system

Abstract

The system according to this embodiment aims to enable beginners to efficiently learn the basic knowledge of app development and actually develop apps. [Solution] The system according to the embodiment comprises a provisioning unit, a designing unit, and a testing unit. The provisioning unit provides educational materials for beginners to learn the basic knowledge of app development. When a user inputs an app idea, the designing unit generates an app design based on that idea using a generation AI, and then performs coding. The testing unit automatically verifies the app's operation using the generation AI, detects bugs, and corrects them.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult for beginners to efficiently learn basic knowledge of app development and actually develop an app.

[0005] The system according to the embodiment aims to enable beginners to efficiently learn basic knowledge of app development and actually develop an app.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a provisioning unit, a designing unit, and a testing unit. The provisioning unit provides educational materials for beginners to learn the basic knowledge of app development. When a user inputs an app idea, the designing unit generates an app design based on that idea using a generation AI, and then performs the coding. The testing unit automatically verifies the app's operation using the generation AI, detects bugs, and corrects them. [Effects of the Invention]

[0007] The system according to this embodiment enables beginners to efficiently learn the basic knowledge of app development and actually develop apps. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

[0016] [First Embodiment]<00第1実施形態に係るデータ処理システム10の構成の一例が示されている。

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The application development support system according to an embodiment of the present invention is a system that utilizes generative AI to build a mass training program that enables even beginners to become application developers. The application development support system provides learning materials for beginners to learn the basic knowledge of application development and uses generative AI to support application design and coding. Specifically, when a user inputs an application idea, the generative AI generates an application blueprint based on that idea and then performs the coding. This makes it possible for even beginners to develop an application in a short period of time. Furthermore, the generative AI also supports testing and debugging of the application under development. For example, the generative AI can automatically verify the operation of the application, detect and correct bugs. This allows developers to complete the application efficiently. This training program makes it possible to train a large number of people with skills in application development using generative AI. This makes it possible to form a group within the company with experience in generative AI and to possess knowledge regarding future AI use within the company. In addition, by building an educational program that can be sold to other companies based on the know-how gained from the trial, it is thought that there will be strong demand from business people, the education industry, the human resource development industry, the consulting industry, and IT companies. Specific methods of this program include providing learning materials for application development using generative AI, application design and coding support, and testing and debugging support. This allows even beginners to become app developers in a short period of time, fostering talent that will shape the future of AI utilization. The app development support system enables even beginners to develop apps and complete them efficiently.

[0029] The application development support system according to this embodiment comprises a provisioning unit, a designing unit, and a testing unit. The provisioning unit provides learning materials for beginners to learn the basic knowledge of application development. The provisioning unit can provide, for example, online courses, video materials, and interactive learning tools. The provisioning unit can also periodically update the content of the learning materials to keep up with the latest technologies and trends. For example, the provisioning unit can provide learning materials for learning the basic concepts of programming and the basic procedures of application development. When a user inputs an application idea, the designing unit generates a blueprint of the application based on that idea using a generative AI, and then performs coding. For example, the designing unit generates blueprints such as wireframes, flowcharts, and ER diagrams based on the idea input by the user. The designing unit can also automatically perform coding based on the blueprint using the generative AI. For example, the designing unit inputs the idea input by the user as a prompt to the generative AI and generates a blueprint. The generative AI uses natural language processing technology to analyze the user's idea and generate an appropriate blueprint. Furthermore, the designing unit can also automatically perform coding based on the blueprint using the generative AI. For example, the design department performs coding using a specific programming language and framework based on the design blueprint generated by the generative AI. The testing department automatically verifies the application's operation using the generative AI, detecting and fixing bugs. The testing department automatically executes tests such as unit tests, integration tests, and system tests. Furthermore, the testing department can also use the generative AI to analyze test results, detect and fix bugs. For example, the testing department automatically verifies the application's operation using the generative AI, detecting and fixing bugs. The generative AI automatically generates test cases and verifies the application's operation. Furthermore, the testing department can also use the generative AI to analyze test results, detect and fix bugs. For example, the testing department automatically verifies the application's operation using the generative AI, detecting and fixing bugs. As a result, the application development support system according to this embodiment enables even beginners to develop applications and complete applications efficiently.

[0030] The service provider offers learning materials for beginners to acquire fundamental knowledge of app development. These materials can include online courses, video tutorials, and interactive learning tools. Specifically, online courses are structured in a modular format, allowing for step-by-step learning, with each module containing quizzes and exercises. Video tutorials include expert explanations and actual coding demonstrations, making them visually easy to understand. Interactive learning tools provide an environment where users can actually write and run code, receiving real-time feedback. The service provider also regularly updates the materials to keep up with the latest technologies and trends. For example, when new programming languages ​​or frameworks emerge, new materials including explanations and usage instructions are added. Furthermore, the service provider tracks user learning progress and suggests optimal learning plans for each individual user. This allows the service provider to help beginners efficiently acquire fundamental app development knowledge and smoothly transition to actual development.

[0031] The design team receives app ideas from users, and a generative AI generates a blueprint of the app based on those ideas, and then performs the coding. Specifically, the user inputs their ideas as prompts to the generative AI, which then generates the blueprint. The generative AI uses natural language processing technology to analyze the user's ideas and generate an appropriate blueprint. For example, if a user inputs "I want to make a task management app," the generative AI analyzes the request and generates wireframes and flowcharts that include features such as adding, editing, deleting tasks, and reminder functions. Furthermore, the design team can also use the generative AI to automatically code based on the blueprint. For example, they can code using a specific programming language and framework based on the blueprint generated by the generative AI. The generative AI selects the optimal programming language and framework according to the user's requirements and generates code efficiently. For example, in the case of a task management app, the generative AI selects Python and the Django framework and automatically generates the backend API and database schema. It also automatically generates frontend UI components, making the app ready for immediate use by the user. This allows the design department to significantly simplify the process of users translating app ideas into concrete blueprints and code, enabling them to develop apps more quickly.

[0032] The testing unit uses a generative AI to automatically verify the application's operation, detect bugs, and fix them. Specifically, it automatically executes tests such as unit tests, integration tests, and system tests. The generative AI automatically generates test cases and verifies that each function of the application works correctly. For example, in the case of a task management application, it generates test cases to verify that the task addition, editing, and deletion functions work correctly. Furthermore, the generative AI can also analyze test results to detect and fix bugs. For example, by analyzing test results, if a particular function does not work as expected, it can identify the cause and automatically generate code to fix it. The generative AI learns from past test data and bug fix history, gaining the ability to detect and fix bugs more efficiently. In addition, the testing unit works in conjunction with the continuous integration (CI) / continuous delivery (CD) pipeline, automatically running tests every time there is a code change to ensure quality. As a result, the testing unit can maintain a high level of application quality and provide users with a reliable application.

[0033] The training department can cultivate a large number of people with skills in app development utilizing generative AI. The training department can, for example, provide online courses, workshops, and interactive learning tools. Furthermore, the training department can regularly update its materials to keep up with the latest technologies and trends. For example, the training department can provide materials for learning fundamental programming concepts and basic app development procedures. In addition, the training department can provide training programs to acquire practical app development skills using generative AI. For example, the training department can provide training programs where generative AI automatically assists with app design and coding. This allows for the cultivation of a large number of people with skills in app development utilizing generative AI. Some or all of the processes described above in the training department may be performed using AI, for example, or not. For example, the training department can provide training programs where generative AI automatically assists with app design and coding.

[0034] The Formation Department can create a group within the company with experience in generative AI. For example, the Formation Department can create such a group through internal projects and training programs. Furthermore, the Formation Department can accumulate internal knowledge by sharing successful case studies and know-how from projects utilizing generative AI. For example, the Formation Department can share successful case studies from projects utilizing generative AI within the company, thereby creating a group with experience in generative AI. In addition, the Formation Department can provide training programs utilizing generative AI to cultivate internal talent. For example, the Formation Department provides training programs utilizing generative AI to cultivate internal talent. This allows the company to create a group with experience in generative AI and possess internal knowledge regarding AI utilization. Some or all of the above processes in the Formation Department may be performed using AI, or not. For example, the Formation Department can provide training programs utilizing generative AI to cultivate internal talent.

[0035] The Development Department can build educational programs that can be sold to other companies based on the know-how gained from trials. For example, the Development Department can provide online courses, workshops, and interactive learning tools. Furthermore, the Development Department can regularly update the content of the materials to keep up with the latest technologies and trends. For example, the Development Department can provide materials for learning fundamental programming concepts and basic application development procedures. In addition, the Development Department can provide training programs using generative AI to acquire practical application development skills. For example, the Development Department can provide training programs where generative AI automatically assists with application design and coding. This allows the Development Department to build educational programs that can be sold to other companies. Some or all of the above processes in the Development Department may be performed using AI, or not. For example, the Development Department can provide training programs where generative AI automatically assists with application design and coding.

[0036] The delivery unit can analyze the user's learning history and determine the optimal order in which learning materials are provided. For example, the delivery unit can suggest the next learning material based on what the user has learned in the past. The delivery unit can also analyze the user's learning speed and provide materials at an appropriate pace. Furthermore, the delivery unit can evaluate the user's level of understanding and provide necessary review materials. For example, the delivery unit analyzes the user's learning history and determines the optimal order in which learning materials are provided. This enables efficient learning by determining the optimal order in which learning materials are provided based on the user's learning history. Some or all of the above processes in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's learning history into AI and have the AI ​​determine the optimal order in which learning materials are provided.

[0037] The service provider can provide customized learning materials based on the user's interests and preferences. For example, the service provider can provide materials related to topics the user is interested in. Furthermore, the service provider can provide materials that include specific examples based on the user's areas of interest. In addition, the service provider can provide materials tailored to the user's hobbies and preferences. For example, the service provider can provide customized learning materials based on the user's interests and preferences. This can increase the user's motivation to learn. Some or all of the above-described processes in the service provider may be performed using AI, or not. For example, the service provider can input the user's interests and preferences into an AI and have the AI ​​generate customized learning materials.

[0038] The service provider can provide educational materials that include region-specific examples, taking into account the user's geographical location information. For example, the service provider can provide educational materials that include examples related to the area where the user lives. The service provider can also provide educational materials that solve region-specific problems based on the user's geographical location. Furthermore, the service provider can provide educational materials that are tailored to the culture and customs of the user's region. For example, the service provider can provide educational materials that include region-specific examples, taking into account the user's geographical location information. This enhances the effectiveness of learning by providing educational materials that include region-specific examples based on the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into AI and have the AI ​​generate educational materials that include region-specific examples.

[0039] The service provider can analyze the user's social media activity when providing educational materials and provide relevant materials. For example, the service provider can provide materials related to topics the user has shown interest in on social media. The service provider can also provide materials that enhance the user's motivation to learn based on their social media activity. Furthermore, the service provider can provide materials that include specific examples based on the user's social media interactions. For example, the service provider can analyze the user's social media activity and provide relevant materials. This can enhance the user's motivation to learn by providing relevant materials based on their social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity into AI and have the AI ​​generate relevant materials.

[0040] The design department can select the optimal design methodology during the design phase by referring to the user's past project history. For example, the design department can refer to the design methodology of the user's past successful projects. Furthermore, the design department can propose the optimal design methodology based on the user's past project history. In addition, the design department can analyze the failures of the user's past projects and select an improved design methodology. For example, the design department selects the optimal design methodology by referring to the user's past project history. This enables efficient design by selecting the optimal design methodology based on the user's past project history. Some or all of the above processes in the design department may be performed using AI, or not. For example, the design department can input the user's past project history into an AI and have the AI ​​select the optimal design methodology.

[0041] The design department can customize the design process according to the user's current skill level during the design phase. For example, if the user is a beginner, the design department can provide a basic design process. If the user is an intermediate user, the design department can also provide an advanced design process. Furthermore, if the user is an advanced user, the design department can provide a specialized design process. For example, the design department customizes the design process according to the user's current skill level. This allows the design department to provide the optimal design process for the user by customizing the design process according to the user's skill level. Some or all of the above processes in the design department may be performed using AI, for example, or not using AI. For example, the design department can input the user's skill level into the AI ​​and have the AI ​​customize the design process.

[0042] The design department can generate design drawings that include region-specific requirements by considering the user's geographical location information during the design phase. For example, the design department can generate design drawings that take into account the legal regulations of the area where the user lives. The design department can also generate design drawings that reflect region-specific needs based on the user's geographical location. Furthermore, the design department can generate design drawings that take into account the climate and environmental conditions of the user's region. For example, the design department can generate design drawings that include region-specific requirements by considering the user's geographical location information. This makes it possible to design in a way that addresses region-specific needs by generating design drawings that include region-specific requirements based on the user's geographical location information. Some or all of the above processes in the design department may be performed using AI, for example, or not using AI. For example, the design department can input the user's geographical location information into AI and have the AI ​​generate design drawings that include region-specific requirements.

[0043] The design department can analyze users' social media activity during the design phase and propose relevant design methodologies. For example, the design department can propose design methodologies that users have shown interest in on social media. Furthermore, the design department can propose design methodologies that enhance learning motivation based on users' social media activity. In addition, the design department can propose design methodologies that include specific examples based on the content of users' social media interactions. For example, the design department analyzes users' social media activity and proposes relevant design methodologies. This allows the design department to provide the optimal design methodology for users by proposing relevant methodologies based on their social media activity. Some or all of the above processes in the design department may be performed using AI, or not. For example, the design department can input users' social media activity into AI and have the AI ​​propose relevant design methodologies.

[0044] The testing unit can select the optimal test case during testing by referring to past bug history. For example, the testing unit can select the optimal test case based on patterns of bugs that have occurred in the past. The testing unit can also select test cases to prevent recurrence from the user's past bug history. Furthermore, the testing unit can analyze past bug history and select efficient test cases. For example, the testing unit can select the optimal test case by referring to past bug history. This enables efficient testing by selecting the optimal test case based on past bug history. Some or all of the above processes in the testing unit may be performed using AI, for example, or without using AI. For example, the testing unit can input past bug history into AI and have the AI ​​select the optimal test case.

[0045] The testing unit can customize the testing process during testing according to the user's current development status. For example, if the user is in the initial stages, the testing unit provides a basic testing process. If the user is in the intermediate stages, the testing unit can also provide an advanced testing process. Furthermore, if the user is in the final stages, the testing unit can provide a detailed testing process. For example, the testing unit customizes the testing process according to the user's current development status. By customizing the testing process according to the user's development status, efficient testing becomes possible. Some or all of the above processes in the testing unit may be performed using AI, for example, or not using AI. For example, the testing unit can input the user's development status into AI and have the AI ​​customize the testing process.

[0046] The testing unit can prioritize the detection of region-specific bugs during testing by considering the user's geographical location information. For example, the testing unit can prioritize the detection of bugs specific to the area where the user lives. The testing unit can also conduct tests to resolve region-specific problems based on the user's geographical location. Furthermore, the testing unit can conduct tests that take into account the environmental conditions of the user's region. For example, the testing unit prioritizes the detection of region-specific bugs by considering the user's geographical location information. This allows for the efficient resolution of region-specific problems by prioritizing the detection of region-specific bugs based on the user's geographical location information. Some or all of the above processing in the testing unit may be performed using AI, for example, or without AI. For example, the testing unit can input the user's geographical location information into AI and have the AI ​​detect region-specific bugs.

[0047] The testing unit can analyze the user's social media activity during testing and propose relevant test methods. For example, the testing unit can propose test methods that the user has shown interest in on social media. It can also propose test methods that enhance learning motivation based on the user's social media activity. Furthermore, the testing unit can propose test methods that include specific examples based on the content of the user's social media interactions. For example, the testing unit analyzes the user's social media activity and proposes relevant test methods. By proposing relevant test methods based on the user's social media activity, it is possible to provide the user with the most suitable test method. Some or all of the above processing in the testing unit may be performed using AI, for example, or not using AI. For example, the testing unit can input the user's social media activity into AI and have the AI ​​propose relevant test methods.

[0048] The training unit can select the optimal training method by referring to the user's past learning history during training. For example, the training unit can suggest the next training method to be learned based on what the user has learned in the past. The training unit can also analyze the user's learning speed and provide training methods at an appropriate pace. Furthermore, the training unit can evaluate the user's level of understanding and provide necessary review methods. For example, the training unit selects the optimal training method by referring to the user's past learning history. This enables efficient training by selecting the optimal training method based on the user's past learning history. Some or all of the above processes in the training unit may be performed using AI, for example, or without AI. For example, the training unit can input the user's past learning history into AI and have the AI ​​select the optimal training method.

[0049] The training department can provide region-specific training programs during training, taking into account the user's geographical location information. For example, the training department can provide training programs that include case studies related to the area where the user lives. The training department can also provide training programs that solve region-specific problems based on the user's geographical location. Furthermore, the training department can provide training programs that are tailored to the culture and customs of the user's region. For example, the training department can provide region-specific training programs that take into account the user's geographical location information. This makes it possible to provide training that meets region-specific needs by providing region-specific training programs based on the user's geographical location information. Some or all of the above processing in the training department may be performed using AI, for example, or not using AI. For example, the training department can input the user's geographical location information into AI and have the AI ​​generate region-specific training programs.

[0050] The formation unit can create an optimal group by referring to the user's past project history during the formation process. For example, the formation unit may refer to the members of the user's past successful projects. The formation unit can also propose an optimal group based on the user's past project history. Furthermore, the formation unit can analyze the failures of the user's past projects and create an improved group. For example, the formation unit can create an optimal group by referring to the user's past project history. This enables efficient group formation by creating an optimal group based on the user's past project history. Some or all of the above processes in the formation unit may be performed using AI, for example, or without AI. For example, the formation unit can input the user's past project history into AI and have the AI ​​propose an optimal group.

[0051] The formation unit can create region-specific groups by considering the user's geographical location information during the formation process. For example, the formation unit can create groups that reflect the specific needs of the area where the user lives. The formation unit can also create groups that solve region-specific problems based on the user's geographical location. Furthermore, the formation unit can create groups that are in line with the culture and customs of the user's region. For example, the formation unit can create region-specific groups by considering the user's geographical location information. This makes it possible to create groups that respond to region-specific needs by creating region-specific groups based on the user's geographical location information. Some or all of the above-described processes in the formation unit may be performed using AI, for example, or without AI. For example, the formation unit can input the user's geographical location information into AI and have AI create region-specific groups.

[0052] The program development unit can construct an optimal program by referencing the results of past educational programs during the development process. For example, the development unit can refer to the methods of successful educational programs in the past. The development unit can also analyze the results of past educational programs and construct an optimal program. Furthermore, the development unit can analyze the failures of past educational programs and construct an improved program. For example, the development unit can construct an optimal program by referencing the results of past educational programs. This enables efficient education by constructing an optimal program based on the results of past educational programs. Some or all of the above processes in the development unit may be performed using AI, for example, or without AI. For example, the development unit can input the results of past educational programs into AI and have the AI ​​construct an optimal program.

[0053] The development unit can provide region-specific educational programs by considering the user's geographical location information during development. For example, the development unit can provide educational programs that reflect the specific needs of the area where the user lives. Furthermore, the development unit can provide educational programs that solve region-specific problems based on the user's geographical location. In addition, the development unit can provide educational programs tailored to the culture and customs of the user's region. For example, the development unit provides region-specific educational programs by considering the user's geographical location information. This enables education that addresses region-specific needs by providing region-specific educational programs based on the user's geographical location information. Some or all of the above processing in the development unit may be performed using AI, for example, or without AI. For example, the development unit can input the user's geographical location information into AI and have the AI ​​generate region-specific educational programs.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The service provider can analyze users' learning styles and provide the most suitable learning materials. For example, it can provide visual materials to users who prefer visual learning and audio materials to users who prefer auditory learning. The service provider can also adjust the pace of the materials to match the user's learning speed. Furthermore, it can customize the content of the materials based on user feedback. This allows for the provision of optimal learning materials tailored to each user's learning style, thereby enhancing learning effectiveness.

[0056] The training department can create individualized learning plans based on users' learning history. For example, it can analyze past learning content and progress to suggest what to learn next. The training department can also provide customized training programs tailored to the user's learning goals. Furthermore, it can assess the user's level of understanding and provide review materials as needed. This allows for efficient learning by providing individualized learning plans based on the user's learning history.

[0057] The team building function can assemble appropriate project teams based on the user's skill level. For example, it can assign basic projects to beginners, advanced projects to intermediate users, and specialized projects to advanced users. The team building function can also select projects based on the user's interests and preferences. Furthermore, it can select the most suitable team members by referring to the user's past project history. This allows for efficient project execution by forming project teams that match the user's skill level and interests.

[0058] The development team can continuously improve the content of educational programs based on user feedback. For example, they can collect opinions and requests from users and adjust the program's content and format. They can also analyze user learning outcomes and implement effective teaching methods. Furthermore, they can regularly update the content of educational materials to keep up with the latest technologies and trends. This allows for more effective learning by improving the educational program based on user feedback.

[0059] The system can analyze a user's learning history and determine the optimal order in which learning materials are provided. For example, it can suggest the next learning material based on what the user has previously studied. The system can also analyze the user's learning speed and provide materials at an appropriate pace. Furthermore, it can evaluate the user's level of understanding and provide necessary review materials. This allows for efficient learning by determining the optimal order of material delivery based on the user's learning history.

[0060] The service provider can offer customized learning materials based on the user's interests. For example, they can provide materials related to topics the user is interested in. They can also provide materials that include specific examples based on the user's areas of interest. Furthermore, they can provide materials tailored to the user's hobbies and preferences. By providing customized learning materials based on the user's interests, their motivation to learn can be enhanced.

[0061] The service provider can provide educational materials that include region-specific examples, taking into account the user's geographical location. For example, it can provide materials that include examples related to the area where the user lives. Furthermore, the service provider can provide educational materials that address region-specific problems based on the user's geographical location. In addition, the service provider can provide educational materials tailored to the culture and customs of the user's region. By providing educational materials that include region-specific examples based on the user's geographical location, the effectiveness of learning can be enhanced.

[0062] The service provider can analyze users' social media activity when providing educational materials and offer relevant materials. For example, they can provide materials related to topics that users have shown interest in on social media. Furthermore, the service provider can offer materials that enhance learning motivation based on users' social media activity. They can also provide materials that include specific examples based on users' social media interactions. This allows for increased learning motivation by providing relevant materials based on users' social media activity.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The provider will provide learning materials for beginners to acquire basic knowledge of app development. For example, they will provide online courses, video materials, and interactive learning tools. The provider can also regularly update the content of the materials to keep up with the latest technologies and trends. Step 2: The design department receives an app idea from the user, and the generating AI generates a blueprint of the app based on that idea, and then performs the coding. For example, it generates blueprints such as wireframes, flowcharts, and ER diagrams based on the idea entered by the user. It can also use the generating AI to automatically perform coding based on the blueprint. Step 3: The testing unit uses a generation AI to automatically verify the application's operation, detect bugs, and fix them. For example, it automatically executes tests such as unit tests, integration tests, and system tests. The generation AI automatically generates test cases, verifies the application's operation, analyzes the test results, and detects and fixes bugs.

[0065] (Example of form 2) The application development support system according to an embodiment of the present invention is a system that utilizes generative AI to build a mass training program that enables even beginners to become application developers. The application development support system provides learning materials for beginners to learn the basic knowledge of application development and uses generative AI to support application design and coding. Specifically, when a user inputs an application idea, the generative AI generates an application blueprint based on that idea and then performs the coding. This makes it possible for even beginners to develop an application in a short period of time. Furthermore, the generative AI also supports testing and debugging of the application under development. For example, the generative AI can automatically verify the operation of the application, detect and correct bugs. This allows developers to complete the application efficiently. This training program makes it possible to train a large number of people with skills in application development using generative AI. This makes it possible to form a group within the company with experience in generative AI and to possess knowledge regarding future AI use within the company. In addition, by building an educational program that can be sold to other companies based on the know-how gained from the trial, it is thought that there will be strong demand from business people, the education industry, the human resource development industry, the consulting industry, and IT companies. Specific methods of this program include providing learning materials for application development using generative AI, application design and coding support, and testing and debugging support. This allows even beginners to become app developers in a short period of time, fostering talent that will shape the future of AI utilization. The app development support system enables even beginners to develop apps and complete them efficiently.

[0066] The application development support system according to this embodiment comprises a provisioning unit, a designing unit, and a testing unit. The provisioning unit provides learning materials for beginners to learn the basic knowledge of application development. The provisioning unit can provide, for example, online courses, video materials, and interactive learning tools. The provisioning unit can also periodically update the content of the learning materials to keep up with the latest technologies and trends. For example, the provisioning unit can provide learning materials for learning the basic concepts of programming and the basic procedures of application development. When a user inputs an application idea, the designing unit generates a blueprint of the application based on that idea using a generative AI, and then performs coding. For example, the designing unit generates blueprints such as wireframes, flowcharts, and ER diagrams based on the idea input by the user. The designing unit can also automatically perform coding based on the blueprint using the generative AI. For example, the designing unit inputs the idea input by the user as a prompt to the generative AI and generates a blueprint. The generative AI uses natural language processing technology to analyze the user's idea and generate an appropriate blueprint. Furthermore, the designing unit can also automatically perform coding based on the blueprint using the generative AI. For example, the design department performs coding using a specific programming language and framework based on the design blueprint generated by the generative AI. The testing department automatically verifies the application's operation using the generative AI, detecting and fixing bugs. The testing department automatically executes tests such as unit tests, integration tests, and system tests. Furthermore, the testing department can also use the generative AI to analyze test results, detect and fix bugs. For example, the testing department automatically verifies the application's operation using the generative AI, detecting and fixing bugs. The generative AI automatically generates test cases and verifies the application's operation. Furthermore, the testing department can also use the generative AI to analyze test results, detect and fix bugs. For example, the testing department automatically verifies the application's operation using the generative AI, detecting and fixing bugs. As a result, the application development support system according to this embodiment enables even beginners to develop applications and complete applications efficiently.

[0067] The service provider offers learning materials for beginners to acquire fundamental knowledge of app development. These materials can include online courses, video tutorials, and interactive learning tools. Specifically, online courses are structured in a modular format, allowing for step-by-step learning, with each module containing quizzes and exercises. Video tutorials include expert explanations and actual coding demonstrations, making them visually easy to understand. Interactive learning tools provide an environment where users can actually write and run code, receiving real-time feedback. The service provider also regularly updates the materials to keep up with the latest technologies and trends. For example, when new programming languages ​​or frameworks emerge, new materials including explanations and usage instructions are added. Furthermore, the service provider tracks user learning progress and suggests optimal learning plans for each individual user. This allows the service provider to help beginners efficiently acquire fundamental app development knowledge and smoothly transition to actual development.

[0068] The design team receives app ideas from users, and a generative AI generates a blueprint of the app based on those ideas, and then performs the coding. Specifically, the user inputs their ideas as prompts to the generative AI, which then generates the blueprint. The generative AI uses natural language processing technology to analyze the user's ideas and generate an appropriate blueprint. For example, if a user inputs "I want to make a task management app," the generative AI analyzes the request and generates wireframes and flowcharts that include features such as adding, editing, deleting tasks, and reminder functions. Furthermore, the design team can also use the generative AI to automatically code based on the blueprint. For example, they can code using a specific programming language and framework based on the blueprint generated by the generative AI. The generative AI selects the optimal programming language and framework according to the user's requirements and generates code efficiently. For example, in the case of a task management app, the generative AI selects Python and the Django framework and automatically generates the backend API and database schema. It also automatically generates frontend UI components, making the app ready for immediate use by the user. This allows the design department to significantly simplify the process of users translating app ideas into concrete blueprints and code, enabling them to develop apps more quickly.

[0069] The testing unit uses a generative AI to automatically verify the application's operation, detect bugs, and fix them. Specifically, it automatically executes tests such as unit tests, integration tests, and system tests. The generative AI automatically generates test cases and verifies that each function of the application works correctly. For example, in the case of a task management application, it generates test cases to verify that the task addition, editing, and deletion functions work correctly. Furthermore, the generative AI can also analyze test results to detect and fix bugs. For example, by analyzing test results, if a particular function does not work as expected, it can identify the cause and automatically generate code to fix it. The generative AI learns from past test data and bug fix history, gaining the ability to detect and fix bugs more efficiently. In addition, the testing unit works in conjunction with the continuous integration (CI) / continuous delivery (CD) pipeline, automatically running tests every time there is a code change to ensure quality. As a result, the testing unit can maintain a high level of application quality and provide users with a reliable application.

[0070] The training department can cultivate a large number of people with skills in app development utilizing generative AI. The training department can, for example, provide online courses, workshops, and interactive learning tools. Furthermore, the training department can regularly update its materials to keep up with the latest technologies and trends. For example, the training department can provide materials for learning fundamental programming concepts and basic app development procedures. In addition, the training department can provide training programs to acquire practical app development skills using generative AI. For example, the training department can provide training programs where generative AI automatically assists with app design and coding. This allows for the cultivation of a large number of people with skills in app development utilizing generative AI. Some or all of the processes described above in the training department may be performed using AI, for example, or not. For example, the training department can provide training programs where generative AI automatically assists with app design and coding.

[0071] The Formation Department can create a group within the company with experience in generative AI. For example, the Formation Department can create such a group through internal projects and training programs. Furthermore, the Formation Department can accumulate internal knowledge by sharing successful case studies and know-how from projects utilizing generative AI. For example, the Formation Department can share successful case studies from projects utilizing generative AI within the company, thereby creating a group with experience in generative AI. In addition, the Formation Department can provide training programs utilizing generative AI to cultivate internal talent. For example, the Formation Department provides training programs utilizing generative AI to cultivate internal talent. This allows the company to create a group with experience in generative AI and possess internal knowledge regarding AI utilization. Some or all of the above processes in the Formation Department may be performed using AI, or not. For example, the Formation Department can provide training programs utilizing generative AI to cultivate internal talent.

[0072] The Development Department can build educational programs that can be sold to other companies based on the know-how gained from trials. For example, the Development Department can provide online courses, workshops, and interactive learning tools. Furthermore, the Development Department can regularly update the content of the materials to keep up with the latest technologies and trends. For example, the Development Department can provide materials for learning fundamental programming concepts and basic application development procedures. In addition, the Development Department can provide training programs using generative AI to acquire practical application development skills. For example, the Development Department can provide training programs where generative AI automatically assists with application design and coding. This allows the Development Department to build educational programs that can be sold to other companies. Some or all of the above processes in the Development Department may be performed using AI, or not. For example, the Development Department can provide training programs where generative AI automatically assists with application design and coding.

[0073] The service provider can estimate the user's emotions and adjust the difficulty level of the learning materials based on the estimated emotions. For example, if the user is feeling stressed, the service provider can provide easy materials to reduce the learning burden. Conversely, if the user is relaxed, the service provider can provide more challenging materials to encourage learning. Furthermore, if the user is excited, the service provider can provide interactive materials to increase learning motivation. For example, the service provider estimates the user's emotions and adjusts the difficulty level of the learning materials based on the estimated emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for reduced learning burden and increased learning motivation by adjusting the difficulty level of the learning materials according to the user's emotions. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can estimate the user's emotions and adjust the difficulty level of the learning materials based on the estimated emotions.

[0074] The delivery unit can analyze the user's learning history and determine the optimal order in which learning materials are provided. For example, the delivery unit can suggest the next learning material based on what the user has learned in the past. The delivery unit can also analyze the user's learning speed and provide materials at an appropriate pace. Furthermore, the delivery unit can evaluate the user's level of understanding and provide necessary review materials. For example, the delivery unit analyzes the user's learning history and determines the optimal order in which learning materials are provided. This enables efficient learning by determining the optimal order in which learning materials are provided based on the user's learning history. Some or all of the above processes in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's learning history into AI and have the AI ​​determine the optimal order in which learning materials are provided.

[0075] The service provider can provide customized learning materials based on the user's interests and preferences. For example, the service provider can provide materials related to topics the user is interested in. Furthermore, the service provider can provide materials that include specific examples based on the user's areas of interest. In addition, the service provider can provide materials tailored to the user's hobbies and preferences. For example, the service provider can provide customized learning materials based on the user's interests and preferences. This can increase the user's motivation to learn. Some or all of the above-described processes in the service provider may be performed using AI, or not. For example, the service provider can input the user's interests and preferences into an AI and have the AI ​​generate customized learning materials.

[0076] The service provider can estimate the user's emotions and select the format of the learning materials based on those emotions. For example, if the user is tired, the service provider can provide text-based learning materials that are visually less burdensome. If the user is excited, the service provider can also provide interactive video learning materials. Furthermore, if the user is relaxed, the service provider can provide video learning materials that include detailed explanations. For example, the service provider estimates the user's emotions and selects the format of the learning materials based on those emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. By selecting the format of the learning materials according to the user's emotions, the burden of learning can be reduced and motivation to learn can be increased. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's emotions into an AI and have the AI ​​select the format of the learning materials.

[0077] The service provider can provide educational materials that include region-specific examples, taking into account the user's geographical location information. For example, the service provider can provide educational materials that include examples related to the area where the user lives. The service provider can also provide educational materials that solve region-specific problems based on the user's geographical location. Furthermore, the service provider can provide educational materials that are tailored to the culture and customs of the user's region. For example, the service provider can provide educational materials that include region-specific examples, taking into account the user's geographical location information. This enhances the effectiveness of learning by providing educational materials that include region-specific examples based on the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into AI and have the AI ​​generate educational materials that include region-specific examples.

[0078] The service provider can analyze the user's social media activity when providing educational materials and provide relevant materials. For example, the service provider can provide materials related to topics the user has shown interest in on social media. The service provider can also provide materials that enhance the user's motivation to learn based on their social media activity. Furthermore, the service provider can provide materials that include specific examples based on the user's social media interactions. For example, the service provider can analyze the user's social media activity and provide relevant materials. This can enhance the user's motivation to learn by providing relevant materials based on their social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity into AI and have the AI ​​generate relevant materials.

[0079] The design department can estimate the user's emotions and adjust the level of detail in the design drawing based on the estimated emotions. For example, if the user is stressed, the design department can generate a simple design drawing. It can also generate a detailed design drawing if the user is relaxed. Furthermore, if the user is excited, it can generate a visually appealing design drawing. For example, the design department estimates the user's emotions and adjusts the level of detail in the design drawing based on the estimated emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for the provision of the optimal design drawing for the user by adjusting the level of detail in the design drawing according to the user's emotions. Some or all of the above processing in the design department may be performed using AI, or not. For example, the design department can input the user's emotions into the AI ​​and have the AI ​​adjust the level of detail in the design drawing.

[0080] The design department can select the optimal design methodology during the design phase by referring to the user's past project history. For example, the design department can refer to the design methodology of the user's past successful projects. Furthermore, the design department can propose the optimal design methodology based on the user's past project history. In addition, the design department can analyze the failures of the user's past projects and select an improved design methodology. For example, the design department selects the optimal design methodology by referring to the user's past project history. This enables efficient design by selecting the optimal design methodology based on the user's past project history. Some or all of the above processes in the design department may be performed using AI, or not. For example, the design department can input the user's past project history into an AI and have the AI ​​select the optimal design methodology.

[0081] The design department can customize the design process according to the user's current skill level during the design phase. For example, if the user is a beginner, the design department can provide a basic design process. If the user is an intermediate user, the design department can also provide an advanced design process. Furthermore, if the user is an advanced user, the design department can provide a specialized design process. For example, the design department customizes the design process according to the user's current skill level. This allows the design department to provide the optimal design process for the user by customizing the design process according to the user's skill level. Some or all of the above processes in the design department may be performed using AI, for example, or not using AI. For example, the design department can input the user's skill level into the AI ​​and have the AI ​​customize the design process.

[0082] The design department can estimate the user's emotions and adjust the display method of the design diagram based on the estimated emotions. For example, if the user is tense, the design department can provide a simple and highly visible display method. If the user is relaxed, the design department can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the design department can provide a display method that gets straight to the point. For example, the design department estimates the user's emotions and adjusts the display method of the design diagram based on the estimated emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the design department to provide the optimal display method for the user by adjusting the display method of the design diagram according to the user's emotions. Some or all of the above processing in the design department may be performed using AI, for example, or not using AI. For example, the design department can input the user's emotions into the AI ​​and have the AI ​​adjust the display method of the design diagram.

[0083] The design department can generate design drawings that include region-specific requirements by considering the user's geographical location information during the design phase. For example, the design department can generate design drawings that take into account the legal regulations of the area where the user lives. The design department can also generate design drawings that reflect region-specific needs based on the user's geographical location. Furthermore, the design department can generate design drawings that take into account the climate and environmental conditions of the user's region. For example, the design department can generate design drawings that include region-specific requirements by considering the user's geographical location information. This makes it possible to design in a way that addresses region-specific needs by generating design drawings that include region-specific requirements based on the user's geographical location information. Some or all of the above processes in the design department may be performed using AI, for example, or not using AI. For example, the design department can input the user's geographical location information into AI and have the AI ​​generate design drawings that include region-specific requirements.

[0084] The design department can analyze users' social media activity during the design phase and propose relevant design methodologies. For example, the design department can propose design methodologies that users have shown interest in on social media. Furthermore, the design department can propose design methodologies that enhance learning motivation based on users' social media activity. In addition, the design department can propose design methodologies that include specific examples based on the content of users' social media interactions. For example, the design department analyzes users' social media activity and proposes relevant design methodologies. This allows the design department to provide the optimal design methodology for users by proposing relevant methodologies based on their social media activity. Some or all of the above processes in the design department may be performed using AI, or not. For example, the design department can input users' social media activity into AI and have the AI ​​propose relevant design methodologies.

[0085] The testing unit can estimate the user's emotions and prioritize tests based on those emotions. For example, if the user is stressed, the testing unit may prioritize simpler tests. Conversely, if the user is relaxed, the testing unit may prioritize more detailed tests. Furthermore, if the user is in a hurry, the testing unit may prioritize more important tests. For example, the testing unit estimates the user's emotions and prioritizes tests based on those emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This enables efficient testing by prioritizing tests according to the user's emotions. Some or all of the above processing in the testing unit may be performed using AI, or not. For example, the testing unit can input the user's emotions into an AI and have the AI ​​determine the test priorities.

[0086] The testing unit can select the optimal test case during testing by referring to past bug history. For example, the testing unit can select the optimal test case based on patterns of bugs that have occurred in the past. The testing unit can also select test cases to prevent recurrence from the user's past bug history. Furthermore, the testing unit can analyze past bug history and select efficient test cases. For example, the testing unit can select the optimal test case by referring to past bug history. This enables efficient testing by selecting the optimal test case based on past bug history. Some or all of the above processes in the testing unit may be performed using AI, for example, or without using AI. For example, the testing unit can input past bug history into AI and have the AI ​​select the optimal test case.

[0087] The testing unit can customize the testing process during testing according to the user's current development status. For example, if the user is in the initial stages, the testing unit provides a basic testing process. If the user is in the intermediate stages, the testing unit can also provide an advanced testing process. Furthermore, if the user is in the final stages, the testing unit can provide a detailed testing process. For example, the testing unit customizes the testing process according to the user's current development status. By customizing the testing process according to the user's development status, efficient testing becomes possible. Some or all of the above processes in the testing unit may be performed using AI, for example, or not using AI. For example, the testing unit can input the user's development status into AI and have the AI ​​customize the testing process.

[0088] The testing unit can estimate the user's emotions and adjust the display method of the test results based on the estimated emotions. For example, if the user is nervous, the testing unit can provide a simple and highly visible display method. If the user is relaxed, the testing unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the testing unit can provide a concise display method. For example, the testing unit estimates the user's emotions and adjusts the display method of the test results based on the estimated emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the system to provide the optimal display method for the user by adjusting the display method of the test results according to the user's emotions. Some or all of the above processing in the testing unit may be performed using AI, for example, or not using AI. For example, the testing unit can input the user's emotions into the AI ​​and have the AI ​​adjust the display method of the test results.

[0089] The testing unit can prioritize the detection of region-specific bugs during testing by considering the user's geographical location information. For example, the testing unit can prioritize the detection of bugs specific to the area where the user lives. The testing unit can also conduct tests to resolve region-specific problems based on the user's geographical location. Furthermore, the testing unit can conduct tests that take into account the environmental conditions of the user's region. For example, the testing unit prioritizes the detection of region-specific bugs by considering the user's geographical location information. This allows for the efficient resolution of region-specific problems by prioritizing the detection of region-specific bugs based on the user's geographical location information. Some or all of the above processing in the testing unit may be performed using AI, for example, or without AI. For example, the testing unit can input the user's geographical location information into AI and have the AI ​​detect region-specific bugs.

[0090] The testing unit can analyze the user's social media activity during testing and propose relevant test methods. For example, the testing unit can propose test methods that the user has shown interest in on social media. It can also propose test methods that enhance learning motivation based on the user's social media activity. Furthermore, the testing unit can propose test methods that include specific examples based on the content of the user's social media interactions. For example, the testing unit analyzes the user's social media activity and proposes relevant test methods. By proposing relevant test methods based on the user's social media activity, it is possible to provide the user with the most suitable test method. Some or all of the above processing in the testing unit may be performed using AI, for example, or not using AI. For example, the testing unit can input the user's social media activity into AI and have the AI ​​propose relevant test methods.

[0091] The training unit can estimate the user's emotions and adjust the pace of the training program based on the estimated emotions. For example, if the user is stressed, the training unit can slow down the pace to reduce the learning burden. Conversely, if the user is relaxed, the training unit can speed up the pace to promote learning. Furthermore, if the user is excited, the training unit can increase the interactive elements to enhance learning motivation. For example, the training unit estimates the user's emotions and adjusts the pace of the training program based on the estimated emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for reducing the learning burden and increasing learning motivation by adjusting the pace of the training program according to the user's emotions. Some or all of the above processing in the training unit may be performed using AI, for example, or without AI. For example, the training unit can input the user's emotions into the AI ​​and have the AI ​​adjust the pace of the training program.

[0092] The training unit can select the optimal training method by referring to the user's past learning history during training. For example, the training unit can suggest the next training method to be learned based on what the user has learned in the past. The training unit can also analyze the user's learning speed and provide training methods at an appropriate pace. Furthermore, the training unit can evaluate the user's level of understanding and provide necessary review methods. For example, the training unit selects the optimal training method by referring to the user's past learning history. This enables efficient training by selecting the optimal training method based on the user's past learning history. Some or all of the above processes in the training unit may be performed using AI, for example, or without AI. For example, the training unit can input the user's past learning history into AI and have the AI ​​select the optimal training method.

[0093] The training unit can estimate the user's emotions and customize the content of the training program based on those emotions. For example, if the user is stressed, the training unit can provide easy content to reduce the learning burden. Conversely, if the user is relaxed, the training unit can provide more challenging content to encourage learning. Furthermore, if the user is excited, the training unit can provide interactive content to increase motivation to learn. For example, the training unit estimates the user's emotions and customizes the content of the training program based on those emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for the reduction of learning burden and increased motivation by customizing the content of the training program according to the user's emotions. Some or all of the above processing in the training unit may be performed using AI, for example, or without AI. For example, the training unit can input the user's emotions into the AI ​​and have the AI ​​customize the content of the training program.

[0094] The training department can provide region-specific training programs during training, taking into account the user's geographical location information. For example, the training department can provide training programs that include case studies related to the area where the user lives. The training department can also provide training programs that solve region-specific problems based on the user's geographical location. Furthermore, the training department can provide training programs that are tailored to the culture and customs of the user's region. For example, the training department can provide region-specific training programs that take into account the user's geographical location information. This makes it possible to provide training that meets region-specific needs by providing region-specific training programs based on the user's geographical location information. Some or all of the above processing in the training department may be performed using AI, for example, or not using AI. For example, the training department can input the user's geographical location information into AI and have the AI ​​generate region-specific training programs.

[0095] The formation unit can estimate the user's emotions and adjust the group formation method based on the estimated user emotions. For example, if the user is stressed, the formation unit can form a small group to reduce the learning burden. Conversely, if the user is relaxed, the formation unit can form a large group to promote interaction. Furthermore, if the user is excited, the formation unit can form an interactive group to increase learning motivation. For example, the formation unit estimates the user's emotions and adjusts the group formation method based on the estimated user emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for reducing the learning burden and increasing learning motivation by adjusting the group formation method according to the user's emotions. Some or all of the above processing in the formation unit may be performed using AI, for example, or without AI. For example, the formation unit can input the user's emotions into the AI ​​and have the AI ​​adjust the group formation method.

[0096] The formation unit can create an optimal group by referring to the user's past project history during the formation process. For example, the formation unit may refer to the members of the user's past successful projects. The formation unit can also propose an optimal group based on the user's past project history. Furthermore, the formation unit can analyze the failures of the user's past projects and create an improved group. For example, the formation unit can create an optimal group by referring to the user's past project history. This enables efficient group formation by creating an optimal group based on the user's past project history. Some or all of the above processes in the formation unit may be performed using AI, for example, or without AI. For example, the formation unit can input the user's past project history into AI and have the AI ​​propose an optimal group.

[0097] The formation unit can estimate the user's emotions and determine the group's role assignments based on those estimated emotions. For example, if the user is stressed, the formation unit can assign a less burdensome role. Conversely, if the user is relaxed, the formation unit can assign a challenging role. Furthermore, if the user is excited, the formation unit can assign an interactive role. For example, the formation unit estimates the user's emotions and determines the group's role assignments based on those estimated emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This enables efficient role assignment by determining the group's role assignments according to the user's emotions. Some or all of the above processing in the formation unit may be performed using AI, or not. For example, the formation unit can input the user's emotions into an AI and have the AI ​​determine the group's role assignments.

[0098] The formation unit can create region-specific groups by considering the user's geographical location information during the formation process. For example, the formation unit can create groups that reflect the specific needs of the area where the user lives. The formation unit can also create groups that solve region-specific problems based on the user's geographical location. Furthermore, the formation unit can create groups that are in line with the culture and customs of the user's region. For example, the formation unit can create region-specific groups by considering the user's geographical location information. This makes it possible to create groups that respond to region-specific needs by creating region-specific groups based on the user's geographical location information. Some or all of the above-described processes in the formation unit may be performed using AI, for example, or without AI. For example, the formation unit can input the user's geographical location information into AI and have AI create region-specific groups.

[0099] The program builder can estimate the user's emotions and adjust the content of the educational program based on those emotions. For example, if the user is stressed, the program builder can provide easy content to reduce the learning burden. If the user is relaxed, the program builder can provide more challenging content to encourage learning. Furthermore, if the user is excited, the program builder can provide interactive content to increase learning motivation. For example, the program builder estimates the user's emotions and adjusts the content of the educational program based on those emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for reducing the learning burden and increasing learning motivation by adjusting the content of the educational program according to the user's emotions. Some or all of the above processing in the program builder may be performed using AI, for example, or not using AI. For example, the program builder can input the user's emotions into the AI ​​and have the AI ​​adjust the content of the educational program.

[0100] The program development unit can construct an optimal program by referencing the results of past educational programs during the development process. For example, the development unit can refer to the methods of successful educational programs in the past. The development unit can also analyze the results of past educational programs and construct an optimal program. Furthermore, the development unit can analyze the failures of past educational programs and construct an improved program. For example, the development unit can construct an optimal program by referencing the results of past educational programs. This enables efficient education by constructing an optimal program based on the results of past educational programs. Some or all of the above processes in the development unit may be performed using AI, for example, or without AI. For example, the development unit can input the results of past educational programs into AI and have the AI ​​construct an optimal program.

[0101] The development unit can estimate the user's emotions and adjust the delivery method of the educational program based on the estimated emotions. For example, if the user is nervous, the development unit can provide a simple and highly visible delivery method. If the user is relaxed, the development unit can also provide a delivery method that includes detailed information. Furthermore, if the user is in a hurry, the development unit can provide a delivery method that gets straight to the point. For example, the development unit estimates the user's emotions and adjusts the delivery method of the educational program based on the estimated emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. By adjusting the delivery method of the educational program according to the user's emotions, the burden of learning can be reduced and motivation to learn can be increased. Some or all of the above processing in the development unit may be performed using AI, for example, or not using AI. For example, the development unit can input the user's emotions into AI and have AI adjust the delivery method of the educational program.

[0102] The development unit can provide region-specific educational programs by considering the user's geographical location information during development. For example, the development unit can provide educational programs that reflect the specific needs of the area where the user lives. Furthermore, the development unit can provide educational programs that solve region-specific problems based on the user's geographical location. In addition, the development unit can provide educational programs tailored to the culture and customs of the user's region. For example, the development unit provides region-specific educational programs by considering the user's geographical location information. This enables education that addresses region-specific needs by providing region-specific educational programs based on the user's geographical location information. Some or all of the above processing in the development unit may be performed using AI, for example, or without AI. For example, the development unit can input the user's geographical location information into AI and have the AI ​​generate region-specific educational programs.

[0103] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0104] The service provider can analyze users' learning styles and provide the most suitable learning materials. For example, it can provide visual materials to users who prefer visual learning and audio materials to users who prefer auditory learning. The service provider can also adjust the pace of the materials to match the user's learning speed. Furthermore, it can customize the content of the materials based on user feedback. This allows for the provision of optimal learning materials tailored to each user's learning style, thereby enhancing learning effectiveness.

[0105] The training department can create individualized learning plans based on users' learning history. For example, it can analyze past learning content and progress to suggest what to learn next. The training department can also provide customized training programs tailored to the user's learning goals. Furthermore, it can assess the user's level of understanding and provide review materials as needed. This allows for efficient learning by providing individualized learning plans based on the user's learning history.

[0106] The team building function can assemble appropriate project teams based on the user's skill level. For example, it can assign basic projects to beginners, advanced projects to intermediate users, and specialized projects to advanced users. The team building function can also select projects based on the user's interests and preferences. Furthermore, it can select the most suitable team members by referring to the user's past project history. This allows for efficient project execution by forming project teams that match the user's skill level and interests.

[0107] The development team can continuously improve the content of educational programs based on user feedback. For example, they can collect opinions and requests from users and adjust the program's content and format. They can also analyze user learning outcomes and implement effective teaching methods. Furthermore, they can regularly update the content of educational materials to keep up with the latest technologies and trends. This allows for more effective learning by improving the educational program based on user feedback.

[0108] The system can estimate the user's emotions and adjust the learning environment based on those emotions. For example, if the user is stressed, it can provide a relaxing environment to reduce the burden of learning. If the user is relaxed, it can provide an environment that enhances concentration. Furthermore, if the user is excited, it can provide an interactive learning environment to increase motivation. By providing an optimal learning environment tailored to the user's emotions, the learning effect can be enhanced.

[0109] The system can analyze a user's learning history and determine the optimal order in which learning materials are provided. For example, it can suggest the next learning material based on what the user has previously studied. The system can also analyze the user's learning speed and provide materials at an appropriate pace. Furthermore, it can evaluate the user's level of understanding and provide necessary review materials. This allows for efficient learning by determining the optimal order of material delivery based on the user's learning history.

[0110] The service provider can offer customized learning materials based on the user's interests. For example, they can provide materials related to topics the user is interested in. They can also provide materials that include specific examples based on the user's areas of interest. Furthermore, they can provide materials tailored to the user's hobbies and preferences. By providing customized learning materials based on the user's interests, their motivation to learn can be enhanced.

[0111] The system can estimate the user's emotions and select the format of the learning materials based on those emotions. For example, if the user is tired, it can provide text-based learning materials that are less visually taxing. If the user is excited, it can provide interactive video learning materials. Furthermore, if the user is relaxed, it can provide video learning materials that include detailed explanations. By selecting the format of learning materials according to the user's emotions, the learning burden can be reduced and motivation to learn can be increased.

[0112] The service provider can provide educational materials that include region-specific examples, taking into account the user's geographical location. For example, it can provide materials that include examples related to the area where the user lives. Furthermore, the service provider can provide educational materials that address region-specific problems based on the user's geographical location. In addition, the service provider can provide educational materials tailored to the culture and customs of the user's region. By providing educational materials that include region-specific examples based on the user's geographical location, the effectiveness of learning can be enhanced.

[0113] The service provider can analyze users' social media activity when providing educational materials and offer relevant materials. For example, they can provide materials related to topics that users have shown interest in on social media. Furthermore, the service provider can offer materials that enhance learning motivation based on users' social media activity. They can also provide materials that include specific examples based on users' social media interactions. This allows for increased learning motivation by providing relevant materials based on users' social media activity.

[0114] The following briefly describes the processing flow for example form 2.

[0115] Step 1: The provider will provide learning materials for beginners to acquire basic knowledge of app development. For example, they will provide online courses, video materials, and interactive learning tools. The provider can also regularly update the content of the materials to keep up with the latest technologies and trends. Step 2: The design department receives an app idea from the user, and the generating AI generates a blueprint of the app based on that idea, and then performs the coding. For example, it generates blueprints such as wireframes, flowcharts, and ER diagrams based on the idea entered by the user. It can also use the generating AI to automatically perform coding based on the blueprint. Step 3: The testing unit uses a generation AI to automatically verify the application's operation, detect bugs, and fix them. For example, it automatically executes tests such as unit tests, integration tests, and system tests. The generation AI automatically generates test cases, verifies the application's operation, analyzes the test results, and detects and fixes bugs.

[0116] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0117] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0118] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0119] Each of the multiple elements described above, including the provisioning unit, designing unit, testing unit, training unit, formation unit, and construction unit, is realized by at least one of the smart device 14 and the data processing unit 12. For example, the provisioning unit is realized by the control unit 46A of the smart device 14 and provides online courses and video teaching materials. The design unit is realized by the specific processing unit 290 of the data processing unit 12 and generates application blueprints using generation AI and performs coding. The testing unit is realized by the specific processing unit 290 of the data processing unit 12 and the generation AI automatically verifies the operation of the application, detects and corrects bugs. The training unit is realized by the control unit 46A of the smart device 14 and trains personnel with skills in application development utilizing generation AI. The formation unit is realized by the specific processing unit 290 of the data processing unit 12 and forms a group within the company with experience in generation AI. The construction unit is realized by the control unit 46A of the smart device 14 and builds educational programs that can be sold to other companies. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0120] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0121] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0122] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0123] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0124] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0126] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0127] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0128] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0129] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0130] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0131] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0132] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0134] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0135] Each of the multiple elements described above, including the provisioning unit, designing unit, testing unit, training unit, formation unit, and construction unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the provisioning unit is implemented by the control unit 46A of the smart glasses 214 and provides online courses and video teaching materials. The design unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates application blueprints using generative AI and performs coding. The testing unit is implemented by the specific processing unit 290 of the data processing unit 12 and the generative AI automatically verifies the operation of the application, detects and corrects bugs. The training unit is implemented by the control unit 46A of the smart glasses 214 and trains personnel with skills in application development utilizing generative AI. The formation unit is implemented by the specific processing unit 290 of the data processing unit 12 and forms a group within the company with experience in generative AI. The construction unit is implemented by the control unit 46A of the smart glasses 214 and constructs educational programs that can be sold to other companies. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0136] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0137] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0138] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0139] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0140] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0142] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0143] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements described above, including the provisioning unit, designing unit, testing unit, training unit, formation unit, and construction unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the provisioning unit is implemented by the control unit 46A of the headset terminal 314 and provides online courses and video teaching materials. The design unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates application blueprints using generative AI and performs coding. The testing unit is implemented by the specific processing unit 290 of the data processing unit 12 and the generative AI automatically verifies the operation of the application, detects and corrects bugs. The training unit is implemented by the control unit 46A of the headset terminal 314 and trains personnel with skills in application development utilizing generative AI. The formation unit is implemented by the specific processing unit 290 of the data processing unit 12 and forms a group within the company with experience in generative AI. The construction unit is implemented by the control unit 46A of the headset terminal 314 and constructs educational programs that can be sold to other companies. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0152] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0153] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0154] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0155] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0156] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0157] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0158] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0159] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0160] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0161] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0162] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0163] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0164] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0165] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0166] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0167] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0168] Each of the multiple elements described above, including the provisioning unit, designing unit, testing unit, training unit, forming unit, and construction unit, is realized by at least one of the robot 414 and the data processing unit 12. For example, the provisioning unit is realized by the control unit 46A of the robot 414 and provides online courses and video teaching materials. The design unit is realized by the specific processing unit 290 of the data processing unit 12 and generates application blueprints using generative AI and performs coding. The testing unit is realized by the specific processing unit 290 of the data processing unit 12 and the generative AI automatically verifies the operation of the application, detects and corrects bugs. The training unit is realized by the control unit 46A of the robot 414 and trains personnel with skills in application development utilizing generative AI. The forming unit is realized by the specific processing unit 290 of the data processing unit 12 and forms a group within the company with experience in generative AI. The construction unit is realized by the control unit 46A of the robot 414 and constructs educational programs that can be sold to other companies. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0169] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0170] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0171] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0172] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0173] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0174] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0175] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0176] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0177] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0178] 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.

[0179] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0180] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0181] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0182] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0183] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0184] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0185] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0186] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0187] (Note 1) The service provider offers educational materials for beginners to learn the basics of app development, When a user inputs an app idea, a generation AI generates a blueprint for the app based on that idea, and then the design department handles the coding. It includes a testing unit in which a generation AI automatically verifies the operation of the application, detects and corrects bugs. A system characterized by the following features. (Note 2) We have a training department that cultivates a large number of people with the skills to develop applications using generative AI. The system described in Appendix 1, characterized by the features described herein. (Note 3) The company has a Formation Department that forms a group with a proven track record in generative AI. The system described in Appendix 1, characterized by the features described herein. (Note 4) We have a development department that builds educational programs that can be sold to other companies based on the know-how gained from trials. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the difficulty level of the learning materials based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, The system analyzes the user's learning history to determine the optimal order in which learning materials are provided. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, When providing educational materials, we offer customized materials based on the user's interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned supply unit is, The system estimates the user's emotions and selects the format of the learning materials based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned supply unit is, When providing educational materials, we will take into account the user's geographical location and provide materials that include region-specific examples. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned supply unit is, When providing educational materials, we analyze users' social media activity and provide relevant materials. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned design department, It estimates the user's emotions and adjusts the level of detail in the design based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned design department, During the design phase, the optimal design methodology is selected by referring to the user's past project history. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned design department, During the design phase, customize the design process according to the user's current skill level. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned design department, It estimates the user's emotions and adjusts how the blueprints are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned design department, During the design phase, the system generates design drawings that include region-specific requirements, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned design department, During the design phase, we analyze users' social media activity and propose relevant design methodologies. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned test unit is We estimate user emotions and determine test priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned test unit is During testing, the optimal test cases are selected by referring to past bug history. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned test unit is During testing, customize the testing process according to the user's current development status. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned test unit is It estimates the user's emotions and adjusts how test results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned test unit is During testing, region-specific bugs are prioritized for detection by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned test unit is During testing, we analyze users' social media activity and propose relevant testing methodologies. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned training department, It estimates the user's emotions and adjusts the pace of the training program based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 24) The aforementioned training department, During training, the optimal training method is selected by referring to the user's past learning history. The system described in Appendix 2, characterized by the features described herein. (Note 25) The aforementioned training department, It estimates the user's emotions and customizes the content of the training program based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned training department, During training, the program provides region-specific training programs that take into account the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 27) The formed portion is It estimates user sentiment and adjusts group formation methods based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 28) The formed portion is During the formation process, the optimal group is formed by referencing the user's past project history. The system described in Appendix 3, characterized by the features described herein. (Note 29) The formed portion is The system estimates the user's emotions and determines the group's role assignments based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 30) The formed portion is During the formation process, region-specific groups are formed by considering the user's geographical location information. The system described in Appendix 3, characterized by the features described herein. (Note 31) The aforementioned construction unit is The system estimates the user's emotions and adjusts the content of the educational program based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 32) The aforementioned construction unit is During the development process, we will create the optimal program by referring to the results of past educational programs. The system described in Appendix 4, characterized by the features described herein. (Note 33) The aforementioned construction unit is The system estimates user emotions and adjusts the delivery method of educational programs based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 34) The aforementioned construction unit is During development, the system provides region-specific educational programs that take into account the user's geographical location. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0188] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The service provider offers educational materials for beginners to learn the basics of app development, When a user inputs an app idea, a generation AI generates a blueprint for the app based on that idea, and then a design department handles the coding. It includes a testing unit in which a generating AI automatically verifies the operation of the application, detects and corrects bugs. A system characterized by the following features.

2. We have a training department that cultivates a large number of people with the skills to develop applications using generative AI. The system according to feature 1.

3. The company has a Formation Department that forms a group with a proven track record in generative AI. The system according to feature 1.

4. We have a development department that builds educational programs that can be sold to other companies based on the know-how gained from trials. The system according to feature 1.

5. The aforementioned supply unit is, The system estimates the user's emotions and adjusts the difficulty level of the learning materials based on those estimated emotions. The system according to feature 1.

6. The aforementioned supply unit is, The system analyzes the user's learning history to determine the optimal order in which learning materials are provided. The system according to feature 1.

7. The aforementioned supply unit is, When providing educational materials, we offer customized materials based on the user's interests and preferences. The system according to feature 1.

8. The aforementioned supply unit is, The system estimates the user's emotions and selects the format of the learning materials based on those estimated emotions. The system according to feature 1.

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