between AIs from different cultures and regions, generating new ideas from a global perspective. For example, a Japanese music generation AI and a French painting generation AI could collaborate to create an internationally renowned artwork. The collaboration unit also creates new ideas and expressions between AIs from different cultures and regions, building a system that generates new ideas from a global perspective. For example, a Chinese poetry generation AI and an American story generation AI could collaborate to create a story that blends Eastern and Western cultures. The collaboration unit also creates new ideas and expressions between AIs from different cultures and regions, generating new ideas from a global perspective, improving the performance of the AIs. For example, a Spanish cooking recipe generation AI and an Italian fashion design generation AI could collaborate to create a work that combines cooking and fashion. This allows the creation of new ideas and expressions between AIs from different cultures and regions, generating new ideas from a global perspective, improving the performance of the AIs.

[0083] The Collaboration Department can apply the creation of new ideas and expressions to a company's product development and service improvements, thereby benefiting actual business. For example, the Collaboration Department can apply the creation of new ideas and expressions to a company's product development and service improvements, thereby benefiting actual business. For example, a music generation AI and a painting generation AI can work together to create artwork for a company's marketing campaign. The Collaboration Department can also build a system that applies the new ideas and expressions created by the generative AI to a company's product development and service improvements, thereby benefiting actual business. For example, a poetry generation AI and a story generation AI can work together to create a company's brand story. The Collaboration Department can also improve the performance of the generative AI by applying the creation of new ideas and expressions to a company's product development and service improvements, thereby benefiting actual business. For example, a cooking recipe generation AI and a fashion design generation AI can work together to propose new products and services for a company. In this way, the creation of new ideas and expressions can be applied to a company's product development and service improvements, thereby benefiting actual business, thereby improving the performance of the generative AI.

[0084] The collaboration unit can use the emotion estimation function to suggest new genres and styles based on the user's emotions when creating new ideas and expressions. For example, the collaboration unit uses the emotion estimation function to suggest new genres and styles based on the user's emotions when creating new ideas and expressions. For example, a music generation AI and a painting generation AI cooperate to suggest a new art style that matches the user's emotions. The collaboration unit also uses the emotion estimation function to build a system in which the generation AI suggests new genres and styles based on the user's emotions when creating new ideas and expressions. For example, a poetry generation AI and a story generation AI cooperate to suggest a new story style that matches the user's emotions. The collaboration unit also utilizes the emotion estimation function to suggest new genres and styles based on the user's emotions when creating new ideas and expressions. For example, a cooking recipe generation AI and a fashion design generation AI cooperate to suggest a new lifestyle that matches the user's emotions. This makes it possible to improve user satisfaction by using the emotion estimation function to suggest new genres and styles based on the user's emotions when creating new ideas and expressions.

[0085] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0086] The generative AI cooperation and competition system can further estimate the user's emotions and adjust the generative AI's output based on those emotions. For example, if a user is feeling sad, a poetry generation AI can generate comforting poetry, and a music generation AI can provide soothing music. If a user is excited, a story generation AI can generate an adventurous story, and a painting generation AI can provide vivid visuals. This can improve user satisfaction by providing content that is in tune with the user's emotions.

[0087] The generative AI cooperation and competition system can further estimate the user's emotions and adjust the generative AI's output based on those emotions. For example, if a user is feeling sad, a poetry generation AI can generate comforting poetry, and a music generation AI can provide soothing music. If a user is excited, a story generation AI can generate an adventurous story, and a painting generation AI can provide vivid visuals. This can improve user satisfaction by providing content that is in tune with the user's emotions.

[0088] The generative AI cooperation and competition system can further estimate the user's emotions and adjust the generative AI's output based on those emotions. For example, if a user is feeling sad, a poetry generation AI can generate comforting poetry, and a music generation AI can provide soothing music. If a user is excited, a story generation AI can generate an adventurous story, and a painting generation AI can provide vivid visuals. This can improve user satisfaction by providing content that is in tune with the user's emotions.

[0089] The generative AI cooperation and competition system can further estimate the user's emotions and adjust the generative AI's output based on those emotions. For example, if a user is feeling sad, a poetry generation AI can generate comforting poetry, and a music generation AI can provide soothing music. If a user is excited, a story generation AI can generate an adventurous story, and a painting generation AI can provide vivid visuals. This can improve user satisfaction by providing content that is in tune with the user's emotions.

[0090] The generative AI cooperation and competition system can further estimate the user's emotions and adjust the generative AI's output based on those emotions. For example, if a user is feeling sad, a poetry generation AI can generate comforting poetry, and a music generation AI can provide soothing music. If a user is excited, a story generation AI can generate an adventurous story, and a painting generation AI can provide vivid visuals. This can improve user satisfaction by providing content that is in tune with the user's emotions.

[0091] The generative AI cooperation and competition system can further estimate the user's emotions and adjust the generative AI's output based on those emotions. For example, if a user is feeling sad, a poetry generation AI can generate comforting poetry, and a music generation AI can provide soothing music. If a user is excited, a story generation AI can generate an adventurous story, and a painting generation AI can provide vivid visuals. This can improve user satisfaction by providing content that is in tune with the user's emotions.

[0092] The generative AI cooperation and competition system can further estimate the user's emotions and adjust the generative AI's output based on those emotions. For example, if a user is feeling sad, a poetry generation AI can generate comforting poetry, and a music generation AI can provide soothing music. If a user is excited, a story generation AI can generate an adventurous story, and a painting generation AI can provide vivid visuals. This can improve user satisfaction by providing content that is in tune with the user's emotions.

[0093] The generative AI cooperation and competition system can further estimate the user's emotions and adjust the generative AI's output based on those emotions. For example, if a user is feeling sad, a poetry generation AI can generate comforting poetry, and a music generation AI can provide soothing music. If a user is excited, a story generation AI can generate an adventurous story, and a painting generation AI can provide vivid visuals. This can improve user satisfaction by providing content that is in tune with the user's emotions.

[0094] The generative AI cooperation and competition system can further estimate the user's emotions and adjust the generative AI's output based on those emotions. For example, if a user is feeling sad, a poetry generation AI can generate comforting poetry, and a music generation AI can provide soothing music. If a user is excited, a story generation AI can generate an adventurous story, and a painting generation AI can provide vivid visuals. This can improve user satisfaction by providing content that is in tune with the user's emotions.

[0095] The generative AI cooperation and competition system can further estimate the user's emotions and adjust the generative AI's output based on those emotions. For example, if a user is feeling sad, a poetry generation AI can generate comforting poetry, and a music generation AI can provide soothing music. If a user is excited, a story generation AI can generate an adventurous story, and a painting generation AI can provide vivid visuals. This can improve user satisfaction by providing content that is in tune with the user's emotions.

[0096] The processing flow of the second embodiment will be briefly explained below.

[0097] Step 1: There are various types of generative AI, such as text generation AI (e.g., LLM), image generation AI, and music generation AI. Step 2: The Collaboration Department manages collaboration between the generative AIs. For example, a music generation AI and a painting generation AI collaborate to create an artwork that combines music and visuals. The Collaboration Department manages collaborative projects between the generative AIs, and monitors the allocation of tasks and progress. Step 3: The competition department manages the competition between the generation AIs. For example, a poetry generation AI and a story generation AI compete to see which can generate the most moving text. The competition department sets the competition rules for the generation AIs and establishes evaluation criteria. Step 4: The collaboration unit manages collaboration between generation AIs from different fields and genres. For example, a cooking recipe generation AI and a fashion design generation AI can collaborate to propose a new lifestyle that combines cooking and fashion. The collaboration unit manages data exchange and protocols between the generation AIs. Step 5: The participation department manages user participation. For example, the user specifies a theme and conditions for the generation AI, and the generation AI generates a work based on those conditions. The participation department collects user feedback and uses it as learning data for the generation AI.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0111] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0126] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0142] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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]

[0165] 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. Generative AI and A cooperation department that manages cooperation between the generation AIs, The Competition Department manages the competition between the generation AIs, A collaboration department that manages the collaboration of AI generation in different fields and genres, a participation unit that manages user participation; A system characterized by:

2. The Cooperation Department: When the generative AIs cooperate with each other, the system analyzes the emotional interactions that arise during the cooperation process and proposes the optimal cooperation method.

2. The system of claim 1.

3. The Cooperation Department: The generative AI monitors the progress of collaborative projects in real time and automatically redistributes tasks as needed.

2. The system of claim 1.

4. The Cooperation Department: Introducing a dynamic role-sharing algorithm to maximize the use of each generative AI's strengths 2. The system of claim 1.

5. The linking unit is Collaboration between AIs from different cultures and languages ​​to generate works that reflect cultural diversity 2. The system of claim 1.

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