system

Abstract

An object of the system according to the embodiment is to further receive the benefit of the generation AI by using the GUI-based content creation tool using the Markdown and the CSS.SOLUTION: A system according to an embodiment includes a generation AI, a GUI-based content creation tool, a Markdown inputter, and a CSS inputter. The generation AI analyzes the text inputted by the Markdown inputting unit. The GUI-based content creation tool creates a figure or slide based on the text parsed by the generation AI.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Previous technology had the problem that generative AI was not sufficiently capable of recognizing and generating non-text content such as diagrams and slides.

[0005] The system according to the embodiment aims to enable users to take full advantage of generative AI by using GUI-based content creation tools that use Markdown and CSS. [Means for solving the problem]

[0006] A system according to an embodiment includes a generation AI, a GUI-based content creation tool, a Markdown input unit, and a CSS input unit. The generation AI analyzes text entered by the Markdown input unit. The GUI-based content creation tool creates diagrams or slides based on the text analyzed by the generation AI. [Effects of the Invention]

[0007] The system according to the embodiment can use GUI-based content creation tools using Markdown and CSS to better benefit from generative AI. [Brief explanation of the drawings]

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

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

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

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

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

[0013] In the following embodiments, the coded storage is one or more nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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) A content creation system according to an embodiment of the present invention uses a generative AI to analyze text and create diagrams and slides using Markdown and CSS. This makes the content creation system more efficient and intuitive to use when creating text-based content.

[0029] A content creation system according to an embodiment includes a generation AI, a GUI-based content creation tool, a Markdown input unit, and a CSS input unit. The generation AI analyzes text input via the Markdown input unit. For example, the generation AI analyzes text in Markdown format using a text generation AI (e.g., LLM). The generation AI can also analyze the content of the text using a multimodal generation AI. The generation AI can also automatically collect relevant background information to understand the context and incorporate it into the analysis results. For example, the text generation AI has trained on large amounts of text data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI collects related news articles and academic papers and references them to understand the context. The GUI-based content creation tool creates figures and slides based on the text analyzed by the generation AI. For example, the GUI-based content creation tool automatically generates the layout and design of figures and slides based on text written in Markdown format. The GUI-based content creation tool can also easily create figures and slides by specifying styles using CSS. For example, by entering "# title" in Markdown and specifying "h1 { color: blue;}" in CSS, a slide with a blue title can be created. The Markdown input unit provides an interface for a user to input text in Markdown format. For example, the Markdown input unit provides a text editor, allowing a user to input text using Markdown notation. The CSS input unit provides an interface for a user to input CSS. For example, the CSS input unit provides a style sheet editor, allowing a user to specify styles using CSS. As a result, the content creation system according to the embodiment improves the efficiency of text-based content creation and enables intuitive operation.For example, users can easily create diagrams and slides by entering text in Markdown format and specifying styles using CSS. The generation AI also automatically adjusts the layout and design, eliminating the need for users to configure detailed settings.

[0030] Generative AI can analyze text written in Markdown format and automatically generate diagrams and slides based on its content. For example, generative AI can analyze text written in Markdown format and automatically generate diagrams and slides based on its content. For example, generative AI can extract keywords and phrases contained in the text and generate appropriate diagrams and slides based on them. Generative AI can also automatically search for related images and videos to understand the context and incorporate them into the analysis results. For example, if the text contains the word "cat," it will search for images and videos of cats and incorporate them into the analysis results. This improves the efficiency of content creation by analyzing text written in Markdown format and automatically generating diagrams and slides.

[0031] Generative AI can analyze text written in Markdown format and generate appropriate diagrams based on its content. For example, generative AI can analyze text written in Markdown format and generate appropriate diagrams based on its content. For example, generative AI can extract keywords and phrases contained in the text and generate appropriate diagrams based on them. Generative AI can also refer to the user's past input history and provide personalized analysis results. For example, if the user has input a lot about "cats" in the past, it can provide detailed analysis results about cats. This reduces the user's workload by analyzing Markdown text and generating appropriate diagrams.

[0032] Generative AI can analyze text written in Markdown format and generate appropriate slides based on its content. For example, generative AI can analyze text written in Markdown format and generate appropriate slides based on its content. For example, generative AI can extract keywords and phrases contained in the text and generate appropriate slides based on them. Generative AI can also use emotion estimation functionality to generate analysis results based on the user's emotional state and adjust them to elicit positive emotions. For example, if the user is feeling stressed, it can provide relaxing content. This reduces the user's workload by analyzing Markdown text and generating appropriate slides.

[0033] Generative AI can analyze text written in Markdown format and generate appropriate diagrams and slides based on its content. For example, generative AI can analyze text written in Markdown format and generate appropriate diagrams and slides based on its content. For example, generative AI can extract keywords and phrases contained in the text and generate appropriate diagrams and slides based on them. Generative AI can also analyze text in different languages ​​simultaneously and provide analysis results in multiple languages. For example, it can analyze text in English and Japanese simultaneously and provide analysis results in both languages. This reduces the workload of users by analyzing text in Markdown format and generating appropriate diagrams and slides.

[0034] Generative AI can analyze text written in Markdown format and generate appropriate diagrams and slides based on its content. For example, generative AI can analyze text written in Markdown format and generate appropriate diagrams and slides based on its content. For example, generative AI can extract keywords and phrases contained in the text and generate appropriate diagrams and slides based on that. Generative AI can also accept voice input, convert the voice to text, and analyze that text. For example, it can convert what a user says into text and analyze that text. This reduces the workload on users by analyzing text in Markdown format and generating appropriate diagrams and slides.

[0035] Generative AI can analyze text written in Markdown format and generate appropriate diagrams and slides based on its content. For example, generative AI can analyze text written in Markdown format and generate appropriate diagrams and slides based on its content. For example, generative AI can extract keywords and phrases contained in the text and generate appropriate diagrams and slides based on them. Generative AI can also use its emotion estimation function to analyze the emotions in the text entered by the user in real time and provide feedback according to the emotions. For example, if the user is angry, it can provide feedback to calm down. This reduces the workload of the user by analyzing text in Markdown format and generating appropriate diagrams and slides.

[0036] The summary generation unit can refer to background information and topic models to understand the context. For example, when the generation AI creates a summary, the summary generation unit automatically collects related background information and refers to it to understand the context. For example, it collects related news articles and academic papers. The summary generation unit also uses topic models to understand the context when the generation AI creates a summary. For example, it extracts related keywords and phrases based on the topic model. The summary generation unit also references related background information and topic models when the generation AI creates a summary, building a system for understanding the context. For example, it automatically collects related information and reflects it in the summary. This enables more accurate summaries by referring to background information and topic models to understand the context.

[0037] The summary generation unit can analyze the logical structure of an answer and the development of the arguments to generate a logical summary. For example, the summary generation unit uses a generation AI to analyze the logical structure of an answer and generate a logical summary. For example, it analyzes the development of arguments and logical consistency and reflects this in the summary. The summary generation unit also analyzes the development of arguments in an answer and builds a system in which the generation AI generates a logical summary. For example, it generates a summary based on the importance and relevance of arguments. The summary generation unit also develops an algorithm for the generation AI to analyze the logical structure of an answer and the development of arguments to generate a logical summary. For example, it evaluates the logical consistency and the importance of arguments. This makes it possible to generate a logical summary by analyzing the logical structure of an answer and the development of arguments in a question.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The content creation system can further include a voice input unit. The voice input unit provides an interface that allows the user to give instructions by voice. For example, when the user says, "Create the next slide," the voice input unit converts the instruction into text, and the generation AI analyzes the text to automatically generate the next slide. The voice input unit can also convert what the user speaks into text in real time and input it directly into the Markdown input unit. This allows the user to create content using only their voice without using a keyboard, improving work efficiency.

[0040] The content creation system can further include a data visualization unit. The data visualization unit automatically generates graphs and charts based on data provided by the user. For example, when a user inputs sales data, the data visualization unit analyzes the data and generates appropriate graphs and charts. The data visualization unit can also adjust the design of graphs and charts based on styles and themes specified by the user. This makes it easier for users to visually understand data and improves the quality of presentations.

[0041] The content creation system may further include a real-time collaboration unit. The real-time collaboration unit provides an interface that allows multiple users to edit content simultaneously. For example, team members can edit the same slide from different locations, and changes are reflected in real time. The real-time collaboration unit may also provide chat and comment functions to support communication between users. This allows the entire team to create content efficiently.

[0042] The content creation system can further include an automatic translation unit. The automatic translation unit automatically translates text entered by a user into multiple languages. For example, it can translate text entered by a user in English into Japanese or French and generate corresponding slides. The automatic translation unit can also provide an interface that allows a user to check the translation results and correct them if necessary. This makes it easier to share content between users who speak different languages, allowing work to proceed efficiently even in international projects.

[0043] The content creation system may further include a template library unit. The template library unit provides a variety of templates from which users can select. Examples include templates for business presentations, educational templates, and event templates. The template library unit may also provide a function that allows users to save and reuse templates they have created. This allows users to create content quickly and improves work efficiency.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The Markdown input unit provides an interface for a user to input text in Markdown format. For example, the Markdown input unit provides a text editor that allows a user to input text using Markdown notation. Step 2: The CSS input unit provides an interface for the user to input CSS. For example, the CSS input unit provides a style sheet editor, allowing the user to specify styles using CSS. Step 3: The generation AI analyzes the text entered by the Markdown input unit. For example, the generation AI analyzes the text in Markdown format using a text generation AI (e.g., LLM). The generation AI can also analyze the content of the text using a multimodal generation AI. The generation AI can also automatically collect relevant background information to understand the context and reflect it in the analysis results. Step 4: The GUI-based content creation tool creates figures and slides based on the text analyzed by the generative AI. For example, the GUI-based content creation tool automatically generates the layout and design of figures and slides based on text written in Markdown format. The GUI-based content creation tool also makes it easy to create figures and slides by specifying styles using CSS.

[0046] (Example 2) A content creation system according to an embodiment of the present invention uses a generative AI to analyze text and create diagrams and slides using Markdown and CSS. This makes the content creation system more efficient and intuitive to use when creating text-based content.

[0047] A content creation system according to an embodiment includes a generation AI, a GUI-based content creation tool, a Markdown input unit, and a CSS input unit. The generation AI analyzes text input via the Markdown input unit. For example, the generation AI analyzes text in Markdown format using a text generation AI (e.g., LLM). The generation AI can also analyze the content of the text using a multimodal generation AI. The generation AI can also automatically collect relevant background information to understand the context and incorporate it into the analysis results. For example, the text generation AI has trained on large amounts of text data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI collects related news articles and academic papers and references them to understand the context. The GUI-based content creation tool creates figures and slides based on the text analyzed by the generation AI. For example, the GUI-based content creation tool automatically generates the layout and design of figures and slides based on text written in Markdown format. The GUI-based content creation tool can also easily create figures and slides by specifying styles using CSS. For example, by entering "# title" in Markdown and specifying "h1 { color: blue;}" in CSS, a slide with a blue title can be created. The Markdown input unit provides an interface for a user to input text in Markdown format. For example, the Markdown input unit provides a text editor, allowing a user to input text using Markdown notation. The CSS input unit provides an interface for a user to input CSS. For example, the CSS input unit provides a style sheet editor, allowing a user to specify styles using CSS. As a result, the content creation system according to the embodiment improves the efficiency of text-based content creation and enables intuitive operation.For example, users can easily create diagrams and slides by entering text in Markdown format and specifying styles using CSS. The generation AI also automatically adjusts the layout and design, eliminating the need for users to configure detailed settings.

[0048] Generative AI can analyze text written in Markdown format and automatically generate diagrams and slides based on its content. For example, generative AI can analyze text written in Markdown format and automatically generate diagrams and slides based on its content. For example, generative AI can extract keywords and phrases contained in the text and generate appropriate diagrams and slides based on them. Generative AI can also automatically search for related images and videos to understand the context and incorporate them into the analysis results. For example, if the text contains the word "cat," it will search for images and videos of cats and incorporate them into the analysis results. This improves the efficiency of content creation by analyzing text written in Markdown format and automatically generating diagrams and slides.

[0049] Generative AI can analyze text written in Markdown format and generate appropriate diagrams based on its content. For example, generative AI can analyze text written in Markdown format and generate appropriate diagrams based on its content. For example, generative AI can extract keywords and phrases contained in the text and generate appropriate diagrams based on them. Generative AI can also refer to the user's past input history and provide personalized analysis results. For example, if the user has input a lot about "cats" in the past, it can provide detailed analysis results about cats. This reduces the user's workload by analyzing Markdown text and generating appropriate diagrams.

[0050] Generative AI can analyze text written in Markdown format and generate appropriate slides based on its content. For example, generative AI can analyze text written in Markdown format and generate appropriate slides based on its content. For example, generative AI can extract keywords and phrases contained in the text and generate appropriate slides based on them. Generative AI can also use emotion estimation functionality to generate analysis results based on the user's emotional state and adjust them to elicit positive emotions. For example, if the user is feeling stressed, it can provide relaxing content. This reduces the user's workload by analyzing Markdown text and generating appropriate slides.

[0051] Generative AI can analyze text written in Markdown format and generate appropriate diagrams and slides based on its content. For example, generative AI can analyze text written in Markdown format and generate appropriate diagrams and slides based on its content. For example, generative AI can extract keywords and phrases contained in the text and generate appropriate diagrams and slides based on them. Generative AI can also analyze text in different languages ​​simultaneously and provide analysis results in multiple languages. For example, it can analyze text in English and Japanese simultaneously and provide analysis results in both languages. This reduces the workload of users by analyzing text in Markdown format and generating appropriate diagrams and slides.

[0052] Generative AI can analyze text written in Markdown format and generate appropriate diagrams and slides based on its content. For example, generative AI can analyze text written in Markdown format and generate appropriate diagrams and slides based on its content. For example, generative AI can extract keywords and phrases contained in the text and generate appropriate diagrams and slides based on that. Generative AI can also accept voice input, convert the voice to text, and analyze that text. For example, it can convert what a user says into text and analyze that text. This reduces the workload on users by analyzing text in Markdown format and generating appropriate diagrams and slides.

[0053] Generative AI can analyze text written in Markdown format and generate appropriate diagrams and slides based on its content. For example, generative AI can analyze text written in Markdown format and generate appropriate diagrams and slides based on its content. For example, generative AI can extract keywords and phrases contained in the text and generate appropriate diagrams and slides based on them. Generative AI can also use its emotion estimation function to analyze the emotions in the text entered by the user in real time and provide feedback according to the emotions. For example, if the user is angry, it can provide feedback to calm down. This reduces the workload of the user by analyzing text in Markdown format and generating appropriate diagrams and slides.

[0054] The summary generation unit can refer to background information and topic models to understand the context. For example, when the generation AI creates a summary, the summary generation unit automatically collects related background information and refers to it to understand the context. For example, it collects related news articles and academic papers. The summary generation unit also uses topic models to understand the context when the generation AI creates a summary. For example, it extracts related keywords and phrases based on the topic model. The summary generation unit also references related background information and topic models when the generation AI creates a summary, building a system for understanding the context. For example, it automatically collects related information and reflects it in the summary. This enables more accurate summaries by referring to background information and topic models to understand the context.

[0055] The summary generation unit can analyze the logical structure of an answer and the development of the arguments to generate a logical summary. For example, the summary generation unit uses a generation AI to analyze the logical structure of an answer and generate a logical summary. For example, it analyzes the development of arguments and logical consistency and reflects this in the summary. The summary generation unit also analyzes the development of arguments in an answer and builds a system in which the generation AI generates a logical summary. For example, it generates a summary based on the importance and relevance of arguments. The summary generation unit also develops an algorithm for the generation AI to analyze the logical structure of an answer and the development of arguments to generate a logical summary. For example, it evaluates the logical consistency and the importance of arguments. This makes it possible to generate a logical summary by analyzing the logical structure of an answer and the development of arguments in a question.

[0056] The summary generation unit uses the emotion estimation function to generate a summary that captures the emotional nuances of the answer, allowing the emotional elements to be reflected in the evaluation. For example, when the generation AI summarizes, the summary generation unit uses the emotion estimation function to capture the emotional nuances of the answer. For example, it generates a summary based on an emotion score. The summary generation unit also uses the emotion estimation function to build a system in which the generation AI reflects the emotional elements of the answer in the evaluation. For example, it performs the evaluation based on the emotion score. The summary generation unit also develops an algorithm for the generation AI to use the emotion estimation function to generate a summary that captures the emotional nuances of the answer. For example, it generates a summary based on the emotion score and reflects that in the evaluation. In this way, by generating a summary that captures the emotional nuances, the emotional elements can also be reflected in the evaluation.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The content creation system can further include a voice input unit. The voice input unit provides an interface that allows the user to give instructions by voice. For example, when the user says, "Create the next slide," the voice input unit converts the instruction into text, and the generation AI analyzes the text to automatically generate the next slide. The voice input unit can also convert what the user speaks into text in real time and input it directly into the Markdown input unit. This allows the user to create content using only their voice without using a keyboard, improving work efficiency.

[0059] The content creation system can further use the emotion estimation function to provide feedback according to the user's emotions. For example, if the user is feeling stressed, the system can suggest colors and designs that will help them relax. Or, if the user is excited, the system can provide advice to help them stay calm. Furthermore, the emotion estimation function can automatically adjust the appropriate content layout and design based on the user's emotional state. This makes it possible to create optimal content according to the user's emotional state.

[0060] The content creation system can further include a data visualization unit. The data visualization unit automatically generates graphs and charts based on data provided by the user. For example, when a user inputs sales data, the data visualization unit analyzes the data and generates appropriate graphs and charts. The data visualization unit can also adjust the design of graphs and charts based on styles and themes specified by the user. This makes it easier for users to visually understand data and improves the quality of presentations.

[0061] The content creation system can also use emotion estimation to suggest content based on the user's emotions. For example, if the user is feeling sad, the system can suggest uplifting slides. If the user is feeling happy, the system can suggest designs and layouts that will further enhance that emotion. Furthermore, the emotion estimation function can automatically adjust the appropriate tone and style of content according to the user's emotional state. This makes it possible to create content that is in tune with the user's emotions.

[0062] The content creation system may further include a real-time collaboration unit. The real-time collaboration unit provides an interface that allows multiple users to edit content simultaneously. For example, team members can edit the same slide from different locations, and changes are reflected in real time. The real-time collaboration unit may also provide chat and comment functions to support communication between users. This allows the entire team to create content efficiently.

[0063] The content creation system can further use the emotion estimation function to evaluate content based on the user's emotions. For example, it can evaluate whether the content of a slide created by the user evokes positive emotions and provide feedback. It can also point out any potential negative emotions in the content created by the user and suggest improvements. Furthermore, the emotion estimation function can adjust the tone and content of the feedback appropriately depending on the user's emotional state. This allows users to create content that takes emotions into consideration.

[0064] The content creation system can further include an automatic translation unit. The automatic translation unit automatically translates text entered by a user into multiple languages. For example, it can translate text entered by a user in English into Japanese or French and generate corresponding slides. The automatic translation unit can also provide an interface that allows a user to check the translation results and correct them if necessary. This makes it easier to share content between users who speak different languages, allowing work to proceed efficiently even in international projects.

[0065] The content creation system can further use the emotion estimation function to customize content based on the user's emotions. For example, if the user is nervous, the system can suggest colors and designs that will help them relax. Or, if the user is concentrating, the system can suggest simple designs to help them maintain their concentration. Furthermore, the emotion estimation function can automatically adjust the appropriate tone and style of content depending on the user's emotional state. This makes it possible to create content that is in tune with the user's emotions.

[0066] The content creation system may further include a template library unit. The template library unit provides a variety of templates from which users can select. Examples include templates for business presentations, educational templates, and event templates. The template library unit may also provide a function that allows users to save and reuse templates they have created. This allows users to create content quickly and improves work efficiency.

[0067] The content creation system can further use emotion estimation to optimize content based on the user's emotions. For example, if the user is tired, the system can suggest concise and easy-to-understand content. Or, if the user is excited, the system can suggest stimulating designs that maintain that excitement. Furthermore, the emotion estimation function can automatically adjust the appropriate tone and style of content according to the user's emotional state. This makes it possible to create content that is in tune with the user's emotions.

[0068] The processing flow of the second embodiment will be briefly explained below.

[0069] Step 1: The Markdown input unit provides an interface for a user to input text in Markdown format. For example, the Markdown input unit provides a text editor that allows a user to input text using Markdown notation. Step 2: The CSS input unit provides an interface for the user to input CSS. For example, the CSS input unit provides a style sheet editor, allowing the user to specify styles using CSS. Step 3: The generation AI analyzes the text entered by the Markdown input unit. For example, the generation AI analyzes the text in Markdown format using a text generation AI (e.g., LLM). The generation AI can also analyze the content of the text using a multimodal generation AI. The generation AI can also automatically collect relevant background information to understand the context and reflect it in the analysis results. Step 4: The GUI-based content creation tool creates figures and slides based on the text analyzed by the generative AI. For example, the GUI-based content creation tool automatically generates the layout and design of figures and slides based on text written in Markdown format. The GUI-based content creation tool also makes it easy to create figures and slides by specifying styles using CSS.

[0070] 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.

[0071] 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.

[0072] 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.

[0073] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0074] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] 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).

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0089] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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."

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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]

[0137] 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. Generative AI and A GUI-based content creation tool and Markdown input section, a CSS input unit; The generated AI is Analyzing the text input by the Markdown input unit; The GUI-based content creation tool Create a diagram or slide based on the text analyzed by the generative AI A system characterized by:

2. The generated AI is Analyzing the text written in the Markdown format and automatically generating the diagram or slide based on the content of the text.

2. The system of claim 1.

3. The generated AI is Analyzing the text written in the Markdown format and generating the appropriate figure or slide based on the content of the text 2. The system of claim 1.

4. The generated AI is Analyzing the text written in the Markdown format and generating the appropriate figure or slide based on the content of the text 2. The system of claim 1.

5. The generated AI is Analyzing the text written in the Markdown format and generating the appropriate figure or slide based on the content of the text 2. The system of claim 1.

Images