System

Abstract

An object of a system according to an embodiment is to efficiently create a material and shape the material into a format specified by a user.SOLUTION: A system includes a reception unit, a generation unit, a shaping unit, and a provision unit. The reception unit receives an input of a theme or a purpose of a material. The generation unit generates the content of the material based on the information received by the reception unit. The shaping unit shapes the material generated by the generation unit into a format specified by a user. The providing unit provides the material shaped by the shaping 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 an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

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

[0004] Conventional techniques have had the problem that creating materials requires a lot of time and effort, making it difficult to create them efficiently.

[0005] The system according to the embodiment aims to efficiently create materials and format them in a format specified by the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a formatting unit, and a provision unit. The reception unit receives an input of the theme and purpose of the material. The generation unit generates the content of the material based on the information received by the reception unit. The formatting unit formats the material generated by the generation unit into the format specified by the user. The provision unit provides the material formatted by the formatting unit.

Effect of the Invention

[0007] The system according to the embodiment can efficiently create a material and format it into the format specified by the user.

Brief Description of the Drawings

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A document creation system according to an embodiment of the present invention utilizes a generation AI to automatically create a large number of interesting and attractive documents. In the document creation system, a user inputs the theme and purpose of the document, and the generation AI generates the content of the document based on the input. The generated document is then automatically formatted and provided according to the user's specified format. For example, in the document creation system, a user inputs a theme such as "Creating materials for a contest using SB generation AI." This information is input to the generation AI. The generation AI then generates the content of the document based on the input theme and purpose. The generation AI collects relevant information and automatically creates the structure and content of the document. For example, the system generates documents that include a contest overview, participation instructions, and an analysis of past winning entries. The generated documents are automatically formatted according to the user's specified format. For example, the documents are formatted in a format tailored to the user's needs, such as a presentation slide format or report format. This allows users to easily create a large number of documents. For example, multiple documents for participating in a contest can be created in a short amount of time. Furthermore, because the generation AI automatically creates documents, users can significantly reduce the time and effort required for document creation. Furthermore, the generated documents are optimized to increase their chances of ranking in the contest. For example, based on the analysis results of past winning works, it is possible to create materials that emphasize points that are likely to be evaluated. This allows users to increase their chances of success in the contest. This allows the material creation system to enable users to easily create large amounts of materials and increase their chances of ranking in the contest. For example, users can significantly reduce the time and effort required to create materials, and create materials quickly and efficiently. Furthermore, the generated materials can increase their chances of success in the contest by emphasizing points that are likely to be evaluated.

[0029] A material creation system according to an embodiment includes a reception unit, a generation unit, a formatting unit, and a providing unit. The reception unit receives input of the theme and purpose of the material. For example, a user inputs a theme such as "Creating materials for a contest utilizing SB generation AI." The generation unit uses a generation AI to generate the content of the material based on the information received by the reception unit. The generation unit collects information, for example, from public databases on the Internet or past contest materials, and determines the structure of the material. The generation AI generates the content of the material using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation AI generates materials including a contest overview, how to participate, and an analysis of past winning works. The formatting unit formats the material generated by the generation unit into a format specified by the user. The formatting unit formats the material in a format tailored to the user's needs, such as a slide format for a presentation or a report format. The formatting unit can also perform detailed customization of font size, color, layout, and the like. The providing unit provides the material formatted by the formatting unit. The providing unit provides materials that emphasize points that are likely to be evaluated based on, for example, the results of analyzing past award-winning works. This allows the material creation system according to the embodiment to automate the process from inputting the theme and purpose of the material to generating, formatting, and providing it, thereby efficiently creating materials. For example, users can significantly reduce the time and effort required to create materials, allowing them to create materials quickly and efficiently. Furthermore, by emphasizing points that are likely to be evaluated, the generated materials can increase the chances of success in a contest.

[0030] The generation unit can collect information from public databases on the Internet or from past contest materials. The generation unit, for example, collects information from public databases on the Internet. Examples include academic databases and patent databases. The generation unit can also collect information from past contest materials. Examples include materials from business contests and technical contests. As a result, the content of the materials is enriched by the information collected by the generation unit. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input information collected from public databases on the Internet into a generation AI and have the generation AI generate the content of the materials.

[0031] The generation unit can determine the structure of the material based on the collected information. The generation unit, for example, determines the structure of the material based on the collected information. For example, this includes the order of chapters and sections. By determining the structure of the material based on the collected information, the generation unit improves the quality of the material. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the collected information into a generation AI and have the generation AI determine the structure of the material.

[0032] The formatting unit can format the materials according to the type of format specified by the user. For example, the formatting unit formats the materials according to the type of format specified by the user. Examples include PDF, PowerPoint, Word, etc. The formatting unit can provide materials that meet the user's needs by formatting the materials in a format that meets the user's needs. Some or all of the above-mentioned processing in the formatting unit may be performed using AI, for example, or may be performed without using AI. For example, the formatting unit can input the generated materials to a generation AI and cause the generation AI to perform processing to format the materials into the specified format.

[0033] The formatting unit can perform detailed customization of font size, color, and layout. The formatting unit performs detailed customization of font size, color, and layout, for example. This includes, for example, a font size range, a color palette, and a layout template. The formatting unit performs detailed customization to improve the appearance of the document. Some or all of the above-described processing in the formatting unit may be performed using, for example, AI, or may be performed without using AI. For example, the formatting unit can input the generated document into a generation AI and have the generation AI perform customization of font size, color, and layout.

[0034] The providing unit can provide materials that emphasize points that are likely to be evaluated based on the analysis results of past award-winning works. The providing unit provides materials that emphasize points that are likely to be evaluated based on the analysis results of past award-winning works, for example. These points may include originality, practicality, and quality of presentation. By emphasizing points that are likely to be evaluated, the providing unit increases the probability of success in the contest. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the analysis results of past award-winning works into the generating AI and cause the generating AI to provide materials that emphasize points that are likely to be evaluated.

[0035] The reception unit can analyze the user's past input history and provide optimal input assistance. For example, the reception unit can automatically display themes and purposes that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest themes and purposes to be used in a specific time period based on the user's past input history. In this way, optimal input assistance can be provided to the user by analyzing the past input history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history to a generation AI and cause the generation AI to provide optimal input assistance.

[0036] When inputting a theme or purpose, the reception unit can present input candidates based on the user's current project or field of interest. For example, the reception unit can automatically display themes and purposes related to the user's current project as candidates. The reception unit can also suggest related themes and purposes based on the user's field of interest. The reception unit can also analyze the user's past project history and suggest related themes and purposes. This makes user input more efficient by presenting input candidates based on the user's current project or field of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's current project or field of interest into the generation AI and cause the generation AI to present input candidates.

[0037] The reception unit can select the optimal input means according to the user's input method when inputting a theme or purpose. For example, if the user selects voice input, the reception unit inputs the theme or purpose using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also provide an interface that supports keyboard input. Furthermore, if the user selects image input, the reception unit can also input the theme or purpose using image recognition technology. This improves input efficiency by selecting the optimal input means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data of the user's input method to the generation AI and cause the generation AI to select the optimal input means.

[0038] The reception unit can present highly relevant input candidates by taking into account the user's geographical location information when the user inputs a theme or purpose. For example, if the user is in a specific area, the reception unit can automatically display themes and purposes related to that area as candidates. The reception unit can also suggest related events and projects based on the user's current location. The reception unit can also analyze the user's past location information and suggest related themes and purposes. In this way, highly relevant input candidates can be presented to the user by taking geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to a generation AI and cause the generation AI to present highly relevant input candidates.

[0039] The reception unit can analyze the user's social media activity when inputting a theme or purpose and present related input candidates. The reception unit can, for example, suggest related themes or purposes based on content shared by the user on social media. The reception unit can also analyze the user's social media activity history and present related themes or purposes. The reception unit can also suggest related themes or purposes based on the activities of the user's friends on social media. In this way, by analyzing social media activity, relevant input candidates can be presented to the user. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data of the user's social media activity to a generation AI and cause the generation AI to present related input candidates.

[0040] The reception unit can customize the input method by reflecting the user's past feedback when inputting a theme or purpose. The reception unit can, for example, suggest an optimal input method based on feedback provided by the user in the past. The reception unit can also analyze the user's past feedback and customize the input interface. The reception unit can also optimize the input procedure based on the user's past feedback. This makes it possible to provide the user with an optimal input method by reflecting the past feedback. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's past feedback into a generation AI and have the generation AI customize the input method.

[0041] When generating materials, the generation unit can adjust the level of detail of the content based on the importance of the theme or purpose. For example, in the case of an important theme or purpose, the generation unit generates content containing detailed information. In addition, in the case of a general theme or purpose, the generation unit can also generate content containing concise information. In addition, in the case of a theme or purpose related to a specific field of expertise, the generation unit can also generate content containing specialized information. In this way, by adjusting the level of detail of the content based on the importance of the theme or purpose, it is possible to generate materials with an appropriate amount of information. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input data on the importance of the theme or purpose into the generation AI and have the generation AI adjust the level of detail of the content.

[0042] When generating materials, the generation unit can apply different generation algorithms depending on the theme or purpose category. For example, if the theme or purpose is business-related, the generation unit can apply a business-oriented generation algorithm. Furthermore, if the theme or purpose is education-related, the generation unit can also apply an education-oriented generation algorithm. Furthermore, if the theme or purpose is entertainment-related, the generation unit can also apply an entertainment-oriented generation algorithm. In this way, by applying a generation algorithm according to the category, more appropriate materials can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data of the theme or purpose category into the generation AI and cause the generation AI to apply different generation algorithms.

[0043] When generating materials, the generation unit can improve the accuracy of the content by referring to the user's past generation results. For example, the generation unit analyzes the content of materials generated by the user in the past and improves the accuracy. The generation unit can also generate content by extracting optimal information from the user's past generation results. The generation unit can also adjust the content to maintain consistency based on the user's past generation results. In this way, the accuracy of the content is improved by referring to the past generation results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data of the user's past generation results into the generation AI and have the generation AI improve the accuracy of the content.

[0044] When generating materials, the generation unit can determine the priority of content based on the submission time of the theme or purpose. For example, for a theme or purpose with an upcoming deadline, the generation unit prioritizes generating the most important information. The generation unit can also generate content including detailed information for a theme or purpose with a distant submission time. The generation unit can also adjust the priority of information according to the submission time and generate optimal content. In this way, by determining the priority of content based on the submission time, important information can be provided at the appropriate time. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the submission time of the theme or purpose into the generation AI and have the generation AI determine the priority of content.

[0045] When generating materials, the generation unit can adjust the order of content based on the relevance of the theme or purpose. For example, the generation unit can prioritize information related to important themes or purposes. The generation unit can also postpone information related to general themes or purposes. The generation unit can also group highly related information and adjust the order. This allows for a deeper understanding of the materials by adjusting the order of content based on relevance. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the relevance of the theme or purpose into the generation AI and have the generation AI adjust the order of the content.

[0046] When generating materials, the generation unit can adjust the use of technical terms in the content according to the user's level of expertise. For example, if the user is an expert, the generation unit can generate content that uses a lot of technical terms. Furthermore, if the user is a layperson, the generation unit can generate easy-to-understand content that avoids technical terms. Furthermore, the generation unit can adjust the frequency of use of technical terms according to the user's level of expertise. This allows for the provision of materials that are easy for users to understand by adjusting the use of technical terms according to the level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terms.

[0047] When formatting a document, the formatting unit can adjust the level of detail of the formatting based on the importance of the format. For example, in the case of an important format, the formatting unit performs detailed customization to improve the accuracy of the formatting. Furthermore, in the case of a general format, the formatting unit can perform basic formatting to efficiently format the document. Furthermore, in the case of a format for a specific purpose, the formatting unit can perform formatting that is optimal for the purpose. In this way, appropriate formatting is possible by adjusting the level of detail of the formatting based on the importance of the format. Some or all of the above-mentioned processing in the formatting unit may be performed using, for example, AI, or may be performed without using AI. For example, the formatting unit can input data on the importance of the format to the generation AI and have the generation AI adjust the level of detail of the formatting.

[0048] When formatting a document, the formatting unit can apply different formatting algorithms depending on the type of format. For example, in the case of a slide format for a presentation, the formatting unit can apply a formatting algorithm for slides. Furthermore, in the case of a report format, the formatting unit can also apply a formatting algorithm for reports. Furthermore, in the case of a document including graphs and charts, the formatting unit can also apply a formatting algorithm for graphs and charts. This enables appropriate formatting by applying a formatting algorithm depending on the type of format. Some or all of the above-described processing in the formatting unit may be performed using, for example, AI, or may be performed without using AI. For example, the formatting unit can input data on the type of format to the generation AI and cause the generation AI to apply different formatting algorithms.

[0049] When formatting a document, the formatting unit can improve the accuracy of the formatting by referring to the user's past formatting results. For example, the formatting unit analyzes the design of documents that the user has formatted in the past to improve accuracy. The formatting unit can also extract an optimal design from the user's past formatting results and format the document. The formatting unit can also adjust the design to maintain consistency based on the user's past formatting results. This improves the accuracy of the formatting by referring to the past formatting results. Some or all of the above-mentioned processing in the formatting unit may be performed using, for example, AI, or may be performed without using AI. For example, the formatting unit can input data of the user's past formatting results into the generation AI and cause the generation AI to improve the accuracy of the formatting.

[0050] When formatting documents, the formatting unit can determine the formatting priority based on the submission date of the format. For example, in the case of a format with an approaching deadline, the formatting unit prioritizes formatting the most important information. In addition, in the case of a format with a distant submission date, the formatting unit can also format the document to include detailed information. The formatting unit can also adjust the priority of information according to the submission date and perform optimal formatting. In this way, by determining the formatting priority based on the submission date, important information can be provided at the appropriate time. Some or all of the above-mentioned processing in the formatting unit may be performed using, for example, AI, or may be performed without using AI. For example, the formatting unit can input data on the submission date of the format into the generation AI and have the generation AI determine the formatting priority.

[0051] When formatting the document, the formatting unit can adjust the order of formatting based on the relevance of the format. For example, the formatting unit prioritizes information related to important formats. The formatting unit can also postpone information related to general formats. The formatting unit can also group highly related information and adjust the order. This allows for a deeper understanding of the document by adjusting the order of formatting based on relevance. Some or all of the above-described processing in the formatting unit may be performed using, or without, AI. For example, the formatting unit can input data on the relevance of formats to the generation AI and have the generation AI adjust the order of formatting.

[0052] When formatting the document, the formatting unit can adjust the use of technical terms in the formatting depending on the user's level of expertise. For example, if the user is an expert, the formatting unit may perform formatting that uses a lot of technical terms. Furthermore, if the user is a layperson, the formatting unit may perform formatting that avoids technical terms and is easier to understand. The formatting unit can also adjust the frequency of use of technical terms depending on the user's level of expertise. This allows for adjusting the use of technical terms depending on the level of expertise, thereby providing materials that are easy for the user to understand. Some or all of the above-described processing in the formatting unit may be performed using, for example, AI, or may be performed without using AI. For example, the formatting unit may input data on the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terms.

[0053] When providing the materials, the providing unit can emphasize points that are likely to be evaluated based on the results of an analysis of past award-winning works. For example, the providing unit can analyze commonalities among past award-winning works and emphasize points that are likely to be evaluated. The providing unit can also adjust the design of the materials by referring to the designs of past award-winning works. The providing unit can also analyze the content of past award-winning works and emphasize information that is likely to be evaluated. This increases the probability of success in the contest by emphasizing points that are likely to be evaluated. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the results of an analysis of past award-winning works into the generating AI and cause the generating AI to provide materials that emphasize points that are likely to be evaluated.

[0054] The providing unit can improve the method of providing materials by reflecting the user's past feedback. For example, the providing unit can suggest an optimal method of providing materials based on feedback provided by the user in the past. The providing unit can also analyze the user's past feedback and customize the method of providing materials. The providing unit can also optimize the procedure of providing materials based on the user's past feedback. This allows the optimal method of providing materials to the user by reflecting the past feedback. Some or all of the above-described processing in the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input the user's past feedback into the generation AI and cause the generation AI to improve the method of providing materials.

[0055] When providing materials, the providing unit can customize the provided content based on the user's current project or area of ​​interest. For example, the providing unit can prioritize providing materials related to the user's current project. The providing unit can also provide related materials based on the user's area of ​​interest. The providing unit can also analyze the user's past project history and provide related materials. In this way, by customizing the provided content based on the user's current project or area of ​​interest, it is possible to provide useful materials to the user. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the user's current project or area of ​​interest into the generation AI and cause the generation AI to customize the provided content.

[0056] When providing materials, the providing unit can prioritize providing highly relevant materials by taking into account the user's geographical location information. For example, if the user is in a specific area, the providing unit can prioritize providing materials related to that area. The providing unit can also provide materials for related events or projects based on the user's current location. The providing unit can also analyze the user's past location information and provide related materials. In this way, highly relevant materials can be provided to the user by taking the geographical location information into consideration. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's geographical location information into the generation AI and cause the generation AI to provide highly relevant materials.

[0057] When providing materials, the providing unit can analyze the user's social media activity and provide related materials. The providing unit can provide related materials based on, for example, content shared by the user on social media. The providing unit can also analyze the user's social media activity history and provide related materials. The providing unit can also provide related materials by referring to the activities of the user's friends on social media. In this way, related materials can be provided to the user by analyzing social media activity. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input data on the user's social media activity into a generation AI and cause the generation AI to provide related materials.

[0058] The providing unit can customize the method of providing materials by reflecting the user's past feedback. The providing unit can, for example, suggest an optimal method of providing materials based on feedback provided by the user in the past. The providing unit can also analyze the user's past feedback and customize the method of providing materials. The providing unit can also optimize the procedure of providing materials based on the user's past feedback. This allows the optimal method of providing materials to the user by reflecting the past feedback. Some or all of the above-described processing in the providing unit can be performed using AI, for example, or can be performed without using AI. For example, the providing unit can input the user's past feedback into the generation AI and cause the generation AI to customize the method of providing materials.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The reception unit can analyze the user's input in real time and suggest related additional information based on the input. For example, if a user inputs "marketing strategy," the reception unit can suggest related additional information such as "market analysis" and "competitive analysis." If a user inputs "technological innovation," the reception unit can suggest related additional information such as "patent information" and "technology trends." If a user inputs "project management," the reception unit can suggest related additional information such as "Gantt charts" and "risk management." This allows users to efficiently collect necessary information and improve the quality of their documents. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can input the user's input to a generation AI and have the generation AI suggest related additional information.

[0061] When formatting a document, the formatting unit can improve the accuracy of the formatting by referring to the user's past formatting results. For example, the formatting unit can analyze the designs of documents that the user has formatted in the past and improve the accuracy. The formatting unit can also extract and format the optimal design from the user's past formatting results. The formatting unit can also adjust the design to maintain consistency based on the user's past formatting results. This improves the accuracy of the formatting by referring to the past formatting results. Some or all of the above-mentioned processing in the formatting unit may be performed using, for example, AI, or may be performed without using AI. For example, the formatting unit can input data of the user's past formatting results into the generation AI and have the generation AI improve the accuracy of the formatting.

[0062] The reception unit can analyze the user's past input history and provide optimal input assistance. For example, themes and purposes that the user has frequently input in the past can be automatically displayed as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest themes and purposes to be used during a specific time period based on the user's past input history. In this way, optimal input assistance can be provided to the user by analyzing the past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history into a generation AI and cause the generation AI to provide optimal input assistance.

[0063] When generating materials, the generation unit can adjust the level of detail of the content based on the importance of the theme or purpose. For example, in the case of an important theme or purpose, the generation unit can generate content containing detailed information. In addition, in the case of a general theme or purpose, the generation unit can generate content containing concise information. In addition, in the case of a theme or purpose related to a specific field of expertise, the generation unit can generate content containing specialized information. In this way, by adjusting the level of detail of the content based on the importance of the theme or purpose, it is possible to generate materials with an appropriate amount of information. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the importance of the theme or purpose into the generation AI and have the generation AI adjust the level of detail of the content.

[0064] When providing materials, the providing unit can customize the provided content based on the user's current project or area of ​​interest. For example, the providing unit can prioritize providing materials related to the user's current project. The providing unit can also provide related materials based on the user's area of ​​interest. The providing unit can also analyze the user's past project history and provide related materials. In this way, by customizing the provided content based on the user's current project or area of ​​interest, it is possible to provide useful materials to the user. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the user's current project or area of ​​interest into the generating AI and cause the generating AI to customize the provided content.

[0065] When generating materials, the generation unit can apply different generation algorithms depending on the theme or purpose category. For example, if the theme or purpose is business-related, a business-oriented generation algorithm can be applied. Furthermore, if the theme or purpose is education-related, the generation unit can also apply an education-oriented generation algorithm. Furthermore, if the theme or purpose is entertainment-related, the generation unit can also apply an entertainment-oriented generation algorithm. In this way, by applying a generation algorithm according to the category, more appropriate materials can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data of the theme or purpose category into the generation AI and cause the generation AI to apply different generation algorithms.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The reception unit accepts input of the theme and purpose of the materials. For example, the user inputs a theme such as "Creating materials for a contest utilizing SB generation AI." Step 2: The generation unit uses a generation AI to generate the content of the materials based on the information received by the reception unit. The generation unit collects information, for example, from public databases on the Internet and past contest materials, and determines the structure of the materials. The generation AI generates the content of the materials using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation AI generates materials that include an overview of the contest, how to participate, and an analysis of past winning entries. Step 3: The formatter formats the materials generated by the generator into a format specified by the user. The formatter formats the materials in a format that meets the user's needs, such as a slide format for a presentation or a report format. The formatter can also perform detailed customization of font size, color, layout, etc. Step 4: The providing department provides the materials formatted by the formatting department. For example, the providing department provides materials that emphasize points that are likely to be evaluated based on the results of an analysis of past award-winning works.

[0068] (Example 2) A document creation system according to an embodiment of the present invention utilizes a generation AI to automatically create a large number of interesting and attractive documents. In the document creation system, a user inputs the theme and purpose of the document, and the generation AI generates the content of the document based on the input. The generated document is then automatically formatted and provided according to the user's specified format. For example, in the document creation system, a user inputs a theme such as "Creating materials for a contest using SB generation AI." This information is input to the generation AI. The generation AI then generates the content of the document based on the input theme and purpose. The generation AI collects relevant information and automatically creates the structure and content of the document. For example, the system generates documents that include a contest overview, participation instructions, and an analysis of past winning entries. The generated documents are automatically formatted according to the user's specified format. For example, the documents are formatted in a format tailored to the user's needs, such as a presentation slide format or report format. This allows users to easily create a large number of documents. For example, multiple documents for participating in a contest can be created in a short amount of time. Furthermore, because the generation AI automatically creates documents, users can significantly reduce the time and effort required for document creation. Furthermore, the generated documents are optimized to increase their chances of ranking in the contest. For example, based on the analysis results of past winning works, it is possible to create materials that emphasize points that are likely to be evaluated. This allows users to increase their chances of success in the contest. This allows the material creation system to enable users to easily create large amounts of materials and increase their chances of ranking in the contest. For example, users can significantly reduce the time and effort required to create materials, and create materials quickly and efficiently. Furthermore, the generated materials can increase their chances of success in the contest by emphasizing points that are likely to be evaluated.

[0069] A material creation system according to an embodiment includes a reception unit, a generation unit, a formatting unit, and a providing unit. The reception unit receives input of the theme and purpose of the material. For example, a user inputs a theme such as "Creating materials for a contest utilizing SB generation AI." The generation unit uses a generation AI to generate the content of the material based on the information received by the reception unit. The generation unit collects information, for example, from public databases on the Internet or past contest materials, and determines the structure of the material. The generation AI generates the content of the material using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation AI generates materials including a contest overview, how to participate, and an analysis of past winning works. The formatting unit formats the material generated by the generation unit into a format specified by the user. The formatting unit formats the material in a format tailored to the user's needs, such as a slide format for a presentation or a report format. The formatting unit can also perform detailed customization of font size, color, layout, and the like. The providing unit provides the material formatted by the formatting unit. The providing unit provides materials that emphasize points that are likely to be evaluated based on, for example, the results of analyzing past award-winning works. This allows the material creation system according to the embodiment to automate the process from inputting the theme and purpose of the material to generating, formatting, and providing it, thereby efficiently creating materials. For example, users can significantly reduce the time and effort required to create materials, allowing them to create materials quickly and efficiently. Furthermore, by emphasizing points that are likely to be evaluated, the generated materials can increase the chances of success in a contest.

[0070] The generation unit can collect information from public databases on the Internet or from past contest materials. The generation unit, for example, collects information from public databases on the Internet. Examples include academic databases and patent databases. The generation unit can also collect information from past contest materials. Examples include materials from business contests and technical contests. As a result, the content of the materials is enriched by the information collected by the generation unit. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input information collected from public databases on the Internet into a generation AI and have the generation AI generate the content of the materials.

[0071] The generation unit can determine the structure of the material based on the collected information. The generation unit, for example, determines the structure of the material based on the collected information. For example, this includes the order of chapters and sections. By determining the structure of the material based on the collected information, the generation unit improves the quality of the material. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the collected information into a generation AI and have the generation AI determine the structure of the material.

[0072] The formatting unit can format the materials according to the type of format specified by the user. For example, the formatting unit formats the materials according to the type of format specified by the user. Examples include PDF, PowerPoint, Word, etc. The formatting unit can provide materials that meet the user's needs by formatting the materials in a format that meets the user's needs. Some or all of the above-mentioned processing in the formatting unit may be performed using AI, for example, or may be performed without using AI. For example, the formatting unit can input the generated materials to a generation AI and cause the generation AI to perform processing to format the materials into the specified format.

[0073] The formatting unit can perform detailed customization of font size, color, and layout. The formatting unit performs detailed customization of font size, color, and layout, for example. This includes, for example, a font size range, a color palette, and a layout template. The formatting unit performs detailed customization to improve the appearance of the document. Some or all of the above-described processing in the formatting unit may be performed using, for example, AI, or may be performed without using AI. For example, the formatting unit can input the generated document into a generation AI and have the generation AI perform customization of font size, color, and layout.

[0074] The providing unit can provide materials that emphasize points that are likely to be evaluated based on the analysis results of past award-winning works. The providing unit provides materials that emphasize points that are likely to be evaluated based on the analysis results of past award-winning works, for example. These points may include originality, practicality, and quality of presentation. By emphasizing points that are likely to be evaluated, the providing unit increases the probability of success in the contest. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the analysis results of past award-winning works into the generating AI and cause the generating AI to provide materials that emphasize points that are likely to be evaluated.

[0075] The reception unit can estimate the user's emotions and adjust the input method for the theme or purpose based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable the user to quickly input the theme or purpose. This adjusts the input method according to the user's emotions, reducing the user's stress and enabling efficient input. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI adjust the input method based on the emotion.

[0076] The reception unit can analyze the user's past input history and provide optimal input assistance. For example, the reception unit can automatically display themes and purposes that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest themes and purposes to be used in a specific time period based on the user's past input history. In this way, optimal input assistance can be provided to the user by analyzing the past input history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history to a generation AI and cause the generation AI to provide optimal input assistance.

[0077] When inputting a theme or purpose, the reception unit can present input candidates based on the user's current project or field of interest. For example, the reception unit can automatically display themes and purposes related to the user's current project as candidates. The reception unit can also suggest related themes and purposes based on the user's field of interest. The reception unit can also analyze the user's past project history and suggest related themes and purposes. This makes user input more efficient by presenting input candidates based on the user's current project or field of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's current project or field of interest into the generation AI and cause the generation AI to present input candidates.

[0078] The reception unit can select the optimal input means according to the user's input method when inputting a theme or purpose. For example, if the user selects voice input, the reception unit inputs the theme or purpose using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also provide an interface that supports keyboard input. Furthermore, if the user selects image input, the reception unit can also input the theme or purpose using image recognition technology. This improves input efficiency by selecting the optimal input means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data of the user's input method to the generation AI and cause the generation AI to select the optimal input means.

[0079] The reception unit can estimate the user's emotions and prioritize input content based on the estimated user emotions. For example, if the user is nervous, the reception unit can prioritize displaying important input items to simplify input. Furthermore, if the user is relaxed, the reception unit can display detailed input items and provide a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize displaying the most important input items to enable quick input. This allows important information to be prioritized by prioritizing input content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI prioritize the input content based on the emotion.

[0080] The reception unit can present highly relevant input candidates by taking into account the user's geographical location information when the user inputs a theme or purpose. For example, if the user is in a specific area, the reception unit can automatically display themes and purposes related to that area as candidates. The reception unit can also suggest related events and projects based on the user's current location. The reception unit can also analyze the user's past location information and suggest related themes and purposes. In this way, highly relevant input candidates can be presented to the user by taking geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to a generation AI and cause the generation AI to present highly relevant input candidates.

[0081] The reception unit can analyze the user's social media activity when inputting a theme or purpose and present related input candidates. The reception unit can, for example, suggest related themes or purposes based on content shared by the user on social media. The reception unit can also analyze the user's social media activity history and present related themes or purposes. The reception unit can also suggest related themes or purposes based on the activities of the user's friends on social media. In this way, by analyzing social media activity, relevant input candidates can be presented to the user. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data of the user's social media activity to a generation AI and cause the generation AI to present related input candidates.

[0082] The reception unit can customize the input method by reflecting the user's past feedback when inputting a theme or purpose. The reception unit can, for example, suggest an optimal input method based on feedback provided by the user in the past. The reception unit can also analyze the user's past feedback and customize the input interface. The reception unit can also optimize the input procedure based on the user's past feedback. This makes it possible to provide the user with an optimal input method by reflecting the past feedback. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's past feedback into a generation AI and have the generation AI customize the input method.

[0083] The generation unit can estimate the user's emotions and adjust the content of the materials based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate humorous content. If the user is in a hurry, the generation unit can generate concise content that focuses on the main points. If the user is excited, the generation unit can generate content that adds visually stimulating effects. This allows the content of the materials to be adjusted according to the user's emotions, thereby generating more appropriate materials. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the generation unit can input user emotion data into the generation AI and cause the generation AI to adjust the content of the materials based on the emotion.

[0084] When generating materials, the generation unit can adjust the level of detail of the content based on the importance of the theme or purpose. For example, in the case of an important theme or purpose, the generation unit generates content containing detailed information. In addition, in the case of a general theme or purpose, the generation unit can also generate content containing concise information. In addition, in the case of a theme or purpose related to a specific field of expertise, the generation unit can also generate content containing specialized information. In this way, by adjusting the level of detail of the content based on the importance of the theme or purpose, it is possible to generate materials with an appropriate amount of information. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input data on the importance of the theme or purpose into the generation AI and have the generation AI adjust the level of detail of the content.

[0085] When generating materials, the generation unit can apply different generation algorithms depending on the theme or purpose category. For example, if the theme or purpose is business-related, the generation unit can apply a business-oriented generation algorithm. Furthermore, if the theme or purpose is education-related, the generation unit can also apply an education-oriented generation algorithm. Furthermore, if the theme or purpose is entertainment-related, the generation unit can also apply an entertainment-oriented generation algorithm. In this way, by applying a generation algorithm according to the category, more appropriate materials can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data of the theme or purpose category into the generation AI and cause the generation AI to apply different generation algorithms.

[0086] When generating materials, the generation unit can improve the accuracy of the content by referring to the user's past generation results. For example, the generation unit analyzes the content of materials generated by the user in the past and improves the accuracy. The generation unit can also generate content by extracting optimal information from the user's past generation results. The generation unit can also adjust the content to maintain consistency based on the user's past generation results. In this way, the accuracy of the content is improved by referring to the past generation results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data of the user's past generation results into the generation AI and have the generation AI improve the accuracy of the content.

[0087] The generation unit can estimate the user's emotions and adjust the length of the material based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can generate short, concise material. If the user is relaxed, the generation unit can generate longer material with detailed explanations. If the user is excited, the generation unit can generate material with visually stimulating effects. By adjusting the length of the material according to the user's emotions, it is possible to generate material of an appropriate length. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI, or without an AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the material based on the emotion.

[0088] When generating materials, the generation unit can determine the priority of content based on the submission time of the theme or purpose. For example, for a theme or purpose with an upcoming deadline, the generation unit prioritizes generating the most important information. The generation unit can also generate content including detailed information for a theme or purpose with a distant submission time. The generation unit can also adjust the priority of information according to the submission time and generate optimal content. In this way, by determining the priority of content based on the submission time, important information can be provided at the appropriate time. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the submission time of the theme or purpose into the generation AI and have the generation AI determine the priority of content.

[0089] When generating materials, the generation unit can adjust the order of content based on the relevance of the theme or purpose. For example, the generation unit can prioritize information related to important themes or purposes. The generation unit can also postpone information related to general themes or purposes. The generation unit can also group highly related information and adjust the order. This allows for a deeper understanding of the materials by adjusting the order of content based on relevance. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the relevance of the theme or purpose into the generation AI and have the generation AI adjust the order of the content.

[0090] When generating materials, the generation unit can adjust the use of technical terms in the content according to the user's level of expertise. For example, if the user is an expert, the generation unit can generate content that uses a lot of technical terms. Furthermore, if the user is a layperson, the generation unit can generate easy-to-understand content that avoids technical terms. Furthermore, the generation unit can adjust the frequency of use of technical terms according to the user's level of expertise. This allows for the provision of materials that are easy for users to understand by adjusting the use of technical terms according to the level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terms.

[0091] The formatting unit can estimate the user's emotions and adjust the formatting expression based on the estimated user's emotions. For example, if the user is relaxed, the formatting unit can use soft colors and fonts to format the text. If the user is in a hurry, the formatting unit can also use a simple, highly visible format. If the user is excited, the formatting unit can also use a visually stimulating design. This allows for adjusting the formatting expression based on the user's emotions, thereby providing more appropriate materials. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the formatting unit can be performed using AI, for example, or without AI. For example, the formatting unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the formatting expression based on the emotion.

[0092] When formatting a document, the formatting unit can adjust the level of detail of the formatting based on the importance of the format. For example, in the case of an important format, the formatting unit performs detailed customization to improve the accuracy of the formatting. Furthermore, in the case of a general format, the formatting unit can perform basic formatting to efficiently format the document. Furthermore, in the case of a format for a specific purpose, the formatting unit can perform formatting that is optimal for the purpose. In this way, appropriate formatting is possible by adjusting the level of detail of the formatting based on the importance of the format. Some or all of the above-mentioned processing in the formatting unit may be performed using, for example, AI, or may be performed without using AI. For example, the formatting unit can input data on the importance of the format to the generation AI and have the generation AI adjust the level of detail of the formatting.

[0093] When formatting a document, the formatting unit can apply different formatting algorithms depending on the type of format. For example, in the case of a slide format for a presentation, the formatting unit can apply a formatting algorithm for slides. Furthermore, in the case of a report format, the formatting unit can also apply a formatting algorithm for reports. Furthermore, in the case of a document including graphs and charts, the formatting unit can also apply a formatting algorithm for graphs and charts. This enables appropriate formatting by applying a formatting algorithm depending on the type of format. Some or all of the above-described processing in the formatting unit may be performed using, for example, AI, or may be performed without using AI. For example, the formatting unit can input data on the type of format to the generation AI and cause the generation AI to apply different formatting algorithms.

[0094] When formatting a document, the formatting unit can improve the accuracy of the formatting by referring to the user's past formatting results. For example, the formatting unit analyzes the design of documents that the user has formatted in the past to improve accuracy. The formatting unit can also extract an optimal design from the user's past formatting results and format the document. The formatting unit can also adjust the design to maintain consistency based on the user's past formatting results. This improves the accuracy of the formatting by referring to the past formatting results. Some or all of the above-mentioned processing in the formatting unit may be performed using, for example, AI, or may be performed without using AI. For example, the formatting unit can input data of the user's past formatting results into the generation AI and cause the generation AI to improve the accuracy of the formatting.

[0095] The formatting unit can estimate the user's emotions and adjust the length of the formatting based on the estimated user's emotions. For example, if the user is in a hurry, the formatting unit can perform a short, to-the-point formatting. If the user is relaxed, the formatting unit can also perform a longer formatting that includes detailed explanations. If the user is excited, the formatting unit can also perform formatting that adds visually stimulating effects. This allows for adjusting the length of the formatting based on the user's emotions, thereby providing materials of an appropriate length. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the formatting unit can be performed using, for example, AI, or without AI. For example, the formatting unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the formatting based on the emotion.

[0096] When formatting documents, the formatting unit can determine the formatting priority based on the submission date of the format. For example, in the case of a format with an approaching deadline, the formatting unit prioritizes formatting the most important information. In addition, in the case of a format with a distant submission date, the formatting unit can also format the document to include detailed information. The formatting unit can also adjust the priority of information according to the submission date and perform optimal formatting. In this way, by determining the formatting priority based on the submission date, important information can be provided at the appropriate time. Some or all of the above-mentioned processing in the formatting unit may be performed using, for example, AI, or may be performed without using AI. For example, the formatting unit can input data on the submission date of the format into the generation AI and have the generation AI determine the formatting priority.

[0097] When formatting the document, the formatting unit can adjust the order of formatting based on the relevance of the format. For example, the formatting unit prioritizes information related to important formats. The formatting unit can also postpone information related to general formats. The formatting unit can also group highly related information and adjust the order. This allows for a deeper understanding of the document by adjusting the order of formatting based on relevance. Some or all of the above-described processing in the formatting unit may be performed using, or without, AI. For example, the formatting unit can input data on the relevance of formats to the generation AI and have the generation AI adjust the order of formatting.

[0098] When formatting the document, the formatting unit can adjust the use of technical terms in the formatting depending on the user's level of expertise. For example, if the user is an expert, the formatting unit may perform formatting that uses a lot of technical terms. Furthermore, if the user is a layperson, the formatting unit may perform formatting that avoids technical terms and is easier to understand. The formatting unit can also adjust the frequency of use of technical terms depending on the user's level of expertise. This allows for adjusting the use of technical terms depending on the level of expertise, thereby providing materials that are easy for the user to understand. Some or all of the above-described processing in the formatting unit may be performed using, for example, AI, or may be performed without using AI. For example, the formatting unit may input data on the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terms.

[0099] The providing unit can estimate the user's emotions and adjust the presentation method based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can provide the materials in a soft tone. If the user is in a hurry, the providing unit can also provide the materials in a quick and concise manner. If the user is excited, the providing unit can also provide the materials in a visually stimulating manner. This allows for more appropriate materials to be provided by adjusting the presentation method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the presentation method based on the emotion.

[0100] When providing the materials, the providing unit can emphasize points that are likely to be evaluated based on the results of an analysis of past award-winning works. For example, the providing unit can analyze commonalities among past award-winning works and emphasize points that are likely to be evaluated. The providing unit can also adjust the design of the materials by referring to the designs of past award-winning works. The providing unit can also analyze the content of past award-winning works and emphasize information that is likely to be evaluated. This increases the probability of success in the contest by emphasizing points that are likely to be evaluated. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the results of an analysis of past award-winning works into the generating AI and cause the generating AI to provide materials that emphasize points that are likely to be evaluated.

[0101] The providing unit can improve the method of providing materials by reflecting the user's past feedback. For example, the providing unit can suggest an optimal method of providing materials based on feedback provided by the user in the past. The providing unit can also analyze the user's past feedback and customize the method of providing materials. The providing unit can also optimize the procedure of providing materials based on the user's past feedback. This allows the optimal method of providing materials to the user by reflecting the past feedback. Some or all of the above-described processing in the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input the user's past feedback into the generation AI and cause the generation AI to improve the method of providing materials.

[0102] When providing materials, the providing unit can customize the provided content based on the user's current project or area of ​​interest. For example, the providing unit can prioritize providing materials related to the user's current project. The providing unit can also provide related materials based on the user's area of ​​interest. The providing unit can also analyze the user's past project history and provide related materials. In this way, by customizing the provided content based on the user's current project or area of ​​interest, it is possible to provide useful materials to the user. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the user's current project or area of ​​interest into the generation AI and cause the generation AI to customize the provided content.

[0103] The providing unit can estimate the user's emotions and determine the priority of materials to be provided based on the estimated user emotions. For example, if the user is nervous, the providing unit can prioritize providing important materials. Furthermore, if the user is relaxed, the providing unit can also provide detailed materials. Furthermore, if the user is in a hurry, the providing unit can prioritize providing the most important materials. Thus, by determining the priority of materials according to the user's emotions, important information can be provided preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to determine the priority of materials based on emotions.

[0104] When providing materials, the providing unit can prioritize providing highly relevant materials by taking into account the user's geographical location information. For example, if the user is in a specific area, the providing unit can prioritize providing materials related to that area. The providing unit can also provide materials for related events or projects based on the user's current location. The providing unit can also analyze the user's past location information and provide related materials. In this way, highly relevant materials can be provided to the user by taking the geographical location information into consideration. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's geographical location information into the generation AI and cause the generation AI to provide highly relevant materials.

[0105] When providing materials, the providing unit can analyze the user's social media activity and provide related materials. The providing unit can provide related materials based on, for example, content shared by the user on social media. The providing unit can also analyze the user's social media activity history and provide related materials. The providing unit can also provide related materials by referring to the activities of the user's friends on social media. In this way, related materials can be provided to the user by analyzing social media activity. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input data on the user's social media activity into a generation AI and cause the generation AI to provide related materials.

[0106] The providing unit can customize the method of providing materials by reflecting the user's past feedback. The providing unit can, for example, suggest an optimal method of providing materials based on feedback provided by the user in the past. The providing unit can also analyze the user's past feedback and customize the method of providing materials. The providing unit can also optimize the procedure of providing materials based on the user's past feedback. This allows the optimal method of providing materials to the user by reflecting the past feedback. Some or all of the above-described processing in the providing unit can be performed using AI, for example, or can be performed without using AI. For example, the providing unit can input the user's past feedback into the generation AI and cause the generation AI to customize the method of providing materials. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, formatting unit, and providing unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can receive input of a user's theme or purpose using the reception device 38 of the smart device 14. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates the content of the material using a generation AI. The formatting unit formats the material generated by the control unit 46A of the smart device 14 into a format specified by the user. The providing unit provides the material formatted by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, formatting unit, and provision unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can receive input of the user's theme or purpose using the microphone 238 of the smart glasses 214. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates the content of the material using a generation AI. The formatting unit formats the material generated by the control unit 46A of the smart glasses 214 into a format specified by the user. The provision unit provides the material formatted by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, formatting unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit can receive input of the user's theme or purpose using the microphone 238 of the headset type terminal 314. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates the content of the material using a generation AI. The formatting unit formats the material generated by the control unit 46A of the headset type terminal 314 into a format specified by the user. The provision unit provides the material formatted by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, formatting unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can receive input of the user's theme and purpose using the microphone 238 of the robot 414. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates the content of the material using a generation AI. The formatting unit formats the material generated by the control unit 46A of the robot 414 into a format specified by the user. The provision unit provides the material formatted by the specific processing unit 290 of the data processing device 12.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] The reception unit can analyze the user's input in real time and suggest related additional information based on the input. For example, if a user inputs "marketing strategy," the reception unit can suggest related additional information such as "market analysis" and "competitive analysis." If a user inputs "technological innovation," the reception unit can suggest related additional information such as "patent information" and "technology trends." If a user inputs "project management," the reception unit can suggest related additional information such as "Gantt charts" and "risk management." This allows users to efficiently collect necessary information and improve the quality of their documents. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can input the user's input to a generation AI and have the generation AI suggest related additional information.

[0109] The generation unit can estimate the user's emotions and adjust the tone and style of the materials based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit can generate materials with a simple and intuitive tone. Alternatively, if the user is relaxed, the generation unit can generate materials with detailed and in-depth content. Furthermore, if the user is excited, the generation unit can generate materials with a visually appealing and energetic tone. This allows for more effective materials to be provided by adjusting the tone and style of the materials according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the tone and style of the materials based on the emotion.

[0110] When formatting a document, the formatting unit can improve the accuracy of the formatting by referring to the user's past formatting results. For example, the formatting unit can analyze the designs of documents that the user has formatted in the past and improve the accuracy. The formatting unit can also extract and format the optimal design from the user's past formatting results. The formatting unit can also adjust the design to maintain consistency based on the user's past formatting results. This improves the accuracy of the formatting by referring to the past formatting results. Some or all of the above-mentioned processing in the formatting unit may be performed using, for example, AI, or may be performed without using AI. For example, the formatting unit can input data of the user's past formatting results into the generation AI and have the generation AI improve the accuracy of the formatting.

[0111] The providing unit can estimate the user's emotions and adjust the presentation method based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can provide the materials in a soft tone. If the user is in a hurry, the providing unit can provide the materials in a quick and concise manner. If the user is excited, the providing unit can provide the materials in a visually stimulating manner. This allows the presentation method to be adjusted according to the user's emotions, thereby providing more appropriate materials. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or without AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the presentation method based on the emotion.

[0112] The reception unit can analyze the user's past input history and provide optimal input assistance. For example, themes and purposes that the user has frequently input in the past can be automatically displayed as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest themes and purposes to be used during a specific time period based on the user's past input history. In this way, optimal input assistance can be provided to the user by analyzing the past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history into a generation AI and cause the generation AI to provide optimal input assistance.

[0113] When generating materials, the generation unit can adjust the level of detail of the content based on the importance of the theme or purpose. For example, in the case of an important theme or purpose, the generation unit can generate content containing detailed information. In addition, in the case of a general theme or purpose, the generation unit can generate content containing concise information. In addition, in the case of a theme or purpose related to a specific field of expertise, the generation unit can generate content containing specialized information. In this way, by adjusting the level of detail of the content based on the importance of the theme or purpose, it is possible to generate materials with an appropriate amount of information. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the importance of the theme or purpose into the generation AI and have the generation AI adjust the level of detail of the content.

[0114] The formatting unit can estimate the user's emotions and adjust the formatting expression based on the estimated user's emotions. For example, if the user is relaxed, the formatting unit can use soft colors and fonts. If the user is in a hurry, the formatting unit can use a simple, highly visible format. If the user is excited, the formatting unit can use a visually stimulating design. This allows for adjusting the formatting expression according to the user's emotions, thereby providing more appropriate materials. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the formatting unit can be performed using AI, for example, or without AI. For example, the formatting unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the formatting expression based on the emotion.

[0115] When providing materials, the providing unit can customize the provided content based on the user's current project or area of ​​interest. For example, the providing unit can prioritize providing materials related to the user's current project. The providing unit can also provide related materials based on the user's area of ​​interest. The providing unit can also analyze the user's past project history and provide related materials. In this way, by customizing the provided content based on the user's current project or area of ​​interest, it is possible to provide useful materials to the user. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the user's current project or area of ​​interest into the generating AI and cause the generating AI to customize the provided content.

[0116] The reception unit can estimate the user's emotions and adjust the input method for the theme or purpose based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable the user to quickly input the theme or purpose. This adjusts the input method according to the user's emotions, reducing the user's stress and enabling efficient input. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI adjust the input method based on the emotion.

[0117] When generating materials, the generation unit can apply different generation algorithms depending on the theme or purpose category. For example, if the theme or purpose is business-related, a business-oriented generation algorithm can be applied. Furthermore, if the theme or purpose is education-related, the generation unit can also apply an education-oriented generation algorithm. Furthermore, if the theme or purpose is entertainment-related, the generation unit can also apply an entertainment-oriented generation algorithm. In this way, by applying a generation algorithm according to the category, more appropriate materials can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data of the theme or purpose category into the generation AI and cause the generation AI to apply different generation algorithms.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The reception unit accepts input of the theme and purpose of the materials. For example, the user inputs a theme such as "Creating materials for a contest utilizing SB generation AI." Step 2: The generation unit uses a generation AI to generate the content of the materials based on the information received by the reception unit. The generation unit collects information, for example, from public databases on the Internet and past contest materials, and determines the structure of the materials. The generation AI generates the content of the materials using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation AI generates materials that include an overview of the contest, how to participate, and an analysis of past winning entries. Step 3: The formatter formats the materials generated by the generator into a format specified by the user. The formatter formats the materials in a format that meets the user's needs, such as a slide format for a presentation or a report format. The formatter can also perform detailed customization of font size, color, layout, etc. Step 4: The providing department provides the materials formatted by the formatting department. For example, the providing department provides materials that emphasize points that are likely to be evaluated based on the results of an analysis of past award-winning works.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] 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 engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 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.

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

[0127] The 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.

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

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

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

[0131] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] 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.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0142] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0143] 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.

[0144] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0147] 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.

[0148] 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.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 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.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 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.

[0158] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0159] 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.

[0160] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0161] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0162] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0163] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0164] 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.

[0165] 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.

[0166] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0167] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0168] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0169] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0174] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0175] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0176] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0177] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0178] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0179] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0180] 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.

[0181] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0182] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0183] 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.

[0184] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0185] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0186] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0187] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0188] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0189] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0190] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0191] [Explanation of symbols]

[0192] 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. A reception section that accepts input of the subject and purpose of the materials; a generation unit that generates content of the material based on the information received by the reception unit; a formatting unit that formats the material generated by the generation unit into a format designated by a user; a providing unit that provides the material formatted by the formatting unit. A system characterized by:

2. The generation unit Gather information from public databases on the Internet or past contest materials 2. The system of claim 1.

3. The generation unit Decide the structure of the materials based on the information collected 2. The system of claim 1.

4. The shaping unit is Formatting the document according to the format type specified by the user 2. The system of claim 1.

5. The shaping unit is Deep customization of font size, color, and layout 2. The system of claim 1.

6. The providing unit Provide materials that emphasize points that are likely to be evaluated based on the results of an analysis of past award-winning works 2. The system of claim 1.

7. The reception unit Inferring user emotions and adjusting the input method for themes and goals based on the estimated user emotions 2. The system of claim 1.

8. The reception unit Analyzes the user's input history and provides optimal input assistance 2. The system of claim 1.

9. The reception unit As you type your topic or purpose, suggestions are provided based on your current projects and interests.

2. The system of claim 1.

10. The reception unit When entering a topic or purpose, select the most appropriate input method depending on the user's input method.

2. The system of claim 1.

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