System

Abstract

To provide a system that enables users to effectively learn and utilize prompt engineering.SOLUTION: A system according to the embodiment comprises an education unit, a support unit, a generation unit, and a provision unit. The education unit allows AI to educate users on prompt engineering. The support unit allows the AI to support the users in creating prompts. The generation unit generates content based on prompts created by the user with support from the support unit. The provision unit provides the content generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have the drawback that it is difficult for users to effectively learn and utilize prompt engineering.

[0005] The system according to the embodiment aims to enable users to effectively learn and utilize prompt engineering. [Means for solving the problem]

[0006] The system according to the embodiment includes an education unit, a support unit, a generation unit, and a provision unit. The education unit uses AI to educate a user on prompt engineering. The support unit uses AI to support the user in creating prompts. The generation unit generates content based on prompts created by the user with support from the support unit. The provision unit provides the content generated by the generation unit. Effect of the Invention

[0007] The system according to the embodiment can enable users to effectively learn and utilize prompt engineering. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

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

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

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) The prompt engineering support system according to the embodiment of the present invention is a system for more effective use of the generation AI while maintaining its safety and ethics. The prompt engineering support system aims to turn all citizens into prompt engineers through two businesses. First, an educational service is provided in which an AI becomes a teacher and teaches prompt engineering. In this educational service, an AI interactively teaches prompt engineering to a user, and the user can learn how to create a prompt through dialogue with the AI. For example, when a user asks "how should I create a prompt?", the AI ​​explains it while giving a specific example. Next, a system is provided in which an AI supports prompt engineering. In this system, an AI supports a user when creating a prompt, and provides a proposal for a prompt based on the user's input. For example, when a user inputs "I want to create a product description," the AI ​​presents an appropriate proposal for a prompt. Furthermore, the generation AI generates content based on the prompt created by the user. The generated content is in response to the user's request, and includes, for example, a product description and advertising copy. The generated content is provided to the user through the provision unit. This system makes it possible to educate and support prompt engineering while maintaining the safety and ethics of the generation AI, and all citizens can acquire the skills of a prompt engineer. This enables the Prompt Engineering Support System to educate and support prompt engineering while maintaining the safety and ethics of the generative AI.

[0029] The prompt engineering support system according to the embodiment includes an education unit, a support unit, a generation unit, and a provision unit. The education unit is configured to have an AI provide prompt engineering education to a user. The education unit provides education in an interactive manner, for example. The interactive education is performed in a format in which a user asks an AI a question, and the AI ​​answers by giving a specific example. For example, when a user asks "how should I create a prompt?", the AI ​​explains a specific method for creating a prompt. The support unit supports the user in creating a prompt. The support unit provides a prompt proposal based on, for example, a user's input. For example, when a user inputs "I want to create a product description," the AI ​​presents an appropriate prompt proposal. The generation unit generates content based on a prompt created by the user with support from the support unit. The generation unit generates content using, for example, a generation AI. The generated content includes, for example, a product description and an advertisement copy. The provision unit provides the content generated by the generation unit. The provision unit provides the generated content by filtering the generated content. The filtering includes, for example, removing inappropriate content and evaluating the quality. This enables the prompt engineering support system to educate and support the user in prompt engineering and provide generated content.

[0030] In the education department, the AI ​​educates users on prompt engineering. The education department, for example, provides education in an interactive format. Interactive education is conducted in the form of a user asking the AI ​​a question, and the AI ​​responds by providing specific examples. For example, if a user asks "how should I create a prompt?", the AI ​​explains how to create a specific prompt. Specifically, the education department provides teaching materials and tutorials for users to understand the basic concepts of prompt engineering. This includes the structure of a prompt, the selection of effective keywords, and how to adjust it to the target audience. In addition, the education department has a function in which the AI ​​provides real-time feedback on specific challenges that users face when actually creating a prompt. For example, the AI ​​evaluates the effectiveness of a prompt created by a user and suggests improvements. In addition, the education department saves the history of prompts created by users in the past, and supports the user in improving their skills based on this. This allows users to check their own growth and continue to learn. In addition, the education department also provides a community function that allows users to share their knowledge and experience with other users. This allows users to receive feedback and advice from other users and learn more effective prompt engineering.

[0031] In the support section, AI supports the user in creating prompts. For example, the support section provides prompt suggestions based on the user's input. For example, when a user inputs "I want to create a product description," the AI ​​presents appropriate prompt suggestions. Specifically, the support section analyzes the keywords and phrases entered by the user and makes suggestions for generating optimal prompts based on them. The AI ​​learns effective prompt patterns based on past data and success cases, and uses this to provide specific advice to the user. For example, if a user inputs "I want to highlight the features of a new product," the AI ​​proposes specific phrases and structures to highlight the features of the product. In addition, the support section provides real-time feedback on the prompts created by the user and suggests corrections as necessary. This allows users to quickly improve their prompts and generate more effective content. In addition, the support section also has a function in which the AI ​​suggests the structure and keywords of the prompt that are best suited to the purpose when the user creates a prompt tailored to a specific purpose or target audience. This allows users to efficiently create prompts that best suit their needs.

[0032] The generation unit generates content based on the prompts created by the user with the support of the support unit. The generation unit generates content using, for example, a generation AI. The generated content includes, for example, product descriptions and advertising copy. Specifically, the generation unit receives the prompts created by the user as input and executes an algorithm for generating high-quality content based on the prompts. The generation AI uses natural language processing technology to accurately understand the user's intention and generate text accordingly. For example, if a user inputs "I want to create an advertising copy that emphasizes the features of a new product," the generation AI generates advertising copy using specific phrases and structures to effectively convey the features of the product. The generation unit also has a function to evaluate the quality of the generated content and make corrections as necessary. This ensures that the generated content meets the user's expectations. In addition, the generation unit also provides a function to save the content generated by the user and make it available for later reuse. This allows the user to efficiently create new content based on content generated in the past. The generation unit supports the user in quickly generating high-quality content that meets diverse needs, improving the efficiency and effectiveness of the entire system.

[0033] The providing unit provides the content generated by the generating unit. The providing unit, for example, filters the generated content and provides it. Filtering, for example, removes inappropriate content and evaluates the quality. Specifically, the providing unit processes a large amount of generated content at once and executes an algorithm to select high-quality content from among them. AI evaluates the grammar, style, and content consistency of the generated content, and automatically detects and removes inappropriate expressions and content that may be misleading. The providing unit also has a function to customize content according to the needs and purposes of the user. For example, when generating advertising copy for a specific target audience, the content is adjusted to match the characteristics and preferences of the audience. Furthermore, the providing unit also provides a function to support multiple formats and platforms when providing the generated content to the user. This allows the user to effectively utilize the generated content in various media and channels. For example, the generated advertising copy is converted into an optimal format for use on a website, SNS, email marketing, etc. The providing unit also collects feedback from users and provides data to continuously improve the quality and effectiveness of the generated content. This allows the providing unit to provide high-quality and effective content to users and improve the performance of the entire system.

[0034] The education department can provide education on prompt engineering in an interactive manner. The education department, for example, provides education in an interactive manner using a chatbot. The chatbot provides appropriate answers to questions entered by the user. For example, when the user asks "How should I create a prompt?", the chatbot explains how to create a specific prompt. The education department can also provide education in an interactive manner using voice dialogue. The voice dialogue is performed in a format in which the user asks a question by voice and the AI ​​answers by voice. For example, when the user asks by voice, "Please tell me how to create a prompt," the AI ​​explains by voice how to create a specific prompt. This allows the user to learn more effectively by providing education in an interactive manner.

[0035] The support unit can provide a proposal for a prompt based on the user's input. For example, when the user inputs "I want to create a product description," the AI ​​presents an appropriate proposal for a prompt. The proposal for a prompt includes, for example, a template or a sample prompt. The template is provided in a pre-prepared format so that the user can easily create a prompt. The sample prompt is provided as a concrete example that the user can refer to. For example, when the user inputs "I want to create a product description," the support unit presents templates such as "a prompt that emphasizes the product's features" and "a prompt that explains the benefits of the product." In addition, when the user inputs "I want to create a product description," the support unit presents a sample prompt such as "This product is of high quality and lasts a long time." This makes it possible to support the creation of a prompt by providing a proposal for a prompt based on the user's input.

[0036] The providing unit can filter the generated content and provide it. The providing unit, for example, filters the generated content and provides it. Filtering, for example, removes inappropriate content and evaluates quality. Removal of inappropriate content is a process of removing inappropriate expressions or information when the generated content includes such expressions or information. Quality evaluation is a process of evaluating the quality of the generated content and checking whether the content satisfies a certain standard. For example, when the generated content includes inappropriate expressions, the providing unit automatically detects and removes the inappropriate expressions. The providing unit can also evaluate the quality of the generated content and regenerate the content when the quality is low. In this way, appropriate content can be provided to the user by filtering the generated content and providing it.

[0037] The education unit can analyze the user's past learning history and select an appropriate education method. The education unit, for example, provides a curriculum that focuses on reviewing topics that the user had difficulty with in the past. For example, if the user feels that "a specific part of creating a prompt is difficult," the education unit provides review materials focusing on that part. The education unit also suggests an education program including more advanced content based on the user's field of expertise. For example, if the user feels that "I understand the basic method of creating a prompt," the education unit provides practical tasks and techniques for creating applied prompts. Furthermore, the education unit adjusts the progress speed according to the user's learning pace and encourages review at an appropriate time. For example, if the user feels that the learning pace is "too fast," the education unit slows down the progress speed and ensures time to deepen understanding. This enables effective learning by selecting the optimal education method based on the user's past learning history. The analysis of the learning history is performed using data such as past test results and study time. A part or all of the above-mentioned processing in the education unit may be performed using, for example, AI, or may be performed without using AI.

[0038] The education unit can customize the educational content based on the user's interests and concerns. The education unit, for example, explains the educational content using cases and examples related to the topic in which the user is interested. For example, if the user feels that "I am interested in a specific field in prompt creation," the education unit provides cases and examples related to that field. In addition, the education unit provides related additional materials and references based on the user's field of interest. For example, if the user feels that "I want to know more information about prompt creation," the education unit provides related additional materials and references. Furthermore, the education unit creates an individual learning plan based on the topic in which the user shows interest. For example, if the user feels that "I want to deepen my learning about a specific topic," the education unit provides an individual learning plan focusing on that topic. This improves the effectiveness of learning by customizing the educational content based on the user's interests and concerns. The interests and concerns are identified using, for example, a method such as a questionnaire or an analysis of behavioral history. A part or all of the above-mentioned processing in the education unit may be performed using, for example, AI, or may be performed without using AI.

[0039] The education unit can localize the educational content based on the geographical location information of the user. The education unit, for example, explains the educational content using examples and data related to the user's region. For example, if the user feels that "I want to know examples specific to the region when creating a prompt," the education unit provides examples and data related to the region. In addition, the education unit provides educational materials that match the language and culture of the user's region. For example, if the user feels that "I want to know materials that match the language and culture of the region when creating a prompt," the education unit provides educational materials that match the language and culture of the region. In addition, the education unit provides content that conforms to the educational curriculum of the user's region. For example, if the user feels that "I want to know the content of prompt creation that conforms to the educational curriculum of the region," the education unit provides content that conforms to the educational curriculum of the region. This enables more appropriate education by localizing the educational content based on the geographical location information of the user. The geographical location information is acquired using a method such as GPS data or an IP address. A part or all of the above-mentioned processing in the education unit may be performed using, for example, AI, or may be performed without using AI.

[0040] The education unit can explain the educational content using relevant examples based on the user's social media activity. For example, the education unit explains the educational content using examples related to a topic in which the user showed interest on social media. For example, if the user feels that "I am interested in a specific topic in creating a prompt," the education unit provides examples related to that topic. In addition, the education unit provides related additional materials based on the user's social media activity history. For example, if the user feels that "I want to know more information about creating a prompt," the education unit provides related additional materials. In addition, the education unit provides educational content that incorporates the opinions of experts and influencers that the user follows on social media. For example, if the user feels that "I want to know the opinions of specific experts and influencers in creating a prompt," the education unit provides educational content that incorporates those opinions. As a result, the effectiveness of learning is improved by explaining the educational content based on the user's social media activity. The social media activity is acquired using, for example, a method such as analysis of the posted content and followers. A part or all of the above-mentioned processing in the education unit may be performed using, for example, AI, or may be performed without using AI.

[0041] The support unit can analyze the user's past prompt creation history and select the optimal support method. The support unit, for example, proposes a similar prompt based on a successful example of a prompt created by the user in the past. For example, if the user feels that "I want to create a new prompt based on a prompt that was successful in the past," the support unit proposes a similar prompt based on the past successful example. In addition, the support unit provides support with a focus on a point of prompt creation that the user was weak at in the past. For example, if the user feels that "I want to overcome a point of prompt creation that was weak in the past," the support unit provides support with a focus on that point. In addition, the support unit analyzes the user's past prompt creation history and proposes the optimal support timing. For example, if the user feels that "I want to receive support at the optimal timing," the support unit proposes the optimal support timing based on the past history. This enables effective support by selecting the optimal support method based on the user's past prompt creation history. The analysis of the prompt creation history is performed using data such as past prompts and success rates. A part or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI.

[0042] The support unit can customize the support content based on the user's current project and field of interest. The support unit provides support content related to the project the user is currently working on, for example. For example, if the user feels that "I want to receive support related to the current project," the support unit provides support content related to the project. In addition, the support unit provides related cases and reference materials based on the user's field of interest. For example, if the user feels that "I want to know cases and materials related to the field of interest," the support unit provides cases and reference materials related to that field. In addition, the support unit provides appropriate support according to the progress of the user's current project. For example, if the user feels that "I want to receive support according to the progress of the project," the support unit provides support according to the progress. This enables effective support by customizing the support content based on the user's current project and field of interest. The current project and field of interest are identified using, for example, a project management tool, a questionnaire, or other methods. A part or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI.

[0043] The support unit can localize the support content based on the geographical location information of the user. The support unit explains the support content using examples and data related to the user's region, for example. For example, if the user feels that "I want to know examples specific to the region when creating a prompt," the support unit provides examples and data related to the region. In addition, the support unit provides support materials that match the language and culture of the user's region. For example, if the user feels that "I want to know materials that match the language and culture of the region when creating a prompt," the support unit provides support materials that match the language and culture of the region. In addition, the support unit proposes a support method according to the characteristics of the user's region. For example, if the user feels that "I want to receive support that matches the characteristics of the region," the support unit proposes a support method that matches the characteristics. This makes it possible to provide more appropriate support by localizing the support content based on the geographical location information of the user. The geographical location information is acquired using, for example, GPS data, IP addresses, or other methods. A part or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI.

[0044] The support unit can explain the support content using relevant examples based on the user's social media activity. The support unit explains the support content using examples related to topics in which the user has shown interest on social media, for example. For example, if the user feels that "I am interested in a specific topic in creating a prompt," the support unit provides examples related to that topic. In addition, the support unit provides related additional materials based on the user's social media activity history. For example, if the user feels that "I want to know more information about creating a prompt," the support unit provides related additional materials. In addition, the support unit provides support content that incorporates the opinions of experts and influencers that the user follows on social media. For example, if the user feels that "I want to know the opinions of specific experts and influencers in creating a prompt," the support unit provides support content that incorporates those opinions. This enables effective support by explaining the support content based on the user's social media activity. The social media activity is acquired using, for example, a method such as analysis of the posted content and followers. A part or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI.

[0045] The generating unit can customize the content to be generated based on the user's past creation history. For example, the generating unit generates new content in a similar style based on the style of content created by the user in the past. For example, if the user feels that "I want to create new content based on the style of content created in the past," the generating unit generates new content in a similar style based on the past style. In addition, the generating unit generates content incorporating keywords and phrases used by the user in the past. For example, if the user feels that "I want to create content incorporating keywords and phrases used in the past," the generating unit generates content incorporating the keywords and phrases. Furthermore, the generating unit analyzes the user's past creation history and proposes an optimal content configuration. For example, if the user feels that "I want to know the optimal content configuration," the generating unit proposes an optimal content configuration based on the past history. In this way, more appropriate content can be generated by customizing the content to be generated based on the user's past creation history. The creation history is acquired using data such as past projects and success rates. A part or all of the above-mentioned processing in the generating unit may be performed using, for example, a generation AI, or may be performed without using a generation AI.

[0046] The generating unit can optimize the content to be generated based on the user's current project or field of interest. The generating unit generates content related to the project that the user is currently working on, for example. For example, if the user feels that "I want to create content related to the current project," the generating unit generates content related to the project. The generating unit also generates content incorporating related cases and data based on the user's field of interest. For example, if the user feels that "I want to create content incorporating cases and data related to the field of interest," the generating unit generates content incorporating cases and data related to the field. Furthermore, the generating unit generates appropriate content according to the progress of the user's current project. For example, if the user feels that "I want to create content according to the progress of the project," the generating unit generates content according to the progress. As a result, more appropriate content can be generated by optimizing the content to be generated based on the user's current project or field of interest. The current project or field of interest is identified using a method such as a project management tool or a questionnaire. A part or all of the above-mentioned processing in the generating unit may be performed using, for example, a generating AI, or may be performed without using a generating AI.

[0047] The generation unit can localize the content to be generated based on the geographical location information of the user. The generation unit generates the content using examples and data related to the user's region, for example. For example, when the user feels that "I want to know examples specific to the region when creating a prompt," the generation unit provides examples and data related to the region. In addition, the generation unit generates content that matches the language and culture of the user's region. For example, when the user feels that "I want to know content that matches the language and culture of the region when creating a prompt," the generation unit provides content that matches the language and culture of the region. In addition, the generation unit generates content according to the characteristics of the user's region. For example, when the user feels that "I want to create content that matches the characteristics of the region," the generation unit generates content according to the characteristics. As a result, more appropriate content can be generated by localizing the content to be generated based on the geographical location information of the user. The geographical location information is acquired using a method such as GPS data or an IP address. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI.

[0048] The generation unit can generate the content to be generated using highly relevant examples based on the user's social media activity. The generation unit generates the content using examples related to topics in which the user has shown interest on social media, for example. For example, if the user feels that "I am interested in a specific topic in creating a prompt," the generation unit provides examples related to that topic. The generation unit also provides related additional materials based on the user's social media activity history. For example, if the user feels that "I want to know more information about creating a prompt," the generation unit provides related additional materials. Furthermore, the generation unit generates content incorporating the opinions of experts and influencers that the user follows on social media. For example, if the user feels that "I want to know the opinions of specific experts and influencers in creating a prompt," the generation unit provides content incorporating those opinions. In this way, more appropriate content can be generated by generating content to be generated based on the user's social media activity. The social media activity is acquired using, for example, a method such as analysis of the posted content and followers. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI.

[0049] The providing unit can customize the content to be provided based on the user's past browsing history. For example, the providing unit provides new content in a similar style based on the style of content previously viewed by the user. For example, when the user feels that "I want to know new content based on the style of content previously viewed," the providing unit provides new content in a similar style based on the past style. Furthermore, the providing unit provides content incorporating keywords or phrases previously viewed by the user. For example, when the user feels that "I want to know content incorporating keywords or phrases previously viewed," the providing unit provides content incorporating the keywords or phrases. Furthermore, the providing unit analyzes the user's past browsing history and proposes an optimal content configuration. For example, when the user feels that "I want to know the optimal content configuration," the providing unit proposes an optimal content configuration based on the past history. As a result, more appropriate content can be provided by customizing the content to be provided based on the user's past browsing history. The browsing history is acquired using data such as past viewed pages and browsing time. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI.

[0050] The providing unit can optimize the content to be provided based on the user's current project or field of interest. The providing unit provides, for example, content related to a project that the user is currently working on. For example, if the user feels that "I want to know content related to the current project," the providing unit provides content related to the project. Furthermore, the providing unit provides content incorporating related cases and data based on the user's field of interest. For example, if the user feels that "I want to know content incorporating cases and data related to the field of interest," the providing unit provides content incorporating cases and data related to the field. Furthermore, the providing unit provides appropriate content according to the progress of the user's current project. For example, if the user feels that "I want to know content according to the progress of the project," the providing unit provides content according to the progress. As a result, more appropriate content can be provided by optimizing the content to be provided based on the user's current project or field of interest. The current project or field of interest is identified using, for example, a project management tool or a questionnaire. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI.

[0051] The providing unit can localize the content to be provided based on the geographical location information of the user. The providing unit provides the content using examples and data related to the user's region, for example. For example, when the user feels that "I want to know examples specific to the region when creating a prompt," the providing unit provides examples and data related to the region. In addition, the providing unit provides content that matches the language and culture of the user's region. For example, when the user feels that "I want to know content that matches the language and culture of the region when creating a prompt," the providing unit provides content that matches the language and culture of the region. In addition, the providing unit provides content according to the characteristics of the user's region. For example, when the user feels that "I want to create content that matches the characteristics of the region," the providing unit provides content according to the characteristics. As a result, more appropriate content can be provided by localizing the content to be provided based on the geographical location information of the user. The geographical location information is acquired using a method such as GPS data or an IP address. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI.

[0052] The providing unit can provide the content to be provided using highly relevant examples based on the user's social media activity. The providing unit provides the content using examples related to a topic in which the user has shown interest on social media, for example. For example, if the user feels that "I am interested in a specific topic in creating a prompt," the providing unit provides examples related to that topic. In addition, the providing unit provides related additional materials based on the user's social media activity history. For example, if the user feels that "I want to know more information about creating a prompt," the providing unit provides related additional materials. Furthermore, the providing unit provides content that incorporates the opinions of experts and influencers that the user follows on social media. For example, if the user feels that "I want to know the opinions of specific experts and influencers in creating a prompt," the providing unit provides content that incorporates those opinions. As a result, more appropriate content can be provided by providing content to be provided based on the user's social media activity. The social media activity is acquired using, for example, a method such as analysis of the posted content and followers. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The education department can customize the teaching method based on the user's learning style. For example, a user who prefers visual learning can be provided with teaching materials that make extensive use of diagrams and graphs. A user who prefers auditory learning can be provided with teaching materials in the form of audio guides or podcasts. Furthermore, a user who prefers practical learning can be provided with interactive simulations and practical assignments. This can improve the learning effect by providing the optimal teaching method according to the user's learning style.

[0055] The support unit can monitor the progress of the user's prompt creation in real time and provide feedback at an appropriate time. For example, if the user is stuck in creating a prompt, the support unit can immediately provide advice or hints. If the user is progressing smoothly, the support unit can send an encouraging message. Furthermore, if the user repeatedly makes mistakes in a particular part, the support unit can provide a detailed explanation or additional resources for that part. This makes it possible to improve the efficiency of prompt creation by providing appropriate support according to the user's progress.

[0056] The providing unit can collect user feedback and continuously improve the quality of the content to be provided. For example, the providing unit can enable users to leave ratings and comments on the provided content. The providing unit can also analyze user feedback to identify common problems and areas for improvement. Furthermore, the providing unit can correct and update the content based on user feedback. This makes it possible to provide high-quality content that meets the needs of users.

[0057] The education department can create an individual learning plan based on the user's learning goals. For example, if the user wants to master a specific skill, a curriculum focusing on that skill can be provided. Also, if the user wants to achieve a goal within a specific deadline, a learning schedule can be created to match that deadline. Furthermore, the learning plan can be flexibly adjusted according to the user's progress. This makes it possible to support effective learning by providing an optimal learning plan according to the user's learning goals.

[0058] The process flow of the first embodiment will be briefly described below.

[0059] Step 1: The education department has the AI ​​educate the user on prompt engineering. The education department, for example, provides education in an interactive format. Interactive education is conducted in the form of the user asking the AI ​​a question, and the AI ​​responding by providing specific examples. For example, if the user asks, "How should I create a prompt?", the AI ​​will explain how to create a specific prompt. Step 2: The support department uses AI to support the user in creating a prompt. The support department provides prompt suggestions based on the user's input, for example. For example, if the user inputs "I want to create a product description," the AI ​​will suggest an appropriate prompt. Step 3: The generation unit generates content based on the prompts created by the user with support from the support unit. The generation unit generates the content using, for example, a generation AI. The generated content includes, for example, product descriptions and advertising copy. Step 4: The providing unit provides the content generated by the generating unit. The providing unit, for example, filters the generated content and provides it. The filtering includes, for example, removing inappropriate content and evaluating the quality of the content.

[0060] (Example 2) The prompt engineering support system according to the embodiment of the present invention is a system for more effective use of the generation AI while maintaining its safety and ethics. The prompt engineering support system aims to turn all citizens into prompt engineers through two businesses. First, an educational service is provided in which an AI becomes a teacher and teaches prompt engineering. In this educational service, an AI interactively teaches prompt engineering to a user, and the user can learn how to create a prompt through dialogue with the AI. For example, when a user asks "how should I create a prompt?", the AI ​​explains it while giving a specific example. Next, a system is provided in which an AI supports prompt engineering. In this system, an AI supports a user when creating a prompt, and provides a proposal for a prompt based on the user's input. For example, when a user inputs "I want to create a product description," the AI ​​presents an appropriate proposal for a prompt. Furthermore, the generation AI generates content based on the prompt created by the user. The generated content is in response to the user's request, and includes, for example, a product description and advertising copy. The generated content is provided to the user through the provision unit. This system makes it possible to educate and support prompt engineering while maintaining the safety and ethics of the generation AI, and all citizens can acquire the skills of a prompt engineer. This enables the Prompt Engineering Support System to educate and support prompt engineering while maintaining the safety and ethics of the generative AI.

[0061] The prompt engineering support system according to the embodiment includes an education unit, a support unit, a generation unit, and a provision unit. The education unit is configured to have an AI provide prompt engineering education to a user. The education unit provides education in an interactive manner, for example. The interactive education is performed in a format in which a user asks an AI a question, and the AI ​​answers by giving a specific example. For example, when a user asks "how should I create a prompt?", the AI ​​explains a specific method for creating a prompt. The support unit supports the user in creating a prompt. The support unit provides a prompt proposal based on, for example, a user's input. For example, when a user inputs "I want to create a product description," the AI ​​presents an appropriate prompt proposal. The generation unit generates content based on a prompt created by the user with support from the support unit. The generation unit generates content using, for example, a generation AI. The generated content includes, for example, a product description and an advertisement copy. The provision unit provides the content generated by the generation unit. The provision unit provides the generated content by filtering the generated content. The filtering includes, for example, removing inappropriate content and evaluating the quality. This enables the prompt engineering support system to educate and support the user in prompt engineering and provide generated content.

[0062] In the education department, the AI ​​educates users on prompt engineering. The education department, for example, provides education in an interactive format. Interactive education is conducted in the form of a user asking the AI ​​a question, and the AI ​​responds by providing specific examples. For example, if a user asks "how should I create a prompt?", the AI ​​explains how to create a specific prompt. Specifically, the education department provides teaching materials and tutorials for users to understand the basic concepts of prompt engineering. This includes the structure of a prompt, the selection of effective keywords, and how to adjust it to the target audience. In addition, the education department has a function in which the AI ​​provides real-time feedback on specific challenges that users face when actually creating a prompt. For example, the AI ​​evaluates the effectiveness of a prompt created by a user and suggests improvements. In addition, the education department saves the history of prompts created by users in the past, and supports the user in improving their skills based on this. This allows users to check their own growth and continue to learn. In addition, the education department also provides a community function that allows users to share their knowledge and experience with other users. This allows users to receive feedback and advice from other users and learn more effective prompt engineering.

[0063] In the support section, AI supports the user in creating prompts. For example, the support section provides prompt suggestions based on the user's input. For example, when a user inputs "I want to create a product description," the AI ​​presents appropriate prompt suggestions. Specifically, the support section analyzes the keywords and phrases entered by the user and makes suggestions for generating optimal prompts based on them. The AI ​​learns effective prompt patterns based on past data and success cases, and uses this to provide specific advice to the user. For example, if a user inputs "I want to highlight the features of a new product," the AI ​​proposes specific phrases and structures to highlight the features of the product. In addition, the support section provides real-time feedback on the prompts created by the user and suggests corrections as necessary. This allows users to quickly improve their prompts and generate more effective content. In addition, the support section also has a function in which the AI ​​suggests the structure and keywords of the prompt that are best suited to the purpose when the user creates a prompt tailored to a specific purpose or target audience. This allows users to efficiently create prompts that best suit their needs.

[0064] The generation unit generates content based on the prompts created by the user with the support of the support unit. The generation unit generates content using, for example, a generation AI. The generated content includes, for example, product descriptions and advertising copy. Specifically, the generation unit receives the prompts created by the user as input and executes an algorithm for generating high-quality content based on the prompts. The generation AI uses natural language processing technology to accurately understand the user's intention and generate text accordingly. For example, if a user inputs "I want to create an advertising copy that emphasizes the features of a new product," the generation AI generates advertising copy using specific phrases and structures to effectively convey the features of the product. The generation unit also has a function to evaluate the quality of the generated content and make corrections as necessary. This ensures that the generated content meets the user's expectations. In addition, the generation unit also provides a function to save the content generated by the user and make it available for later reuse. This allows the user to efficiently create new content based on content generated in the past. The generation unit supports the user in quickly generating high-quality content that meets diverse needs, improving the efficiency and effectiveness of the entire system.

[0065] The providing unit provides the content generated by the generating unit. The providing unit, for example, filters the generated content and provides it. Filtering, for example, removes inappropriate content and evaluates the quality. Specifically, the providing unit processes a large amount of generated content at once and executes an algorithm to select high-quality content from among them. AI evaluates the grammar, style, and content consistency of the generated content, and automatically detects and removes inappropriate expressions and content that may be misleading. The providing unit also has a function to customize content according to the needs and purposes of the user. For example, when generating advertising copy for a specific target audience, the content is adjusted to match the characteristics and preferences of the audience. Furthermore, the providing unit also provides a function to support multiple formats and platforms when providing the generated content to the user. This allows the user to effectively utilize the generated content in various media and channels. For example, the generated advertising copy is converted into an optimal format for use on a website, SNS, email marketing, etc. The providing unit also collects feedback from users and provides data to continuously improve the quality and effectiveness of the generated content. This allows the providing unit to provide high-quality and effective content to users and improve the performance of the entire system.

[0066] The education department can provide education on prompt engineering in an interactive manner. The education department, for example, provides education in an interactive manner using a chatbot. The chatbot provides appropriate answers to questions entered by the user. For example, when the user asks "How should I create a prompt?", the chatbot explains how to create a specific prompt. The education department can also provide education in an interactive manner using voice dialogue. The voice dialogue is performed in a format in which the user asks a question by voice and the AI ​​answers by voice. For example, when the user asks by voice, "Please tell me how to create a prompt," the AI ​​explains by voice how to create a specific prompt. This allows the user to learn more effectively by providing education in an interactive manner.

[0067] The support unit can provide a proposal for a prompt based on the user's input. For example, when the user inputs "I want to create a product description," the AI ​​presents an appropriate proposal for a prompt. The proposal for a prompt includes, for example, a template or a sample prompt. The template is provided in a pre-prepared format so that the user can easily create a prompt. The sample prompt is provided as a concrete example that the user can refer to. For example, when the user inputs "I want to create a product description," the support unit presents templates such as "a prompt that emphasizes the product's features" and "a prompt that explains the benefits of the product." In addition, when the user inputs "I want to create a product description," the support unit presents a sample prompt such as "This product is of high quality and lasts a long time." This makes it possible to support the creation of a prompt by providing a proposal for a prompt based on the user's input.

[0068] The providing unit can filter the generated content and provide it. The providing unit, for example, filters the generated content and provides it. Filtering, for example, removes inappropriate content and evaluates quality. Removal of inappropriate content is a process of removing inappropriate expressions or information when the generated content includes such expressions or information. Quality evaluation is a process of evaluating the quality of the generated content and checking whether the content satisfies a certain standard. For example, when the generated content includes inappropriate expressions, the providing unit automatically detects and removes the inappropriate expressions. The providing unit can also evaluate the quality of the generated content and regenerate the content when the quality is low. In this way, appropriate content can be provided to the user by filtering the generated content and providing it.

[0069] The education unit can estimate the user's emotions and adjust the difficulty of the educational content based on the estimated user's emotions. For example, if the user feels stressed, the education unit lowers the difficulty of the educational content and starts the explanation from basic concepts. For example, if the user feels that "creating prompts is difficult," the education unit explains the basic structure and elements of prompts in detail. Furthermore, if the user is relaxed, the education unit increases the difficulty of the educational content and provides applied tasks. For example, if the user feels that "I have become accustomed to creating prompts," the education unit introduces more advanced prompt creation techniques and application examples. Furthermore, if the user is excited, the education unit provides educational content in the form of an interactive quiz or game. For example, if the user feels that "I want to test my prompt creation skills," the education unit asks prompt creation questions in the form of a quiz, and the user proceeds with learning in the form of answering the questions. This allows for more effective education by adjusting the difficulty of the educational content according to the user's emotions. The emotion estimation is performed using technologies such as facial expression recognition and voice analysis. A part or all of the above-mentioned processing in the education unit may be performed using, for example, AI, or may be performed without using AI.

[0070] The education unit can analyze the user's past learning history and select an appropriate education method. The education unit, for example, provides a curriculum that focuses on reviewing topics that the user had difficulty with in the past. For example, if the user feels that "a specific part of creating a prompt is difficult," the education unit provides review materials focusing on that part. The education unit also suggests an education program including more advanced content based on the user's field of expertise. For example, if the user feels that "I understand the basic method of creating a prompt," the education unit provides practical tasks and techniques for creating applied prompts. Furthermore, the education unit adjusts the progress speed according to the user's learning pace and encourages review at an appropriate time. For example, if the user feels that the learning pace is "too fast," the education unit slows down the progress speed and ensures time to deepen understanding. This enables effective learning by selecting the optimal education method based on the user's past learning history. The analysis of the learning history is performed using data such as past test results and study time. A part or all of the above-mentioned processing in the education unit may be performed using, for example, AI, or may be performed without using AI.

[0071] The education unit can customize the educational content based on the user's interests and concerns. The education unit, for example, explains the educational content using cases and examples related to the topic in which the user is interested. For example, if the user feels that "I am interested in a specific field in prompt creation," the education unit provides cases and examples related to that field. In addition, the education unit provides related additional materials and references based on the user's field of interest. For example, if the user feels that "I want to know more information about prompt creation," the education unit provides related additional materials and references. Furthermore, the education unit creates an individual learning plan based on the topic in which the user shows interest. For example, if the user feels that "I want to deepen my learning about a specific topic," the education unit provides an individual learning plan focusing on that topic. This improves the effectiveness of learning by customizing the educational content based on the user's interests and concerns. The interests and concerns are identified using, for example, a method such as a questionnaire or an analysis of behavioral history. A part or all of the above-mentioned processing in the education unit may be performed using, for example, AI, or may be performed without using AI.

[0072] The education unit can estimate the user's emotions and adjust the speed of the education based on the estimated user's emotions. For example, when the user feels impatient, the education unit slows down the speed of the education to ensure time for deepening understanding. For example, when the user feels that "progress in prompt creation is too fast," the education unit slows down the speed of the education and explains basic concepts again. Furthermore, when the user feels relaxed, the education unit speeds up the education to efficiently proceed with learning. For example, when the user feels that "progress in prompt creation is too slow," the education unit speeds up the education and provides applied tasks. Furthermore, when the user is excited, the education unit adjusts the speed of the education and increases the interactive elements. For example, when the user feels that "I want to test my prompt creation skills," the education unit asks prompt creation questions in the form of a quiz, and the user proceeds with learning in the form of answering the questions. In this way, the effectiveness of learning is improved by adjusting the speed of the education according to the user's emotions. Emotions are estimated using technologies such as facial expression recognition and voice analysis. Some or all of the above-mentioned processing in the education department may be performed using AI, for example, or may be performed without using AI.

[0073] The education unit can localize the educational content based on the geographical location information of the user. The education unit, for example, explains the educational content using examples and data related to the user's region. For example, if the user feels that "I want to know examples specific to the region when creating a prompt," the education unit provides examples and data related to the region. In addition, the education unit provides educational materials that match the language and culture of the user's region. For example, if the user feels that "I want to know materials that match the language and culture of the region when creating a prompt," the education unit provides educational materials that match the language and culture of the region. In addition, the education unit provides content that conforms to the educational curriculum of the user's region. For example, if the user feels that "I want to know the content of prompt creation that conforms to the educational curriculum of the region," the education unit provides content that conforms to the educational curriculum of the region. This enables more appropriate education by localizing the educational content based on the geographical location information of the user. The geographical location information is acquired using a method such as GPS data or an IP address. A part or all of the above-mentioned processing in the education unit may be performed using, for example, AI, or may be performed without using AI.

[0074] The education unit can explain the educational content using relevant examples based on the user's social media activity. For example, the education unit explains the educational content using examples related to a topic in which the user showed interest on social media. For example, if the user feels that "I am interested in a specific topic in creating a prompt," the education unit provides examples related to that topic. In addition, the education unit provides related additional materials based on the user's social media activity history. For example, if the user feels that "I want to know more information about creating a prompt," the education unit provides related additional materials. In addition, the education unit provides educational content that incorporates the opinions of experts and influencers that the user follows on social media. For example, if the user feels that "I want to know the opinions of specific experts and influencers in creating a prompt," the education unit provides educational content that incorporates those opinions. As a result, the effectiveness of learning is improved by explaining the educational content based on the user's social media activity. The social media activity is acquired using, for example, a method such as analysis of the posted content and followers. A part or all of the above-mentioned processing in the education unit may be performed using, for example, AI, or may be performed without using AI.

[0075] The support unit can estimate the user's emotion and adjust the support method based on the estimated user's emotion. For example, when the user is stressed, the support unit provides simple and intuitive support. For example, when the user feels "stressed in creating a prompt", the support unit provides simple and intuitive support. Furthermore, when the user is relaxed, the support unit provides detailed explanations and additional support options. For example, when the user feels "relaxed in creating a prompt", the support unit provides detailed explanations and additional support options. Furthermore, when the user is excited, the support unit provides an interactive support method. For example, when the user feels "excited in creating a prompt", the support unit provides an interactive support method. This allows for more effective support by adjusting the support method according to the user's emotion. The emotion is estimated using, for example, a technology such as facial expression recognition or voice analysis. A part or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI.

[0076] The support unit can analyze the user's past prompt creation history and select the optimal support method. The support unit, for example, proposes a similar prompt based on a successful example of a prompt created by the user in the past. For example, if the user feels that "I want to create a new prompt based on a prompt that was successful in the past," the support unit proposes a similar prompt based on the past successful example. In addition, the support unit provides support with a focus on a point of prompt creation that the user was weak at in the past. For example, if the user feels that "I want to overcome a point of prompt creation that was weak in the past," the support unit provides support with a focus on that point. In addition, the support unit analyzes the user's past prompt creation history and proposes the optimal support timing. For example, if the user feels that "I want to receive support at the optimal timing," the support unit proposes the optimal support timing based on the past history. This enables effective support by selecting the optimal support method based on the user's past prompt creation history. The analysis of the prompt creation history is performed using data such as past prompts and success rates. A part or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI.

[0077] The support unit can customize the support content based on the user's current project and field of interest. The support unit provides support content related to the project the user is currently working on, for example. For example, if the user feels that "I want to receive support related to the current project," the support unit provides support content related to the project. In addition, the support unit provides related cases and reference materials based on the user's field of interest. For example, if the user feels that "I want to know cases and materials related to the field of interest," the support unit provides cases and reference materials related to that field. In addition, the support unit provides appropriate support according to the progress of the user's current project. For example, if the user feels that "I want to receive support according to the progress of the project," the support unit provides support according to the progress. This enables effective support by customizing the support content based on the user's current project and field of interest. The current project and field of interest are identified using, for example, a project management tool, a questionnaire, or other methods. A part or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI.

[0078] The support unit can estimate the user's emotion and determine the priority of support based on the estimated user's emotion. For example, when the user is stressed, the support unit provides support with a high degree of urgency with priority. For example, when the user feels "stressed in creating a prompt", the support unit provides support with a high degree of urgency with priority. Furthermore, when the user is relaxed, the support unit provides normal support. For example, when the user feels "relaxed in creating a prompt", the support unit provides normal support. Furthermore, when the user is excited, the support unit provides interactive support with priority. For example, when the user feels "excited in creating a prompt", the support unit provides interactive support with priority. This enables more effective support by determining the priority of support according to the user's emotion. The emotion is estimated using, for example, a technology such as facial expression recognition or voice analysis. A part or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI.

[0079] The support unit can localize the support content based on the geographical location information of the user. The support unit explains the support content using examples and data related to the user's region, for example. For example, if the user feels that "I want to know examples specific to the region when creating a prompt," the support unit provides examples and data related to the region. In addition, the support unit provides support materials that match the language and culture of the user's region. For example, if the user feels that "I want to know materials that match the language and culture of the region when creating a prompt," the support unit provides support materials that match the language and culture of the region. In addition, the support unit proposes a support method according to the characteristics of the user's region. For example, if the user feels that "I want to receive support that matches the characteristics of the region," the support unit proposes a support method that matches the characteristics. This makes it possible to provide more appropriate support by localizing the support content based on the geographical location information of the user. The geographical location information is acquired using, for example, GPS data, IP addresses, or other methods. A part or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI.

[0080] The support unit can explain the support content using relevant examples based on the user's social media activity. The support unit explains the support content using examples related to topics in which the user has shown interest on social media, for example. For example, if the user feels that "I am interested in a specific topic in creating a prompt," the support unit provides examples related to that topic. In addition, the support unit provides related additional materials based on the user's social media activity history. For example, if the user feels that "I want to know more information about creating a prompt," the support unit provides related additional materials. In addition, the support unit provides support content that incorporates the opinions of experts and influencers that the user follows on social media. For example, if the user feels that "I want to know the opinions of specific experts and influencers in creating a prompt," the support unit provides support content that incorporates those opinions. This enables effective support by explaining the support content based on the user's social media activity. The social media activity is acquired using, for example, a method such as analysis of the posted content and followers. A part or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI.

[0081] The generation unit can estimate the user's emotion and adjust the style of the generated content based on the estimated user's emotion. For example, when the user is relaxed, the generation unit generates content written in a gentle tone. For example, when the user feels "relaxed", the generation unit generates content written in a gentle tone. Furthermore, when the user is in a hurry, the generation unit generates content that is concise and to the point. For example, when the user feels "in a hurry", the generation unit generates content that is concise and to the point. Furthermore, when the user is excited, the generation unit generates content to which a visually stimulating effect is added. For example, when the user feels "excited", the generation unit generates content to which a visually stimulating effect is added. This allows more appropriate content to be generated by adjusting the style of the generated content according to the user's emotion. The emotion is estimated using, for example, a technology such as facial expression recognition or voice analysis. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI.

[0082] The generating unit can customize the content to be generated based on the user's past creation history. For example, the generating unit generates new content in a similar style based on the style of content created by the user in the past. For example, if the user feels that "I want to create new content based on the style of content created in the past," the generating unit generates new content in a similar style based on the past style. In addition, the generating unit generates content incorporating keywords and phrases used by the user in the past. For example, if the user feels that "I want to create content incorporating keywords and phrases used in the past," the generating unit generates content incorporating the keywords and phrases. Furthermore, the generating unit analyzes the user's past creation history and proposes an optimal content configuration. For example, if the user feels that "I want to know the optimal content configuration," the generating unit proposes an optimal content configuration based on the past history. In this way, more appropriate content can be generated by customizing the content to be generated based on the user's past creation history. The creation history is acquired using data such as past projects and success rates. A part or all of the above-mentioned processing in the generating unit may be performed using, for example, a generation AI, or may be performed without using a generation AI.

[0083] The generating unit can optimize the content to be generated based on the user's current project or field of interest. The generating unit generates content related to the project that the user is currently working on, for example. For example, if the user feels that "I want to create content related to the current project," the generating unit generates content related to the project. The generating unit also generates content incorporating related cases and data based on the user's field of interest. For example, if the user feels that "I want to create content incorporating cases and data related to the field of interest," the generating unit generates content incorporating cases and data related to the field. Furthermore, the generating unit generates appropriate content according to the progress of the user's current project. For example, if the user feels that "I want to create content according to the progress of the project," the generating unit generates content according to the progress. As a result, more appropriate content can be generated by optimizing the content to be generated based on the user's current project or field of interest. The current project or field of interest is identified using a method such as a project management tool or a questionnaire. A part or all of the above-mentioned processing in the generating unit may be performed using, for example, a generating AI, or may be performed without using a generating AI.

[0084] The generation unit can estimate the user's emotion and determine the priority of the content to be generated based on the estimated user's emotion. For example, when the user is stressed, the generation unit generates high-urgency content with priority. For example, when the user feels "stressed in creating the prompt", the generation unit generates high-urgency content with priority. Furthermore, when the user is relaxed, the generation unit generates normal content. For example, when the user feels "relaxed in creating the prompt", the generation unit generates normal content. Furthermore, when the user is excited, the generation unit generates visually stimulating content with priority. For example, when the user feels "excited in creating the prompt", the generation unit generates visually stimulating content with priority. In this way, by determining the priority of the content to be generated according to the user's emotion, more appropriate content can be generated. The emotion is estimated using, for example, a technology such as facial expression recognition or voice analysis. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI.

[0085] The generation unit can localize the content to be generated based on the geographical location information of the user. The generation unit generates the content using examples and data related to the user's region, for example. For example, when the user feels that "I want to know examples specific to the region when creating a prompt," the generation unit provides examples and data related to the region. In addition, the generation unit generates content that matches the language and culture of the user's region. For example, when the user feels that "I want to know content that matches the language and culture of the region when creating a prompt," the generation unit provides content that matches the language and culture of the region. In addition, the generation unit generates content according to the characteristics of the user's region. For example, when the user feels that "I want to create content that matches the characteristics of the region," the generation unit generates content according to the characteristics. As a result, more appropriate content can be generated by localizing the content to be generated based on the geographical location information of the user. The geographical location information is acquired using a method such as GPS data or an IP address. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI.

[0086] The generation unit can generate the content to be generated using highly relevant examples based on the user's social media activity. The generation unit generates the content using examples related to topics in which the user has shown interest on social media, for example. For example, if the user feels that "I am interested in a specific topic in creating a prompt," the generation unit provides examples related to that topic. The generation unit also provides related additional materials based on the user's social media activity history. For example, if the user feels that "I want to know more information about creating a prompt," the generation unit provides related additional materials. Furthermore, the generation unit generates content incorporating the opinions of experts and influencers that the user follows on social media. For example, if the user feels that "I want to know the opinions of specific experts and influencers in creating a prompt," the generation unit provides content incorporating those opinions. In this way, more appropriate content can be generated by generating content to be generated based on the user's social media activity. The social media activity is acquired using, for example, a method such as analysis of the posted content and followers. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI.

[0087] The providing unit can estimate the user's emotion and adjust the display method of the content to be provided based on the estimated emotion of the user. For example, when the user is nervous, the providing unit provides a simple and highly visible display method. For example, when the user feels "nervous in creating a prompt", the providing unit provides a simple and highly visible display method. Furthermore, when the user is relaxed, the providing unit provides a display method including detailed information. For example, when the user feels "relaxed in creating a prompt", the providing unit provides a display method including detailed information. Furthermore, when the user is in a hurry, the providing unit provides a display method that focuses on the main points. For example, when the user feels "in a hurry in creating a prompt", the providing unit provides a display method that focuses on the main points. This allows for more appropriate display by adjusting the display method of the content to be provided according to the emotion of the user. The emotion is estimated using, for example, a technology such as facial expression recognition or voice analysis. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI.

[0088] The providing unit can customize the content to be provided based on the user's past browsing history. For example, the providing unit provides new content in a similar style based on the style of content previously viewed by the user. For example, when the user feels that "I want to know new content based on the style of content previously viewed," the providing unit provides new content in a similar style based on the past style. Furthermore, the providing unit provides content incorporating keywords or phrases previously viewed by the user. For example, when the user feels that "I want to know content incorporating keywords or phrases previously viewed," the providing unit provides content incorporating the keywords or phrases. Furthermore, the providing unit analyzes the user's past browsing history and proposes an optimal content configuration. For example, when the user feels that "I want to know the optimal content configuration," the providing unit proposes an optimal content configuration based on the past history. As a result, more appropriate content can be provided by customizing the content to be provided based on the user's past browsing history. The browsing history is acquired using data such as past viewed pages and browsing time. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI.

[0089] The providing unit can optimize the content to be provided based on the user's current project or field of interest. The providing unit provides, for example, content related to a project that the user is currently working on. For example, if the user feels that "I want to know content related to the current project," the providing unit provides content related to the project. Furthermore, the providing unit provides content incorporating related cases and data based on the user's field of interest. For example, if the user feels that "I want to know content incorporating cases and data related to the field of interest," the providing unit provides content incorporating cases and data related to the field. Furthermore, the providing unit provides appropriate content according to the progress of the user's current project. For example, if the user feels that "I want to know content according to the progress of the project," the providing unit provides content according to the progress. As a result, more appropriate content can be provided by optimizing the content to be provided based on the user's current project or field of interest. The current project or field of interest is identified using, for example, a project management tool or a questionnaire. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI.

[0090] The providing unit can estimate the user's emotion and determine the priority of the content to be provided based on the estimated user's emotion. For example, when the user is stressed, the providing unit provides content with a high degree of urgency with priority. For example, when the user feels "stressed in creating a prompt", the providing unit provides content with a high degree of urgency with priority. Furthermore, when the user is relaxed, the providing unit provides normal content. For example, when the user feels "relaxed in creating a prompt", the providing unit provides normal content. Furthermore, when the user is excited, the providing unit provides visually stimulating content with priority. For example, when the user feels "excited in creating a prompt", the providing unit provides visually stimulating content with priority. This allows more appropriate content to be provided by determining the priority of the content to be provided according to the user's emotion. The emotion is estimated using, for example, a technique such as facial expression recognition or voice analysis. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI.

[0091] The providing unit can localize the content to be provided based on the geographical location information of the user. The providing unit provides the content using examples and data related to the user's region, for example. For example, when the user feels that "I want to know examples specific to the region when creating a prompt," the providing unit provides examples and data related to the region. In addition, the providing unit provides content that matches the language and culture of the user's region. For example, when the user feels that "I want to know content that matches the language and culture of the region when creating a prompt," the providing unit provides content that matches the language and culture of the region. In addition, the providing unit provides content according to the characteristics of the user's region. For example, when the user feels that "I want to create content that matches the characteristics of the region," the providing unit provides content according to the characteristics. As a result, more appropriate content can be provided by localizing the content to be provided based on the geographical location information of the user. The geographical location information is acquired using a method such as GPS data or an IP address. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI.

[0092] The providing unit can provide the content to be provided using highly relevant examples based on the user's social media activity. The providing unit provides the content using examples related to a topic in which the user has shown interest on social media, for example. For example, if the user feels that "I am interested in a specific topic in creating a prompt," the providing unit provides examples related to that topic. In addition, the providing unit provides related additional materials based on the user's social media activity history. For example, if the user feels that "I want to know more information about creating a prompt," the providing unit provides related additional materials. Furthermore, the providing unit provides content that incorporates the opinions of experts and influencers that the user follows on social media. For example, if the user feels that "I want to know the opinions of specific experts and influencers in creating a prompt," the providing unit provides content that incorporates those opinions. As a result, more appropriate content can be provided by providing content to be provided based on the user's social media activity. The social media activity is acquired using, for example, a method such as analysis of the posted content and followers. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The education department can customize the teaching method based on the user's learning style. For example, a user who prefers visual learning can be provided with teaching materials that make extensive use of diagrams and graphs. A user who prefers auditory learning can be provided with teaching materials in the form of audio guides or podcasts. Furthermore, a user who prefers practical learning can be provided with interactive simulations and practical assignments. This can improve the learning effect by providing the optimal teaching method according to the user's learning style.

[0095] The support unit can monitor the progress of the user's prompt creation in real time and provide feedback at an appropriate time. For example, if the user is stuck in creating a prompt, the support unit can immediately provide advice or hints. If the user is progressing smoothly, the support unit can send an encouraging message. Furthermore, if the user repeatedly makes mistakes in a particular part, the support unit can provide a detailed explanation or additional resources for that part. This makes it possible to improve the efficiency of prompt creation by providing appropriate support according to the user's progress.

[0096] The providing unit can collect user feedback and continuously improve the quality of the content to be provided. For example, the providing unit can enable users to leave ratings and comments on the provided content. The providing unit can also analyze user feedback to identify common problems and areas for improvement. Furthermore, the providing unit can correct and update the content based on user feedback. This makes it possible to provide high-quality content that meets the needs of users.

[0097] The education department can create an individual learning plan based on the user's learning goals. For example, if the user wants to master a specific skill, a curriculum focusing on that skill can be provided. Also, if the user wants to achieve a goal within a specific deadline, a learning schedule can be created to match that deadline. Furthermore, the learning plan can be flexibly adjusted according to the user's progress. This makes it possible to support effective learning by providing an optimal learning plan according to the user's learning goals.

[0098] The support unit can estimate the user's emotions and personalize the content of the support based on the estimated user's emotions. For example, if the user feels anxious, the support unit can provide a message that gives a sense of security. If the user feels confident, the support unit can provide a challenging task. Furthermore, if the user is excited, the support unit can provide support that includes interactive elements. In this way, the user's satisfaction can be improved by providing optimal support according to the user's emotions.

[0099] The providing unit can estimate the user's emotion and adjust the format of the content to be provided based on the estimated user's emotion. For example, when the user is tired, short and concise content can be provided. When the user is concentrating, detailed and in-depth content can be provided. Furthermore, when the user is relaxed, visually appealing content can be provided. In this way, the user's satisfaction can be improved by providing an optimal content format according to the user's emotion.

[0100] The generation unit can estimate the user's emotion and adjust the tone of the generated content based on the estimated user's emotion. For example, if the user is sad, content written in an encouraging tone can be generated. If the user is happy, content written in a bright and positive tone can be generated. Furthermore, if the user is angry, content written in a calm and collected tone can be generated. This makes it possible to improve user satisfaction by providing content with an optimal tone according to the user's emotion.

[0101] The education unit can estimate the user's emotions and adjust the format of the educational content based on the estimated user's emotions. For example, if the user is tired, educational content using short videos or infographics can be provided. Also, if the user is focused, educational content using detailed text or long videos can be provided. Furthermore, if the user is relaxed, educational content in the form of interactive quizzes or games can be provided. In this way, the learning effect can be improved by providing the optimal format of educational content according to the user's emotions.

[0102] The providing unit can estimate the user's emotions and adjust the timing of providing content based on the estimated user's emotions. For example, if the user is feeling stressed, content can be provided during a time period when the user can relax. Also, if the user is concentrating, content can be provided during a time period when learning is most effective. Furthermore, if the user is relaxed, content can be provided during a free time period. In this way, content can be provided at the optimal timing according to the user's emotions, thereby improving user satisfaction.

[0103] The generation unit can estimate the user's emotion and adjust the theme of the generated content based on the estimated user's emotion. For example, if the user is sad, content with a theme of encouragement or hope can be generated. If the user is happy, content with a theme of celebration or success can be generated. Furthermore, if the user is angry, content with a theme of calmness or solution can be generated. In this way, user satisfaction can be improved by providing content with an optimal theme according to the user's emotion.

[0104] The process flow of the second embodiment will be briefly described below.

[0105] Step 1: The education department has the AI ​​educate the user on prompt engineering. The education department, for example, provides education in an interactive format. Interactive education is conducted in the form of the user asking the AI ​​a question, and the AI ​​responding by providing specific examples. For example, if the user asks, "How should I create a prompt?", the AI ​​will explain how to create a specific prompt. Step 2: The support department uses AI to support the user in creating a prompt. The support department provides prompt suggestions based on the user's input, for example. For example, if the user inputs "I want to create a product description," the AI ​​will suggest an appropriate prompt. Step 3: The generation unit generates content based on the prompts created by the user with support from the support unit. The generation unit generates the content using, for example, a generation AI. The generated content includes, for example, product descriptions and advertising copy. Step 4: The providing unit provides the content generated by the generating unit. The providing unit, for example, filters the generated content and provides it. The filtering includes, for example, removing inappropriate content and evaluating the quality of the content.

[0106] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0107] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0108] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0109] For example, the education unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the support unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0112] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0117] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0120] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0122] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0124] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0125] For example, the education unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the support unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0128] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0133] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0136] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0140] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0141] For example, the education unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the support unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0144] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0145] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0146] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0148] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0149] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0150] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0151] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0153] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0157] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0158] For example, the education unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the support unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0159] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0160] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0161] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0162] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0163] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0164] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0165] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0166] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0167] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0168] In addition, 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 upon request from the data processing device 12.

[0169] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0170] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0171] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0172] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0173] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0174] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0175] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0176] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0177] (Appendix 1) The education department provides prompt engineering training to users through AI, and A support unit that uses AI to support the user in creating a prompt; a generating unit that generates content based on a prompt created by the user with support from the support unit; a providing unit that provides the content generated by the generating unit; A system characterized by: (Appendix 2) The Ministry of Education, Making Prompt Engineering Education Interactive 2. The system of claim 1, (Appendix 3) The support portion is Providing suggested prompts based on user input 2. The system of claim 1, (Appendix 4) The providing unit is Filter and serve generated content 2. The system of claim 1, (Appendix 5) The Ministry of Education, To estimate a user's emotion and adjust the difficulty of educational content based on the estimated user's emotion. 2. The system of claim 1, (Appendix 6) The Ministry of Education, Analyze the user's past learning history and select the appropriate teaching method 2. The system of claim 1, (Appendix 7) The Ministry of Education, Customize educational content based on user interests 2. The system of claim 1, (Appendix 8) The Ministry of Education, To estimate a user's emotion and adjust a progress speed of the education based on the estimated emotion of the user. 2. The system of claim 1, (Appendix 9) The Ministry of Education, Localize educational content based on the user's geographic location 2. The system of claim 1, (Appendix 10) The Ministry of Education, Illustrate educational content with relevant examples based on users' social media activity 2. The system of claim 1, (Appendix 11) The support portion is To estimate a user's emotion and adjust a support method based on the estimated user's emotion. 2. The system of claim 1, (Appendix 12) The support portion is Analyze the user's prompt creation history and choose the best way to support them 2. The system of claim 1, (Appendix 13) The support portion is Customize support based on your current projects and interests 2. The system of claim 1, (Appendix 14) The support portion is Estimate user's emotions and determine priority of support based on the estimated user's emotions 2. The system of claim 1, (Appendix 15) The support portion is Localize support based on the user's geographic location 2. The system of claim 1, (Appendix 16) The support portion is Explain support with relevant examples based on users' social media activity 2. The system of claim 1, (Appendix 17) The generation unit is Estimating user emotions and adjusting the style of generated content based on the estimated user emotions 2. The system of claim 1, (Appendix 18) The generation unit is Customize generated content based on the user's past creation history 2. The system of claim 1, (Appendix 19) The generation unit is Tailor the content you generate based on your current projects and interests 2. The system of claim 1, (Appendix 20) The generation unit is Estimating user emotions and determining the priority of content to be generated based on the estimated user emotions 2. The system of claim 1, (Appendix 21) The generation unit is Localize the content you generate based on the user's geographic location 2. The system of claim 1, (Appendix 22) The generation unit is Generate content using relevant examples based on users' social media activity 2. The system of claim 1, (Appendix 23) The providing unit is To estimate a user's emotion and adjust a method of displaying provided content based on the estimated user's emotion. 2. The system of claim 1, (Appendix 24) The providing unit is Customize the content you provide based on your past browsing history 2. The system of claim 1, (Appendix 25) The providing unit is Optimize the content you provide based on your current projects and interests 2. The system of claim 1, (Appendix 26) The providing unit is To estimate a user's emotions and determine the priority of contents to be provided based on the estimated user's emotions 2. The system of claim 1, (Appendix 27) The providing unit is Localize the content you provide based on the user's geographic location 2. The system of claim 1, (Appendix 28) The providing unit is Providing content with relevant examples based on users' social media activity 2. The system of claim 1, [Explanation of symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An education department where AI teaches prompt engineering to users; A support unit that uses AI to support the user in creating a prompt; a generating unit that generates content based on a prompt created by the user with support from the support unit; a providing unit that provides the content generated by the generating unit; A system characterized by:

2. The Ministry of Education, Making Prompt Engineering Education Interactive 2. The system of claim 1 .

3. The support portion is Providing suggested prompts based on the user's input.

2. The system of claim 1 .

4. The providing unit is The content generated by the generating unit is filtered and provided.

2. The system of claim 1 .

5. The Ministry of Education, The user's emotion is estimated, and the difficulty level of the educational content is adjusted based on the estimated user's emotion.

2. The system of claim 1 .

6. The Ministry of Education, Analyzing the user's past learning history and selecting an appropriate teaching method 2. The system of claim 1 .

7. The Ministry of Education, Customize educational content based on the user's interests and concerns 2. The system of claim 1 .

8. The Ministry of Education, The user's emotion is estimated, and a progress speed of the education is adjusted based on the estimated emotion of the user.

2. The system of claim 1 .

9. The Ministry of Education, Localizing educational content based on the user's geographic location information.

2. The system of claim 1 .

10. The Ministry of Education, Explain educational content with relevant examples based on the user's social media activity 2. The system of claim 1 .

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