system

Abstract

The system according to this embodiment aims to improve the capabilities of the generative AIs by having them generate content through cooperation and competition among themselves. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, an evaluation unit, an evolution unit, and a cooperation unit. The reception unit receives input of themes and tasks. The generation unit generates content based on the themes and tasks received by the reception unit. The evaluation unit evaluates the content generated by the generation unit. The evolution unit evolves the generation AI based on the evaluation results from the evaluation unit. The cooperation unit generates content through cooperation between the generation AIs.

Description

Technical Field

[0004] ,

[0006] , , , , ,

[0005] , , , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, generative AIs often generate content alone, and there is room for improvement in improving the capabilities of generative AIs through cooperation and competition.

[0005] The system according to the embodiment aims to enable generative AIs to generate content through cooperation and competition and improve the capabilities of generative AIs.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, an evaluation unit, an evolution unit, and a cooperation unit. The reception unit receives input of themes and tasks. The generation unit generates content based on the themes and tasks received by the reception unit. The evaluation unit evaluates the content generated by the generation unit. The evolution unit evolves the generation AI based on the evaluation results from the evaluation unit. The cooperation unit generates content through cooperation between the generation AIs. [Effects of the Invention]

[0007] The system according to this embodiment can generate content through cooperation and competition among generating AIs, thereby improving the capabilities of the generating AIs. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The platform according to an embodiment of the present invention is a system in which generative AIs with different areas of expertise cooperate and compete. In this platform, users set themes or tasks, and generative AIs generate content based on those themes or tasks. The generated content is evaluated by users and other AIs, and the best content is selected. Through this process, it is expected that the generative AIs will learn from each other and evolve. For example, there is a reception unit for users to set themes or tasks, and a generation unit for generative AIs to generate content based on those themes or tasks. The generated content is evaluated by an evaluation unit, and there is an evolution unit in which the generative AIs evolve based on the evaluation results. It also has a cooperation unit for generative AIs to cooperate in generating content. As a result, the platform can generate superior content through the evolution of generative AIs with different areas of expertise through cooperation and competition.

[0029] The platform according to this embodiment comprises a reception unit, a generation unit, an evaluation unit, an evolution unit, and a cooperation unit. The reception unit receives input from the user to set themes and tasks. The reception unit, for example, stores the themes and tasks entered by the user in a database. The reception unit can also analyze the user's input and convert it into an appropriate format. The generation unit generates content based on the themes and tasks received by the reception unit using a generation AI. The generation unit generates text using, for example, a text generation AI (e.g., LLM). The generation unit can also generate images using an image generation AI. Furthermore, the generation unit can generate content that combines text and images using a multimodal generation AI. The evaluation unit evaluates the content generated by the generation unit. The evaluation unit, for example, collects user feedback and calculates an evaluation score. The evaluation unit can also evaluate the content based on evaluations by other generation AIs. Furthermore, the evaluation unit can evaluate the quality of the content using quantitative indicators. The evolution unit evolves the generation AI based on the results evaluated by the evaluation unit. The evolution unit, for example, improves the algorithms of the generative AI to enhance its performance. It can also update the training data of the generative AI to give it more advanced generative capabilities. Furthermore, it can optimize the parameters of the generative AI to improve generation accuracy. The cooperation unit supports the process of generative AIs collaborating to generate content. For example, it divides tasks among the generative AIs to efficiently generate content. It can also share information among the generative AIs to improve the accuracy of their collaboration. Furthermore, it can manage the collaboration history of the generative AIs and suggest the optimal collaboration method. As a result, the platform according to this embodiment allows generative AIs with different areas of expertise to evolve through collaboration and competition, generating superior content.

[0030] The reception section accepts input from users to set themes and tasks. Specifically, it has the function of saving themes and tasks entered by users to a database. For example, information entered by users through the web interface is saved to the database in real time and used for subsequent processing. The reception section can also analyze the user's input and convert it into an appropriate format. For example, it uses natural language processing technology to convert free-form text entered by users into structured data and prepare it in a format that is easy for the generation section to use. Furthermore, the reception section has the function of automatically supplementing relevant information based on the user's input. For example, it automatically suggests keywords and reference materials related to the theme entered by the user, helping the user set more specific tasks. In this way, the reception section accurately understands the user's intent and supports the smooth progress of the subsequent generation process.

[0031] The generation unit uses generation AI to generate content based on themes and tasks received by the reception unit. Specifically, it generates text using text generation AI (e.g., LLM). For example, it collects relevant information based on a user-defined theme and generates a logical and consistent text. The generation unit can also generate images using image generation AI. For example, it generates original images based on a style or theme specified by the user. Furthermore, the generation unit can generate content combining text and images using multimodal generation AI. For example, it can simultaneously generate explanatory text and corresponding illustrations based on a user-defined theme. The generation unit efficiently utilizes these generation AIs to provide diverse content that meets user needs. In addition, the generation unit has the function to receive user feedback in real time during the generation process and adjust the generated content accordingly. This allows the generation unit to quickly provide high-quality content that meets user expectations.

[0032] The evaluation unit evaluates the content generated by the generation unit. Specifically, it collects user feedback and calculates an evaluation score. For example, users evaluate the generated content, and the evaluation unit calculates a score based on the evaluation results. The evaluation unit can also evaluate content based on evaluations by other generation AIs. For example, it can evaluate content generated by different generation AIs against each other and integrate the results to make a final evaluation. Furthermore, the evaluation unit can evaluate the quality of content using quantitative indicators. For example, it can set specific evaluation criteria such as the consistency of the text, the accuracy of the information, the resolution of images, and the degree of style consistency, and evaluate the content based on these criteria. Based on these evaluation results, the evaluation unit provides feedback to the generation and evolution units to support improvements in the generation process. In addition, the evaluation unit can store the evaluation results in a database and use them for long-term trend analysis and performance evaluation of generation AIs. This allows the evaluation unit to improve the quality of generated content and increase user satisfaction.

[0033] The evolution unit evolves the generative AI based on the evaluation results from the evaluation unit. Specifically, it improves the generative AI's algorithm and enhances its performance. For example, it adjusts the model parameters of the generative AI based on the evaluation results to achieve higher accuracy generation. The evolution unit can also update the generative AI's training data to give it more advanced generation capabilities. For example, it adds newly collected data and user feedback to the training data to expand the generative AI's knowledge base. Furthermore, the evolution unit can optimize the generative AI's parameters to improve generation accuracy. For example, it tunes hyperparameters and improves the model architecture to maximize the generative AI's performance. The evolution unit can automate these evolution processes and continuously improve the generative AI's performance. As a result, the evolution unit can ensure that the generative AI is always evolving based on the latest technology and data, and continues to provide users with high-quality content.

[0034] The Collaboration Department supports the process of generating content through collaboration between generative AIs. Specifically, it divides tasks among generative AIs to efficiently generate content. For example, a text generation AI and an image generation AI can collaborate to simultaneously generate text and its corresponding image. The Collaboration Department can also share information between generative AIs to improve the accuracy of their collaboration. For example, a text generation AI can provide the content of text it has generated to an image generation AI, which then generates a related image based on that. Furthermore, the Collaboration Department can manage the collaboration history between generative AIs and suggest the optimal collaboration method. For example, it can analyze data from past collaboration projects to identify the most effective collaboration patterns and plan new collaboration projects based on them. Through these functions, the Collaboration Department can facilitate smooth collaboration among generative AIs, enabling efficient and high-quality content generation. In this way, the Collaboration Department supports generative AIs with different areas of expertise to evolve through collaboration and competition, and generate superior content.

[0035] The reception desk can analyze the user's past theme and task setting history and suggest the optimal input method. For example, the reception desk can automatically display themes and tasks that the user has frequently set in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest themes and tasks that the user will use during specific time periods based on the user's past setting history. This improves user convenience by suggesting the optimal input method based on past setting history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past setting history data into a generating AI and have the generating AI suggest the optimal input method.

[0036] The input field can provide an auto-completion function based on the user's current areas of interest when inputting themes or topics. For example, the input field can automatically complete relevant themes and topics based on keywords the user has recently searched for or content they have viewed. The input field can also analyze the user's social media activity and suggest themes and topics based on their areas of interest. Furthermore, the input field can combine the user's past settings history with their current areas of interest to automatically complete the most suitable themes and topics. This improves input efficiency by providing an auto-completion function based on the user's areas of interest. Some or all of the above processing in the input field may be performed using AI, for example, or not. For example, the input field can input the user's areas of interest data into a generating AI and have the generating AI perform the auto-completion function.

[0037] The reception desk can suggest highly relevant themes and tasks based on the user's geographical location information when they input a theme or task. For example, if the user is in a specific region, the reception desk can suggest themes and tasks related to that region. Furthermore, if the user is traveling, the reception desk can suggest themes and tasks related to their travel destination. Additionally, if the user is at home, the reception desk can suggest themes and tasks that can be done at home. This ensures that themes and tasks are highly relevant to the user by providing suggestions based on geographical location information. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location data into a generating AI and have the AI ​​generate suggestions for highly relevant themes and tasks.

[0038] The reception desk can analyze a user's social media activity when they input a theme or topic, and suggest relevant themes and topics. For example, the reception desk can suggest relevant themes and topics based on posts the user has recently "liked" or shared. It can also analyze the content of posts from accounts the user follows and suggest themes and topics of interest. Furthermore, the reception desk can suggest themes and topics that are in line with current trends based on the user's social media activity history. In this way, by providing suggestions based on social media activity, themes and topics that match the user's interests are provided. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI suggest relevant themes and topics.

[0039] The generation unit can adjust the level of detail of the generated content based on the importance of the theme or issue during generation. For example, for high-importance themes or issues, the generation unit generates content that includes detailed explanations and background information. Conversely, for low-importance themes or issues, the generation unit can generate concise and to-the-point content. Furthermore, the generation unit can generate content with an appropriate level of detail based on the importance level specified by the user. This ensures that appropriate content is generated by adjusting the level of detail according to the importance of the theme or issue. Some or all of the above-described processes in the generation unit may be performed using AI, or not. For example, the generation unit can input theme and issue importance data into a generation AI and have the generation AI adjust the level of detail of the content.

[0040] The generation unit can apply different generation algorithms depending on the theme or topic category during generation. For example, for themes and topics related to science and technology, the generation unit can apply a generation algorithm that incorporates specialized knowledge. It can also apply a generation algorithm that emphasizes creativity for themes and topics related to art and design. Furthermore, it can apply a generation algorithm that emphasizes practicality for themes and topics related to business and economics. This allows for the generation of more specialized content by applying a generation algorithm appropriate to the category. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input theme and topic category data into a generation AI and have the generation AI apply an appropriate generation algorithm.

[0041] The generation unit can determine the priority of content to generate based on the submission deadlines for themes and assignments. For example, the generation unit will prioritize generating content for themes and assignments with approaching deadlines. It can also postpone generating content for themes and assignments with later submission deadlines. Furthermore, the generation unit can generate content with appropriate priorities based on the submission deadlines specified by the user. This ensures that content is generated at the appropriate time by setting priorities based on submission deadlines. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input theme and assignment submission deadline data into a generation AI and have the generation AI determine the priorities.

[0042] The generation unit can adjust the order in which it generates content based on the relevance of themes and issues during generation. For example, the generation unit can prioritize generating content for highly relevant themes and issues. It can also postpone generating content for less relevant themes and issues. Furthermore, the generation unit can generate content in an appropriate order based on user-specified relevance. This ensures that content is generated in an appropriate order by setting a relevance-based order. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input theme and issue relevance data into a generation AI and have the generation AI adjust the content order.

[0043] The evaluation unit can improve the accuracy of its evaluations based on the interrelationships between content. For example, if multiple content pieces are related, the evaluation unit will consider their interrelationships when performing the evaluation. The evaluation unit can also analyze the relationships between content pieces to improve the accuracy of the evaluation. Furthermore, the evaluation unit can perform a comprehensive evaluation based on the interrelationships between content pieces. This improves the accuracy of the evaluation by considering the interrelationships between content pieces. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input content interrelationship data into a generating AI and have the generating AI perform the evaluation accuracy improvement.

[0044] The evaluation unit can perform evaluations based on the attribute information of the content submitter. For example, the evaluation unit may consider the submitter's expertise and experience. The evaluation unit may also refer to the submitter's past evaluation history. Furthermore, the evaluation unit may apply appropriate evaluation criteria based on the submitter's attribute information. This allows for a more appropriate evaluation by considering the submitter's attribute information. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the submitter's attribute information data into a generating AI and have the generating AI perform the evaluation.

[0045] The evaluation unit can perform evaluations based on the geographical distribution of content. For example, if the content is related to a specific region, the evaluation unit will consider the characteristics of that region when performing the evaluation. The evaluation unit can also analyze the geographical distribution of content to improve the accuracy of the evaluation. Furthermore, the evaluation unit can apply appropriate evaluation criteria based on geographical factors. This allows for a more appropriate evaluation by considering geographical distribution. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input geographical distribution data of the content into a generating AI and have the generating AI perform the evaluation.

[0046] The evaluation unit can improve the accuracy of its evaluation based on relevant literature for the content during the evaluation process. For example, the evaluation unit can refer to relevant literature for the content to improve the accuracy of the evaluation. The evaluation unit can also adjust the evaluation criteria for the content based on relevant literature. Furthermore, the evaluation unit can reflect information from relevant literature when evaluating the content. This improves the accuracy of the evaluation by referring to relevant literature. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input relevant literature data into a generating AI and have the generating AI perform the evaluation.

[0047] The evolution unit can optimize the evolution algorithm based on past evaluation data during evolution. For example, the evolution unit can analyze past evaluation data and select the most effective evolution algorithm. The evolution unit can also adjust the evolution algorithm based on trends in the evaluation data. Furthermore, the evolution unit can refer to past evaluation data and optimize the parameters of the evolution algorithm. This improves the accuracy of the evolution algorithm by referring to past evaluation data. Some or all of the above processes in the evolution unit may be performed using AI, for example, or without AI. For example, the evolution unit can input past evaluation data into a generating AI and have the generating AI perform the optimization of the evolution algorithm.

[0048] The evolution unit can determine the direction of evolution based on the past performance of the generative AI during evolution. For example, the evolution unit can determine the direction of evolution based on the past performance data of the generative AI. The evolution unit can also prioritize the evolution of algorithms that have shown performance improvements. Furthermore, the evolution unit can analyze the past performance of the generative AI and formulate an optimal evolution strategy. This allows the optimal direction of evolution to be determined by analyzing past performance. Some or all of the above processes in the evolution unit may be performed using AI, for example, or without AI. For example, the evolution unit can input the past performance data of the generative AI into the generative AI and have the generative AI determine the direction of evolution.

[0049] The evolution unit can select the optimal evolution method based on the geographical location information of the generating AI during evolution. For example, if the generating AI is associated with a specific region, the evolution unit will select an evolution method considering the characteristics of that region. The evolution unit can also select the optimal evolution method based on geographical factors. Furthermore, the evolution unit can refer to the geographical location information of the generating AI and adjust the evolution method. This allows for more appropriate evolution by selecting an evolution method based on geographical location information. Some or all of the above-described processes in the evolution unit may be performed using AI, for example, or without AI. For example, the evolution unit can input the geographical location information data of the generating AI and have the generating AI select the optimal evolution method.

[0050] The evolution unit can propose evolutionary methods based on the social media activity of the generative AI during evolution. For example, the evolution unit can analyze the social media activity of the generative AI and propose evolutionary methods. The evolution unit can also determine the direction of evolution based on the activity history on social media. Furthermore, the evolution unit can refer to the social media activity of the generative AI and select the optimal evolutionary method. In this way, the optimal evolutionary method is proposed by analyzing the social media activity. Some or all of the above processing in the evolution unit may be performed using AI, for example, or without AI. For example, the evolution unit can input the social media activity data of the generative AI into the generative AI and have the generative AI execute the proposed evolutionary methods.

[0051] The cooperation unit can select the optimal cooperation method based on the past cooperation history of the generating AIs during a cooperation process. For example, the cooperation unit can analyze the past cooperation history of the generating AIs and select the optimal cooperation method. The cooperation unit can also adjust the cooperation method based on trends in the cooperation history. Furthermore, the cooperation unit can refer to the past cooperation history of the generating AIs and optimize the parameters of the cooperation method. In this way, the optimal cooperation method is selected by analyzing the past cooperation history. Some or all of the above processing in the cooperation unit may be performed using AI, for example, or without AI. For example, the cooperation unit can input past cooperation history data of the generating AIs into the generating AI and have the generating AI perform the selection of the cooperation method.

[0052] The cooperation unit can assign roles to the generating AI based on their respective expertise during collaboration. For example, the cooperation unit can determine the optimal role assignment by considering the expertise of the generating AI. Furthermore, the cooperation unit can adjust the role assignment when generating AIs with different expertise collaborate. In addition, the cooperation unit can optimize the role assignment based on the expertise of the generating AI. This allows for more effective collaboration by assigning roles according to the expertise of the generating AI. Some or all of the above-described processes in the cooperation unit may be performed using AI, or not. For example, the cooperation unit can input expertise data of the generating AI into the generating AI and have the generating AI determine the role assignment.

[0053] The cooperation unit can select the optimal cooperation method based on the geographical location information of the generating AI during cooperation. For example, if the generating AI is related to a specific region, the cooperation unit will select a cooperation method considering the characteristics of that region. The cooperation unit can also select the optimal cooperation method based on geographical factors. Furthermore, the cooperation unit can refer to the geographical location information of the generating AI and adjust the cooperation method. This allows for more appropriate cooperation by selecting a cooperation method based on geographical location information. Some or all of the above processing in the cooperation unit may be performed using AI, for example, or without AI. For example, the cooperation unit can input the geographical location information data of the generating AI and have the generating AI perform the selection of a cooperation method.

[0054] The collaboration unit can improve the accuracy of collaboration based on the relevant literature of the generating AI during the collaboration process. For example, the collaboration unit can refer to the relevant literature of the generating AI to improve the accuracy of collaboration. The collaboration unit can also adjust the collaboration method based on the relevant literature. Furthermore, the collaboration unit can reflect the information of the relevant literature when the generating AI collaborates. This improves the accuracy of collaboration by referring to the relevant literature. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without using AI. For example, the collaboration unit can input the relevant literature data of the generating AI into the generating AI and have the generating AI perform the improvement of collaboration accuracy.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The reception desk can automatically suggest the most suitable themes and tasks based on the user's past input history. For example, it can automatically display themes and tasks that the user has frequently set in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest themes and tasks that the user will use during specific time periods based on their past settings history. This improves user convenience by suggesting the most suitable input method based on past settings history.

[0057] The reception desk can suggest highly relevant themes and tasks based on the user's geographical location. For example, if a user is in a specific region, it can suggest themes and tasks related to that region. If a user is traveling, it can suggest themes and tasks related to their travel destination. Furthermore, if a user is at home, it can suggest themes and tasks that can be done at home. In this way, by providing suggestions based on geographical location, the system can offer users themes and tasks that are highly relevant to them.

[0058] The generation unit can apply different generation algorithms depending on the category of content being generated. For example, for themes and topics related to science and technology, a generation algorithm with specialized knowledge can be applied. For themes and topics related to art and design, a generation algorithm that emphasizes creativity can be applied. Furthermore, for themes and topics related to business and economics, a generation algorithm that emphasizes practicality can be applied. In this way, by applying a generation algorithm appropriate to the category, more specialized content can be generated.

[0059] The evaluation unit can improve the accuracy of its evaluations based on the interrelationships between content. For example, if multiple content pieces are related, the evaluation will take these interrelationships into consideration. It can also analyze the relationships between content pieces to improve the accuracy of the evaluation. Furthermore, it can perform a comprehensive evaluation based on the interrelationships of content. In this way, considering the interrelationships of content improves the accuracy of the evaluation.

[0060] The evolution section can determine the direction of evolution based on the past performance of the generative AI. For example, it can determine the direction of evolution based on the generative AI's past performance data. It can also prioritize the evolution of algorithms that have shown performance improvements. Furthermore, it can analyze the generative AI's past performance and formulate the optimal evolution strategy. In this way, the optimal direction of evolution is determined by analyzing past performance.

[0061] The cooperation function can select the optimal cooperation method based on the past cooperation history of the generating AIs. For example, it can analyze the past cooperation history of the generating AIs and select the most suitable method. It can also adjust the cooperation method based on trends in the cooperation history. Furthermore, it can optimize the parameters of the cooperation method by referring to the past cooperation history of the generating AIs. In this way, the optimal cooperation method is selected by analyzing past cooperation history.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The reception desk receives input from users to set themes and tasks. For example, the reception desk saves the themes and tasks entered by users to a database. The reception desk can also analyze the user's input and convert it into an appropriate format. Step 2: The generation unit uses a generation AI to generate content based on the themes and tasks received by the reception unit. For example, the generation unit generates text using a text generation AI (e.g., LLM). The generation unit can also generate images using an image generation AI. Furthermore, the generation unit can generate content that combines text and images using a multimodal generation AI. Step 3: The evaluation unit evaluates the content generated by the generation unit. The evaluation unit, for example, collects user feedback and calculates an evaluation score. The evaluation unit can also evaluate the content based on evaluations from other generation AIs. Furthermore, the evaluation unit can evaluate the quality of the content using quantitative metrics. Step 4: The evolution unit evolves the generative AI based on the evaluation results from the evaluation unit. For example, the evolution unit improves the generative AI's algorithm to enhance its performance. The evolution unit can also update the generative AI's training data to give it more advanced generative capabilities. Furthermore, the evolution unit can optimize the generative AI's parameters to improve its generation accuracy. Step 5: The Collaboration Department supports the process of generating content collaboratively by the generative AIs. For example, the Collaboration Department divides tasks among the generative AIs to efficiently generate content. The Collaboration Department can also share information among the generative AIs to improve the accuracy of their collaboration. Furthermore, the Collaboration Department can manage the collaboration history of the generative AIs and suggest the optimal collaboration methods.

[0064] (Example of form 2) The platform according to an embodiment of the present invention is a system in which generative AIs with different areas of expertise cooperate and compete. In this platform, users set themes or tasks, and generative AIs generate content based on those themes or tasks. The generated content is evaluated by users and other AIs, and the best content is selected. Through this process, it is expected that the generative AIs will learn from each other and evolve. For example, there is a reception unit for users to set themes or tasks, and a generation unit for generative AIs to generate content based on those themes or tasks. The generated content is evaluated by an evaluation unit, and there is an evolution unit in which the generative AIs evolve based on the evaluation results. It also has a cooperation unit for generative AIs to cooperate in generating content. As a result, the platform can generate superior content through the evolution of generative AIs with different areas of expertise through cooperation and competition.

[0065] The platform according to this embodiment comprises a reception unit, a generation unit, an evaluation unit, an evolution unit, and a cooperation unit. The reception unit receives input from the user to set themes and tasks. The reception unit, for example, stores the themes and tasks entered by the user in a database. The reception unit can also analyze the user's input and convert it into an appropriate format. The generation unit generates content based on the themes and tasks received by the reception unit using a generation AI. The generation unit generates text using, for example, a text generation AI (e.g., LLM). The generation unit can also generate images using an image generation AI. Furthermore, the generation unit can generate content that combines text and images using a multimodal generation AI. The evaluation unit evaluates the content generated by the generation unit. The evaluation unit, for example, collects user feedback and calculates an evaluation score. The evaluation unit can also evaluate the content based on evaluations by other generation AIs. Furthermore, the evaluation unit can evaluate the quality of the content using quantitative indicators. The evolution unit evolves the generation AI based on the results evaluated by the evaluation unit. The evolution unit, for example, improves the algorithms of the generative AI to enhance its performance. It can also update the training data of the generative AI to give it more advanced generative capabilities. Furthermore, it can optimize the parameters of the generative AI to improve generation accuracy. The cooperation unit supports the process of generative AIs collaborating to generate content. For example, it divides tasks among the generative AIs to efficiently generate content. It can also share information among the generative AIs to improve the accuracy of their collaboration. Furthermore, it can manage the collaboration history of the generative AIs and suggest the optimal collaboration method. As a result, the platform according to this embodiment allows generative AIs with different areas of expertise to evolve through collaboration and competition, generating superior content.

[0066] The reception section accepts input from users to set themes and tasks. Specifically, it has the function of saving themes and tasks entered by users to a database. For example, information entered by users through the web interface is saved to the database in real time and used for subsequent processing. The reception section can also analyze the user's input and convert it into an appropriate format. For example, it uses natural language processing technology to convert free-form text entered by users into structured data and prepare it in a format that is easy for the generation section to use. Furthermore, the reception section has the function of automatically supplementing relevant information based on the user's input. For example, it automatically suggests keywords and reference materials related to the theme entered by the user, helping the user set more specific tasks. In this way, the reception section accurately understands the user's intent and supports the smooth progress of the subsequent generation process.

[0067] The generation unit uses generation AI to generate content based on themes and tasks received by the reception unit. Specifically, it generates text using text generation AI (e.g., LLM). For example, it collects relevant information based on a user-defined theme and generates a logical and consistent text. The generation unit can also generate images using image generation AI. For example, it generates original images based on a style or theme specified by the user. Furthermore, the generation unit can generate content combining text and images using multimodal generation AI. For example, it can simultaneously generate explanatory text and corresponding illustrations based on a user-defined theme. The generation unit efficiently utilizes these generation AIs to provide diverse content that meets user needs. In addition, the generation unit has the function to receive user feedback in real time during the generation process and adjust the generated content accordingly. This allows the generation unit to quickly provide high-quality content that meets user expectations.

[0068] The evaluation unit evaluates the content generated by the generation unit. Specifically, it collects user feedback and calculates an evaluation score. For example, users evaluate the generated content, and the evaluation unit calculates a score based on the evaluation results. The evaluation unit can also evaluate content based on evaluations by other generation AIs. For example, it can evaluate content generated by different generation AIs against each other and integrate the results to make a final evaluation. Furthermore, the evaluation unit can evaluate the quality of content using quantitative indicators. For example, it can set specific evaluation criteria such as the consistency of the text, the accuracy of the information, the resolution of images, and the degree of style consistency, and evaluate the content based on these criteria. Based on these evaluation results, the evaluation unit provides feedback to the generation and evolution units to support improvements in the generation process. In addition, the evaluation unit can store the evaluation results in a database and use them for long-term trend analysis and performance evaluation of generation AIs. This allows the evaluation unit to improve the quality of generated content and increase user satisfaction.

[0069] The evolution unit evolves the generative AI based on the evaluation results from the evaluation unit. Specifically, it improves the generative AI's algorithm and enhances its performance. For example, it adjusts the model parameters of the generative AI based on the evaluation results to achieve higher accuracy generation. The evolution unit can also update the generative AI's training data to give it more advanced generation capabilities. For example, it adds newly collected data and user feedback to the training data to expand the generative AI's knowledge base. Furthermore, the evolution unit can optimize the generative AI's parameters to improve generation accuracy. For example, it tunes hyperparameters and improves the model architecture to maximize the generative AI's performance. The evolution unit can automate these evolution processes and continuously improve the generative AI's performance. As a result, the evolution unit can ensure that the generative AI is always evolving based on the latest technology and data, and continues to provide users with high-quality content.

[0070] The Collaboration Department supports the process of generating content through collaboration between generative AIs. Specifically, it divides tasks among generative AIs to efficiently generate content. For example, a text generation AI and an image generation AI can collaborate to simultaneously generate text and its corresponding image. The Collaboration Department can also share information between generative AIs to improve the accuracy of their collaboration. For example, a text generation AI can provide the content of text it has generated to an image generation AI, which then generates a related image based on that. Furthermore, the Collaboration Department can manage the collaboration history between generative AIs and suggest the optimal collaboration method. For example, it can analyze data from past collaboration projects to identify the most effective collaboration patterns and plan new collaboration projects based on them. Through these functions, the Collaboration Department can facilitate smooth collaboration among generative AIs, enabling efficient and high-quality content generation. In this way, the Collaboration Department supports generative AIs with different areas of expertise to evolve through collaboration and competition, and generate superior content.

[0071] The reception desk can estimate the user's emotions and customize the input interface for themes and tasks based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple and intuitive interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of themes and tasks. This ensures that the input of themes and tasks is smooth by providing an interface that responds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0072] The reception desk can analyze the user's past theme and task setting history and suggest the optimal input method. For example, the reception desk can automatically display themes and tasks that the user has frequently set in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest themes and tasks that the user will use during specific time periods based on the user's past setting history. This improves user convenience by suggesting the optimal input method based on past setting history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past setting history data into a generating AI and have the generating AI suggest the optimal input method.

[0073] The input field can provide an auto-completion function based on the user's current areas of interest when inputting themes or topics. For example, the input field can automatically complete relevant themes and topics based on keywords the user has recently searched for or content they have viewed. The input field can also analyze the user's social media activity and suggest themes and topics based on their areas of interest. Furthermore, the input field can combine the user's past settings history with their current areas of interest to automatically complete the most suitable themes and topics. This improves input efficiency by providing an auto-completion function based on the user's areas of interest. Some or all of the above processing in the input field may be performed using AI, for example, or not. For example, the input field can input the user's areas of interest data into a generating AI and have the generating AI perform the auto-completion function.

[0074] The reception desk can estimate the user's emotions and determine the priority of input themes and tasks based on the estimated emotions. For example, if the user is stressed, the reception desk will prioritize suggesting relaxing themes and tasks. It can also prioritize suggesting challenging themes and tasks if the user is excited. Furthermore, if the user is tired, it can prioritize suggesting easy and quick themes and tasks. This allows for the suggestion of more appropriate themes and tasks by setting priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI determine the priorities.

[0075] The reception desk can suggest highly relevant themes and tasks based on the user's geographical location information when they input a theme or task. For example, if the user is in a specific region, the reception desk can suggest themes and tasks related to that region. Furthermore, if the user is traveling, the reception desk can suggest themes and tasks related to their travel destination. Additionally, if the user is at home, the reception desk can suggest themes and tasks that can be done at home. This ensures that themes and tasks are highly relevant to the user by providing suggestions based on geographical location information. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location data into a generating AI and have the AI ​​generate suggestions for highly relevant themes and tasks.

[0076] The reception desk can analyze a user's social media activity when they input a theme or topic, and suggest relevant themes and topics. For example, the reception desk can suggest relevant themes and topics based on posts the user has recently "liked" or shared. It can also analyze the content of posts from accounts the user follows and suggest themes and topics of interest. Furthermore, the reception desk can suggest themes and topics that are in line with current trends based on the user's social media activity history. In this way, by providing suggestions based on social media activity, themes and topics that match the user's interests are provided. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI suggest relevant themes and topics.

[0077] The generation unit can estimate the user's emotions and adjust the way the generated content is expressed based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate content expressed in a calm tone. If the user is excited, the generation unit can also generate content expressed in an energetic tone. Furthermore, if the user is sad, the generation unit can generate content expressed in a comforting tone. By adjusting the expression method according to the user's emotions, more appropriate content is generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the way the content is expressed.

[0078] The generation unit can adjust the level of detail of the generated content based on the importance of the theme or issue during generation. For example, for high-importance themes or issues, the generation unit generates content that includes detailed explanations and background information. Conversely, for low-importance themes or issues, the generation unit can generate concise and to-the-point content. Furthermore, the generation unit can generate content with an appropriate level of detail based on the importance level specified by the user. This ensures that appropriate content is generated by adjusting the level of detail according to the importance of the theme or issue. Some or all of the above-described processes in the generation unit may be performed using AI, or not. For example, the generation unit can input theme and issue importance data into a generation AI and have the generation AI adjust the level of detail of the content.

[0079] The generation unit can apply different generation algorithms depending on the theme or topic category during generation. For example, for themes and topics related to science and technology, the generation unit can apply a generation algorithm that incorporates specialized knowledge. It can also apply a generation algorithm that emphasizes creativity for themes and topics related to art and design. Furthermore, it can apply a generation algorithm that emphasizes practicality for themes and topics related to business and economics. This allows for the generation of more specialized content by applying a generation algorithm appropriate to the category. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input theme and topic category data into a generation AI and have the generation AI apply an appropriate generation algorithm.

[0080] The generation unit can estimate the user's emotions and adjust the length of the generated content based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate short, concise content. If the user is relaxed, the generation unit can also generate longer content with detailed explanations. Furthermore, if the user is excited, the generation unit can generate content with visually stimulating effects. By adjusting the length of the content according to the user's emotions, more appropriate content is generated. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the length of the content.

[0081] The generation unit can determine the priority of content to generate based on the submission deadlines for themes and assignments. For example, the generation unit will prioritize generating content for themes and assignments with approaching deadlines. It can also postpone generating content for themes and assignments with later submission deadlines. Furthermore, the generation unit can generate content with appropriate priorities based on the submission deadlines specified by the user. This ensures that content is generated at the appropriate time by setting priorities based on submission deadlines. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input theme and assignment submission deadline data into a generation AI and have the generation AI determine the priorities.

[0082] The generation unit can adjust the order in which it generates content based on the relevance of themes and issues during generation. For example, the generation unit can prioritize generating content for highly relevant themes and issues. It can also postpone generating content for less relevant themes and issues. Furthermore, the generation unit can generate content in an appropriate order based on user-specified relevance. This ensures that content is generated in an appropriate order by setting a relevance-based order. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input theme and issue relevance data into a generation AI and have the generation AI adjust the content order.

[0083] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated emotions. For example, if the user is relaxed, the evaluation unit can apply lenient evaluation criteria. It can also apply strict evaluation criteria if the user requests a harsh evaluation. Furthermore, if the user is conducting the evaluation from a neutral standpoint, the evaluation unit can apply standard evaluation criteria. This allows for more appropriate evaluations by setting evaluation criteria that align with the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the evaluation unit may be performed using AI, or not. For example, the evaluation unit can input user emotion data into a generative AI and have the generative AI adjust the evaluation criteria.

[0084] The evaluation unit can improve the accuracy of its evaluations based on the interrelationships between content. For example, if multiple content pieces are related, the evaluation unit will consider their interrelationships when performing the evaluation. The evaluation unit can also analyze the relationships between content pieces to improve the accuracy of the evaluation. Furthermore, the evaluation unit can perform a comprehensive evaluation based on the interrelationships between content pieces. This improves the accuracy of the evaluation by considering the interrelationships between content pieces. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input content interrelationship data into a generating AI and have the generating AI perform the evaluation accuracy improvement.

[0085] The evaluation unit can perform evaluations based on the attribute information of the content submitter. For example, the evaluation unit may consider the submitter's expertise and experience. The evaluation unit may also refer to the submitter's past evaluation history. Furthermore, the evaluation unit may apply appropriate evaluation criteria based on the submitter's attribute information. This allows for a more appropriate evaluation by considering the submitter's attribute information. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the submitter's attribute information data into a generating AI and have the generating AI perform the evaluation.

[0086] The evaluation unit can estimate the user's emotions and adjust the display order of the evaluation results based on the estimated emotions. For example, if the user is relaxed, the evaluation unit may prioritize displaying detailed evaluation results. It can also prioritize displaying concise evaluation results if the user is in a hurry. Furthermore, if the user is excited, the evaluation unit may prioritize displaying visually stimulating evaluation results. This allows for a more appropriate display of evaluation results by setting the display order according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the evaluation unit may be performed using AI, or not. For example, the evaluation unit can input user emotion data into the generative AI and have the generative AI adjust the display order of the evaluation results.

[0087] The evaluation unit can perform evaluations based on the geographical distribution of content. For example, if the content is related to a specific region, the evaluation unit will consider the characteristics of that region when performing the evaluation. The evaluation unit can also analyze the geographical distribution of content to improve the accuracy of the evaluation. Furthermore, the evaluation unit can apply appropriate evaluation criteria based on geographical factors. This allows for a more appropriate evaluation by considering geographical distribution. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input geographical distribution data of the content into a generating AI and have the generating AI perform the evaluation.

[0088] The evaluation unit can improve the accuracy of its evaluation based on relevant literature for the content during the evaluation process. For example, the evaluation unit can refer to relevant literature for the content to improve the accuracy of the evaluation. The evaluation unit can also adjust the evaluation criteria for the content based on relevant literature. Furthermore, the evaluation unit can reflect information from relevant literature when evaluating the content. This improves the accuracy of the evaluation by referring to relevant literature. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input relevant literature data into a generating AI and have the generating AI perform the evaluation.

[0089] The evolution unit can estimate the user's emotions and adjust the evolution method based on the estimated emotions. For example, if the user is relaxed, the evolution unit can apply a gentle evolution method. It can also apply an aggressive evolution method if the user is excited. Furthermore, if the user is stressed, the evolution unit can apply an evolution method that reduces stress. By setting an evolution method according to the user's emotions, more appropriate evolution can be achieved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the evolution unit may be performed using AI, for example, or not using AI. For example, the evolution unit can input user emotion data into a generative AI and have the generative AI adjust the evolution method.

[0090] The evolution unit can optimize the evolution algorithm based on past evaluation data during evolution. For example, the evolution unit can analyze past evaluation data and select the most effective evolution algorithm. The evolution unit can also adjust the evolution algorithm based on trends in the evaluation data. Furthermore, the evolution unit can refer to past evaluation data and optimize the parameters of the evolution algorithm. This improves the accuracy of the evolution algorithm by referring to past evaluation data. Some or all of the above processes in the evolution unit may be performed using AI, for example, or without AI. For example, the evolution unit can input past evaluation data into a generating AI and have the generating AI perform the optimization of the evolution algorithm.

[0091] The evolution unit can determine the direction of evolution based on the past performance of the generative AI during evolution. For example, the evolution unit can determine the direction of evolution based on the past performance data of the generative AI. The evolution unit can also prioritize the evolution of algorithms that have shown performance improvements. Furthermore, the evolution unit can analyze the past performance of the generative AI and formulate an optimal evolution strategy. This allows the optimal direction of evolution to be determined by analyzing past performance. Some or all of the above processes in the evolution unit may be performed using AI, for example, or without AI. For example, the evolution unit can input the past performance data of the generative AI into the generative AI and have the generative AI determine the direction of evolution.

[0092] The evolution unit can estimate the user's emotions and determine evolutionary priorities based on those emotions. For example, if the user is relaxed, the evolution unit may set a low evolutionary priority. If the user is excited, the evolution unit may set a high evolutionary priority. Furthermore, if the user is stressed, the evolution unit may set a medium evolutionary priority. This allows for more appropriate evolution by setting priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the evolution unit may be performed using AI or not using AI. For example, the evolution unit can input user emotion data into a generative AI and have the generative AI determine the evolutionary priorities.

[0093] The evolution unit can select the optimal evolution method based on the geographical location information of the generating AI during evolution. For example, if the generating AI is associated with a specific region, the evolution unit will select an evolution method considering the characteristics of that region. The evolution unit can also select the optimal evolution method based on geographical factors. Furthermore, the evolution unit can refer to the geographical location information of the generating AI and adjust the evolution method. This allows for more appropriate evolution by selecting an evolution method based on geographical location information. Some or all of the above-described processes in the evolution unit may be performed using AI, for example, or without AI. For example, the evolution unit can input the geographical location information data of the generating AI and have the generating AI select the optimal evolution method.

[0094] The evolution unit can propose evolutionary methods based on the social media activity of the generative AI during evolution. For example, the evolution unit can analyze the social media activity of the generative AI and propose evolutionary methods. The evolution unit can also determine the direction of evolution based on the activity history on social media. Furthermore, the evolution unit can refer to the social media activity of the generative AI and select the optimal evolutionary method. In this way, the optimal evolutionary method is proposed by analyzing the social media activity. Some or all of the above processing in the evolution unit may be performed using AI, for example, or without AI. For example, the evolution unit can input the social media activity data of the generative AI into the generative AI and have the generative AI execute the proposed evolutionary methods.

[0095] The cooperation unit can estimate the user's emotions and adjust its cooperation method based on the estimated emotions. For example, if the user is relaxed, the cooperation unit can apply a gentle cooperation method. It can also apply an aggressive cooperation method if the user is excited. Furthermore, if the user is stressed, the cooperation unit can apply a stress-reducing cooperation method. This allows for more appropriate cooperation by setting cooperation methods according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the cooperation unit may be performed using AI, or not. For example, the cooperation unit can input user emotion data into a generative AI and have the generative AI adjust the cooperation method.

[0096] The cooperation unit can select the optimal cooperation method based on the past cooperation history of the generating AIs during a cooperation process. For example, the cooperation unit can analyze the past cooperation history of the generating AIs and select the optimal cooperation method. The cooperation unit can also adjust the cooperation method based on trends in the cooperation history. Furthermore, the cooperation unit can refer to the past cooperation history of the generating AIs and optimize the parameters of the cooperation method. In this way, the optimal cooperation method is selected by analyzing the past cooperation history. Some or all of the above processing in the cooperation unit may be performed using AI, for example, or without AI. For example, the cooperation unit can input past cooperation history data of the generating AIs into the generating AI and have the generating AI perform the selection of the cooperation method.

[0097] The cooperation unit can assign roles to the generating AI based on their respective expertise during collaboration. For example, the cooperation unit can determine the optimal role assignment by considering the expertise of the generating AI. Furthermore, the cooperation unit can adjust the role assignment when generating AIs with different expertise collaborate. In addition, the cooperation unit can optimize the role assignment based on the expertise of the generating AI. This allows for more effective collaboration by assigning roles according to the expertise of the generating AI. Some or all of the above-described processes in the cooperation unit may be performed using AI, or not. For example, the cooperation unit can input expertise data of the generating AI into the generating AI and have the generating AI determine the role assignment.

[0098] The cooperation unit can estimate the user's emotions and determine cooperation priorities based on those estimated emotions. For example, if the user is relaxed, the cooperation unit may set a low priority for cooperation. Conversely, if the user is excited, the cooperation unit may set a high priority for cooperation. Furthermore, if the user is stressed, the cooperation unit may set a medium priority for cooperation. This allows for more appropriate cooperation by setting priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the cooperation unit may be performed using AI, or not using AI. For example, the cooperation unit can input user emotion data into a generative AI and have the generative AI determine the cooperation priorities.

[0099] The cooperation unit can select the optimal cooperation method based on the geographical location information of the generating AI during cooperation. For example, if the generating AI is related to a specific region, the cooperation unit will select a cooperation method considering the characteristics of that region. The cooperation unit can also select the optimal cooperation method based on geographical factors. Furthermore, the cooperation unit can refer to the geographical location information of the generating AI and adjust the cooperation method. This allows for more appropriate cooperation by selecting a cooperation method based on geographical location information. Some or all of the above processing in the cooperation unit may be performed using AI, for example, or without AI. For example, the cooperation unit can input the geographical location information data of the generating AI and have the generating AI perform the selection of a cooperation method.

[0100] The collaboration unit can improve the accuracy of collaboration based on the relevant literature of the generating AI during the collaboration process. For example, the collaboration unit can refer to the relevant literature of the generating AI to improve the accuracy of collaboration. The collaboration unit can also adjust the collaboration method based on the relevant literature. Furthermore, the collaboration unit can reflect the information of the relevant literature when the generating AI collaborates. This improves the accuracy of collaboration by referring to the relevant literature. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without using AI. For example, the collaboration unit can input the relevant literature data of the generating AI into the generating AI and have the generating AI perform the improvement of collaboration accuracy.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] The reception desk can automatically suggest the most suitable themes and tasks based on the user's past input history. For example, it can automatically display themes and tasks that the user has frequently set in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest themes and tasks that the user will use during specific time periods based on their past settings history. This improves user convenience by suggesting the most suitable input method based on past settings history.

[0103] The generation unit can estimate the user's emotions and adjust the tone of the generated content based on those emotions. For example, if the user is relaxed, it can generate content expressed in a calm tone. If the user is excited, it can generate content expressed in an energetic tone. Furthermore, if the user is sad, it can generate content expressed in a comforting tone. By adjusting the tone according to the user's emotions, more appropriate content can be generated.

[0104] The evaluation unit can estimate the user's emotions when evaluating the generated content and adjust the evaluation criteria based on those emotions. For example, if the user is relaxed, a lenient evaluation criterion can be applied. Conversely, if the user is seeking a critical evaluation, a strict evaluation criterion can be applied. Furthermore, if the user is providing a neutral evaluation, a standard evaluation criterion can be applied. This allows for more appropriate evaluations by setting evaluation criteria that align with the user's emotions.

[0105] The evolution unit can estimate the user's emotions during the evolution of the generating AI and adjust the evolution method based on the estimated emotions. For example, if the user is relaxed, a gentle evolution method can be applied. If the user is excited, an aggressive evolution method can be applied. Furthermore, if the user is stressed, an evolution method that reduces stress can be applied. By setting an evolution method that corresponds to the user's emotions, more appropriate evolution can be achieved.

[0106] The cooperation unit can estimate the user's emotions when generating AIs cooperate and adjust the method of cooperation based on the estimated emotions. For example, if the user is relaxed, a gentle cooperation method will be applied. If the user is excited, an aggressive cooperation method may be applied. Furthermore, if the user is stressed, a cooperation method that reduces stress may be applied. In this way, more appropriate cooperation can be achieved by setting a cooperation method that is appropriate to the user's emotions.

[0107] The reception desk can suggest highly relevant themes and tasks based on the user's geographical location. For example, if a user is in a specific region, it can suggest themes and tasks related to that region. If a user is traveling, it can suggest themes and tasks related to their travel destination. Furthermore, if a user is at home, it can suggest themes and tasks that can be done at home. In this way, by providing suggestions based on geographical location, the system can offer users themes and tasks that are highly relevant to them.

[0108] The generation unit can apply different generation algorithms depending on the category of content being generated. For example, for themes and topics related to science and technology, a generation algorithm with specialized knowledge can be applied. For themes and topics related to art and design, a generation algorithm that emphasizes creativity can be applied. Furthermore, for themes and topics related to business and economics, a generation algorithm that emphasizes practicality can be applied. In this way, by applying a generation algorithm appropriate to the category, more specialized content can be generated.

[0109] The evaluation unit can improve the accuracy of its evaluations based on the interrelationships between content. For example, if multiple content pieces are related, the evaluation will take these interrelationships into consideration. It can also analyze the relationships between content pieces to improve the accuracy of the evaluation. Furthermore, it can perform a comprehensive evaluation based on the interrelationships of content. In this way, considering the interrelationships of content improves the accuracy of the evaluation.

[0110] The evolution section can determine the direction of evolution based on the past performance of the generative AI. For example, it can determine the direction of evolution based on the generative AI's past performance data. It can also prioritize the evolution of algorithms that have shown performance improvements. Furthermore, it can analyze the generative AI's past performance and formulate the optimal evolution strategy. In this way, the optimal direction of evolution is determined by analyzing past performance.

[0111] The cooperation function can select the optimal cooperation method based on the past cooperation history of the generating AIs. For example, it can analyze the past cooperation history of the generating AIs and select the most suitable method. It can also adjust the cooperation method based on trends in the cooperation history. Furthermore, it can optimize the parameters of the cooperation method by referring to the past cooperation history of the generating AIs. In this way, the optimal cooperation method is selected by analyzing past cooperation history.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The reception desk receives input from users to set themes and tasks. For example, the reception desk saves the themes and tasks entered by users to a database. The reception desk can also analyze the user's input and convert it into an appropriate format. Step 2: The generation unit uses a generation AI to generate content based on the themes and tasks received by the reception unit. For example, the generation unit generates text using a text generation AI (e.g., LLM). The generation unit can also generate images using an image generation AI. Furthermore, the generation unit can generate content that combines text and images using a multimodal generation AI. Step 3: The evaluation unit evaluates the content generated by the generation unit. The evaluation unit, for example, collects user feedback and calculates an evaluation score. The evaluation unit can also evaluate the content based on evaluations from other generation AIs. Furthermore, the evaluation unit can evaluate the quality of the content using quantitative metrics. Step 4: The evolution unit evolves the generative AI based on the evaluation results from the evaluation unit. For example, the evolution unit improves the generative AI's algorithm to enhance its performance. The evolution unit can also update the generative AI's training data to give it more advanced generative capabilities. Furthermore, the evolution unit can optimize the generative AI's parameters to improve its generation accuracy. Step 5: The Collaboration Department supports the process of generating content collaboratively by the generative AIs. For example, the Collaboration Department divides tasks among the generative AIs to efficiently generate content. The Collaboration Department can also share information among the generative AIs to improve the accuracy of their collaboration. Furthermore, the Collaboration Department can manage the collaboration history of the generative AIs and suggest the optimal collaboration methods.

[0114] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0116] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0117] Each of the multiple elements described above, including the reception unit, generation unit, evaluation unit, evolution unit, and cooperation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit receives user themes and tasks via the control unit 46A of the smart device 14. The generation unit generates content using a generation AI via the specific processing unit 290 of the data processing unit 12. The evaluation unit evaluates the generated content via the specific processing unit 290 of the data processing unit 12. The evolution unit improves the algorithm of the generation AI and enhances its performance via the specific processing unit 290 of the data processing unit 12. The cooperation unit distributes tasks among the generation AIs via the control unit 46A of the smart device 14 to efficiently generate content. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0120] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0121] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0122] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0124] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0125] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0126] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0127] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0128] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0130] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0132] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] Each of the multiple elements described above, including the reception unit, generation unit, evaluation unit, evolution unit, and cooperation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit receives the user's theme or task input via the control unit 46A of the smart glasses 214. The generation unit generates content using a generation AI via the specific processing unit 290 of the data processing unit 12. The evaluation unit evaluates the generated content via the specific processing unit 290 of the data processing unit 12. The evolution unit improves the algorithm of the generation AI and enhances its performance via the specific processing unit 290 of the data processing unit 12. The cooperation unit distributes tasks among the generation AIs via the control unit 46A of the smart glasses 214 to efficiently generate content. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0136] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0137] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0140] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0141] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the reception unit, generation unit, evaluation unit, evolution unit, and cooperation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit receives user themes and tasks via the control unit 46A of the headset terminal 314. The generation unit generates content using a generation AI via the specific processing unit 290 of the data processing unit 12. The evaluation unit evaluates the generated content via the specific processing unit 290 of the data processing unit 12. The evolution unit improves the algorithm of the generation AI and enhances its performance via the specific processing unit 290 of the data processing unit 12. The cooperation unit distributes tasks among the generation AIs via the control unit 46A of the headset terminal 314 to efficiently generate content. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0152] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0153] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0154] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0156] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0157] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0158] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0159] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0160] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0161] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0162] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0163] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0164] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0165] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0166] Each of the multiple elements described above, including the reception unit, generation unit, evaluation unit, evolution unit, and cooperation unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the reception unit receives user themes and tasks via the control unit 46A of the robot 414. The generation unit generates content using a generation AI via the specific processing unit 290 of the data processing unit 12. The evaluation unit evaluates the content generated by the specific processing unit 290 of the data processing unit 12. The evolution unit improves the algorithm of the generation AI and enhances its performance via the specific processing unit 290 of the data processing unit 12. The cooperation unit distributes tasks among the generation AIs via the control unit 46A of the robot 414 to efficiently generate content. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0167] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0168] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0169] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0170] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0171] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0172] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0173] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0174] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0175] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0176] 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.

[0177] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0178] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0179] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0180] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0181] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0182] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0183] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0184] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0185] (Note 1) A reception desk that accepts submissions of themes and assignments, A generation unit that generates content based on themes and issues received by the reception unit, An evaluation unit that evaluates the content generated by the generation unit, Based on the results evaluated by the aforementioned evaluation unit, the evolution unit evolves the generated AI, It includes a cooperative unit where generating AIs work together to generate content. A system characterized by the following features. (Note 2) The aforementioned reception unit is It estimates the user's emotions and customizes the input interface for themes and tasks based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is It analyzes the user's past theme and task setting history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is When entering themes or tasks, the system provides an auto-completion feature based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It estimates the user's emotions and determines the priority of the entered themes and issues based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is When users input themes or challenges, the system suggests highly relevant themes and challenges based on their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When users input themes or issues, the system analyzes their social media activity and suggests related themes and issues. The system described in Appendix 1, characterized by the features described herein. (Note 8) The generating unit is It estimates user emotions and adjusts how generated content is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The generating unit is During generation, adjust the level of detail of the generated content based on the importance of the theme or issue. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is During generation, different generation algorithms are applied depending on the theme or category of the issue. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is It estimates the user's emotions and adjusts the length of the generated content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is During generation, the priority of the content to be generated is determined based on the theme and the submission deadline for the assignment. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is During generation, the order of generated content is adjusted based on the relevance of themes and issues. The system described in Appendix 1, characterized by the features described herein. (Note 14) The evaluation unit, It estimates the user's emotions and adjusts the evaluation criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The evaluation unit, During evaluation, improve the accuracy of the evaluation based on the interrelationships of the content. The system described in Appendix 1, characterized by the features described herein. (Note 16) The evaluation unit, During the evaluation process, the content will be evaluated based on the attribute information of the content submitter. The system described in Appendix 1, characterized by the features described herein. (Note 17) The evaluation unit, The system estimates the user's emotions and adjusts the display order of evaluation results based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The evaluation unit, During the evaluation process, the content will be evaluated based on its geographical distribution. The system described in Appendix 1, characterized by the features described herein. (Note 19) The evaluation unit, During evaluation, improve the accuracy of the evaluation based on relevant literature for the content. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned evolutionary section is It estimates user emotions and adjusts the evolution method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned evolutionary section is During evolution, the evolutionary algorithm is optimized based on past evaluation data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned evolutionary section is During evolution, the direction of evolution is determined based on the past performance of the generative AI. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned evolutionary section is It estimates user emotions and determines evolutionary priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned evolutionary section is During evolution, the optimal evolutionary method is selected based on the geographical location information of the generating AI. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned evolutionary section is During evolution, we propose evolutionary methods based on the social media activity of generative AI. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned cooperation department, It estimates the user's emotions and adjusts the method of collaboration based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned cooperation department, During collaboration, the optimal collaboration method is selected based on the past collaboration history of the generated AIs. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned cooperation department, When collaborating, roles will be divided based on the expertise of the generating AI. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned cooperation department, It estimates the user's emotions and determines the priority of collaboration based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned cooperation department, During collaboration, the optimal collaboration method is selected based on the geographical location information of the generated AI. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned cooperation department, When collaborating, improve the accuracy of the collaboration based on relevant literature on generative AI. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts submissions of themes and assignments, A generation unit that generates content based on themes and issues received by the reception unit, An evaluation unit that evaluates the content generated by the generation unit, Based on the results evaluated by the aforementioned evaluation unit, the evolution unit evolves the generated AI, It includes a cooperative unit in which generating AIs work together to generate content. A system characterized by the following features.

2. The aforementioned reception unit is It estimates the user's emotions and customizes the input interface for themes and tasks based on those estimated emotions. The system according to feature 1.

3. The aforementioned reception unit is It analyzes the user's past theme and task setting history and suggests the optimal input method. The system according to feature 1.

4. The aforementioned reception unit is When entering themes or tasks, the system provides an auto-completion feature based on the user's current areas of interest. The system according to feature 1.

5. The aforementioned reception unit is It estimates the user's emotions and determines the priority of the entered themes and issues based on the estimated user emotions. The system according to feature 1.

6. The aforementioned reception unit is When users input themes or challenges, the system suggests highly relevant themes and challenges based on their geographical location. The system according to feature 1.

7. The aforementioned reception unit is When users input themes or issues, the system analyzes their social media activity and suggests related themes and issues. The system according to feature 1.

8. The generating unit is It estimates user emotions and adjusts how generated content is presented based on those estimated emotions. The system according to feature 1.

9. The generating unit is During generation, adjust the level of detail of the generated content based on the importance of the theme or issue. The system according to feature 1.

10. The generating unit is During generation, different generation algorithms are applied depending on the theme or category of the issue. The system according to feature 1.

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