System

Abstract

To provide a system for efficiently generating a WEB page material from a document.SOLUTION: A system includes a reception unit, an analysis unit, a generation unit, and a provision unit. The receiving unit receives an input of a document. The analysis unit analyzes the document received by the reception unit. The generation part generates a WEB page material on the basis of the document analyzed by the analysis part. The provision part provides the WEB page material generated by the generation part.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 technologies have had the problem of making it difficult to efficiently generate web page materials from documents.

[0005] The system according to the embodiment aims to efficiently generate web page materials from documents. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, an analysis unit, a generation unit, and a providing unit. The receiving unit receives document input. The analysis unit analyzes the document received by the receiving unit. The generation unit generates web page materials based on the document analyzed by the analysis unit. The providing unit provides the web page materials generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently generate web page materials from documents. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

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

[0028] (Example 1) A system according to an embodiment of the present invention is a system for generating web page materials from documents. In this system, a user inputs a document, a generation AI analyzes the document, and generates the web page materials. The generated materials include HTML files, CSS files, JavaScript (registered trademark) files, and the like. The generated web page materials are provided to users so that they can easily create web pages. This allows the system to easily create web pages simply by users inputting documents. For example, when publishing corporate reports or presentation materials as web pages, materials can be generated without much effort. Furthermore, because the generation AI analyzes the content of the document, users can create high-quality web pages without specialized knowledge.

[0029] A web page generation system according to an embodiment includes a receiving unit, an analysis unit, a generation unit, and a providing unit. The receiving unit receives a document input from a user. Examples of documents include, but are not limited to, text files, PDF files, and Word files. For example, when a user inputs a PDF file, the receiving unit analyzes the content of the PDF file using a generation AI. The analysis unit uses the generation AI to analyze the document received by the receiving unit. The generation AI understands the content of the document and extracts elements necessary for a web page. For example, elements such as headings, paragraphs, images, and links are extracted. The generation unit generates web page materials based on the document analyzed by the analysis unit. The generated materials include, for example, HTML files, CSS files, and JavaScript files. For example, headings are generated as HTML tags, and paragraphs are generated as tags. The providing unit provides the web page materials generated by the generation unit. The providing unit can provide the generated web page materials via, for example, a download link or cloud storage. This allows the web page generation system according to an embodiment to perform a consistent process from document input to the generation and provision of web page materials.

[0030] The analysis unit can analyze the document using natural language processing technology or image recognition technology. Natural language processing technology includes, for example, morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit can use morphological analysis to segment words in the document and grammatical analysis to analyze the structure of sentences. The analysis unit can also use semantic analysis to understand the content of the document. Image recognition technology includes, for example, object detection, face recognition, and image classification. For example, the analysis unit can analyze images in the document using object detection and identify people in the images using face recognition. The analysis unit can also determine the category of the image using image classification. This improves the accuracy of document analysis by using natural language processing technology and image recognition technology.

[0031] The generation unit can generate HTML files, CSS files, and JavaScript files. The generation of HTML files includes, for example, the use of templates and dynamic generation. For example, the generation unit can generate HTML files using templates. The generation unit can also dynamically generate HTML files. The generation of CSS files includes, for example, style sheet templates and custom styles. For example, the generation unit can generate CSS files using style sheet templates. The generation unit can also generate CSS files using custom styles. The generation of JavaScript files includes, for example, script templates and dynamic script generation. For example, the generation unit can generate JavaScript files using script templates. The generation unit can also dynamically generate JavaScript files. This makes it possible to generate a variety of web page materials.

[0032] The providing unit may provide the generated web page materials through a download link or cloud storage. Examples of download links include HTTP links and FTP links. For example, the providing unit may provide the generated web page materials using an HTTP link. The providing unit may also provide the generated web page materials using an FTP link. Examples of cloud storage include Google Drive, Dropbox (registered trademark), AWS (registered trademark) S3, etc. For example, the providing unit may provide the generated web page materials using Google (registered trademark) Drive. The providing unit may also provide the generated web page materials using Dropbox. The providing unit may also provide the generated web page materials using AWS S3. This allows the generated web page materials to be provided efficiently.

[0033] The providing unit may provide a function that allows a user to edit a CSS file to change the design. Editing of the CSS file may include, for example, using an online editor or a local editor. For example, the providing unit may allow a user to edit the CSS file using an online editor. Alternatively, the providing unit may allow a user to edit the CSS file using a local editor. This allows a user to freely customize the design of a web page.

[0034] The reception unit can analyze the user's past document input history and select the optimal input method. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also suggest the optimal input method for a specific time period based on the user's past input history. The reception unit can also select the optimal input method based on the format of documents that the user has previously input. This makes it possible to suggest the optimal input method based on the user's past input history.

[0035] The reception unit can filter documents based on the user's current project or area of ​​interest when inputting documents. For example, the reception unit displays only documents related to the user's ongoing project as input candidates. The reception unit can also preferentially input related documents based on the user's area of ​​interest. The reception unit can also filter related documents by referring to the user's past project history. This allows documents related to the user's project or area of ​​interest to be preferentially input.

[0036] When inputting a document, the reception unit can select the optimal input means depending on the user's input method. For example, if the user selects voice input, the reception unit inputs the document using voice recognition technology. Also, if the user selects text input, the reception unit can support keyboard input. Also, if the user selects image input, the reception unit can analyze the document using image recognition technology. This allows the document to be input using the optimal means depending on the user's input method.

[0037] When inputting documents, the reception unit can prioritize inputting highly relevant documents in consideration of the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize inputting documents related to that area. Furthermore, if the user is traveling, the reception unit can prioritize inputting documents related to the travel destination. Furthermore, if the user is at home, the reception unit can also prioritize inputting documents related to the user's home. This allows the optimal documents to be input based on the user's geographical location information.

[0038] The reception unit can analyze the user's social media activity when inputting a document and input related documents. The reception unit can input related documents based on, for example, information shared by the user on social media. The reception unit can also analyze the content of the user's posts on social media and input related documents. The reception unit can also input related documents by referring to the activities of the user's friends on social media. In this way, related documents can be input based on the user's social media activity.

[0039] The reception unit can customize the input method by reflecting the user's past feedback when inputting a document. The reception unit can, for example, suggest the optimal input method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific input method based on the user's past feedback. The reception unit can also customize the input interface by reflecting the user's past feedback. This makes it possible to suggest the optimal input method based on the user's past feedback.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the document. For example, the analysis unit performs a detailed analysis on a document with a high level of importance. The analysis unit can also perform a concise analysis on a document with a low level of importance. The analysis unit can also adjust the depth of the analysis based on the importance of the document. This allows for optimal analysis to be performed based on the importance of the document.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the document category. For example, the analysis unit can apply a specialized analysis algorithm to a technical document. The analysis unit can also apply an analysis algorithm specialized for marketing to a marketing document. The analysis unit can also apply an academic analysis algorithm to an academic paper. This allows the optimal analysis algorithm to be applied depending on the document category.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by extracting specific patterns from the user's past analysis results. The analysis unit can also adjust the level of detail of the analysis by referring to the user's past analysis results. This makes it possible to improve the accuracy of the analysis based on the user's past analysis results.

[0043] During analysis, the analysis unit can determine the analysis priority based on the submission time of the document. For example, the analysis unit prioritizes analysis of documents with an upcoming submission deadline. The analysis unit can also postpone analysis of documents with a distant submission deadline. The analysis unit can also adjust the analysis priority based on the submission time. This makes it possible to determine the optimal analysis priority based on the submission time of the document.

[0044] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the documents. For example, the analysis unit prioritizes analysis of highly relevant documents. The analysis unit can also postpone analysis of less relevant documents. The analysis unit can also adjust the order of analysis based on the relevance of the documents. This allows analysis to be performed in the optimal order based on the relevance of the documents.

[0045] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit uses a lot of technical terms. On the other hand, if the user does not have technical expertise, the analysis unit can avoid technical terms and use concise expressions. The analysis unit can also adjust the way the analysis results are expressed according to the user's level of expertise. This makes it possible to provide optimal analysis results according to the user's level of expertise.

[0046] The generation unit can adjust the level of detail of the generated materials based on the importance of the document during generation. For example, the generation unit generates detailed web page materials for documents with high importance. The generation unit can also generate concise web page materials for documents with low importance. The generation unit can also adjust the level of detail of the generated materials based on the importance of the document. This makes it possible to generate optimal web page materials based on the importance of the document.

[0047] The generation unit can apply different generation algorithms depending on the document category during generation. For example, the generation unit can apply a specialized generation algorithm to a technical document. The generation unit can also apply a marketing-specific generation algorithm to a marketing document. The generation unit can also apply an academic generation algorithm to an academic paper. This allows the optimal generation algorithm to be applied depending on the document category.

[0048] During generation, the generation unit can improve the accuracy of generation by referring to the user's past generation results. The generation unit adjusts the generation algorithm, for example, based on web page materials generated by the user in the past. The generation unit can also improve the accuracy of generation by extracting specific patterns from the user's past generation results. The generation unit can also adjust the level of detail of the materials to be generated by referring to the user's past generation results. This makes it possible to improve the accuracy of generation based on the user's past generation results.

[0049] The generation unit can determine the priority of the materials to be generated based on the submission time of the document at the time of generation. For example, the generation unit generates documents with an upcoming submission deadline as web page materials with priority. The generation unit can also postpone documents with a distant submission deadline. The generation unit can also adjust the priority of the materials to be generated based on the submission time. This makes it possible to generate optimal web page materials according to the submission time of the document.

[0050] The generation unit can adjust the order of generated materials based on the relevance of the documents during generation. For example, the generation unit generates highly relevant documents as web page materials with priority. The generation unit can also postpone less relevant documents. The generation unit can also adjust the order of generated materials based on the relevance of the documents. This allows web page materials to be generated in the optimal order based on the relevance of the documents.

[0051] The generation unit can adjust the use of technical terms in the generated materials according to the user's level of expertise during generation. For example, if the user has technical knowledge, the generation unit can generate web page materials that use a lot of technical terms. Also, if the user does not have technical knowledge, the generation unit can generate web page materials that avoid technical terms and use concise expressions. The generation unit can also adjust the expression method of the generated materials according to the user's level of expertise. This allows the generation of optimal web page materials according to the user's level of expertise.

[0052] The providing unit can select the optimal providing method by referring to the user's past providing history when providing the information. For example, the providing unit can suggest the optimal providing method based on the providing methods used by the user in the past. The providing unit can also extract a specific pattern from the user's past providing history to select the optimal providing method. The providing unit can also customize the providing method by referring to the user's past providing history. This makes it possible to suggest the optimal providing method based on the user's past providing history.

[0053] The providing unit can customize the content of the material to be provided according to the user's current project when providing the material. For example, the providing unit preferentially provides web page materials related to the user's current project. The providing unit can also customize the content of the material to be provided according to the user's current project. The providing unit can also adjust the level of detail of the material to be provided according to the progress of the user's project. This makes it possible to provide the optimal web page material according to the user's current project.

[0054] The providing unit can improve the delivery method by reflecting user feedback at the time of delivery. For example, the providing unit improves the delivery method based on feedback provided by the user regarding the delivery method. Furthermore, the providing unit can preferentially suggest a specific delivery method based on the user feedback. Furthermore, the providing unit can also customize the delivery interface by reflecting user feedback. This makes it possible to suggest the optimal delivery method based on the user feedback.

[0055] The providing unit can select the optimal providing method by taking into consideration the user's geographical location information when providing the web page materials. For example, if the user is in a specific area, the providing unit can provide web page materials related to that area with priority. Furthermore, if the user is traveling, the providing unit can provide web page materials related to the travel destination with priority. Furthermore, if the user is at home, the providing unit can provide web page materials related to the user's home with priority. In this way, optimal web page materials can be provided based on the user's geographical location information.

[0056] The providing unit can analyze the user's social media activity and suggest content to be provided at the time of providing. For example, the providing unit can provide related web page materials based on information shared by the user on social media. The providing unit can also analyze the content posted by the user on social media and provide related web page materials. The providing unit can also provide related web page materials by referring to the activities of the user's friends on social media. This makes it possible to provide optimal web page materials based on the user's social media activity.

[0057] The providing unit can customize the delivery method by reflecting the user's past feedback when providing the information. The providing unit can, for example, propose an optimal delivery method based on feedback provided by the user in the past. The providing unit can also preferentially propose a specific delivery method based on the user's past feedback. The providing unit can also customize the delivery interface by reflecting the user's past feedback. This makes it possible to propose an optimal delivery method based on the user's past feedback.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The reception unit can retrieve additional information from related external databases based on the user's input. For example, if the user inputs a document related to a specific technology, the reception unit can retrieve related patent information from a related patent database. If the user inputs an academic paper, the reception unit can retrieve citations from a related academic database. Furthermore, if the user inputs marketing materials, the reception unit can retrieve the latest market trends from a market research database. This allows the user to easily obtain additional information related to the document they input.

[0060] The generation unit can automatically select a relevant template based on the user's input content. For example, if the user inputs technical documentation, the generation unit can select a template suitable for technical documentation. Also, if the user inputs presentation materials, the generation unit can select a template suitable for presentations. Furthermore, if the user inputs marketing materials, the generation unit can select a template suitable for marketing. In this way, the user can automatically select the template that is most suitable for the input content.

[0061] The provision unit can analyze the user's past usage history and suggest the optimal provision method. For example, if the user has frequently used cloud storage in the past, the provision unit can preferentially suggest cloud storage. Also, if the user has preferred download links in the past, the provision unit can preferentially suggest download links. Furthermore, if the user has received materials during a specific time period in the past, the provision unit can suggest a delivery method that suits that time period. In this way, the optimal delivery method can be suggested based on the user's past usage history.

[0062] The analysis unit can retrieve additional information from relevant external databases based on the user's input. For example, if a user inputs a document related to a specific technology, the analysis unit can retrieve related patent information from a relevant patent database. If a user inputs an academic paper, the analysis unit can retrieve citations from a relevant academic database. Furthermore, if a user inputs marketing materials, the analysis unit can retrieve the latest market trends from a market research database. This allows users to easily obtain additional information related to the documents they input.

[0063] The providing unit can customize the content of the web page materials to be provided based on the progress of the user's current project. For example, if the user is in the early stages of the project, the providing unit can provide web page materials containing basic information. If the user is in the middle stages of the project, the providing unit can provide web page materials containing detailed information. Furthermore, if the user is in the final stages of the project, the providing unit can provide highly complete web page materials. In this way, the most suitable web page materials can be provided according to the progress of the user's project.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The user inputs a document into the reception unit. The document may be, for example, a text file, a PDF file, a Word file, or the like, but is not limited to these examples. If the user inputs a PDF file, its contents are analyzed by the generation AI. Step 2: The analysis unit uses the generation AI to analyze the document received by the reception unit. The generation AI understands the content of the document and extracts elements necessary for a web page. For example, elements such as headings, paragraphs, images, and links are extracted. Step 3: The generator generates web page materials based on the document analyzed by the analyzer. The generated materials include, for example, HTML files, CSS files, JavaScript files, etc. For example, headings are generated as HTML tags, and paragraphs are generated as tags. Step 4: The providing unit provides the web page material generated by the generating unit. For example, the providing unit can provide the generated web page material through a download link or cloud storage.

[0066] (Example 2) A system according to an embodiment of the present invention is a system for generating web page materials from documents. In this system, a user inputs a document, a generation AI analyzes the document, and generates the web page materials. The generated materials include HTML files, CSS files, JavaScript files, and the like. The generated web page materials are provided to users so that they can easily create web pages. This allows the system to easily create web pages simply by users inputting documents. For example, when publishing corporate reports or presentation materials as web pages, materials can be generated without much effort. Furthermore, because the generation AI analyzes the content of the document, users can create high-quality web pages without specialized knowledge.

[0067] A web page generation system according to an embodiment includes a receiving unit, an analysis unit, a generation unit, and a providing unit. The receiving unit receives a document input from a user. Examples of documents include, but are not limited to, text files, PDF files, and Word files. For example, when a user inputs a PDF file, the receiving unit analyzes the content of the PDF file using a generation AI. The analysis unit uses the generation AI to analyze the document received by the receiving unit. The generation AI understands the content of the document and extracts elements necessary for a web page. For example, elements such as headings, paragraphs, images, and links are extracted. The generation unit generates web page materials based on the document analyzed by the analysis unit. The generated materials include, for example, HTML files, CSS files, and JavaScript files. For example, headings are generated as HTML tags, and paragraphs are generated as tags. The providing unit provides the web page materials generated by the generation unit. The providing unit can provide the generated web page materials via, for example, a download link or cloud storage. This allows the web page generation system according to an embodiment to perform a consistent process from document input to the generation and provision of web page materials.

[0068] The analysis unit can analyze the document using natural language processing technology or image recognition technology. Natural language processing technology includes, for example, morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit can use morphological analysis to segment words in the document and grammatical analysis to analyze the structure of sentences. The analysis unit can also use semantic analysis to understand the content of the document. Image recognition technology includes, for example, object detection, face recognition, and image classification. For example, the analysis unit can analyze images in the document using object detection and identify people in the images using face recognition. The analysis unit can also determine the category of the image using image classification. This improves the accuracy of document analysis by using natural language processing technology and image recognition technology.

[0069] The generation unit can generate HTML files, CSS files, and JavaScript files. The generation of HTML files includes, for example, the use of templates and dynamic generation. For example, the generation unit can generate HTML files using templates. The generation unit can also dynamically generate HTML files. The generation of CSS files includes, for example, style sheet templates and custom styles. For example, the generation unit can generate CSS files using style sheet templates. The generation unit can also generate CSS files using custom styles. The generation of JavaScript files includes, for example, script templates and dynamic script generation. For example, the generation unit can generate JavaScript files using script templates. The generation unit can also dynamically generate JavaScript files. This makes it possible to generate a variety of web page materials.

[0070] The providing unit may provide the generated web page materials through a download link or cloud storage. Examples of download links include an HTTP link and an FTP link. For example, the providing unit may provide the generated web page materials using an HTTP link. The providing unit may also provide the generated web page materials using an FTP link. Examples of cloud storage include Google Drive, Dropbox, AWS S3, etc. For example, the providing unit may provide the generated web page materials using Google Drive. The providing unit may also provide the generated web page materials using Dropbox. The providing unit may also provide the generated web page materials using AWS S3. This allows the generated web page materials to be provided efficiently.

[0071] The providing unit may provide a function that allows a user to edit a CSS file to change the design. Editing of the CSS file may include, for example, using an online editor or a local editor. For example, the providing unit may allow a user to edit the CSS file using an online editor. Alternatively, the providing unit may allow a user to edit the CSS file using a local editor. This allows a user to freely customize the design of a web page.

[0072] The reception unit can estimate the user's emotions and adjust the timing of document input based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can delay the input timing to provide a relaxing environment. Furthermore, if the user is concentrating, the reception unit can speed up the input timing to efficiently proceed with work. Furthermore, if the user is tired, the reception unit can adjust the input timing to encourage the user to take a break. This allows the document to be input at the optimal timing according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0073] The reception unit can analyze the user's past document input history and select the optimal input method. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also suggest the optimal input method for a specific time period based on the user's past input history. The reception unit can also select the optimal input method based on the format of documents that the user has previously input. This makes it possible to suggest the optimal input method based on the user's past input history.

[0074] The reception unit can filter documents based on the user's current project or area of ​​interest when inputting documents. For example, the reception unit displays only documents related to the user's ongoing project as input candidates. The reception unit can also preferentially input related documents based on the user's area of ​​interest. The reception unit can also filter related documents by referring to the user's past project history. This allows documents related to the user's project or area of ​​interest to be preferentially input.

[0075] When inputting a document, the reception unit can select the optimal input means depending on the user's input method. For example, if the user selects voice input, the reception unit inputs the document using voice recognition technology. Also, if the user selects text input, the reception unit can support keyboard input. Also, if the user selects image input, the reception unit can analyze the document using image recognition technology. This allows the document to be input using the optimal means depending on the user's input method.

[0076] The reception unit can estimate the user's emotions and determine the priority of documents to be input based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit can prioritize input of documents with low importance. Furthermore, when the user is relaxed, the reception unit can prioritize input of documents with high importance. Furthermore, when the user is in a hurry, the reception unit can also prioritize input of documents with high urgency. This makes it possible to determine the optimal priority of documents according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0077] When inputting documents, the reception unit can prioritize inputting highly relevant documents in consideration of the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize inputting documents related to that area. Furthermore, if the user is traveling, the reception unit can prioritize inputting documents related to the travel destination. Furthermore, if the user is at home, the reception unit can also prioritize inputting documents related to the user's home. This allows the optimal documents to be input based on the user's geographical location information.

[0078] The reception unit can analyze the user's social media activity when inputting a document and input related documents. The reception unit can input related documents based on, for example, information shared by the user on social media. The reception unit can also analyze the content of the user's posts on social media and input related documents. The reception unit can also input related documents by referring to the activities of the user's friends on social media. In this way, related documents can be input based on the user's social media activity.

[0079] The reception unit can customize the input method by reflecting the user's past feedback when inputting a document. The reception unit can, for example, suggest the optimal input method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific input method based on the user's past feedback. The reception unit can also customize the input interface by reflecting the user's past feedback. This makes it possible to suggest the optimal input method based on the user's past feedback.

[0080] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can provide concise analysis results that focus on the main points. If the user is excited, the analysis unit can also provide visually stimulating analysis results. This makes it possible to provide optimal analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0081] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the document. For example, the analysis unit performs a detailed analysis on a document with a high level of importance. The analysis unit can also perform a concise analysis on a document with a low level of importance. The analysis unit can also adjust the depth of the analysis based on the importance of the document. This allows for optimal analysis to be performed based on the importance of the document.

[0082] During analysis, the analysis unit can apply different analysis algorithms depending on the document category. For example, the analysis unit can apply a specialized analysis algorithm to a technical document. The analysis unit can also apply an analysis algorithm specialized for marketing to a marketing document. The analysis unit can also apply an academic analysis algorithm to an academic paper. This allows the optimal analysis algorithm to be applied depending on the document category.

[0083] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by extracting specific patterns from the user's past analysis results. The analysis unit can also adjust the level of detail of the analysis by referring to the user's past analysis results. This makes it possible to improve the accuracy of the analysis based on the user's past analysis results.

[0084] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and to-the-point analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. If the user is excited, the analysis unit can also provide a visually stimulating analysis result. This makes it possible to provide an analysis result of optimal length depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0085] During analysis, the analysis unit can determine the analysis priority based on the submission time of the document. For example, the analysis unit prioritizes analysis of documents with an upcoming submission deadline. The analysis unit can also postpone analysis of documents with a distant submission deadline. The analysis unit can also adjust the analysis priority based on the submission time. This makes it possible to determine the optimal analysis priority based on the submission time of the document.

[0086] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the documents. For example, the analysis unit prioritizes analysis of highly relevant documents. The analysis unit can also postpone analysis of less relevant documents. The analysis unit can also adjust the order of analysis based on the relevance of the documents. This allows analysis to be performed in the optimal order based on the relevance of the documents.

[0087] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit uses a lot of technical terms. On the other hand, if the user does not have technical expertise, the analysis unit can avoid technical terms and use concise expressions. The analysis unit can also adjust the way the analysis results are expressed according to the user's level of expertise. This makes it possible to provide optimal analysis results according to the user's level of expertise.

[0088] The generation unit can estimate the user's emotions and adjust the expression method of the generated web page materials based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate web page materials with a relaxed design. If the user is in a hurry, the generation unit can generate web page materials that are simple and quickly displayed. If the user is excited, the generation unit can also generate web page materials with a visually stimulating design. This makes it possible to generate optimal web page materials according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0089] The generation unit can adjust the level of detail of the generated materials based on the importance of the document during generation. For example, the generation unit generates detailed web page materials for documents with high importance. The generation unit can also generate concise web page materials for documents with low importance. The generation unit can also adjust the level of detail of the generated materials based on the importance of the document. This makes it possible to generate optimal web page materials based on the importance of the document.

[0090] The generation unit can apply different generation algorithms depending on the document category during generation. For example, the generation unit can apply a specialized generation algorithm to a technical document. The generation unit can also apply a marketing-specific generation algorithm to a marketing document. The generation unit can also apply an academic generation algorithm to an academic paper. This allows the optimal generation algorithm to be applied depending on the document category.

[0091] During generation, the generation unit can improve the accuracy of generation by referring to the user's past generation results. The generation unit adjusts the generation algorithm, for example, based on web page materials generated by the user in the past. The generation unit can also improve the accuracy of generation by extracting specific patterns from the user's past generation results. The generation unit can also adjust the level of detail of the materials to be generated by referring to the user's past generation results. This makes it possible to improve the accuracy of generation based on the user's past generation results.

[0092] The generation unit can estimate the user's emotions and adjust the length of the generated material based on the estimated user's emotions. For example, if the user is in a hurry, the generation unit can generate short, to-the-point web page material. If the user is relaxed, the generation unit can generate longer web page material with detailed explanations. If the user is excited, the generation unit can also generate web page material with visually stimulating effects. This makes it possible to generate web page material of an optimal length depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0093] The generation unit can determine the priority of the materials to be generated based on the submission time of the document at the time of generation. For example, the generation unit generates documents with an upcoming submission deadline as web page materials with priority. The generation unit can also postpone documents with a distant submission deadline. The generation unit can also adjust the priority of the materials to be generated based on the submission time. This makes it possible to generate optimal web page materials according to the submission time of the document.

[0094] The generation unit can adjust the order of generated materials based on the relevance of the documents during generation. For example, the generation unit generates highly relevant documents as web page materials with priority. The generation unit can also postpone less relevant documents. The generation unit can also adjust the order of generated materials based on the relevance of the documents. This allows web page materials to be generated in the optimal order based on the relevance of the documents.

[0095] The generation unit can adjust the use of technical terms in the generated materials according to the user's level of expertise during generation. For example, if the user has technical knowledge, the generation unit can generate web page materials that use a lot of technical terms. Also, if the user does not have technical knowledge, the generation unit can generate web page materials that avoid technical terms and use concise expressions. The generation unit can also adjust the expression method of the generated materials according to the user's level of expertise. This allows the generation of optimal web page materials according to the user's level of expertise.

[0096] The providing unit can estimate the user's emotions and adjust the display method of the web page material to be provided based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can display web page material with a relaxed design. If the user is in a hurry, the providing unit can display web page material that is simple and quickly displayed. If the user is excited, the providing unit can display web page material with a visually stimulating design. This makes it possible to provide web page material in an optimal display method depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0097] The providing unit can select the optimal providing method by referring to the user's past providing history when providing the information. For example, the providing unit can suggest the optimal providing method based on the providing methods used by the user in the past. The providing unit can also extract a specific pattern from the user's past providing history to select the optimal providing method. The providing unit can also customize the providing method by referring to the user's past providing history. This makes it possible to suggest the optimal providing method based on the user's past providing history.

[0098] The providing unit can customize the content of the material to be provided according to the user's current project when providing the material. For example, the providing unit preferentially provides web page materials related to the user's current project. The providing unit can also customize the content of the material to be provided according to the user's current project. The providing unit can also adjust the level of detail of the material to be provided according to the progress of the user's project. This makes it possible to provide the optimal web page material according to the user's current project.

[0099] The providing unit can improve the delivery method by reflecting user feedback at the time of delivery. For example, the providing unit improves the delivery method based on feedback provided by the user regarding the delivery method. Furthermore, the providing unit can preferentially suggest a specific delivery method based on the user feedback. Furthermore, the providing unit can also customize the delivery interface by reflecting user feedback. This makes it possible to suggest the optimal delivery method based on the user feedback.

[0100] The providing unit can estimate the user's emotions and determine the priority of materials to be provided based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit can provide materials with a lower priority. Furthermore, if the user is relaxed, the providing unit can provide materials with a higher priority. Furthermore, if the user is in a hurry, the providing unit can provide materials with a higher priority. This makes it possible to provide web page materials with optimal priority according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0101] The providing unit can select the optimal providing method by taking into consideration the user's geographical location information when providing the web page materials. For example, if the user is in a specific area, the providing unit can provide web page materials related to that area with priority. Furthermore, if the user is traveling, the providing unit can provide web page materials related to the travel destination with priority. Furthermore, if the user is at home, the providing unit can provide web page materials related to the user's home with priority. In this way, optimal web page materials can be provided based on the user's geographical location information.

[0102] The providing unit can analyze the user's social media activity and suggest content to be provided at the time of providing. For example, the providing unit can provide related web page materials based on information shared by the user on social media. The providing unit can also analyze the content posted by the user on social media and provide related web page materials. The providing unit can also provide related web page materials by referring to the activities of the user's friends on social media. This makes it possible to provide optimal web page materials based on the user's social media activity.

[0103] The providing unit can customize the delivery method by reflecting the user's past feedback when providing the content. The providing unit, for example, suggests an optimal delivery method based on feedback provided by the user in the past. The providing unit can also prioritize specific delivery methods based on the user's past feedback. The providing unit can also customize the delivery interface by reflecting the user's past feedback. This allows the optimal delivery method to be proposed based on the user's past feedback. === Hard Security 1-1 === Each of the multiple elements, including the reception unit, analysis unit, generation unit, and provision unit, described above, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, and the user inputs a document. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the document using a generation AI. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and generates web page materials based on the analyzed document. The providing unit is realized, for example, by the output device 40 of the smart device 14 and provides the generated web page materials. === Hardware Security 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214, and a user inputs a document. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the document using a generation AI. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates web page materials based on the analyzed document. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides the generated web page materials. === Hardware Security 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12.For example, the reception unit is realized by the microphone 238 of the headset terminal 314, and a user inputs a document. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the document using a generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates web page materials based on the analyzed document. The provision unit is realized by the display 343 of the headset terminal 314, and provides the generated web page materials. === Hardware Security 1-4 === Each of the multiple elements including the reception unit, analysis unit, generation unit, and provision unit described above is realized by at least one of the robot 414 and the data processing device 12, for example. For example, the reception unit is realized by the microphone 238 of the robot 414, and a user inputs a document. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the document using a generation AI. The generating unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates the web page material based on the analyzed document. The providing unit is realized, for example, by the speaker 240 of the robot 414, and provides the generated web page material.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The reception unit can retrieve additional information from related external databases based on the user's input. For example, if the user inputs a document related to a specific technology, the reception unit can retrieve related patent information from a related patent database. If the user inputs an academic paper, the reception unit can retrieve citations from a related academic database. Furthermore, if the user inputs marketing materials, the reception unit can retrieve the latest market trends from a market research database. This allows the user to easily obtain additional information related to the document they input.

[0106] The analysis unit can estimate the user's emotions and adjust the depth of the analysis based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can provide a concise analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide a visually appealing analysis result. This makes it possible to provide optimal analysis results according to the user's emotions.

[0107] The generation unit can automatically select a relevant template based on the user's input content. For example, if the user inputs technical documentation, the generation unit can select a template suitable for technical documentation. Also, if the user inputs presentation materials, the generation unit can select a template suitable for presentations. Furthermore, if the user inputs marketing materials, the generation unit can select a template suitable for marketing. In this way, the user can automatically select the template that is most suitable for the input content.

[0108] The providing unit can estimate the user's emotions and adjust the design of the web page material to be provided based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can provide web page material with a calm design. If the user is in a hurry, the providing unit can provide web page material with a simple and quickly displayed design. Furthermore, if the user is excited, the providing unit can provide web page material with a visually stimulating design. In this way, web page material with an optimal design can be provided according to the user's emotions.

[0109] The provision unit can analyze the user's past usage history and suggest the optimal provision method. For example, if the user has frequently used cloud storage in the past, the provision unit can preferentially suggest cloud storage. Also, if the user has preferred download links in the past, the provision unit can preferentially suggest download links. Furthermore, if the user has received materials during a specific time period in the past, the provision unit can suggest a delivery method that suits that time period. In this way, the optimal delivery method can be suggested based on the user's past usage history.

[0110] The reception unit can estimate the user's emotion and customize the input interface based on the estimated user's emotion. For example, if the user is feeling stressed, the reception unit can provide a simple and intuitive interface. If the user is feeling relaxed, the reception unit can provide an interface including detailed options. Furthermore, if the user is excited, the reception unit can provide a visually appealing interface. In this way, the optimal input interface can be provided according to the user's emotion.

[0111] The analysis unit can retrieve additional information from relevant external databases based on the user's input. For example, if a user inputs a document related to a specific technology, the analysis unit can retrieve related patent information from a relevant patent database. If a user inputs an academic paper, the analysis unit can retrieve citations from a relevant academic database. Furthermore, if a user inputs marketing materials, the analysis unit can retrieve the latest market trends from a market research database. This allows users to easily obtain additional information related to the documents they input.

[0112] The generation unit can estimate the user's emotions and adjust the colors of the generated web page materials based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate web page materials with calm colors. If the user is in a hurry, the generation unit can generate web page materials with simple, bright colors. Furthermore, if the user is excited, the generation unit can generate web page materials with visually stimulating colors. In this way, web page materials with optimal colors can be generated according to the user's emotions.

[0113] The providing unit can customize the content of the web page materials to be provided based on the progress of the user's current project. For example, if the user is in the early stages of the project, the providing unit can provide web page materials containing basic information. If the user is in the middle stages of the project, the providing unit can provide web page materials containing detailed information. Furthermore, if the user is in the final stages of the project, the providing unit can provide highly complete web page materials. In this way, the most suitable web page materials can be provided according to the progress of the user's project.

[0114] The providing unit can estimate the user's emotions and determine the priority of web page materials to be provided based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide materials with lower importance first. Also, if the user is relaxed, the providing unit can provide materials with higher importance first. Furthermore, if the user is in a hurry, the providing unit can provide materials with higher urgency first. In this way, web page materials can be provided with optimal priority according to the user's emotions.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The user inputs a document into the reception unit. The document may be, for example, a text file, a PDF file, a Word file, or the like, but is not limited to these examples. If the user inputs a PDF file, its contents are analyzed by the generation AI. Step 2: The analysis unit uses the generation AI to analyze the document received by the reception unit. The generation AI understands the content of the document and extracts elements necessary for a web page. For example, elements such as headings, paragraphs, images, and links are extracted. Step 3: The generator generates web page materials based on the document analyzed by the analyzer. The generated materials include, for example, HTML files, CSS files, JavaScript files, etc. For example, headings are generated as HTML tags, and paragraphs are generated as tags. Step 4: The providing unit provides the web page material generated by the generating unit. For example, the providing unit can provide the generated web page material through a download link or cloud storage.

[0117] 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.

[0118] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 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.

[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 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.

[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. 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.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[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 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.

[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 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[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 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.

[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 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0164] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] [Explanation of symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives document input; an analysis unit that analyzes the document received by the reception unit; a generation unit that generates web page materials based on the document analyzed by the analysis unit; a providing unit that provides the web page materials generated by the generating unit; Equipped with A system characterized by:

2. The analysis unit Analyze documents using natural language processing or image recognition techniques 2. The system of claim 1.

3. The generation unit Generate HTML, CSS, and JavaScript files 2. The system of claim 1.

4. The providing unit Provide generated web page materials via download link or cloud storage 2. The system of claim 1.

5. The providing unit Provides the ability for users to edit CSS files to change the design 2. The system of claim 1.

6. The reception unit Estimates user emotions and adjusts document input timing based on the estimated user emotions 2. The system of claim 1.

7. The reception unit Analyze the user's past document input history and select the optimal input method 2. The system of claim 1.

8. The reception unit Filter documents as they are entered based on the user's current project or area of ​​interest 2. The system of claim 1.

Images