System

Abstract

An object of the system according to the embodiment is to enable a user to easily generate a material having an optimal layout and design.SOLUTION: A system according to an embodiment includes a title and key point input unit, a material input unit, a layout and design selection unit, and a material generation unit. The title and key point input unit receives a title and a key point from the user. The material input unit receives a material based on the title and the main point received by the title and main point input unit. The layout and design selection unit selects an optimal layout and design based on the material received by the material input unit. The material generation unit generates a material based on the layout and the design selected by the layout and design selection 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 technology requires manual selection of layout and design when creating documents, which is time-consuming and labor-intensive.

[0005] The system according to the embodiment aims to enable a user to easily generate materials with optimal layout and design. [Means for solving the problem]

[0006] The system according to the embodiment includes a title and main points input unit, a document input unit, a layout and design selection unit, and a document generation unit. The title and main points input unit accepts a title and main points from a user. The document input unit accepts documents based on the title and main points accepted by the title and main points input unit. The layout and design selection unit selects an optimal layout and design based on the documents accepted by the document input unit. The document generation unit generates documents based on the layout and design selected by the layout and design selection unit. [Effects of the Invention]

[0007] The system according to the embodiment allows a user to easily generate materials with optimal layout and design. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The automatic material generation system according to an embodiment of the present invention is a system that automatically generates materials such as PDFs and PowerPoint documents by inputting titles, main points, and documents (images, statistical data files, etc.) provided by the user. This allows the automatic material generation system to automatically generate documents based on the information provided by the user.

[0029] The automatic document generation system according to the embodiment includes a title and main points input unit, a document input unit, a layout and design selection unit, and a document generation unit. The title and main points input unit accepts a title and main points from a user. For example, a title such as "Market Analysis of a New Product" and main points such as "Current Market Status, Competitive Analysis, and Future Outlook" can be input. The document input unit accepts documents from a user. For example, product photos and market research graph data can be input. The layout and design selection unit selects an optimal layout and design based on the input title, main points, and documents. For example, a simple and professional design is selected for a business presentation, and a visually easy-to-understand design is selected for educational materials. The document generation unit generates documents based on the selected layout and design. For example, the automatic document generation system generates documents including a title page, table of contents, and content pages for each section. This allows the automatic document generation system to automatically generate documents based on information provided by a user. This system can be used for a variety of purposes, such as business presentations, educational materials, and research reports. Furthermore, the time required for document creation can be significantly reduced.

[0030] The title and main points input section allows the generation AI to automatically suggest related keywords based on the title and main points entered by the user. For example, when a user enters a title and main points, the generation AI automatically suggests related keywords. For example, if the user enters "market analysis of a new product," keywords such as "market trends," "competitive analysis," and "consumer behavior" will be suggested. This allows the content of the document to be enriched by suggesting related keywords based on the content entered by the user.

[0031] The title and main points input section allows the generation AI to automatically search for and suggest related references and data based on the title and main points entered by the user. For example, when a user enters a title and main points, the generation AI automatically searches the internet for related references and data and suggests them. For example, if you enter "competitive analysis," related market reports and academic papers will be displayed. This improves the reliability of the material by suggesting related references and data based on the content entered by the user.

[0032] The title and main points input section can also support voice input and handwriting input. The title and main points input section allows the user to input the title and main points using voice input. For example, if the user speaks "market analysis for new products" using the microphone, the generation AI converts the voice into text and recognizes it as input content. This improves convenience by allowing the user to input the title and main points using voice input and handwriting input.

[0033] The title and main points input section supports the input of titles and main points in different languages, making it possible to generate multilingual materials. The title and main points input section allows users to input titles and main points in different languages. For example, they can input "Market Analysis" in English, and the generation AI will recognize the content and generate materials. This allows the input of titles and main points in different languages ​​to be supported, making it possible to generate multilingual materials.

[0034] In the document input section, the generation AI automatically analyzes the content of the document as it is being input and can suggest related additional materials. For example, when a user inputs a document, the generation AI automatically analyzes the content and suggests related additional materials. For example, when market research graph data is input, related market reports and statistical data are suggested. This allows the content of the document to be enriched by suggesting related additional materials as the document is input.

[0035] The document input section can diversify the document input format, allowing for the input of video and audio data. The document input section allows users to input video and audio data. For example, when a product promotional video or interview audio is input, the generation AI analyzes the content and incorporates it into the document. This diversifies the document input format, allowing for the input of video and audio data, improving the expressiveness of the document.

[0036] The document input unit can link document input with cloud storage, allowing users to easily upload documents. The document input unit, for example, allows users to upload documents directly from cloud storage. For example, a file can be selected from Google Drive or Dropbox and input into the generation AI. This allows document input to be linked with cloud storage, allowing users to easily upload documents.

[0037] In the document input section, the generation AI can automatically summarize the contents of the document as it is input and generate a summary. For example, when a user inputs a document, the generation AI automatically analyzes the content and generates a summary. For example, if a long report is input, the generation AI extracts the main points and creates a short summary. This allows the main points of the document to be grasped concisely by automatically generating a summary as the document is input.

[0038] The layout and design selection unit allows the generation AI to automatically learn the user's past preferences when selecting the layout and design, and propose the optimal design. For example, the layout and design selection unit allows the generation AI to learn the user's past preferences and propose the optimal design when selecting the layout and design. For example, it proposes a design that the user prefers based on the trends of designs selected in the past. In this way, the generation AI can learn the user's past preferences and propose the optimal design, making it possible to generate materials that suit the user's preferences.

[0039] The layout and design selection section allows the generation AI to automatically reflect the latest design trends in the selection of layout and design. For example, the generation AI automatically collects the latest design trends and reflects them in the selection of layout and design. For example, it proposes designs that incorporate the latest color palettes and font styles. This allows visually appealing materials to be generated by reflecting the latest design trends.

[0040] The layout and design selection unit allows the user to customize the selection of layout and design, thereby increasing the degree of freedom. The layout and design selection unit, for example, allows the user to customize the layout and design. For example, it provides an interface that allows the user to freely change the color usage, font, layout, etc. This allows the user to customize the layout and design, increasing the degree of freedom and enabling the generation of materials that meet the user's needs.

[0041] The layout and design selection unit can provide templates specialized for different industries or uses when selecting layouts and designs. For example, the layout and design selection unit allows the user to select templates specialized for different industries or uses by the generation AI. For example, it provides templates for business presentations, educational materials, and research reports. By providing templates specialized for different industries and uses, the user can select the optimal design according to their purpose.

[0042] The document generation unit allows the generation AI to automatically proofread the content of the document when it is automatically generating the document, and correct typos and grammar. For example, the document generation unit automatically detects and corrects typos and omissions in the input text when the generation AI automatically generates the document. For example, it corrects spelling mistakes and grammatical errors. This makes it possible to improve the accuracy of the document by correcting typos and grammar when the document is automatically generated.

[0043] The document generation unit allows the generation AI to automatically add graphics and icons to visually emphasize the content of the document when automatically generating the document. For example, the document generation unit automatically adds graphics and icons to visually emphasize the content when the generation AI automatically generates the document. For example, it inserts icons to emphasize important points. In this way, by adding graphics and icons to visually emphasize the content when automatically generating the document, the visual appeal of the document can be improved.

[0044] The document generation unit can output automatically generated documents in formats optimized for different devices (smartphones, tablets, PCs). For example, the document generation unit uses a generation AI to automatically generate documents and output them in formats optimized for different devices. For example, it generates a PDF optimized for smartphones and a PowerPoint optimized for tablets. This allows automatically generated documents to be output in formats optimized for different devices, enabling users to use the documents on a variety of devices.

[0045] The document generation unit allows the generation AI to automatically read out the contents of the document aloud when automatically generating the document. For example, the document generation unit adds a function where the generation AI automatically generates a document and reads out the contents aloud. For example, it generates presentation materials and provides audio explanations for each slide. By adding a voice reading function to the automatic generation of documents, the contents of the document can be confirmed by audio, for example, by the visually impaired or users who are driving.

[0046] The output format selection unit allows the generation AI to automatically learn the user's past preferences when selecting an output format and suggest the optimal format. For example, the output format selection unit allows the generation AI to learn the user's past preferences and suggest the optimal format when selecting an output format. For example, for a user who previously selected PDF format, the output format selection unit preferentially suggests PDF format. This allows the system to learn the user's past preferences and suggest the optimal format, thereby providing an output format that meets the user's needs.

[0047] The output format selection section allows the generation AI to automatically reflect the latest formats and trends when selecting the output format. For example, the generation AI automatically collects the latest formats and trends and reflects them in the selection of the output format. For example, it suggests formats that incorporate the latest PDF formats or PowerPoint templates. This allows visually appealing materials to be generated by reflecting the latest formats and trends.

[0048] The output format selection unit allows the user to customize the selection of the output format, thereby increasing the degree of freedom. The output format selection unit, for example, allows the user to customize the output format. For example, it provides an interface that allows the user to freely change the page layout of a PDF or the slide design of a PowerPoint. This allows the user to customize the output format, increasing the degree of freedom and enabling the generation of materials that meet the user's needs.

[0049] The output format selection unit can provide templates specialized for different industries or uses when selecting an output format. For example, the output format selection unit allows the generation AI to provide templates specialized for different industries or uses, allowing the user to select one. For example, it provides templates for business presentations, educational materials, and research reports. By providing templates specialized for different industries and uses, the user can select the optimal output format according to their purpose.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The automatic document generation system can also learn from the user's past document creation history and make customization suggestions tailored to the user's preferences. For example, it can automatically suggest designs that the user prefers based on the fonts, colors, and layout patterns used in the past. It can also learn the user's frequently used keywords and phrases and automatically suggest them the next time a document is created. This allows documents tailored to the user's preferences to be generated quickly.

[0052] The automatic document generation system can also suggest related video content based on the title and main points entered by the user. For example, if you enter "market analysis for a new product," links to related market analysis videos and webinars will be displayed. It can also summarize the content of the video selected by the user and incorporate it into the document. This allows you to add visual content to your document, making it more appealing.

[0053] The automatic document generation system can also search for and suggest related social media posts based on the title and main points entered by the user. For example, if you enter "competitive analysis," related tweets and LinkedIn posts will be displayed. By quoting these posts in the document, you can reflect the latest trends and opinions. This allows you to enrich the content of the document with the latest information.

[0054] The automatic document generation system can also suggest related infographics based on the title and main points entered by the user. For example, if you enter "Current Market Status," infographics that visually represent related market data will be displayed. By incorporating these infographics into documents, you can create documents that are visually easy to understand. This improves the visual appeal of the documents.

[0055] The automatic document generation system can also search for and suggest related news articles based on the title and main points entered by the user. For example, if you enter "consumer behavior," the latest related news articles will be displayed. Furthermore, by citing these news articles in the document, the reliability of the document can be improved. This allows the content of the document to be enriched with the latest information.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The title and main points input section accepts the title and main points from the user. For example, you can enter a title such as "Market analysis of new products" and main points such as "Current market situation, competitive analysis, and future outlook." Step 2: The data input section accepts data from the user based on the title and main points received in the title and main points input section. For example, product photos and market research graph data can be input. Step 3: The layout and design selection department selects the optimal layout and design based on the title, main points, and materials entered. For example, for business presentations, a simple and professional design is selected, and for educational materials, a visually easy-to-understand design is selected. Step 4: The document generation unit generates documents based on the selected layout and design, including a title page, table of contents, and content pages for each section.

[0058] (Example 2) The automatic material generation system according to an embodiment of the present invention is a system that automatically generates materials such as PDFs and PowerPoint documents by inputting titles, main points, and documents (images, statistical data files, etc.) provided by the user. This allows the automatic material generation system to automatically generate documents based on the information provided by the user.

[0059] The automatic document generation system according to the embodiment includes a title and main points input unit, a document input unit, a layout and design selection unit, and a document generation unit. The title and main points input unit accepts a title and main points from a user. For example, a title such as "Market Analysis of a New Product" and main points such as "Current Market Status, Competitive Analysis, and Future Outlook" can be input. The document input unit accepts documents from a user. For example, product photos and market research graph data can be input. The layout and design selection unit selects an optimal layout and design based on the input title, main points, and documents. For example, a simple and professional design is selected for a business presentation, and a visually easy-to-understand design is selected for educational materials. The document generation unit generates documents based on the selected layout and design. For example, the automatic document generation system generates documents including a title page, table of contents, and content pages for each section. This allows the automatic document generation system to automatically generate documents based on information provided by a user. This system can be used for a variety of purposes, such as business presentations, educational materials, and research reports. Furthermore, the time required for document creation can be significantly reduced.

[0060] The title and main points input section allows the generation AI to automatically suggest related keywords based on the title and main points entered by the user. For example, when a user enters a title and main points, the generation AI automatically suggests related keywords. For example, if the user enters "market analysis of a new product," keywords such as "market trends," "competitive analysis," and "consumer behavior" will be suggested. This allows the content of the document to be enriched by suggesting related keywords based on the content entered by the user.

[0061] The title and main points input section allows the generation AI to automatically search for and suggest related references and data based on the title and main points entered by the user. For example, when a user enters a title and main points, the generation AI automatically searches the internet for related references and data and suggests them. For example, if you enter "competitive analysis," related market reports and academic papers will be displayed. This improves the reliability of the material by suggesting related references and data based on the content entered by the user.

[0062] The title and main points input section uses an emotion estimation function to analyze the emotions felt by the user regarding the title and main points, and can make suggested revisions to elicit positive emotions. For example, when a user inputs a title and main points, the generation AI uses the emotion estimation function to analyze the emotions felt in the input content and makes suggested revisions to elicit positive emotions. For example, if "market challenges" is input, the suggested revision will be "market growth opportunities." This makes it possible to improve the impression of the document by making suggested revisions to elicit positive emotions regarding the content entered by the user.

[0063] The title and main points input section can also support voice input and handwriting input. The title and main points input section allows the user to input the title and main points using voice input. For example, if the user speaks "market analysis for new products" using the microphone, the generation AI converts the voice into text and recognizes it as input content. This improves convenience by allowing the user to input the title and main points using voice input and handwriting input.

[0064] The title and main points input section supports the input of titles and main points in different languages, making it possible to generate multilingual materials. The title and main points input section allows users to input titles and main points in different languages. For example, they can input "Market Analysis" in English, and the generation AI will recognize the content and generate materials. This allows the input of titles and main points in different languages ​​to be supported, making it possible to generate multilingual materials.

[0065] The title and main points input section uses an emotion estimation function to display other users' emotional reactions to the title and main points entered by the user in real time, allowing the user to receive feedback. For example, when a user enters a title and main points in the title and main points input section, the generation AI uses the emotion estimation function to display other users' emotional reactions in real time. For example, if the user enters "current market situation," the positive and negative reactions of other users will be displayed. This allows the user to see other users' emotional reactions to the content they entered in real time and receive feedback, allowing them to identify areas for improvement in their materials.

[0066] In the document input section, the generation AI automatically analyzes the content of the document as it is being input and can suggest related additional materials. For example, when a user inputs a document, the generation AI automatically analyzes the content and suggests related additional materials. For example, when market research graph data is input, related market reports and statistical data are suggested. This allows the content of the document to be enriched by suggesting related additional materials as the document is input.

[0067] The document input section can diversify the document input format, allowing for the input of video and audio data. The document input section allows users to input video and audio data. For example, when a product promotional video or interview audio is input, the generation AI analyzes the content and incorporates it into the document. This diversifies the document input format, allowing for the input of video and audio data, improving the expressiveness of the document.

[0068] The material input unit can use the emotion estimation function to analyze the user's emotions toward the input material and suggest a material layout that will elicit positive emotions. For example, when a user inputs a material, the material input unit uses the emotion estimation function to analyze the user's emotions toward the material and suggest a layout that will elicit positive emotions. For example, positive images and graphs can be placed in prominent positions. This can improve the impression of the material by analyzing the user's emotions toward the input material and suggesting a material layout that will elicit positive emotions.

[0069] The document input unit can link document input with cloud storage, allowing users to easily upload documents. The document input unit, for example, allows users to upload documents directly from cloud storage. For example, a file can be selected from Google Drive or Dropbox and input into the generation AI. This allows document input to be linked with cloud storage, allowing users to easily upload documents.

[0070] In the document input section, the generation AI can automatically summarize the contents of the document as it is input and generate a summary. For example, when a user inputs a document, the generation AI automatically analyzes the content and generates a summary. For example, if a long report is input, the generation AI extracts the main points and creates a short summary. This allows the main points of the document to be grasped concisely by automatically generating a summary as the document is input.

[0071] The material input unit can use the emotion estimation function to collect other users' emotional reactions to the input material and suggest improvements to the material. For example, when a user inputs a material, the material input unit uses the emotion estimation function to collect other users' emotional reactions and suggest improvements to the material. For example, it makes suggestions to improve parts that have few positive reactions. In this way, by collecting other users' emotional reactions to the input material and suggesting improvements to the material, the quality of the material can be improved.

[0072] The layout and design selection unit allows the generation AI to automatically learn the user's past preferences when selecting the layout and design, and propose the optimal design. For example, the layout and design selection unit allows the generation AI to learn the user's past preferences and propose the optimal design when selecting the layout and design. For example, it proposes a design that the user prefers based on the trends of designs selected in the past. In this way, the generation AI can learn the user's past preferences and propose the optimal design, making it possible to generate materials that suit the user's preferences.

[0073] The layout and design selection section allows the generation AI to automatically reflect the latest design trends in the selection of layout and design. For example, the generation AI automatically collects the latest design trends and reflects them in the selection of layout and design. For example, it proposes designs that incorporate the latest color palettes and font styles. This allows visually appealing materials to be generated by reflecting the latest design trends.

[0074] The layout and design selection unit uses the emotion estimation function to analyze the user's emotions toward the layout and design selected and can make suggested revisions to elicit positive emotions. For example, when a user selects a layout and design, the layout and design selection unit uses the emotion estimation function to analyze the user's emotions toward the selection and makes suggested revisions to elicit positive emotions. For example, it suggests adjusting the color usage or layout. This allows the impression of the document to be improved by analyzing the user's emotions toward the layout and design selected and making suggested revisions to elicit positive emotions.

[0075] The layout and design selection unit allows the user to customize the selection of layout and design, thereby increasing the degree of freedom. The layout and design selection unit, for example, allows the user to customize the layout and design. For example, it provides an interface that allows the user to freely change the color usage, font, layout, etc. This allows the user to customize the layout and design, increasing the degree of freedom and enabling the generation of materials that meet the user's needs.

[0076] The layout and design selection unit can provide templates specialized for different industries or uses when selecting layouts and designs. For example, the layout and design selection unit allows the user to select templates specialized for different industries or uses by the generation AI. For example, it provides templates for business presentations, educational materials, and research reports. By providing templates specialized for different industries and uses, the user can select the optimal design according to their purpose.

[0077] The layout and design selection unit uses the emotion estimation function to collect other users' emotional reactions to the selected layout and design and propose the optimal design. For example, when a user selects a layout and design, the layout and design selection unit uses the emotion estimation function to collect other users' emotional reactions and propose the optimal design. For example, it will prioritize proposing designs that have a high number of positive reactions. This allows the quality of materials to be improved by collecting other users' emotional reactions to the selected layout and design and proposing the optimal design.

[0078] The document generation unit allows the generation AI to automatically proofread the content of the document when it is automatically generating the document, and correct typos and grammar. For example, the document generation unit automatically detects and corrects typos and omissions in the input text when the generation AI automatically generates the document. For example, it corrects spelling mistakes and grammatical errors. This makes it possible to improve the accuracy of the document by correcting typos and grammar when the document is automatically generated.

[0079] The document generation unit allows the generation AI to automatically add graphics and icons to visually emphasize the content of the document when automatically generating the document. For example, the document generation unit automatically adds graphics and icons to visually emphasize the content when the generation AI automatically generates the document. For example, it inserts icons to emphasize important points. In this way, by adding graphics and icons to visually emphasize the content when automatically generating the document, the visual appeal of the document can be improved.

[0080] The document generation unit can use the emotion estimation function to analyze the user's emotions regarding the generated document and make suggestions for revisions to elicit positive emotions. For example, after the generation AI automatically generates a document, the document generation unit can use the emotion estimation function to analyze the user's emotions and make suggestions for revisions to elicit positive emotions. For example, it can make suggestions to add positive expressions or visuals. In this way, the impression of the document can be improved by analyzing the user's emotions regarding the generated document and making suggestions for revisions to elicit positive emotions.

[0081] The document generation unit can output automatically generated documents in formats optimized for different devices (smartphones, tablets, PCs). For example, the document generation unit uses a generation AI to automatically generate documents and output them in formats optimized for different devices. For example, it generates a PDF optimized for smartphones and a PowerPoint optimized for tablets. This allows automatically generated documents to be output in formats optimized for different devices, enabling users to use the documents on a variety of devices.

[0082] The document generation unit allows the generation AI to automatically read out the contents of the document aloud when automatically generating the document. For example, the document generation unit adds a function where the generation AI automatically generates a document and reads out the contents aloud. For example, it generates presentation materials and provides audio explanations for each slide. By adding a voice reading function to the automatic generation of documents, the contents of the document can be confirmed by audio, for example, by the visually impaired or users who are driving.

[0083] The document generation unit can use the emotion estimation function to collect other users' emotional reactions to the generated document and suggest improvements to the document. For example, after the generation AI automatically generates a document, the document generation unit can use the emotion estimation function to collect other users' emotional reactions and suggest improvements to the document. For example, it can make suggestions to improve parts that have few positive reactions. In this way, by collecting other users' emotional reactions to the generated document and suggesting improvements to the document, the quality of the document can be improved.

[0084] The output format selection unit allows the generation AI to automatically learn the user's past preferences when selecting an output format and suggest the optimal format. For example, the output format selection unit allows the generation AI to learn the user's past preferences and suggest the optimal format when selecting an output format. For example, for a user who previously selected PDF format, the output format selection unit preferentially suggests PDF format. This allows the system to learn the user's past preferences and suggest the optimal format, thereby providing an output format that meets the user's needs.

[0085] The output format selection section allows the generation AI to automatically reflect the latest formats and trends when selecting the output format. For example, the generation AI automatically collects the latest formats and trends and reflects them in the selection of the output format. For example, it suggests formats that incorporate the latest PDF formats or PowerPoint templates. This allows visually appealing materials to be generated by reflecting the latest formats and trends.

[0086] The output format selection unit can use the emotion estimation function to analyze the user's emotions regarding the output format selected and make suggestions for revisions to elicit positive emotions. For example, when a user selects an output format, the output format selection unit uses the emotion estimation function to analyze the user's emotions regarding the selection and make suggestions for revisions to elicit positive emotions. For example, it can suggest adjustments to color usage or layout. This allows the user's emotions regarding the selected output format to be analyzed and suggestions for revisions to elicit positive emotions to improve the impression of the document.

[0087] The output format selection unit allows the user to customize the selection of the output format, thereby increasing the degree of freedom. The output format selection unit, for example, allows the user to customize the output format. For example, it provides an interface that allows the user to freely change the page layout of a PDF or the slide design of a PowerPoint. This allows the user to customize the output format, increasing the degree of freedom and enabling the generation of materials that meet the user's needs.

[0088] The output format selection unit can provide templates specialized for different industries or uses when selecting an output format. For example, the output format selection unit allows the generation AI to provide templates specialized for different industries or uses, allowing the user to select one. For example, it provides templates for business presentations, educational materials, and research reports. By providing templates specialized for different industries and uses, the user can select the optimal output format according to their purpose.

[0089] The output format selection unit can use the emotion estimation function to collect other users' emotional reactions to the selected output format and suggest the optimal format. For example, when a user selects an output format, the output format selection unit uses the emotion estimation function to collect other users' emotional reactions and suggest the optimal format. For example, it will prioritize suggesting formats with a high number of positive reactions. This allows the quality of materials to be improved by collecting other users' emotional reactions to the selected output format and suggesting the optimal format.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The automatic document generation system can also learn from the user's past document creation history and make customization suggestions tailored to the user's preferences. For example, it can automatically suggest designs that the user prefers based on the fonts, colors, and layout patterns used in the past. It can also learn the user's frequently used keywords and phrases and automatically suggest them the next time a document is created. This allows documents tailored to the user's preferences to be generated quickly.

[0092] The automatic document generation system can also suggest related video content based on the title and main points entered by the user. For example, if you enter "market analysis for a new product," links to related market analysis videos and webinars will be displayed. It can also summarize the content of the video selected by the user and incorporate it into the document. This allows you to add visual content to your document, making it more appealing.

[0093] The automatic document generation system can also search for and suggest related social media posts based on the title and main points entered by the user. For example, if you enter "competitive analysis," related tweets and LinkedIn posts will be displayed. By quoting these posts in the document, you can reflect the latest trends and opinions. This allows you to enrich the content of the document with the latest information.

[0094] The automatic document generation system can also suggest related infographics based on the title and main points entered by the user. For example, if you enter "Current Market Status," infographics that visually represent related market data will be displayed. By incorporating these infographics into documents, you can create documents that are visually easy to understand. This improves the visual appeal of the documents.

[0095] The automatic document generation system can also search for and suggest related news articles based on the title and main points entered by the user. For example, if you enter "consumer behavior," the latest related news articles will be displayed. Furthermore, by citing these news articles in the document, the reliability of the document can be improved. This allows the content of the document to be enriched with the latest information.

[0096] The automatic document generation system can also estimate the user's emotions and make suggestions for relaxation if the user is feeling stressed. For example, if it estimates that the user is feeling stressed while creating a document, it can suggest taking a short break or listening to relaxing music. Also, if the user is feeling positive, it can display an encouraging message to help them maintain that emotion. This makes it possible to provide support that is tailored to the user's emotional state.

[0097] The automatic document generation system can also estimate the user's emotions, and if the user is feeling positive, it can suggest content to reinforce those emotions. For example, if it is estimated that the user is feeling positive while creating a document, it can suggest content including success stories and positive feedback. Furthermore, if the user is feeling negative, it can also display relaxation techniques and encouraging messages to alleviate those emotions. This allows it to provide support tailored to the user's emotional state.

[0098] The automatic document generation system can also estimate the user's emotions and make suggestions to reduce frustration felt by the user while creating the document. For example, if it estimates that the user is feeling frustrated while creating the document, it can suggest an easy task or a short break to relax. Furthermore, if the user is feeling positive, it can display an encouraging message to help maintain that emotion. This allows the system to provide support tailored to the user's emotional state.

[0099] The automatic document generation system can also estimate the user's emotions and make suggestions to prevent a decrease in motivation while the user is creating the document. For example, if it is estimated that the user's motivation is decreasing while creating the document, it can suggest content including success stories and positive feedback. Also, if the user has positive emotions, it can display encouraging messages to reinforce those emotions. This makes it possible to provide support that is appropriate for the user's emotional state.

[0100] The automatic document generation system can also estimate the user's emotions and make suggestions to reduce fatigue felt while creating documents. For example, if it estimates that the user is feeling fatigued while creating documents, it can suggest taking a short break or stretching to refresh themselves. Furthermore, if the user is feeling positive, it can display encouraging messages to help them maintain those emotions. This allows the system to provide support tailored to the user's emotional state.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The title and main points input section accepts the title and main points from the user. For example, you can enter a title such as "Market analysis of new products" and main points such as "Current market situation, competitive analysis, and future outlook." Step 2: The data input section accepts data from the user based on the title and main points received in the title and main points input section. For example, product photos and market research graph data can be input. Step 3: The layout and design selection department selects the optimal layout and design based on the title, main points, and materials entered. For example, for business presentations, a simple and professional design is selected, and for educational materials, a visually easy-to-understand design is selected. Step 4: The document generation unit generates documents based on the selected layout and design, including a title page, table of contents, and content pages for each section.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] 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.

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] 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.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] 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.

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 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 title and gist input unit for receiving a title and gist from a user; a material input unit that receives material based on the title and gist received by the title and gist input unit; a layout and design selection unit that selects an optimal layout and design based on the materials received by the material input unit; a material generating unit that generates materials based on the layout and design selected by the layout and design selecting unit. A system characterized by:

2. The input unit for the material includes: When inputting materials, the generation AI automatically analyzes the contents of the materials and suggests related additional materials.

2. The system of claim 1.

3. The layout and design selection unit When selecting layout and design, the generative AI automatically learns the user's past preferences and proposes the optimal design.

2. The system of claim 1.

4. The material generation unit When automatically generating documents, the AI ​​automatically proofreads the contents of the documents and corrects typos and grammar.

2. The system of claim 1.

5. The title and main points input section is Using the emotion estimation function, the app analyzes the emotions users feel about the title and main points they enter, and makes suggestions to revise the content to elicit positive emotions.

2. The system of claim 1.

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