System

Abstract

An object of the system according to the embodiment is to improve the efficiency of document creation and management and to reduce human errors.SOLUTION: A system according to an embodiment includes a selection unit, an input unit, a generation unit, and a management unit. The selection unit selects a document type. The input unit inputs the content of the document selected by the selection unit. The generation unit generates a document based on the information input by the input unit. The management unit manages the document generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including 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] With conventional technology, document creation and management requires a lot of man-hours and is prone to human error.

[0005] The system according to the embodiment aims to improve the efficiency of document creation and management and reduce human error. [Means for solving the problem]

[0006] The system according to the embodiment includes a selection unit, an input unit, a generation unit, and a management unit. The selection unit selects a document type. The input unit inputs the content of the document selected by the selection unit. The generation unit generates a document based on the information input by the input unit. The management unit manages the document generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency of document creation and management and reduce human error. [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 document generation and management system according to an embodiment of the present invention utilizes a multimodal LLM to automatically generate and manage various documents related to data center construction, supporting construction project management. In the document generation and management system, users input the type and content of the required documents, and the multimodal LLM analyzes the input information and automatically generates various documents. The generated documents are managed within the system, allowing users to review and modify them. For example, the document generation and management system automatically generates blueprints, preventing manual errors and enabling the rapid creation of accurate blueprints. The document generation and management system also automatically generates permit application forms, ensuring all necessary information is included and permits are obtained quickly. Furthermore, the document generation and management system automatically generates contracts, accurately specifying contract details and preventing problems. This document generation and management system supports the management of data center construction projects, reducing the burden on company personnel and contributing to project success. This document generation and management system reduces the amount of work required for document creation, addresses human error, and improves quality. For example, automatic generation of blueprints can prevent manual errors and allow for the rapid creation of accurate blueprints. Automatic generation of permit application forms ensures that all necessary information is included, allowing permits to be obtained quickly. Automatic generation of contracts also ensures that contract details are accurately recorded, preventing problems.

[0029] A document generation and management system according to an embodiment includes a selection unit, an input unit, a generation unit, and a management unit. The selection unit selects a document type. Examples of document types include, but are not limited to, technical documents, business documents, and legal documents. The selection unit selects the document type through, for example, a user interface. The selection unit may present options using a drop-down menu, radio buttons, or the like. The input unit inputs the content of the document selected by the selection unit. Examples of input methods include, but are not limited to, text input, voice input, and image input. The input unit inputs the content of the document using, for example, a text box. The input unit may also convert voice input into text data using voice recognition technology. The generation unit uses a generation AI to generate a document based on the information input by the input unit. Examples of generation methods include, but are not limited to, a generation algorithm and generation accuracy. The generation unit generates a document using, for example, a generation AI (e.g., LLM). The generation unit may also analyze the content of the document and generate an appropriate document using the generation AI. The management unit manages the documents generated by the generation unit. Management methods include, but are not limited to, version management and access permission management. For example, the management unit manages the versions of the generated documents and records the change history. The management unit can also set access permissions for the generated documents so that only specific users can access them. As a result, the document generation and management system according to the embodiment can perform a consistent process from document type selection to generation and management.

[0030] The generation unit can generate at least one document from among a blueprint, a permit application, and a contract. The generation unit generates, for example, a blueprint. Blueprints include, but are not limited to, architectural blueprints and mechanical blueprints. The generation unit generates, for example, a blueprint using a generation algorithm based on CAD data. The generation unit can also generate a permit application. The permit application includes, but is not limited to, a building permit application and a business license application. The generation unit can also generate a permit application using, for example, a generation algorithm based on legal regulations. The generation unit can also generate a contract. The contract includes, but is not limited to, a sales contract and an employment contract. The generation unit generates a contract using, for example, a generation algorithm including legal provisions. This allows the generation unit to automatically generate a variety of documents.

[0031] The management unit can manage versions of generated documents. For example, the management unit assigns version numbers to generated documents. Methods for assigning version numbers include, but are not limited to, methods using sequential numbers or dates. For example, the management unit manages the revision history and records the changes made to the document. The management unit also has a version comparison function, which allows it to compare different versions of documents. This enables the management unit to manage the version of documents.

[0032] The management unit can set access permissions for the generated document. The management unit can set the access permissions based on, for example, user roles. User roles include, but are not limited to, administrator, editor, viewer, etc. The management unit can set access levels, for example, to allow only specific users to access the document. The management unit can also record a history of changes to the access permissions and track who changed the access permissions and when. This allows the management unit to manage the access permissions for the document.

[0033] The management unit may include a correction unit that allows users to review and correct the generated document. The correction unit provides, for example, an interface that allows users to review the generated document. Examples of the interface include, but are not limited to, a text editor or a preview screen. The correction unit, for example, manages the revision history and records who revised which part. The correction unit also includes an approval process for revisions, and can prevent the document from being made public until a specific user approves the revision. This allows the management unit to allow users to review and correct the generated document.

[0034] The management unit may include a version management unit that manages versions of generated documents. The version management unit may, for example, have a function to automatically update the version of a generated document. Automatic version updates include, but are not limited to, creating a new version every time a document is changed. The version management unit may, for example, have a function to compare versions, allowing different versions of a document to be compared. The version management unit may also have a function to merge versions, allowing multiple versions to be combined into one. This allows the management unit to maintain the quality of the document.

[0035] The selection unit can analyze past selection history and automatically suggest document types suitable for the user. For example, the selection unit preferentially suggests document types that the user has frequently selected in the past. For example, the selection unit suggests document types related to a specific project based on the user's past selection history. The selection unit can also analyze the user's past selection history and suggest the most efficient document type. This allows the selection unit to suggest the optimal document type based on the past selection history.

[0036] The selection unit can dynamically change the type of documents required depending on the progress of the project. For example, in the early stages of the project, the selection unit preferentially proposes blueprints and plans. For example, in the middle stages of the project, the selection unit preferentially proposes permit applications and progress reports. The selection unit can also preferentially propose contracts and completion reports in the final stages of the project. This allows the selection unit to dynamically change the documents required depending on the progress of the project.

[0037] The selection unit can filter appropriate document types based on the user's job content and job title. For example, if the user is a project manager, the selection unit will prioritize suggesting documents necessary for managing the progress of the entire project. For example, if the user is an engineer, the selection unit will prioritize suggesting documents containing technical details. Furthermore, if the user is in charge of legal affairs, the selection unit can also prioritize suggesting contracts and permission applications. This allows the selection unit to filter document types according to the user's job content and job title.

[0038] The selection unit can preferentially present region-specific document types based on the user's geographical location information. For example, if the user is working on a project in a specific region, the selection unit preferentially presents documents that comply with the laws and regulations of that region. For example, if the user is working on multiple projects in different regions, the selection unit can individually present documents that comply with each region. Furthermore, if the user is working on an international project, the selection unit can preferentially present documents that comply with the laws and regulations of each country. This allows the selection unit to preferentially present region-specific document types.

[0039] The selection unit may analyze the user's social media activity and suggest related document types. For example, the selection unit may suggest documents related to projects shared by the user on social media. For example, the selection unit may analyze the content of the user's social media posts and suggest related document types. The selection unit may also suggest related document types based on the activity of the user's friends on social media. This allows the selection unit to suggest related document types based on social media activity.

[0040] The selection unit can customize document options by reflecting the user's past feedback. For example, the selection unit preferentially suggests document types that the user has previously rated highly. For example, the selection unit makes suggestions by excluding document types that the user has previously rated poorly. The selection unit can also analyze the user's past feedback and suggest the most appropriate document type. This allows the selection unit to customize document options based on the user's past feedback.

[0041] The input unit can analyze past input data and suggest the optimal input method to the user. For example, the input unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. For example, the input unit suggests an input method related to a specific project based on the user's past input data. The input unit can also analyze the user's past input data and suggest the most efficient input method. This allows the input unit to suggest the optimal input method based on the past input data.

[0042] The input unit can filter the input content based on the user's current project or area of ​​interest when the input is made. For example, the input unit preferentially displays input content related to a project currently being carried out by the user. For example, the input unit preferentially displays related input content based on the user's area of ​​interest. The input unit can also dynamically change the required input content depending on the progress of the user's current project. This allows the input unit to filter the input content based on the user's current project or area of ​​interest.

[0043] The input unit can select the optimum input means depending on the user's input method (voice, text, image, etc.) at the time of input. For example, if the user selects voice input, the input unit analyzes the input content using voice recognition technology. For example, if the user selects text input, the input unit analyzes the input content using text analysis technology. Furthermore, if the user selects image input, the input unit can also analyze the input content using image recognition technology. This allows the input unit to provide the optimum input means depending on the user's input method.

[0044] During input, the input unit can prioritize acquiring highly relevant input content based on the user's geographical location information. For example, if the user is working on a project in a specific region, the input unit prioritizes displaying input content related to that region. For example, if the user is working on multiple projects in different regions, the input unit can individually display input content related to each region. Furthermore, if the user is working on an international project, the input unit can also prioritize displaying input content related to the laws and regulations of each country. This allows the input unit to prioritize acquiring highly relevant input content based on the geographical location information.

[0045] The input unit can analyze the user's social media activity at the time of input and acquire related input content. For example, the input unit acquires input content related to a project shared by the user on social media. For example, the input unit analyzes the user's social media posts and acquires related input content. The input unit can also acquire related input content by referring to the activities of the user's friends on social media. This allows the input unit to acquire related input content based on social media activity.

[0046] The input unit can customize the input method by reflecting the user's past feedback when inputting. For example, the input unit preferentially suggests input methods that the user has previously rated highly. For example, the input unit suggests input methods that the user has previously rated poorly, excluding such input methods. The input unit can also analyze the user's past feedback and suggest the optimal input method. This allows the input unit to customize the input method based on the past feedback.

[0047] The generation unit can adjust the level of detail of the generation based on the importance of the document during generation. For example, in the case of an important contract, the generation unit generates a document including detailed clauses and conditions. For example, in the case of a simple report, the generation unit generates a concise document that covers the main points. In addition, in the case of a blueprint, the generation unit can also generate a document including detailed technical information. In this way, the generation unit can provide the level of detail of the generation according to the importance of the document.

[0048] The generation unit can apply different generation algorithms depending on the document category during generation. For example, in the case of a blueprint, the generation unit applies a generation algorithm based on CAD data. For example, in the case of a permit application, the generation unit applies a generation algorithm based on legal regulations. In addition, in the case of a contract, the generation unit can also apply a generation algorithm including legal clauses. This allows the generation unit to provide the optimal generation algorithm depending on the document category.

[0049] During generation, the generation unit can improve the accuracy of generation by referring to the user's past generation results. For example, the generation unit generates a new document by referring to the style of documents the user has generated in the past. For example, the generation unit incorporates frequently used phrases and expressions from the user's past generation results. The generation unit can also analyze the user's past generation results and apply the optimal generation pattern. This allows the generation unit to improve the accuracy of generation based on the past generation results.

[0050] The generation unit can determine the generation priority based on the submission time of the document at the time of generation. For example, the generation unit generates documents with a deadline approaching with priority. For example, the generation unit generates documents with a more distant submission time later. The generation unit can also dynamically change the generation order according to the submission time. This allows the generation unit to provide generation priority according to the submission time.

[0051] The generation unit can adjust the order of generation based on the relevance of the documents during generation. For example, the generation unit generates highly relevant documents with priority. For example, the generation unit generates less relevant documents later. The generation unit can also dynamically change the order of generation according to the relevance of the documents. This allows the generation unit to provide an order of generation according to the relevance of the documents.

[0052] The generation unit can adjust the use of technical terms in the generated document according to the user's level of expertise during generation. For example, if the user is an expert, the generation unit generates a document that uses a lot of technical terms. For example, if the user is a beginner, the generation unit generates a document that avoids technical terms. The generation unit can also generate a document that uses appropriate technical terms according to the user's level of expertise. This allows the generation unit to provide the use of technical terms according to the user's level of expertise.

[0053] During management, the management unit can analyze the version history of the document and select the optimal management method. For example, the management unit can prioritize and display the most important version based on the version history of the document. For example, the management unit can analyze the version history of the document and automatically delete unnecessary versions. The management unit can also propose the optimal version management method based on the version history of the document. This allows the management unit to provide the optimal management method based on the version history.

[0054] During management, the management unit can dynamically set document access permissions based on the user's job title and job duties. The management unit, for example, sets document viewing permissions based on the user's job title. The management unit, for example, sets document editing permissions based on the user's job duties. The management unit can also dynamically set optimal access permissions based on the user's job title and job duties. This allows the management unit to provide access permissions according to the user's job title and job duties.

[0055] The management unit can improve the document management method by reflecting user feedback during management. For example, the management unit customizes the document management method based on user feedback. For example, the management unit analyzes user feedback and proposes an optimal management method. The management unit can also continuously improve the document management method by reflecting user feedback. This allows the management unit to provide improvements to the management method based on feedback.

[0056] The management unit can select the optimal management method based on the geographic distribution of documents during management. For example, the management unit proposes the optimal management method based on the geographic distribution of documents. For example, the management unit analyzes the geographic distribution of documents and applies different management methods to each region. The management unit can also dynamically change the optimal management method taking into account the geographic distribution of documents. This allows the management unit to provide the optimal management method based on the geographic distribution.

[0057] The management unit can improve the accuracy of management by referring to related literature of the document during management. The management unit, for example, proposes an optimal management method based on related literature of the document. The management unit, for example, improves the accuracy of management by referring to related literature of the document. The management unit can also apply an optimal management pattern based on related literature of the document. This allows the management unit to provide improved accuracy of management based on related literature.

[0058] The management unit can customize the management method by taking into account the market value of the document during management. For example, the management unit proposes the optimal management method based on the market value of the document. For example, the management unit analyzes the market value of the document and prioritizes management of important documents. The management unit can also dynamically change the optimal management method by taking into account the market value of the document. This allows the management unit to provide customization of the management method based on market value.

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

[0060] The selection unit can analyze the user's past selection history and preferentially present document types that the user frequently selects. For example, if the user has frequently selected technical documents in the past, the selection unit can preferentially present technical documents. The selection unit can also suggest document types related to a specific project based on the user's selection history. Furthermore, the selection unit can suggest the most efficient document types based on the user's selection history. This allows the selection unit to suggest optimal document types based on the user's past selection history.

[0061] The management unit may include a version management unit that manages versions of generated documents. The version management unit may, for example, have a function to automatically update the version of a generated document. Automatic version updates include, but are not limited to, creating a new version every time a document is changed. The version management unit may, for example, have a function to compare versions, allowing different versions of a document to be compared. The version management unit may also have a function to merge versions, allowing multiple versions to be combined into one. This allows the management unit to maintain the quality of the document.

[0062] The generation unit can adjust the level of detail of the generation based on the importance of the document at the time of generation. For example, in the case of an important contract, a document including detailed clauses and conditions can be generated. In addition, in the case of a simple report, a concise document that covers the main points can be generated. Furthermore, in the case of a blueprint, a document including detailed technical information can be generated. In this way, the generation unit can provide the level of detail of the generation according to the importance of the document.

[0063] The selection unit can dynamically change the type of documents required depending on the progress of the project. For example, in the early stages of the project, blueprints and plans can be prioritized. In the middle stages of the project, permit applications and progress reports can be prioritized. Furthermore, in the final stages of the project, contracts and completion reports can be prioritized. This allows the selection unit to dynamically change the documents required depending on the progress of the project.

[0064] The generator can adjust the use of technical terms in the generated document according to the user's level of expertise during generation. For example, if the user is an expert, a document using a lot of technical terms can be generated. On the other hand, if the user is a beginner, a document using less technical terms can be generated. Furthermore, a document using appropriate technical terms can be generated according to the user's level of expertise. In this way, the generator can provide the use of technical terms according to the user's level of expertise.

[0065] During management, the management department can analyze the version history of a document and select the optimal management method. For example, based on the version history of a document, it can prioritize and display the most important versions. It can also analyze the version history of a document and automatically delete unnecessary versions. It can also propose the optimal version management method based on the version history of a document. This allows the management department to provide the optimal management method based on the version history.

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

[0067] Step 1: The selector selects a document type. Document types include technical documents, business documents, legal documents, etc. The selector can select the document type through a user interface, such as a drop-down menu or radio buttons. Step 2: The input unit inputs the content of the document selected by the selection unit. Input methods include text input, voice input, image input, etc. The input unit can input the content of the document using a text box and can also convert voice input into text data using voice recognition technology. Step 3: The generation unit uses a generation AI to generate a document based on the information input by the input unit. The generation method includes the generation algorithm and the accuracy of generation. The generation unit generates a document using a generation AI (e.g., LLM) and analyzes the content of the document to generate an appropriate document. Step 4: The management unit manages the documents generated by the generation unit. Management methods include version control and access permission management. The management unit manages the versions of the generated documents and records the change history. It also sets access permissions for the generated documents so that only specific users can access them.

[0068] (Example 2) A document generation and management system according to an embodiment of the present invention utilizes a multimodal LLM to automatically generate and manage various documents related to data center construction, supporting construction project management. In the document generation and management system, users input the type and content of the required documents, and the multimodal LLM analyzes the input information and automatically generates various documents. The generated documents are managed within the system, allowing users to review and modify them. For example, the document generation and management system automatically generates blueprints, preventing manual errors and enabling the rapid creation of accurate blueprints. The document generation and management system also automatically generates permit application forms, ensuring all necessary information is included and permits are obtained quickly. Furthermore, the document generation and management system automatically generates contracts, accurately specifying contract details and preventing problems. This document generation and management system supports the management of data center construction projects, reducing the burden on company personnel and contributing to project success. This document generation and management system reduces the amount of work required for document creation, addresses human error, and improves quality. For example, automatic generation of blueprints can prevent manual errors and allow for the rapid creation of accurate blueprints. Automatic generation of permit application forms ensures that all necessary information is included, allowing permits to be obtained quickly. Automatic generation of contracts also ensures that contract details are accurately recorded, preventing problems.

[0069] A document generation and management system according to an embodiment includes a selection unit, an input unit, a generation unit, and a management unit. The selection unit selects a document type. Examples of document types include, but are not limited to, technical documents, business documents, and legal documents. The selection unit selects the document type through, for example, a user interface. The selection unit may present options using a drop-down menu, radio buttons, or the like. The input unit inputs the content of the document selected by the selection unit. Examples of input methods include, but are not limited to, text input, voice input, and image input. The input unit inputs the content of the document using, for example, a text box. The input unit may also convert voice input into text data using voice recognition technology. The generation unit uses a generation AI to generate a document based on the information input by the input unit. Examples of generation methods include, but are not limited to, a generation algorithm and generation accuracy. The generation unit generates a document using, for example, a generation AI (e.g., LLM). The generation unit may also analyze the content of the document and generate an appropriate document using the generation AI. The management unit manages the documents generated by the generation unit. Management methods include, but are not limited to, version management and access permission management. For example, the management unit manages the versions of the generated documents and records the change history. The management unit can also set access permissions for the generated documents so that only specific users can access them. As a result, the document generation and management system according to the embodiment can perform a consistent process from document type selection to generation and management.

[0070] The generation unit can generate at least one document from among a blueprint, a permit application, and a contract. The generation unit generates, for example, a blueprint. Blueprints include, but are not limited to, architectural blueprints and mechanical blueprints. The generation unit generates, for example, a blueprint using a generation algorithm based on CAD data. The generation unit can also generate a permit application. The permit application includes, but is not limited to, a building permit application and a business license application. The generation unit can also generate a permit application using, for example, a generation algorithm based on legal regulations. The generation unit can also generate a contract. The contract includes, but is not limited to, a sales contract and an employment contract. The generation unit generates a contract using, for example, a generation algorithm including legal provisions. This allows the generation unit to automatically generate a variety of documents.

[0071] The management unit can manage versions of generated documents. For example, the management unit assigns version numbers to generated documents. Methods for assigning version numbers include, but are not limited to, methods using sequential numbers or dates. For example, the management unit manages the revision history and records the changes made to the document. The management unit also has a version comparison function, which allows it to compare different versions of documents. This enables the management unit to manage the version of documents.

[0072] The management unit can set access permissions for the generated document. The management unit can set the access permissions based on, for example, user roles. User roles include, but are not limited to, administrator, editor, viewer, etc. The management unit can set access levels, for example, to allow only specific users to access the document. The management unit can also record a history of changes to the access permissions and track who changed the access permissions and when. This allows the management unit to manage the access permissions for the document.

[0073] The management unit may include a correction unit that allows users to review and correct the generated document. The correction unit provides, for example, an interface that allows users to review the generated document. Examples of the interface include, but are not limited to, a text editor or a preview screen. The correction unit, for example, manages the revision history and records who revised which part. The correction unit also includes an approval process for revisions, and can prevent the document from being made public until a specific user approves the revision. This allows the management unit to allow users to review and correct the generated document.

[0074] The management unit may include a version management unit that manages versions of generated documents. The version management unit may, for example, have a function to automatically update the version of a generated document. Automatic version updates include, but are not limited to, creating a new version every time a document is changed. The version management unit may, for example, have a function to compare versions, allowing different versions of a document to be compared. The version management unit may also have a function to merge versions, allowing multiple versions to be combined into one. This allows the management unit to maintain the quality of the document.

[0075] The selection unit can estimate the user's emotions and present document type options based on the estimated user emotions. For example, the selection unit captures the user's facial expression with a camera and estimates the emotions using an emotion estimation algorithm. For example, the selection unit calculates an emotion score based on changes in facial expression and presents document type options. The selection unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the selection unit analyzes the tone and speed of the voice, calculates an emotion score, and presents document type options. The selection unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the selection unit calculates an emotion score based on heart rate fluctuations and presents document type options. This allows the selection unit to present document types according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0076] The selection unit can analyze past selection history and automatically suggest document types suitable for the user. For example, the selection unit preferentially suggests document types that the user has frequently selected in the past. For example, the selection unit suggests document types related to a specific project based on the user's past selection history. The selection unit can also analyze the user's past selection history and suggest the most efficient document type. This allows the selection unit to suggest the optimal document type based on the past selection history.

[0077] The selection unit can dynamically change the type of documents required depending on the progress of the project. For example, in the early stages of the project, the selection unit preferentially proposes blueprints and plans. For example, in the middle stages of the project, the selection unit preferentially proposes permit applications and progress reports. The selection unit can also preferentially propose contracts and completion reports in the final stages of the project. This allows the selection unit to dynamically change the documents required depending on the progress of the project.

[0078] The selection unit can filter appropriate document types based on the user's job content and job title. For example, if the user is a project manager, the selection unit will prioritize suggesting documents necessary for managing the progress of the entire project. For example, if the user is an engineer, the selection unit will prioritize suggesting documents containing technical details. Furthermore, if the user is in charge of legal affairs, the selection unit can also prioritize suggesting contracts and permission applications. This allows the selection unit to filter document types according to the user's job content and job title.

[0079] The selection unit can estimate the user's emotions and adjust the document selection order based on the estimated user emotions. For example, if the user is stressed, the selection unit first presents the simplest and most intuitive document type. For example, if the user is relaxed, the selection unit first presents a wide range of options including detailed document types. Furthermore, if the user is in a hurry, the selection unit can also first present the document type that can be generated most quickly. This allows the selection unit to adjust the document selection order 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, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0080] The selection unit can preferentially present region-specific document types based on the user's geographical location information. For example, if the user is working on a project in a specific region, the selection unit preferentially presents documents that comply with the laws and regulations of that region. For example, if the user is working on multiple projects in different regions, the selection unit can individually present documents that comply with each region. Furthermore, if the user is working on an international project, the selection unit can preferentially present documents that comply with the laws and regulations of each country. This allows the selection unit to preferentially present region-specific document types.

[0081] The selection unit may analyze the user's social media activity and suggest related document types. For example, the selection unit may suggest documents related to projects shared by the user on social media. For example, the selection unit may analyze the content of the user's social media posts and suggest related document types. The selection unit may also suggest related document types based on the activity of the user's friends on social media. This allows the selection unit to suggest related document types based on social media activity.

[0082] The selection unit can customize document options by reflecting the user's past feedback. For example, the selection unit preferentially suggests document types that the user has previously rated highly. For example, the selection unit makes suggestions by excluding document types that the user has previously rated poorly. The selection unit can also analyze the user's past feedback and suggest the most appropriate document type. This allows the selection unit to customize document options based on the user's past feedback.

[0083] The input unit can estimate the user's emotions and adjust the input interface based on the estimated user emotions. For example, when the user is feeling stressed, the input unit provides a simple interface and minimizes input steps. For example, when the user is relaxed, the input unit provides detailed input options and suggests a customizable input method. Furthermore, when the user is in a hurry, the input unit can prioritize voice input to enable quick input. This allows the input unit to provide an input interface according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0084] The input unit can analyze past input data and suggest the optimal input method to the user. For example, the input unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. For example, the input unit suggests an input method related to a specific project based on the user's past input data. The input unit can also analyze the user's past input data and suggest the most efficient input method. This allows the input unit to suggest the optimal input method based on the past input data.

[0085] The input unit can filter the input content based on the user's current project or area of ​​interest when the input is made. For example, the input unit preferentially displays input content related to a project currently being carried out by the user. For example, the input unit preferentially displays related input content based on the user's area of ​​interest. The input unit can also dynamically change the required input content depending on the progress of the user's current project. This allows the input unit to filter the input content based on the user's current project or area of ​​interest.

[0086] The input unit can select the optimum input means depending on the user's input method (voice, text, image, etc.) at the time of input. For example, if the user selects voice input, the input unit analyzes the input content using voice recognition technology. For example, if the user selects text input, the input unit analyzes the input content using text analysis technology. Furthermore, if the user selects image input, the input unit can also analyze the input content using image recognition technology. This allows the input unit to provide the optimum input means depending on the user's input method.

[0087] The input unit can estimate the user's emotions and prioritize input contents based on the estimated user emotions. For example, when the user is stressed, the input unit prioritizes displaying the most important input contents. For example, when the user is relaxed, the input unit displays a wide range of options including detailed input contents. Furthermore, when the user is in a hurry, the input unit can prioritize displaying contents that can be input most quickly. This allows the input unit to prioritize input contents 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, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0088] During input, the input unit can prioritize acquiring highly relevant input content based on the user's geographical location information. For example, if the user is working on a project in a specific region, the input unit prioritizes displaying input content related to that region. For example, if the user is working on multiple projects in different regions, the input unit can individually display input content related to each region. Furthermore, if the user is working on an international project, the input unit can also prioritize displaying input content related to the laws and regulations of each country. This allows the input unit to prioritize acquiring highly relevant input content based on the geographical location information.

[0089] The input unit can analyze the user's social media activity at the time of input and acquire related input content. For example, the input unit acquires input content related to a project shared by the user on social media. For example, the input unit analyzes the user's social media posts and acquires related input content. The input unit can also acquire related input content by referring to the activities of the user's friends on social media. This allows the input unit to acquire related input content based on social media activity.

[0090] The input unit can customize the input method by reflecting the user's past feedback when inputting. For example, the input unit preferentially suggests input methods that the user has previously rated highly. For example, the input unit suggests input methods that the user has previously rated poorly, excluding such input methods. The input unit can also analyze the user's past feedback and suggest the optimal input method. This allows the input unit to customize the input method based on the past feedback.

[0091] The generation unit can estimate the user's emotions and adjust the expression style of the generated document based on the estimated user's emotions. For example, if the user is relaxed, the generation unit generates a document using a detailed and polite expression style. For example, if the user is in a hurry, the generation unit generates a document using a concise and to-the-point expression style. Furthermore, if the user is feeling stressed, the generation unit can also generate a document using a simple and intuitive expression style. This allows the generation unit to provide a document expression style that corresponds 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.

[0092] The generation unit can adjust the level of detail of the generation based on the importance of the document during generation. For example, in the case of an important contract, the generation unit generates a document including detailed clauses and conditions. For example, in the case of a simple report, the generation unit generates a concise document that covers the main points. In addition, in the case of a blueprint, the generation unit can also generate a document including detailed technical information. In this way, the generation unit can provide the level of detail of the generation according to the importance of the document.

[0093] The generation unit can apply different generation algorithms depending on the document category during generation. For example, in the case of a blueprint, the generation unit applies a generation algorithm based on CAD data. For example, in the case of a permit application, the generation unit applies a generation algorithm based on legal regulations. In addition, in the case of a contract, the generation unit can also apply a generation algorithm including legal clauses. This allows the generation unit to provide the optimal generation algorithm depending on the document category.

[0094] During generation, the generation unit can improve the accuracy of generation by referring to the user's past generation results. For example, the generation unit generates a new document by referring to the style of documents the user has generated in the past. For example, the generation unit incorporates frequently used phrases and expressions from the user's past generation results. The generation unit can also analyze the user's past generation results and apply the optimal generation pattern. This allows the generation unit to improve the accuracy of generation based on the past generation results.

[0095] The generation unit can estimate the user's emotions and adjust the length of the generated document based on the estimated user emotions. For example, if the user is in a hurry, the generation unit generates a short, to-the-point document. For example, if the user is relaxed, the generation unit generates a longer document with detailed explanations. The generation unit can also generate a simple, intuitive document if the user is stressed. This allows the generation unit to provide a document of a length that corresponds 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.

[0096] The generation unit can determine the generation priority based on the submission time of the document at the time of generation. For example, the generation unit generates documents with a deadline approaching with priority. For example, the generation unit generates documents with a more distant submission time later. The generation unit can also dynamically change the generation order according to the submission time. This allows the generation unit to provide generation priority according to the submission time.

[0097] The generation unit can adjust the order of generation based on the relevance of the documents during generation. For example, the generation unit generates highly relevant documents with priority. For example, the generation unit generates less relevant documents later. The generation unit can also dynamically change the order of generation according to the relevance of the documents. This allows the generation unit to provide an order of generation according to the relevance of the documents.

[0098] The generation unit can adjust the use of technical terms in the generated document according to the user's level of expertise during generation. For example, if the user is an expert, the generation unit generates a document that uses a lot of technical terms. For example, if the user is a beginner, the generation unit generates a document that avoids technical terms. The generation unit can also generate a document that uses appropriate technical terms according to the user's level of expertise. This allows the generation unit to provide the use of technical terms according to the user's level of expertise.

[0099] The management unit can estimate the user's emotions and adjust the document management method based on the estimated user emotions. For example, if the user is feeling stressed, the management unit can provide a simple and intuitive management method. For example, if the user is feeling relaxed, the management unit can provide detailed management options. The management unit can also provide a quickly accessible management method if the user is in a hurry. This allows the management unit to provide a document management method that corresponds 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 such examples.

[0100] During management, the management unit can analyze the version history of the document and select the optimal management method. For example, the management unit can prioritize and display the most important version based on the version history of the document. For example, the management unit can analyze the version history of the document and automatically delete unnecessary versions. The management unit can also propose the optimal version management method based on the version history of the document. This allows the management unit to provide the optimal management method based on the version history.

[0101] During management, the management unit can dynamically set document access permissions based on the user's job title and job duties. The management unit, for example, sets document viewing permissions based on the user's job title. The management unit, for example, sets document editing permissions based on the user's job duties. The management unit can also dynamically set optimal access permissions based on the user's job title and job duties. This allows the management unit to provide access permissions according to the user's job title and job duties.

[0102] The management unit can improve the document management method by reflecting user feedback during management. For example, the management unit customizes the document management method based on user feedback. For example, the management unit analyzes user feedback and proposes an optimal management method. The management unit can also continuously improve the document management method by reflecting user feedback. This allows the management unit to provide improvements to the management method based on feedback.

[0103] The management unit can estimate the user's emotions and determine document management priorities based on the estimated user emotions. For example, when the user is feeling stressed, the management unit prioritizes managing the most important documents. For example, when the user is relaxed, the management unit provides detailed management options. Furthermore, when the user is in a hurry, the management unit can prioritize managing documents that can be accessed quickly. This allows the management unit to provide management priorities 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, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0104] The management unit can select the optimal management method based on the geographic distribution of documents during management. For example, the management unit proposes the optimal management method based on the geographic distribution of documents. For example, the management unit analyzes the geographic distribution of documents and applies different management methods to each region. The management unit can also dynamically change the optimal management method taking into account the geographic distribution of documents. This allows the management unit to provide the optimal management method based on the geographic distribution.

[0105] The management unit can improve the accuracy of management by referring to related literature of the document during management. The management unit, for example, proposes an optimal management method based on related literature of the document. The management unit, for example, improves the accuracy of management by referring to related literature of the document. The management unit can also apply an optimal management pattern based on related literature of the document. This allows the management unit to provide improved accuracy of management based on related literature.

[0106] The management unit can customize the management method by taking into account the market value of the document during management. For example, the management unit proposes the optimal management method based on the market value of the document. For example, the management unit analyzes the market value of the document and prioritizes management of important documents. The management unit can also dynamically change the optimal management method by taking into account the market value of the document. This allows the management unit to provide customization of the management method based on market value. === Hard Collateral 1-1 === Each of the multiple elements including the selection unit, input unit, generation unit, and management unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the selection unit is realized by the control unit 46A of the smart device 14 and provides an interface for the user to select a document type. The input unit is realized using the reception device 38 of the smart device 14 and allows the user to input document content. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a document based on the input information. The management unit is realized by the specific processing unit 290 of the data processing device 12 and manages the generated document. The selection unit estimates the user's emotion using, for example, the camera 42 or microphone 38B of the smart device 14 and presents options for document types based on the emotion. === Hard Collateral 1-2 === Each of the multiple elements including the selection unit, input unit, generation unit, and management unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the selection unit is realized by the control unit 46A of the smart glasses 214 and provides an interface for the user to select a document type. The input unit is realized using the microphone 238 of the smart glasses 214 and allows the user to input document content. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a document based on the input information. The management unit is realized by the specific processing unit 290 of the data processing device 12 and manages the generated document. The selection unit estimates the user's emotion using, for example, the camera 42 or the microphone 238 of the smart glasses 214 and presents options for document types based on the emotion. === Hard Collateral 1-3 === Each of the multiple elements including the selection unit, input unit, generation unit, and management unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the selection unit is realized by the control unit 46A of the headset type terminal 314 and provides an interface for the user to select a document type. The input unit is realized using the microphone 238 of the headset type terminal 314 and allows the user to input the content of the document. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a document based on the input information. The management unit is realized by the specific processing unit 290 of the data processing device 12 and manages the generated document. The selection unit estimates the user's emotion using, for example, the camera 42 or the microphone 238 of the headset type terminal 314 and presents options for document types based on the emotion. === Hard Collateral 1-4 === Each of the multiple elements including the selection unit, input unit, generation unit, and management unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the selection unit is realized by the control unit 46A of the robot 414 and provides an interface for the user to select a document type. The input unit is realized using the microphone 238 of the robot 414 and allows the user to input the content of the document. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a document based on the input information. The management unit is realized by the specific processing unit 290 of the data processing device 12 and manages the generated document. The selection unit estimates the user's emotion using, for example, the camera 42 or the microphone 238 of the robot 414 and presents options for document types based on the emotion.

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

[0108] The selection unit can analyze the user's past selection history and preferentially present document types that the user frequently selects. For example, if the user has frequently selected technical documents in the past, the selection unit can preferentially present technical documents. The selection unit can also suggest document types related to a specific project based on the user's selection history. Furthermore, the selection unit can suggest the most efficient document types based on the user's selection history. This allows the selection unit to suggest optimal document types based on the user's past selection history.

[0109] The generation unit can estimate the user's emotions and adjust the expression style of the generated document based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate a document using detailed and polite expression styles. If the user is in a hurry, the generation unit can generate a document using concise and to-the-point expression styles. Furthermore, if the user is stressed, the generation unit can generate a document using simple and intuitive expression styles. In this way, the generation unit can provide a document expression style that corresponds to the user's emotions.

[0110] The management unit may include a version management unit that manages versions of generated documents. The version management unit may, for example, have a function to automatically update the version of a generated document. Automatic version updates include, but are not limited to, creating a new version every time a document is changed. The version management unit may, for example, have a function to compare versions, allowing different versions of a document to be compared. The version management unit may also have a function to merge versions, allowing multiple versions to be combined into one. This allows the management unit to maintain the quality of the document.

[0111] The selection unit can estimate the user's emotions and present options for document types based on the estimated user emotions. For example, the user's facial expression can be captured with a camera and the emotions can be estimated using an emotion estimation algorithm. Furthermore, the user's voice can be recorded and the emotions can be estimated using voice analysis technology. For example, the tone and speed of the voice can be analyzed, an emotion score can be calculated, and options for document types can be presented. Furthermore, the user's biometric data (heart rate and electrodermal activity) can be collected with a sensor and the emotions can be estimated using an emotion estimation algorithm. This allows the selection unit to present document types according to the user's emotions.

[0112] The input unit can estimate the user's emotion and adjust the input interface based on the estimated user's emotion. For example, if the user is feeling stressed, a simple interface can be provided to minimize input steps. Alternatively, if the user is relaxed, detailed input options can be provided to suggest a customizable input method. Furthermore, if the user is in a hurry, voice input can be prioritized to enable quick input. In this way, the input unit can provide an input interface according to the user's emotion.

[0113] The generation unit can adjust the level of detail of the generation based on the importance of the document at the time of generation. For example, in the case of an important contract, a document including detailed clauses and conditions can be generated. In addition, in the case of a simple report, a concise document that covers the main points can be generated. Furthermore, in the case of a blueprint, a document including detailed technical information can be generated. In this way, the generation unit can provide the level of detail of the generation according to the importance of the document.

[0114] The management unit can estimate the user's emotions and adjust the document management method based on the estimated user's emotions. For example, if the user is feeling stressed, a simple and intuitive management method can be provided. If the user is feeling relaxed, detailed management options can be provided. Furthermore, if the user is in a hurry, a quickly accessible management method can be provided. In this way, the management unit can provide a document management method according to the user's emotions.

[0115] The selection unit can dynamically change the type of documents required depending on the progress of the project. For example, in the early stages of the project, blueprints and plans can be prioritized. In the middle stages of the project, permit applications and progress reports can be prioritized. Furthermore, in the final stages of the project, contracts and completion reports can be prioritized. This allows the selection unit to dynamically change the documents required depending on the progress of the project.

[0116] The generator can adjust the use of technical terms in the generated document according to the user's level of expertise during generation. For example, if the user is an expert, a document using a lot of technical terms can be generated. On the other hand, if the user is a beginner, a document using less technical terms can be generated. Furthermore, a document using appropriate technical terms can be generated according to the user's level of expertise. In this way, the generator can provide the use of technical terms according to the user's level of expertise.

[0117] During management, the management department can analyze the version history of a document and select the optimal management method. For example, based on the version history of a document, it can prioritize and display the most important versions. It can also analyze the version history of a document and automatically delete unnecessary versions. It can also propose the optimal version management method based on the version history of a document. This allows the management department to provide the optimal management method based on the version history.

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

[0119] Step 1: The selector selects a document type. Document types include technical documents, business documents, legal documents, etc. The selector can select the document type through a user interface, such as a drop-down menu or radio buttons. Step 2: The input unit inputs the content of the document selected by the selection unit. Input methods include text input, voice input, image input, etc. The input unit can input the content of the document using a text box and can also convert voice input into text data using voice recognition technology. Step 3: The generation unit uses a generation AI to generate a document based on the information input by the input unit. The generation method includes the generation algorithm and the accuracy of generation. The generation unit generates a document using a generation AI (e.g., LLM) and analyzes the content of the document to generate an appropriate document. Step 4: The management unit manages the documents generated by the generation unit. Management methods include version control and access permission management. The management unit manages the versions of the generated documents and records the change history. It also sets access permissions for the generated documents so that only specific users can access them.

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

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

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

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

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

[0125] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0127] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

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

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

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

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

[0132] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

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

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

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

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

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

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

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

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

[0143] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

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

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

[0147] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0150] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

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

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

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

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

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

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

[0157] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0159] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

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

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

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

[0164] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0165] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0167] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

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

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

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

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

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

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

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

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

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

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

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

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

[0180] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

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

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

[0183] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

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

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

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

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

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

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

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

[0191] [Explanation of symbols]

[0192] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a selection section for selecting a document type; an input unit for inputting the content of the document selected by the selection unit; a generating unit that generates a document based on the information input by the input unit; a management unit that manages the document generated by the generation unit; Equipped with A system characterized by:

2. The generation unit Generate at least one of the following documents: blueprints, permit applications, and contracts 2. The system of claim 1.

3. The management unit Version control of generated documents 2. The system of claim 1.

4. The management unit Set access permissions for generated documents 2. The system of claim 1.

5. The management unit A correction section is provided that allows users to check and correct the generated document.

2. The system of claim 1.

6. The management unit It has a version control section that manages the versions of the generated documents.

2. The system of claim 1.

7. The selection unit Estimates user sentiment and presents document type options based on the estimated user sentiment 2. The system of claim 1.

8. The selection unit Analyzes past selection history and automatically suggests suitable document types for users 2. The system of claim 1.

Images