System

Abstract

An object of a system according to an embodiment is to effectively refine the skill of problem solving using generated AI.SOLUTION: A system according to an embodiment includes a task providing unit, an evaluation unit, an education resource providing unit, a task taking unit, and a promotion unit. The task providing unit provides a task using the generated AI. The evaluation unit evaluates the solution to the problem provided by the problem providing unit from the viewpoint of creativity and technical ability. The education resource providing unit provides an education resource that allows beginners to advanced users to develop skills. The problem take-in section takes in actual industrial and academic problems. The propagation promotion unit promotes the propagation and innovation of the generated AI.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Previous technology lacked a comprehensive platform for effectively honing problem-solving skills using generative AI.

[0005] The system according to the embodiment aims to effectively hone problem-solving skills using generative AI. [Means for solving the problem]

[0006] The system according to the embodiment comprises an assignment providing unit, an evaluation unit, an educational resource providing unit, an assignment adoption unit, and a dissemination promotion unit. The assignment providing unit provides assignments using generative AI. The evaluation unit evaluates solutions to the assignments provided by the assignment providing unit from the perspectives of creativity and technical ability. The educational resource providing unit provides educational resources that allow people from beginners to advanced users to hone their skills. The assignment adoption unit adopts actual assignments from industry and academia. The dissemination promotion unit promotes the adoption and innovation of generative AI. [Effects of the Invention]

[0007] The system according to the embodiment can effectively hone problem-solving skills using generative AI. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An online competition platform according to an embodiment of the present invention is a system that utilizes generative AI to tackle a variety of challenges. This system allows participants to participate individually or in teams to solve problems in fields such as data science, natural language processing, and image generation. This allows the online competition platform to promote the spread and innovation of generative AI and foster generative AI users.

[0029] An online competition platform according to an embodiment includes a challenge providing unit, an evaluation unit, an educational resource providing unit, a challenge adoption unit, and a dissemination promotion unit. The challenge providing unit provides challenges using a generative AI. For example, the generative AI may analyze large amounts of data to find specific patterns and trends for a data science challenge. The generative AI may also analyze text data and extract meaningful information for a natural language processing challenge. The generative AI may also generate high-quality images for an image generation challenge. The evaluation unit evaluates solutions to the challenges provided by the challenge providing unit from the perspectives of creativity and technical ability. For example, the evaluation unit may evaluate the originality of the algorithms and prompts generated by the generative AI. The evaluation unit may also evaluate the sophistication of the implementation of the solutions generated by the generative AI. The evaluation unit may also evaluate the efficiency of the solutions generated by the generative AI. The educational resource providing unit provides educational resources that enable beginners to advanced users to hone their skills. For example, the educational resource providing unit may provide online courses that teach generative AI from the basics to advanced applications. The educational resource provision unit can also provide tutorials for tackling actual problems. The educational resource provision unit can also provide a community where participants can interact with each other and share knowledge and experiences. The problem acquisition unit acquires actual problems from industry and academia. For example, the problem acquisition unit can use data provided by companies to optimize marketing strategies. The problem acquisition unit can also analyze research data provided by academia to discover new findings. The problem acquisition unit can also support product development. The promotion unit promotes the spread and innovation of generative AI. For example, the promotion unit supports the development of new applications and services using generative AI. The promotion unit can also conduct awareness-raising activities to widely disseminate generative AI technology. The promotion unit can also conduct research and development to maximize the potential of generative AI. As a result, the online competition platform according to the embodiment can promote the spread and innovation of generative AI and aim to cultivate generative AI users.

[0030] The task provider can use the generation AI to dynamically adjust the difficulty of tasks in real time and provide optimal tasks according to the participant's skill level. The task provider, for example, uses the generation AI to analyze the participant's past performance data and adjust the difficulty of tasks in real time. For example, the task provider can automatically set the difficulty of the next task based on the success rate and time of tasks that the participant has previously solved. The task provider also uses the generation AI to provide optimal tasks according to the participant's skill level. For example, it can provide basic tasks to beginners and advanced tasks to advanced learners. This maximizes the learning effect by providing optimal tasks according to the participant's skill level.

[0031] The evaluation unit can set evaluation criteria for efficiency, originality, and practicality for solutions generated by the generative AI and comprehensively evaluate them. For example, the evaluation unit sets evaluation criteria for efficiency, originality, and practicality for solutions generated by the generative AI and builds a system for comprehensive evaluation. For example, the execution time and resource consumption of a solution are used as indicators of efficiency. The evaluation unit also evaluates the originality of the solution generated by the generative AI. For example, it calculates a score for originality by comparing it with solutions from other participants. The evaluation unit also evaluates the practicality of the solution generated by the generative AI. For example, it evaluates based on actual usability and range of application. This makes it possible to comprehensively evaluate solutions by using multiple evaluation criteria.

[0032] The educational resource providing unit can use the generation AI to automatically generate a customized learning plan according to the participant's skill level. For example, the educational resource providing unit uses the generation AI to analyze the participant's past learning data and build a system that automatically generates a customized learning plan according to the participant's skill level. For example, it provides basic content to beginners and advanced content to advanced learners. The educational resource providing unit also uses the generation AI to automatically generate a customized learning plan according to the participant's skill level. For example, it provides an optimal learning plan based on the participant's learning progress and level of understanding. This makes it possible to maximize the learning effect by providing a customized learning plan according to the participant's skill level.

[0033] The Promotion Department can provide automation tools to support the development of new applications and services using generative AI. For example, the Promotion Department develops tools that automatically generate prototypes of new applications using generative AI. For example, the promotion department generates the basic structure of an application based on requirements specified by a user. The Promotion Department also provides automation tools to support the development of new services using generative AI. For example, the promotion department provides tools that automate the design and implementation of services. The Promotion Department also provides automation tools to support the development of new applications and services using generative AI. For example, the promotion department generates the basic structure of an application based on requirements specified by a user. This can promote the spread of generative AI and innovation by supporting the development of new applications and services.

[0034] The task provider can use the generative AI to expand the platform so that it can also handle problem-solving in different fields. For example, the task provider uses the generative AI to build a platform for solving music generation problems. For example, it generates original music based on a theme specified by the participant. The task provider also uses the generative AI to build a platform for solving game design problems. For example, it generates the basic structure of a game based on requirements specified by the participant. The task provider also uses the generative AI to expand the platform so that it can also handle problem-solving in different fields. For example, it builds a platform for solving music generation and game design problems. In this way, by expanding the platform so that it can also handle problem-solving in different fields, a wide range of skills can be developed.

[0035] The evaluation unit can add a collaboration function that allows solutions generated by the generative AI to be shared with other participants in real time and for the participants to work together to solve the problem. The evaluation unit, for example, builds a platform for sharing solutions generated by the generative AI in real time. For example, participants share solutions and receive feedback from other participants. The evaluation unit also adds a collaboration function that allows solutions generated by the generative AI to be shared with other participants in real time and for the participants to work together to solve the problem. For example, it provides a real-time sharing function and a collaborative editing tool. The evaluation unit also adds a collaboration function that allows solutions generated by the generative AI to be shared with other participants in real time and for the participants to work together to solve the problem. For example, it provides a real-time sharing function and a collaborative editing tool. This makes it possible to provide a collaborative learning environment by sharing solutions with other participants and working together to solve the problem.

[0036] The educational resource providing unit can add a collaboration function for sharing educational resources generated by the generation AI with other participants and learning together. The educational resource providing unit, for example, builds a platform for sharing educational resources generated by the generation AI with other participants. For example, the content of an online course is shared and students learn together. The educational resource providing unit also adds a collaboration function for sharing educational resources generated by the generation AI with other participants and learning together. For example, it provides group learning tools and collaborative project functions. The educational resource providing unit also adds a collaboration function for sharing educational resources generated by the generation AI with other participants and learning together. For example, it provides group learning tools and collaborative project functions. This makes it possible to provide a collaborative learning environment by sharing educational resources with other participants and learning together.

[0037] The problem intake department can add a collaboration function that allows solutions generated by the generative AI to be shared with industrial and academic partners and they can work together to solve the problem. For example, the problem intake department could build a platform for sharing solutions generated by the generative AI with industrial partners. For example, corporate personnel could evaluate the solutions and work together to improve them. The problem intake department could also add a collaboration function that allows solutions generated by the generative AI to be shared with industrial and academic partners and they can work together to solve the problem. For example, it could provide a real-time sharing function and a collaborative editing tool. The problem intake department could also add a collaboration function that allows solutions generated by the generative AI to be shared with industrial and academic partners and they can work together to solve the problem. For example, it could provide a real-time sharing function and a collaborative editing tool. This allows solutions to be shared with industrial and academic partners and they can work together to solve the problem, thereby contributing to solving real-world problems.

[0038] The evaluation unit can use the generation AI to automatically evaluate the originality of the participant's algorithm or prompt and compare it with other participants. The evaluation unit, for example, uses the generation AI to build a system that automatically evaluates the originality of the algorithm or prompt submitted by the participant. For example, it calculates an originality score by comparing it with the solutions of other participants. The evaluation unit also uses the generation AI to automatically evaluate the originality of the participant's algorithm or prompt and compare it with other participants. For example, it performs the evaluation based on the originality score. The evaluation unit also uses the generation AI to automatically evaluate the originality of the participant's algorithm or prompt and compare it with other participants. For example, it performs the evaluation based on the originality score. In this way, it is possible to provide a fair evaluation by automatically evaluating the originality of the participant's algorithm or prompt and comparing it with other participants.

[0039] The evaluation unit can introduce a system that evaluates the sophistication of the implementation of a solution generated by the generation AI from technical perspectives such as code quality and efficiency. The evaluation unit, for example, builds a system that evaluates the quality of the code of a solution generated by the generation AI. For example, it calculates a quality score based on the readability and maintainability of the code. The evaluation unit also introduces a system that evaluates the sophistication of the implementation of a solution generated by the generation AI from technical perspectives such as code quality and efficiency. For example, to evaluate code efficiency, it performs an evaluation based on execution time and resource consumption. The evaluation unit also introduces a system that evaluates the sophistication of the implementation of a solution generated by the generation AI from technical perspectives such as code quality and efficiency. For example, to evaluate code efficiency, it performs an evaluation based on execution time and resource consumption. In this way, by evaluating the sophistication of the implementation of a solution from a technical perspective, it is possible to promote the improvement of technical capabilities.

[0040] The educational resource providing unit can use the generative AI to provide educational resources corresponding to different fields, allowing a wide range of skills to be acquired. For example, the educational resource providing unit uses the generative AI to build a system that provides educational resources corresponding to the business field. For example, it provides online courses in marketing strategy and business management. The educational resource providing unit can also use the generative AI to build a system that provides educational resources corresponding to the art field. For example, it provides online courses in design and creative writing. The educational resource providing unit can also use the generative AI to provide educational resources corresponding to different fields, allowing a wide range of skills to be acquired. For example, it provides educational resources corresponding to the fields of business and art. In this way, by providing educational resources corresponding to different fields, a wide range of skills can be acquired.

[0041] The Problem Incorporation Department uses generative AI to incorporate problems from different industries and academic fields, enabling the development of a wide range of problem-solving skills. For example, the Problem Incorporation Department uses generative AI to build a system that incorporates problems from different industries. For example, it analyzes problems in the manufacturing and service industries and proposes optimal solutions. The Problem Incorporation Department also uses generative AI to build a system that incorporates problems from different academic fields. For example, it analyzes research data in medicine and engineering to discover new knowledge. The Problem Incorporation Department also uses generative AI to incorporate problems from different industries and academic fields, enabling the development of a wide range of problem-solving skills. For example, it analyzes problems in the manufacturing and service industries and proposes optimal solutions. In this way, by incorporating problems from different industries and academic fields, it is possible to develop a wide range of problem-solving skills.

[0042] The evaluation unit not only compares the solutions generated by the generation AI with other participants, but also compares them with past excellent solutions, thereby raising the evaluation standards. The evaluation unit, for example, builds a system that compares the solutions generated by the generation AI with past excellent solutions. For example, it sets standards for originality and technical ability based on past data. The evaluation unit also compares the solutions generated by the generation AI with other participants, but also compares them with past excellent solutions, thereby raising the evaluation standards. For example, it sets standards for originality and technical ability based on past data. The evaluation unit also compares the solutions generated by the generation AI with other participants, but also compares them with past excellent solutions, thereby raising the evaluation standards. For example, it sets standards for originality and technical ability based on past data. In this way, the evaluation unit can raise the evaluation standards by comparing the solutions with other participants and past excellent solutions.

[0043] The educational resource providing unit can introduce a system that collects feedback in real time on the educational resources generated by the generation AI and continuously improves the content. The educational resource providing unit, for example, builds a system that collects feedback from participants in real time on the educational resources generated by the generation AI. For example, it collects evaluations and comments on the content of an online course. The educational resource providing unit also introduces a system that collects feedback in real time on the educational resources generated by the generation AI and continuously improves the content. For example, it updates the content of the educational resources based on feedback from participants. The educational resource providing unit also introduces a system that collects feedback in real time on the educational resources generated by the generation AI and continuously improves the content. For example, it updates the content of the educational resources based on feedback from participants. In this way, by collecting feedback in real time on the educational resources and continuously improving the content, it is possible to maximize the educational effectiveness.

[0044] The problem intake unit can automatically collect feedback from industry and academic experts on solutions generated by the generative AI and reflect it in the evaluation. The problem intake unit, for example, builds a system that automatically collects feedback from industry experts on solutions generated by the generative AI. For example, a company representative evaluates the solution and provides comments. The problem intake unit also automatically collects feedback from industry and academic experts on solutions generated by the generative AI and reflects it in the evaluation. For example, it updates the evaluation score of the solution based on the expert evaluation. The problem intake unit also automatically collects feedback from industry and academic experts on solutions generated by the generative AI and reflects it in the evaluation. For example, it updates the evaluation score of the solution based on the expert evaluation. In this way, feedback from industry and academic experts on solutions can be automatically collected and reflected in the evaluation, thereby improving the accuracy of the evaluation.

[0045] The evaluation unit can use the generation AI to introduce different evaluation criteria (e.g., social impact, environmental consideration) and perform a comprehensive evaluation. The evaluation unit, for example, uses the generation AI to build a system for evaluating social impact. For example, it quantifies the impact of a solution on society and calculates an evaluation score. The evaluation unit also uses the generation AI to build a system for evaluating environmental consideration. For example, it quantifies the impact of a solution on the environment and calculates an evaluation score. The evaluation unit also uses the generation AI to introduce different evaluation criteria (e.g., social impact, environmental consideration) and perform a comprehensive evaluation. For example, it performs an evaluation based on social impact and environmental consideration. The evaluation unit also uses the generation AI to introduce different evaluation criteria (e.g., social impact, environmental consideration) and perform a comprehensive evaluation. For example, it performs an evaluation based on social impact and environmental consideration. In this way, by introducing different evaluation criteria and performing a comprehensive evaluation, it is possible to increase the diversity and accuracy of the evaluation.

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

[0047] The assignment provider can use the generative AI to provide assignments that are specific to different cultures or regions. For example, the generative AI can generate assignments themed around social issues in a specific region. The generative AI can also provide assignments that promote intercultural understanding by having participants with different cultural backgrounds work on a common assignment. Furthermore, the generative AI can provide region-specific data analysis assignments using region-specific datasets. This allows participants to deepen their understanding of different cultures and regional issues.

[0048] The evaluation unit can use the generative AI to evaluate the social impact of participants' solutions. For example, the generative AI can quantify the positive impact a solution has on society. The generative AI can also evaluate the solution's impact on the environment and evaluate it from a sustainability perspective. Furthermore, the generative AI can evaluate the solution's impact on a specific community and measure its contribution to the local community. This allows for a comprehensive evaluation of the impact of participants' solutions on society as a whole.

[0049] The educational resource provision unit can use the generation AI to provide customized learning resources according to the learning style of the participants. For example, the generation AI can provide resources with a lot of visual content to participants who prefer visual learning. The generation AI can also provide resources in audio or podcast format to participants who prefer auditory learning. Furthermore, the generation AI can provide resources including interactive simulations and experiments to participants who prefer hands-on learning. This makes it possible to provide optimal learning resources according to the learning style of the participants.

[0050] The Promotion Department can use generative AI to help propose new business models. For example, generative AI can analyze existing business data and propose new revenue models. Generative AI can also analyze success stories from different industries and propose business models that can be applied to other industries. Furthermore, generative AI can predict future market trends and propose business strategies based on them. This allows companies to use generative AI to develop new business models and increase their competitiveness.

[0051] The problem acquisition department can use generative AI to collaborate with experts from different fields and provide complex problems. For example, generative AI can generate problems for medical and engineering experts to work on together. Generative AI can also provide problems for environmental science and economics experts to solve together. Generative AI can also generate problems for education and technology experts to work on together. This allows participants to develop the skills to integrate knowledge from different fields to solve complex problems.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The problem provider uses generative AI to provide problems. For example, generative AI can analyze large amounts of data to find specific patterns and trends in data science problems. Generative AI can also analyze text data and extract meaningful information in natural language processing problems. Furthermore, generative AI can generate high-quality images in image generation problems. Step 2: The evaluator evaluates the solutions to the tasks provided by the task provider in terms of creativity and technical ability. For example, the evaluator evaluates the originality of the algorithms and prompts generated by the generator. The evaluator may also evaluate the sophistication and efficiency of the implementation of the solutions generated by the generator. Step 3: The Educational Resources Provider provides educational resources that allow beginners to advanced users to hone their skills. For example, the Educational Resources Provider can offer online courses that teach the basics of generative AI, from basics to applications. It can also provide tutorials for tackling real-world problems and a community where participants can interact with each other and share their knowledge and experiences. Step 4: The Problem Intake Department incorporates actual problems from industry and academia. For example, they can use data provided by companies to optimize marketing strategies. They can also analyze research data provided by academia to discover new knowledge. They can also provide support for product development. Step 5: The Promotion Department will promote the adoption and innovation of generative AI. For example, the Promotion Department will support the development of new applications and services using generative AI. It will also conduct awareness-raising activities to widely disseminate generative AI technology and research and development to maximize the potential of generative AI.

[0054] (Example 2) An online competition platform according to an embodiment of the present invention is a system that utilizes generative AI to tackle a variety of challenges. This system allows participants to participate individually or in teams to solve problems in fields such as data science, natural language processing, and image generation. This allows the online competition platform to promote the spread and innovation of generative AI and foster generative AI users.

[0055] An online competition platform according to an embodiment includes a challenge providing unit, an evaluation unit, an educational resource providing unit, a challenge adoption unit, and a dissemination promotion unit. The challenge providing unit provides challenges using a generative AI. For example, the generative AI may analyze large amounts of data to find specific patterns and trends for a data science challenge. The generative AI may also analyze text data and extract meaningful information for a natural language processing challenge. The generative AI may also generate high-quality images for an image generation challenge. The evaluation unit evaluates solutions to the challenges provided by the challenge providing unit from the perspectives of creativity and technical ability. For example, the evaluation unit may evaluate the originality of the algorithms and prompts generated by the generative AI. The evaluation unit may also evaluate the sophistication of the implementation of the solutions generated by the generative AI. The evaluation unit may also evaluate the efficiency of the solutions generated by the generative AI. The educational resource providing unit provides educational resources that enable beginners to advanced users to hone their skills. For example, the educational resource providing unit may provide online courses that teach generative AI from the basics to advanced applications. The educational resource provision unit can also provide tutorials for tackling actual problems. The educational resource provision unit can also provide a community where participants can interact with each other and share knowledge and experiences. The problem acquisition unit acquires actual problems from industry and academia. For example, the problem acquisition unit can use data provided by companies to optimize marketing strategies. The problem acquisition unit can also analyze research data provided by academia to discover new findings. The problem acquisition unit can also support product development. The promotion unit promotes the spread and innovation of generative AI. For example, the promotion unit supports the development of new applications and services using generative AI. The promotion unit can also conduct awareness-raising activities to widely disseminate generative AI technology. The promotion unit can also conduct research and development to maximize the potential of generative AI. As a result, the online competition platform according to the embodiment can promote the spread and innovation of generative AI and aim to cultivate generative AI users.

[0056] The task provider can use the generation AI to dynamically adjust the difficulty of tasks in real time and provide optimal tasks according to the participant's skill level. The task provider, for example, uses the generation AI to analyze the participant's past performance data and adjust the difficulty of tasks in real time. For example, the task provider can automatically set the difficulty of the next task based on the success rate and time of tasks that the participant has previously solved. The task provider also uses the generation AI to provide optimal tasks according to the participant's skill level. For example, it can provide basic tasks to beginners and advanced tasks to advanced learners. This maximizes the learning effect by providing optimal tasks according to the participant's skill level.

[0057] The evaluation unit can set evaluation criteria for efficiency, originality, and practicality for solutions generated by the generative AI and comprehensively evaluate them. For example, the evaluation unit sets evaluation criteria for efficiency, originality, and practicality for solutions generated by the generative AI and builds a system for comprehensive evaluation. For example, the execution time and resource consumption of a solution are used as indicators of efficiency. The evaluation unit also evaluates the originality of the solution generated by the generative AI. For example, it calculates a score for originality by comparing it with solutions from other participants. The evaluation unit also evaluates the practicality of the solution generated by the generative AI. For example, it evaluates based on actual usability and range of application. This makes it possible to comprehensively evaluate solutions by using multiple evaluation criteria.

[0058] The evaluation unit can use the emotion estimation function to monitor the emotional states of participants in real time and adjust the evaluation criteria according to fluctuations in stress and motivation. The evaluation unit, for example, uses the emotion estimation function to analyze the facial expressions and voices of participants and build a system that monitors the emotional states in real time. For example, the stress levels of participants are measured using a camera or microphone. The evaluation unit also uses the emotion estimation function to monitor the emotional states of participants in real time and adjust the evaluation criteria according to fluctuations in stress and motivation. For example, if the stress level is high, the evaluation criteria can be relaxed. Also, if motivation is high, the evaluation criteria can be tightened. This makes it possible to adjust the evaluation criteria according to the emotional states of participants.

[0059] The educational resource providing unit can use the generation AI to automatically generate a customized learning plan according to the participant's skill level. For example, the educational resource providing unit uses the generation AI to analyze the participant's past learning data and build a system that automatically generates a customized learning plan according to the participant's skill level. For example, it provides basic content to beginners and advanced content to advanced learners. The educational resource providing unit also uses the generation AI to automatically generate a customized learning plan according to the participant's skill level. For example, it provides an optimal learning plan based on the participant's learning progress and level of understanding. This makes it possible to maximize the learning effect by providing a customized learning plan according to the participant's skill level.

[0060] The educational resource providing unit can use the emotion estimation function to monitor the emotional state of participants while they are learning and provide resources for reducing stress. The educational resource providing unit, for example, uses the emotion estimation function to build a system that monitors the emotional state of participants while they are learning in real time. For example, the educational resource providing unit measures the stress level of participants using a camera or microphone. The educational resource providing unit also uses the emotion estimation function to monitor the emotional state of participants while they are learning in real time and provide resources for reducing stress. For example, the educational resource providing unit provides relaxation techniques and support tools. The educational resource providing unit also uses the emotion estimation function to monitor the emotional state of participants while they are learning and provide resources for reducing stress. For example, the educational resource providing unit provides relaxation techniques and support tools. This makes it possible to maximize the learning effect by providing resources for reducing stress for participants while they are learning.

[0061] The Promotion Department can provide automation tools to support the development of new applications and services using generative AI. For example, the Promotion Department develops tools that automatically generate prototypes of new applications using generative AI. For example, the promotion department generates the basic structure of an application based on requirements specified by a user. The Promotion Department also provides automation tools to support the development of new services using generative AI. For example, the promotion department provides tools that automate the design and implementation of services. The Promotion Department also provides automation tools to support the development of new applications and services using generative AI. For example, the promotion department generates the basic structure of an application based on requirements specified by a user. This can promote the spread of generative AI and innovation by supporting the development of new applications and services.

[0062] The promotion department can use the emotion estimation function to monitor the emotional states of participants and provide support for drawing out creative ideas. The promotion department, for example, uses the emotion estimation function to build a system that monitors the emotional states of participants in real time. For example, the emotional states of participants are analyzed using a camera or a microphone. The promotion department also uses the emotion estimation function to monitor the emotional states of participants in real time and provide support for drawing out creative ideas. For example, it provides brainstorming tools and inspiration techniques. The promotion department also uses the emotion estimation function to monitor the emotional states of participants and provide support for drawing out creative ideas. For example, it provides brainstorming tools and inspiration techniques. In this way, creative ideas can be drawn out by providing support according to the emotional states of participants.

[0063] The task provider can use the generative AI to expand the platform so that it can also handle problem-solving in different fields. For example, the task provider uses the generative AI to build a platform for solving music generation problems. For example, it generates original music based on a theme specified by the participant. The task provider also uses the generative AI to build a platform for solving game design problems. For example, it generates the basic structure of a game based on requirements specified by the participant. The task provider also uses the generative AI to expand the platform so that it can also handle problem-solving in different fields. For example, it builds a platform for solving music generation and game design problems. In this way, by expanding the platform so that it can also handle problem-solving in different fields, a wide range of skills can be developed.

[0064] The evaluation unit can add a collaboration function that allows solutions generated by the generative AI to be shared with other participants in real time and for the participants to work together to solve the problem. The evaluation unit, for example, builds a platform for sharing solutions generated by the generative AI in real time. For example, participants share solutions and receive feedback from other participants. The evaluation unit also adds a collaboration function that allows solutions generated by the generative AI to be shared with other participants in real time and for the participants to work together to solve the problem. For example, it provides a real-time sharing function and a collaborative editing tool. The evaluation unit also adds a collaboration function that allows solutions generated by the generative AI to be shared with other participants in real time and for the participants to work together to solve the problem. For example, it provides a real-time sharing function and a collaborative editing tool. This makes it possible to provide a collaborative learning environment by sharing solutions with other participants and working together to solve the problem.

[0065] The educational resource providing unit can add a collaboration function for sharing educational resources generated by the generation AI with other participants and learning together. The educational resource providing unit, for example, builds a platform for sharing educational resources generated by the generation AI with other participants. For example, the content of an online course is shared and students learn together. The educational resource providing unit also adds a collaboration function for sharing educational resources generated by the generation AI with other participants and learning together. For example, it provides group learning tools and collaborative project functions. The educational resource providing unit also adds a collaboration function for sharing educational resources generated by the generation AI with other participants and learning together. For example, it provides group learning tools and collaborative project functions. This makes it possible to provide a collaborative learning environment by sharing educational resources with other participants and learning together.

[0066] The problem intake department can add a collaboration function that allows solutions generated by the generative AI to be shared with industrial and academic partners and they can work together to solve the problem. For example, the problem intake department could build a platform for sharing solutions generated by the generative AI with industrial partners. For example, corporate personnel could evaluate the solutions and work together to improve them. The problem intake department could also add a collaboration function that allows solutions generated by the generative AI to be shared with industrial and academic partners and they can work together to solve the problem. For example, it could provide a real-time sharing function and a collaborative editing tool. The problem intake department could also add a collaboration function that allows solutions generated by the generative AI to be shared with industrial and academic partners and they can work together to solve the problem. For example, it could provide a real-time sharing function and a collaborative editing tool. This allows solutions to be shared with industrial and academic partners and they can work together to solve the problem, thereby contributing to solving real-world problems.

[0067] The evaluation unit can use the generation AI to automatically evaluate the originality of the participant's algorithm or prompt and compare it with other participants. The evaluation unit, for example, uses the generation AI to build a system that automatically evaluates the originality of the algorithm or prompt submitted by the participant. For example, it calculates an originality score by comparing it with the solutions of other participants. The evaluation unit also uses the generation AI to automatically evaluate the originality of the participant's algorithm or prompt and compare it with other participants. For example, it performs the evaluation based on the originality score. The evaluation unit also uses the generation AI to automatically evaluate the originality of the participant's algorithm or prompt and compare it with other participants. For example, it performs the evaluation based on the originality score. In this way, it is possible to provide a fair evaluation by automatically evaluating the originality of the participant's algorithm or prompt and comparing it with other participants.

[0068] The evaluation unit can introduce a system that evaluates the sophistication of the implementation of a solution generated by the generation AI from technical perspectives such as code quality and efficiency. The evaluation unit, for example, builds a system that evaluates the quality of the code of a solution generated by the generation AI. For example, it calculates a quality score based on the readability and maintainability of the code. The evaluation unit also introduces a system that evaluates the sophistication of the implementation of a solution generated by the generation AI from technical perspectives such as code quality and efficiency. For example, to evaluate code efficiency, it performs an evaluation based on execution time and resource consumption. The evaluation unit also introduces a system that evaluates the sophistication of the implementation of a solution generated by the generation AI from technical perspectives such as code quality and efficiency. For example, to evaluate code efficiency, it performs an evaluation based on execution time and resource consumption. In this way, by evaluating the sophistication of the implementation of a solution from a technical perspective, it is possible to promote the improvement of technical capabilities.

[0069] The evaluation unit can use the emotion estimation function to reflect the emotional state of the participants in the evaluation and set evaluation criteria that take into account the effects of stress and motivation. The evaluation unit, for example, uses the emotion estimation function to monitor the emotional state of the participants in real time and build a system that reflects that data in the evaluation. For example, if the stress level is high, the evaluation criteria can be relaxed. The evaluation unit also uses the emotion estimation function to reflect the emotional state of the participants in the evaluation and set evaluation criteria that take into account the effects of stress and motivation. For example, if the stress level is high, the evaluation criteria can be relaxed. Also, if motivation is high, the evaluation criteria can be tightened. In this way, by reflecting the emotional state of the participants in the evaluation, a fair evaluation that takes into account the effects of stress and motivation is possible.

[0070] The educational resource providing unit can use the generative AI to provide educational resources corresponding to different fields, allowing a wide range of skills to be acquired. For example, the educational resource providing unit uses the generative AI to build a system that provides educational resources corresponding to the business field. For example, it provides online courses in marketing strategy and business management. The educational resource providing unit can also use the generative AI to build a system that provides educational resources corresponding to the art field. For example, it provides online courses in design and creative writing. The educational resource providing unit can also use the generative AI to provide educational resources corresponding to different fields, allowing a wide range of skills to be acquired. For example, it provides educational resources corresponding to the fields of business and art. In this way, by providing educational resources corresponding to different fields, a wide range of skills can be acquired.

[0071] The Problem Incorporation Department uses generative AI to incorporate problems from different industries and academic fields, enabling the development of a wide range of problem-solving skills. For example, the Problem Incorporation Department uses generative AI to build a system that incorporates problems from different industries. For example, it analyzes problems in the manufacturing and service industries and proposes optimal solutions. The Problem Incorporation Department also uses generative AI to build a system that incorporates problems from different academic fields. For example, it analyzes research data in medicine and engineering to discover new knowledge. The Problem Incorporation Department also uses generative AI to incorporate problems from different industries and academic fields, enabling the development of a wide range of problem-solving skills. For example, it analyzes problems in the manufacturing and service industries and proposes optimal solutions. In this way, by incorporating problems from different industries and academic fields, it is possible to develop a wide range of problem-solving skills.

[0072] The evaluation unit not only compares the solutions generated by the generation AI with other participants, but also compares them with past excellent solutions, thereby raising the evaluation standards. The evaluation unit, for example, builds a system that compares the solutions generated by the generation AI with past excellent solutions. For example, it sets standards for originality and technical ability based on past data. The evaluation unit also compares the solutions generated by the generation AI with other participants, but also compares them with past excellent solutions, thereby raising the evaluation standards. For example, it sets standards for originality and technical ability based on past data. The evaluation unit also compares the solutions generated by the generation AI with other participants, but also compares them with past excellent solutions, thereby raising the evaluation standards. For example, it sets standards for originality and technical ability based on past data. In this way, the evaluation unit can raise the evaluation standards by comparing the solutions with other participants and past excellent solutions.

[0073] The educational resource providing unit can introduce a system that collects feedback in real time on the educational resources generated by the generation AI and continuously improves the content. The educational resource providing unit, for example, builds a system that collects feedback from participants in real time on the educational resources generated by the generation AI. For example, it collects evaluations and comments on the content of an online course. The educational resource providing unit also introduces a system that collects feedback in real time on the educational resources generated by the generation AI and continuously improves the content. For example, it updates the content of the educational resources based on feedback from participants. The educational resource providing unit also introduces a system that collects feedback in real time on the educational resources generated by the generation AI and continuously improves the content. For example, it updates the content of the educational resources based on feedback from participants. In this way, by collecting feedback in real time on the educational resources and continuously improving the content, it is possible to maximize the educational effectiveness.

[0074] The problem intake unit can automatically collect feedback from industry and academic experts on solutions generated by the generative AI and reflect it in the evaluation. The problem intake unit, for example, builds a system that automatically collects feedback from industry experts on solutions generated by the generative AI. For example, a company representative evaluates the solution and provides comments. The problem intake unit also automatically collects feedback from industry and academic experts on solutions generated by the generative AI and reflects it in the evaluation. For example, it updates the evaluation score of the solution based on the expert evaluation. The problem intake unit also automatically collects feedback from industry and academic experts on solutions generated by the generative AI and reflects it in the evaluation. For example, it updates the evaluation score of the solution based on the expert evaluation. In this way, feedback from industry and academic experts on solutions can be automatically collected and reflected in the evaluation, thereby improving the accuracy of the evaluation.

[0075] The evaluation unit can use the generation AI to introduce different evaluation criteria (e.g., social impact, environmental consideration) and perform a comprehensive evaluation. The evaluation unit, for example, uses the generation AI to build a system for evaluating social impact. For example, it quantifies the impact of a solution on society and calculates an evaluation score. The evaluation unit also uses the generation AI to build a system for evaluating environmental consideration. For example, it quantifies the impact of a solution on the environment and calculates an evaluation score. The evaluation unit also uses the generation AI to introduce different evaluation criteria (e.g., social impact, environmental consideration) and perform a comprehensive evaluation. For example, it performs an evaluation based on social impact and environmental consideration. The evaluation unit also uses the generation AI to introduce different evaluation criteria (e.g., social impact, environmental consideration) and perform a comprehensive evaluation. For example, it performs an evaluation based on social impact and environmental consideration. In this way, by introducing different evaluation criteria and performing a comprehensive evaluation, it is possible to increase the diversity and accuracy of the evaluation.

[0076] The educational resource providing unit can use the emotion estimation function to personalize the learning progress of participants and make suggestions to maintain motivation based on emotional data of participants during learning. The educational resource providing unit, for example, uses the emotion estimation function to build a system that collects emotional data of participants during learning in real time. For example, the emotional state of participants is analyzed using a camera or microphone. The educational resource providing unit also uses the emotion estimation function to personalize the learning progress of participants and make suggestions to maintain motivation based on the emotional data of participants during learning. For example, it provides an individual learning plan and makes suggestions based on the progress status. The educational resource providing unit also uses the emotion estimation function to personalize the learning progress of participants and make suggestions to maintain motivation based on the emotional data of participants during learning. For example, it provides an individual learning plan and makes suggestions based on the progress status. In this way, the learning effect can be maximized by personalizing the learning progress based on the emotional data during learning and making suggestions to maintain motivation.

[0077] The task incorporation unit can use the emotion estimation function to monitor the emotional state of participants when they are working on actual tasks and provide support to reduce stress. The task incorporation unit, for example, uses the emotion estimation function to build a system that monitors the emotional state of participants when they are working on actual tasks in real time. For example, it measures the stress level of participants using a camera or microphone. The task incorporation unit also uses the emotion estimation function to monitor the emotional state of participants when they are working on actual tasks in real time and provide support to reduce stress. For example, it provides relaxation techniques and support tools. The task incorporation unit also uses the emotion estimation function to monitor the emotional state of participants when they are working on actual tasks in real time and provide support to reduce stress. For example, it provides relaxation techniques and support tools. In this way, it is possible to maximize the effectiveness of problem solving by monitoring the emotional state of participants when they are working on actual tasks and providing support to reduce stress.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The assignment provider can use the generative AI to provide assignments that are specific to different cultures or regions. For example, the generative AI can generate assignments themed around social issues in a specific region. The generative AI can also provide assignments that promote intercultural understanding by having participants with different cultural backgrounds work on a common assignment. Furthermore, the generative AI can provide region-specific data analysis assignments using region-specific datasets. This allows participants to deepen their understanding of different cultures and regional issues.

[0080] The evaluation unit can use the generative AI to evaluate the social impact of participants' solutions. For example, the generative AI can quantify the positive impact a solution has on society. The generative AI can also evaluate the solution's impact on the environment and evaluate it from a sustainability perspective. Furthermore, the generative AI can evaluate the solution's impact on a specific community and measure its contribution to the local community. This allows for a comprehensive evaluation of the impact of participants' solutions on society as a whole.

[0081] The educational resource provision unit can use the generation AI to provide customized learning resources according to the learning style of the participants. For example, the generation AI can provide resources with a lot of visual content to participants who prefer visual learning. The generation AI can also provide resources in audio or podcast format to participants who prefer auditory learning. Furthermore, the generation AI can provide resources including interactive simulations and experiments to participants who prefer hands-on learning. This makes it possible to provide optimal learning resources according to the learning style of the participants.

[0082] The Promotion Department can use generative AI to help propose new business models. For example, generative AI can analyze existing business data and propose new revenue models. Generative AI can also analyze success stories from different industries and propose business models that can be applied to other industries. Furthermore, generative AI can predict future market trends and propose business strategies based on them. This allows companies to use generative AI to develop new business models and increase their competitiveness.

[0083] The problem acquisition department can use generative AI to collaborate with experts from different fields and provide complex problems. For example, generative AI can generate problems for medical and engineering experts to work on together. Generative AI can also provide problems for environmental science and economics experts to solve together. Generative AI can also generate problems for education and technology experts to work on together. This allows participants to develop the skills to integrate knowledge from different fields to solve complex problems.

[0084] The evaluation unit can use the emotion estimation function to monitor the emotional state of the participant and personalize the evaluation feedback. For example, the emotion estimation function can be used to analyze how the participant feels about the evaluation results. The evaluation unit can also use the emotion estimation function to adjust the feedback to be more positive if the participant is feeling stressed. Furthermore, the emotion estimation function can also be used to provide challenging feedback if the participant is highly motivated. This makes it possible to provide personalized feedback according to the participant's emotional state.

[0085] The educational resource provider can use the emotion estimation function to monitor the emotional state of participants during learning and provide resources to improve motivation according to the progress of learning. For example, if a participant feels tired during learning, the emotion estimation function can be used to suggest a short break to refresh themselves. If a participant is highly motivated, the emotion estimation function can also be used to provide additional challenges. Furthermore, if a participant feels stressed, the emotion estimation function can also be used to provide relaxation techniques and stress management resources. In this way, it is possible to provide resources to improve motivation according to the participant's emotional state.

[0086] The Promotion Department can use the emotion estimation function to monitor the emotional state of participants and provide support to draw out creative ideas. For example, if participants are in a relaxed state, the emotion estimation function can be used to suggest a brainstorming session. If participants are lacking concentration, the emotion estimation function can also be used to provide resources to help them improve their concentration. Furthermore, if participants are highly motivated, the emotion estimation function can be used to introduce new ideas or techniques to inspire them. In this way, creative ideas can be drawn out by providing support according to the participants' emotional state.

[0087] The task integration unit can use the emotion estimation function to monitor the emotional state of participants as they work on actual tasks and provide support to reduce stress. For example, the emotion estimation function can be used to measure the stress level of participants as they work on tasks and provide relaxation techniques. The emotion estimation function can also be used to provide additional resources and support if participants are highly motivated. Furthermore, the emotion estimation function can also be used to suggest a break if participants are feeling fatigued. This makes it possible to maximize the effectiveness of task solving by providing support according to the participants' emotional state.

[0088] The evaluation unit can use the emotion estimation function to reflect the emotional state of participants in the evaluation and set evaluation criteria that take into account the effects of stress and motivation. For example, a system can be constructed that uses the emotion estimation function to monitor the emotional state of participants in real time and reflect that data in the evaluation. For example, if the stress level is high, the evaluation criteria can be relaxed. The evaluation unit can also use the emotion estimation function to reflect the emotional state of participants in the evaluation and set evaluation criteria that take into account the effects of stress and motivation. For example, if the stress level is high, the evaluation criteria can be relaxed. Also, if motivation is high, the evaluation criteria can be tightened. In this way, by reflecting the emotional state of participants in the evaluation, a fair evaluation that takes into account the effects of stress and motivation is possible.

[0089] The processing flow of the second embodiment will be briefly explained below.

[0090] Step 1: The problem provider uses generative AI to provide problems. For example, generative AI can analyze large amounts of data to find specific patterns and trends in data science problems. Generative AI can also analyze text data and extract meaningful information in natural language processing problems. Furthermore, generative AI can generate high-quality images in image generation problems. Step 2: The evaluator evaluates the solutions to the tasks provided by the task provider in terms of creativity and technical ability. For example, the evaluator evaluates the originality of the algorithms and prompts generated by the generator. The evaluator may also evaluate the sophistication and efficiency of the implementation of the solutions generated by the generator. Step 3: The Educational Resources Provider provides educational resources that allow beginners to advanced users to hone their skills. For example, the Educational Resources Provider can offer online courses that teach the basics of generative AI, from basics to applications. It can also provide tutorials for tackling real-world problems and a community where participants can interact with each other and share their knowledge and experiences. Step 4: The Problem Intake Department incorporates actual problems from industry and academia. For example, they can use data provided by companies to optimize marketing strategies. They can also analyze research data provided by academia to discover new knowledge. They can also provide support for product development. Step 5: The Promotion Department will promote the adoption and innovation of generative AI. For example, the Promotion Department will support the development of new applications and services using generative AI. It will also conduct awareness-raising activities to widely disseminate generative AI technology and research and development to maximize the potential of generative AI.

[0091] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0092] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0093] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0094] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0095] 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.

[0096] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0097] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0098] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0099] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0100] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0101] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0102] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0103] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0104] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0105] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0106] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0107] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0109] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0110] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0111] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0112] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0113] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0115] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0116] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0117] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0119] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0120] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0121] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0124] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0125] 7, a 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.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0132] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0133] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0135] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0137] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0140] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0141] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0142] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0143] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0144] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0145] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0146] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0147] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0148] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0149] 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.

[0150] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0151] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0152] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0153] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0154] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0155] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0156] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0157] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0158] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An assignment provider that provides assignments using a generation AI; an evaluation unit that evaluates solutions to the problems provided by the problem providing unit from the viewpoints of creativity and technical ability; The Educational Resources Department provides educational resources that allow beginners to advanced users to hone their skills, and The problem-taking department takes in actual problems from industry and academia, The organization will have a promotion department that will promote the spread and innovation of generative AI. A system characterized by:

2. The assignment providing unit: The generation AI is used to dynamically adjust the difficulty of the tasks in real time, providing optimal tasks according to the skill level of the participants.

2. The system of claim 1.

3. The evaluation unit The solutions generated by the AI ​​are evaluated comprehensively based on the criteria of efficiency, originality, and practicality.

2. The system of claim 1.

4. The educational resource providing unit The generation AI is used to automatically generate a customized learning plan based on the participant's skill level.

2. The system of claim 1.

5. The problem intake unit Add a collaboration function to share the solutions generated by the generative AI with partners in industry and academia and work together to solve problems.

2. The system of claim 1.

6. The evaluation unit Emotion estimation functionality is used to monitor participants' emotional states in real time, and the evaluation criteria are adjusted according to fluctuations in stress and motivation.

2. The system of claim 1.

7. The educational resource providing unit Monitor participants' emotional state during learning using emotion estimation and provide resources to reduce stress 2. The system of claim 1.

8. The Promotion Department shall: Monitor participants' emotional states using emotion estimation to help them bring out creative ideas 2. The system of claim 1.

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