System

Abstract

An object of a system according to an embodiment is to efficiently search for a file stored in a cloud storage and create a new file.SOLUTION: A system includes a file storage part, a file retrieval part, and a file creation part. The file storage unit stores a related file of a company in a cloud storage service. The file retrieval part presents the storage destination of the file stored by the file storage part by using the generation AI. The file creation unit creates a new file with reference to a file stored in the cloud storage service.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that searching for files stored in cloud storage or creating new files is not performed efficiently.

[0005] The system according to the embodiment aims to efficiently search for files stored in cloud storage and to efficiently create new files. [Means for solving the problem]

[0006] The system according to the embodiment includes a file storage unit, a file search unit, and a file creation unit. The file storage unit stores company-related files in a cloud storage service. The file search unit uses a generation AI to present storage destinations for files stored by the file storage unit. The file creation unit creates new files by referring to files stored in the cloud storage service. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently search for files stored in cloud storage and create new files. [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 pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

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

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

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

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

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

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

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

[0028] (Example 1) A file management system according to an embodiment of the present invention stores company-related files in Google Drive and uses generative AI to efficiently search for and create files, allowing the file management system to efficiently manage company-related files and quickly obtain necessary information.

[0029] A file management system according to an embodiment includes a file storage unit, a file search unit, and a file creation unit. The file storage unit stores company-related files in Google Drive. For example, it creates a folder for each project and organizes related documents and spreadsheets. The file search unit uses a generation AI to suggest storage locations for files related to tasks. For example, when a prompt such as "Tell me the latest report for Project X" is input, the generation AI searches Google Drive and suggests storage locations for the relevant files. The file creation unit creates new files by referring to files stored in Google Drive. For example, when a prompt such as "Create a presentation for a new product" is input, the generation AI references related files in Google Drive and generates presentation materials containing appropriate content. This allows the file management system to efficiently manage company-related files and quickly obtain necessary information.

[0030] The file storage unit can create folders for each project and organize related documents and spreadsheets. For example, the file storage unit creates folders for each project and organizes related documents and spreadsheets. For example, a folder for Project X stores project plans, progress reports, budget management sheets, etc. The file storage unit also sets naming conventions for folders to make file classification easier. For example, a naming convention such as project name_date_document type can be used. By organizing files by project, you can quickly find the files you need.

[0031] The file search unit can use the generation AI to search within a cloud storage service and present the storage location of the relevant file. The file search unit, for example, uses the generation AI to search within a cloud storage service and present the storage location of the relevant file. For example, if a prompt such as "Tell me the latest report for Project X" is input, the generation AI will search within the cloud storage service and present the storage location of the relevant file. The file search unit also visually displays the search results, allowing the user to intuitively understand them. For example, the search results can be displayed in list format or thumbnail format. This allows the generation AI to efficiently present the storage location of the file.

[0032] The file creation unit can automatically create a new file by referring to files stored in a cloud storage service. For example, the file creation unit automatically creates a new file by referring to files stored in a cloud storage service. For example, when a prompt such as "Create a presentation material for a new product" is input, the generation AI references related files in the cloud storage service and generates a presentation material containing appropriate content. The file creation unit can also create a file using a template. For example, it uses a presentation material template to automatically embed content. This allows a new file to be automatically created by referring to files stored in a cloud storage service.

[0033] The file storage unit allows the generation AI to automatically generate metadata when storing a file, describing the file's content and relevance in detail. For example, when storing a file, the file storage unit allows the generation AI to automatically generate metadata to describe the file's content and relevance in detail. For example, information such as the document's creation date, creator, and related projects is added as metadata. The file storage unit also improves file search accuracy based on the metadata. For example, the file's content is described in detail using metadata, and highly relevant files are preferentially displayed during searches. This improves search accuracy by describing the file's content and relevance in detail.

[0034] In the file storage unit, when a file is stored, the generation AI automatically evaluates the importance of the file and optimizes the folder hierarchy based on the importance. For example, when a file is stored, the file storage unit automatically evaluates the importance of the file and optimizes the folder hierarchy based on the importance. For example, the file storage unit evaluates the importance of a file based on the progress of a project or a deadline, and places files with high importance in higher folders. The file storage unit can also dynamically change the folder hierarchy. For example, as the project progresses, the folder arrangement is changed according to changes in importance. In this way, the folder hierarchy is optimized according to the importance of the file, making it easier to access.

[0035] The file storage unit can also work with cloud storage services (e.g., Dropbox or OneDrive) to integrate file management across multiple platforms. The file storage unit can, for example, link Google Drive with other cloud storage services (e.g., Dropbox or OneDrive) to build a system that integrates file management. For example, it synchronizes files using the APIs of each platform. The file storage unit can also automate the movement and copying of files across multiple platforms. For example, files uploaded to Google Drive can be automatically copied to Dropbox as well. This makes it possible to integrate file management across multiple cloud storage services.

[0036] When a file is stored in the file storage unit, the generation AI can automatically summarize the contents of the file and add the summary information as metadata. For example, when a file is stored in the file storage unit, the generation AI can automatically summarize the contents of the file and add the summary information as metadata. For example, a summary can be created by extracting the main points from a long document. The file storage unit also improves the accuracy of file searches based on the summary information. For example, the summary information can be used to describe the contents of the file in detail, and highly relevant files can be displayed preferentially during a search. In this way, by summarizing the contents of the file and adding the summary information as metadata, search accuracy can be improved.

[0037] The file search unit automatically generates previews of related files when the generation AI presents search results, making it easier for users to check the contents. The file search unit, for example, automatically generates previews of related files when the generation AI presents search results. For example, it may display an excerpt of a portion of a document. The file search unit also visually displays the previews, allowing users to intuitively understand the contents. For example, it may display thumbnails or portions of the contents. This automatically generates previews of related files, making it easier for users to check the contents.

[0038] The file search unit allows the generation AI to automatically complete related keywords and phrases in response to search prompts, improving search accuracy. The file search unit, for example, allows the generation AI to automatically complete related keywords and phrases in response to search prompts. For example, when the user starts typing, related candidates are presented. The file search unit can also optimize the input method for search prompts. For example, it supports natural language input, allowing the user to intuitively input search prompts. This allows related keywords and phrases to be automatically completed, improving search accuracy.

[0039] The file search unit can simultaneously present related external resources when the generation AI presents search results. For example, when the generation AI presents search results, the file search unit simultaneously presents related external resources (e.g., web pages and academic papers). For example, it can search for related papers from Google Scholar or PubMed. The file search unit can also evaluate the reliability of external resources. For example, it can evaluate reliability based on the number of citations or author ratings. This allows users to obtain information in a centralized manner by simultaneously presenting related external resources.

[0040] When the generation AI creates a file, the file creation unit learns the content of past similar files and can generate files of higher quality. For example, the file creation unit allows the generation AI to learn the content of past similar files and create new files based on that knowledge. For example, it analyzes past reports and presentation materials and extracts common structures and content. The file creation unit can also evaluate the quality of files based on the content learned by the generation AI. For example, it evaluates the accuracy of the content and the consistency of the format. This allows the generation AI to learn the content of past similar files and generate files of higher quality.

[0041] The file creation unit allows the generation AI to automatically cite references and data sources during the file creation process, improving reliability. For example, the file creation unit allows the generation AI to automatically cite relevant references and data sources when creating a file. For example, it may search for and cite relevant papers from Google Scholar or PubMed. The file creation unit can also automatically format citations. For example, it may format citations according to APA or MLA style. This allows references and data sources to be automatically cited, improving the reliability of the file.

[0042] The file creation unit can improve user convenience by simultaneously generating different formats (e.g., PDF, Word, PowerPoint) when the generation AI creates a file. For example, the file creation unit can output files with the same content in multiple formats. The file creation unit also provides a function for maintaining consistency between formats. For example, it can adjust the layout and style for each format. This allows files to be generated simultaneously in different formats, improving user convenience.

[0043] The file creation unit can automatically summarize the contents of a file after the generation AI has created it and add the summary information as metadata. The file creation unit, for example, can automatically summarize the contents of a file after the generation AI has created it. For example, it can extract the main points from a long document to create a summary. The file creation unit can also add the summary information as metadata. For example, it can use the summary information to describe the contents of a file in detail and prioritize highly relevant files when searching. In this way, by summarizing the contents of a file after it has been created and adding the summary information as metadata, search accuracy can be improved.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The file management system also includes a voice recognition unit, which allows users to search for and create files by voice. For example, the voice recognition unit accepts a voice command such as "Search for the latest report on Project X," and the generation AI searches for files based on that command. The voice recognition unit can also convert voice commands into text and pass it to the file creation unit. This allows users to manage files using only their voice, without using their hands.

[0046] The file storage unit also includes a version control unit. The version control unit automatically records the change history of a file and makes it easy to access past versions. For example, the change history of a project plan can be saved and a previous version can be restored if necessary. The version control unit also provides a function to visually compare changes. This allows the change history of a file to be managed and past versions to be easily accessed.

[0047] The file search unit further includes a relevance evaluation unit. The relevance evaluation unit evaluates the relevance of search results and prioritizes presenting the most relevant files to the user. For example, if multiple files are found in response to a search prompt, the relevance evaluation unit calculates a relevance score based on the file content and metadata, and displays the files in descending order of score. The relevance evaluation unit can also learn the user's past search history and provide search results tailored to individual needs. This allows the user to obtain more accurate search results.

[0048] The file creation unit also includes a collaboration support unit, which provides an environment in which multiple users can edit files simultaneously. For example, multiple users can edit a document shared on Google Drive in real time. The collaboration support unit also provides a function to record editing history and track who edited which part. This allows for efficient team collaboration.

[0049] The file storage unit also includes a security enhancement unit. The security enhancement unit automatically encrypts files when they are stored, ensuring data security. For example, it encrypts files using a powerful encryption algorithm such as AES-256. The security enhancement unit also provides the ability to set detailed access permissions. For example, it allows only specific users or groups to view or edit files. This enhances file security and ensures data safety.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The file storage section stores all your company's related files in Google Drive. For example, create a folder for each project and organize related documents and spreadsheets. Step 2: The file search unit uses the generation AI to suggest where to store files related to the task. For example, if you enter a prompt such as "What is the latest report for Project X?", the generation AI will search Google Drive and suggest where to store the relevant file. Step 3: The file creation unit creates a new file by referring to files stored in Google Drive. For example, if you enter a prompt such as "Create a presentation for a new product," the generation AI will reference related files in Google Drive and generate a presentation containing appropriate content.

[0052] (Example 2) A file management system according to an embodiment of the present invention stores company-related files in Google Drive and uses generative AI to efficiently search for and create files, allowing the file management system to efficiently manage company-related files and quickly obtain necessary information.

[0053] A file management system according to an embodiment includes a file storage unit, a file search unit, and a file creation unit. The file storage unit stores company-related files in Google Drive. For example, it creates a folder for each project and organizes related documents and spreadsheets. The file search unit uses a generation AI to suggest storage locations for files related to tasks. For example, when a prompt such as "Tell me the latest report for Project X" is input, the generation AI searches Google Drive and suggests storage locations for the relevant files. The file creation unit creates new files by referring to files stored in Google Drive. For example, when a prompt such as "Create a presentation for a new product" is input, the generation AI references related files in Google Drive and generates presentation materials containing appropriate content. This allows the file management system to efficiently manage company-related files and quickly obtain necessary information.

[0054] The file storage unit can create folders for each project and organize related documents and spreadsheets. For example, the file storage unit creates folders for each project and organizes related documents and spreadsheets. For example, a folder for Project X stores project plans, progress reports, budget management sheets, etc. The file storage unit also sets naming conventions for folders to make file classification easier. For example, a naming convention such as project name_date_document type can be used. By organizing files by project, you can quickly find the files you need.

[0055] The file search unit can use the generation AI to search within a cloud storage service and present the storage location of the relevant file. The file search unit, for example, uses the generation AI to search within a cloud storage service and present the storage location of the relevant file. For example, if a prompt such as "Tell me the latest report for Project X" is input, the generation AI will search within the cloud storage service and present the storage location of the relevant file. The file search unit also visually displays the search results, allowing the user to intuitively understand them. For example, the search results can be displayed in list format or thumbnail format. This allows the generation AI to efficiently present the storage location of the file.

[0056] The file creation unit can automatically create a new file by referring to files stored in a cloud storage service. For example, the file creation unit automatically creates a new file by referring to files stored in a cloud storage service. For example, when a prompt such as "Create a presentation material for a new product" is input, the generation AI references related files in the cloud storage service and generates a presentation material containing appropriate content. The file creation unit can also create a file using a template. For example, it uses a presentation material template to automatically embed content. This allows a new file to be automatically created by referring to files stored in a cloud storage service.

[0057] The file storage unit allows the generation AI to automatically generate metadata when storing a file, describing the file's content and relevance in detail. For example, when storing a file, the file storage unit allows the generation AI to automatically generate metadata to describe the file's content and relevance in detail. For example, information such as the document's creation date, creator, and related projects is added as metadata. The file storage unit also improves file search accuracy based on the metadata. For example, the file's content is described in detail using metadata, and highly relevant files are preferentially displayed during searches. This improves search accuracy by describing the file's content and relevance in detail.

[0058] In the file storage unit, when a file is stored, the generation AI automatically evaluates the importance of the file and optimizes the folder hierarchy based on the importance. For example, when a file is stored, the file storage unit automatically evaluates the importance of the file and optimizes the folder hierarchy based on the importance. For example, the file storage unit evaluates the importance of a file based on the progress of a project or a deadline, and places files with high importance in higher folders. The file storage unit can also dynamically change the folder hierarchy. For example, as the project progresses, the folder arrangement is changed according to changes in importance. In this way, the folder hierarchy is optimized according to the importance of the file, making it easier to access.

[0059] The file storage unit can use the emotion estimation function to analyze the emotion a user expresses when uploading a file and propose a folder structure that elicits positive emotions. The file storage unit can, for example, use the emotion estimation function to analyze the emotion when a user uploads a file. For example, the file storage unit can analyze the user's facial expression and voice using a camera or microphone and calculate an emotion score. The file storage unit can also propose a folder structure that elicits positive emotions. For example, the file storage unit can propose an intuitive and easy-to-use folder structure that prevents the user from feeling stressed. This makes it possible to analyze the user's emotions and propose a folder structure that elicits positive emotions.

[0060] The file storage unit can also work with cloud storage services (e.g., Dropbox or OneDrive) to integrate file management across multiple platforms. The file storage unit can, for example, link Google Drive with other cloud storage services (e.g., Dropbox or OneDrive) to build a system that integrates file management. For example, it synchronizes files using the APIs of each platform. The file storage unit can also automate the movement and copying of files across multiple platforms. For example, files uploaded to Google Drive can be automatically copied to Dropbox as well. This makes it possible to integrate file management across multiple cloud storage services.

[0061] When a file is stored in the file storage unit, the generation AI can automatically summarize the contents of the file and add the summary information as metadata. For example, when a file is stored in the file storage unit, the generation AI can automatically summarize the contents of the file and add the summary information as metadata. For example, a summary can be created by extracting the main points from a long document. The file storage unit also improves the accuracy of file searches based on the summary information. For example, the summary information can be used to describe the contents of the file in detail, and highly relevant files can be displayed preferentially during a search. In this way, by summarizing the contents of the file and adding the summary information as metadata, search accuracy can be improved.

[0062] The file storage unit can use the emotion estimation function to monitor the emotions of a user when uploading a file in real time, and provide an interface for reducing stress. The file storage unit, for example, uses the emotion estimation function to monitor the emotions of a user when uploading a file in real time. For example, the file storage unit can analyze the user's facial expressions and voice using a camera or microphone and calculate an emotion score. The file storage unit also provides an interface for reducing stress. For example, the file storage unit can provide an interface with colors and designs that help the user relax. This makes it possible to monitor the user's emotions in real time and provide an interface for reducing stress.

[0063] The file search unit automatically generates previews of related files when the generation AI presents search results, making it easier for users to check the contents. The file search unit, for example, automatically generates previews of related files when the generation AI presents search results. For example, it may display an excerpt of a portion of a document. The file search unit also visually displays the previews, allowing users to intuitively understand the contents. For example, it may display thumbnails or portions of the contents. This automatically generates previews of related files, making it easier for users to check the contents.

[0064] The file search unit allows the generation AI to automatically complete related keywords and phrases in response to search prompts, improving search accuracy. The file search unit, for example, allows the generation AI to automatically complete related keywords and phrases in response to search prompts. For example, when the user starts typing, related candidates are presented. The file search unit can also optimize the input method for search prompts. For example, it supports natural language input, allowing the user to intuitively input search prompts. This allows related keywords and phrases to be automatically completed, improving search accuracy.

[0065] The file search unit can simultaneously present related external resources when the generation AI presents search results. For example, when the generation AI presents search results, the file search unit simultaneously presents related external resources (e.g., web pages and academic papers). For example, it can search for related papers from Google Scholar or PubMed. The file search unit can also evaluate the reliability of external resources. For example, it can evaluate reliability based on the number of citations or author ratings. This allows users to obtain information in a centralized manner by simultaneously presenting related external resources.

[0066] When the generation AI creates a file, the file creation unit learns the content of past similar files and can generate files of higher quality. For example, the file creation unit allows the generation AI to learn the content of past similar files and create new files based on that knowledge. For example, it analyzes past reports and presentation materials and extracts common structures and content. The file creation unit can also evaluate the quality of files based on the content learned by the generation AI. For example, it evaluates the accuracy of the content and the consistency of the format. This allows the generation AI to learn the content of past similar files and generate files of higher quality.

[0067] The file creation unit allows the generation AI to automatically cite references and data sources during the file creation process, improving reliability. For example, the file creation unit allows the generation AI to automatically cite relevant references and data sources when creating a file. For example, it may search for and cite relevant papers from Google Scholar or PubMed. The file creation unit can also automatically format citations. For example, it may format citations according to APA or MLA style. This allows references and data sources to be automatically cited, improving the reliability of the file.

[0068] The file creation unit can analyze the emotions expressed when the generation AI instructs the user to create a file, and propose a file structure that will elicit positive emotions. For example, when a user instructs the user to create a file, the file creation unit analyzes the emotions using an emotion estimation function. For example, it can analyze the user's facial expressions and voice using a camera or microphone and calculate an emotion score. The file creation unit also proposes a file structure that will elicit positive emotions. For example, it can propose an intuitive and easy-to-use file structure so that the user does not feel stressed. This makes it possible to analyze the user's emotions and propose a file structure that will elicit positive emotions.

[0069] The file creation unit can improve user convenience by simultaneously generating different formats (e.g., PDF, Word, PowerPoint) when the generation AI creates a file. For example, the file creation unit can output files with the same content in multiple formats. The file creation unit also provides a function for maintaining consistency between formats. For example, it can adjust the layout and style for each format. This allows files to be generated simultaneously in different formats, improving user convenience.

[0070] The file creation unit can automatically summarize the contents of a file after the generation AI has created it and add the summary information as metadata. The file creation unit, for example, can automatically summarize the contents of a file after the generation AI has created it. For example, it can extract the main points from a long document to create a summary. The file creation unit can also add the summary information as metadata. For example, it can use the summary information to describe the contents of a file in detail and prioritize highly relevant files when searching. In this way, by summarizing the contents of a file after it has been created and adding the summary information as metadata, search accuracy can be improved.

[0071] After the generation AI creates a file, the file creation unit can monitor in real time the emotions the user feels toward the created file and provide feedback to reduce negative emotions. The file creation unit, for example, monitors in real time the emotions the user feels toward the created file. For example, it may use a camera or microphone to analyze the user's facial expressions and voice and calculate an emotion score. The file creation unit also provides feedback to reduce negative emotions. For example, it may present advice or areas for improvement. This makes it possible to monitor the user's emotions in real time and provide feedback to reduce negative emotions.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The file management system also includes a voice recognition unit, which allows users to search for and create files by voice. For example, the voice recognition unit accepts a voice command such as "Search for the latest report on Project X," and the generation AI searches for files based on that command. The voice recognition unit can also convert voice commands into text and pass it to the file creation unit. This allows users to manage files using only their voice, without using their hands.

[0074] The file storage unit also includes a version control unit. The version control unit automatically records the change history of a file and makes it easy to access past versions. For example, the change history of a project plan can be saved and a previous version can be restored if necessary. The version control unit also provides a function to visually compare changes. This allows the change history of a file to be managed and past versions to be easily accessed.

[0075] The file search unit further includes a relevance evaluation unit. The relevance evaluation unit evaluates the relevance of search results and prioritizes presenting the most relevant files to the user. For example, if multiple files are found in response to a search prompt, the relevance evaluation unit calculates a relevance score based on the file content and metadata, and displays the files in descending order of score. The relevance evaluation unit can also learn the user's past search history and provide search results tailored to individual needs. This allows the user to obtain more accurate search results.

[0076] The file creation unit also includes a collaboration support unit, which provides an environment in which multiple users can edit files simultaneously. For example, multiple users can edit a document shared on Google Drive in real time. The collaboration support unit also provides a function to record editing history and track who edited which part. This allows for efficient team collaboration.

[0077] The file storage unit also includes a security enhancement unit. The security enhancement unit automatically encrypts files when they are stored, ensuring data security. For example, it encrypts files using a powerful encryption algorithm such as AES-256. The security enhancement unit also provides the ability to set detailed access permissions. For example, it allows only specific users or groups to view or edit files. This enhances file security and ensures data safety.

[0078] The file storage unit uses the emotion estimation function to analyze the emotions expressed when a user uploads files and can suggest a folder structure that elicits positive emotions. For example, if a user is feeling stressed, the emotion estimation function can detect that emotion and suggest a more intuitive and easy-to-use folder structure. It can also suggest folder names and colors that will make the user feel satisfied. This allows for a folder structure that takes the user's emotions into consideration, improving work efficiency.

[0079] The file search unit can use the emotion estimation function to analyze the user's emotions during a search and optimize the way search results are presented. For example, if the user is feeling impatient or anxious, the emotion estimation function can detect that emotion and present search results more quickly. Also, if the user is relaxed, detailed search results can be provided. This allows for a way to present search results that corresponds to the user's emotions, improving the user experience.

[0080] The file creation unit uses the emotion estimation function to analyze the emotions expressed by the user when instructing file creation and can suggest a file structure that elicits positive emotions. For example, if the user is feeling stressed, the emotion estimation function can detect that emotion and suggest a simpler, more intuitive file structure. It can also suggest templates and designs that will make the user feel satisfied. This allows the system to provide a file structure that takes the user's emotions into consideration, improving work efficiency.

[0081] The file creation unit can use the emotion estimation function to monitor in real time the emotions a user feels after creating a file and provide feedback to alleviate negative emotions. For example, if a user is dissatisfied with a created file, the emotion estimation function can detect that emotion and provide suggestions or advice for improvement. It can also provide positive feedback that makes the user feel satisfied. This makes it possible to monitor a user's emotions in real time and provide feedback to alleviate negative emotions.

[0082] The file search unit can use the emotion estimation function to analyze the user's emotions during a search and optimize the way search results are presented. For example, if the user is feeling impatient or anxious, the emotion estimation function can detect that emotion and present search results more quickly. Also, if the user is relaxed, detailed search results can be provided. This allows for a way to present search results that corresponds to the user's emotions, improving the user experience.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The file storage section stores all your company's related files in Google Drive. For example, create a folder for each project and organize related documents and spreadsheets. Step 2: The file search unit uses the generation AI to suggest where to store files related to the task. For example, if you enter a prompt such as "What is the latest report for Project X?", the generation AI will search Google Drive and suggest where to store the relevant file. Step 3: The file creation unit creates a new file by referring to files stored in Google Drive. For example, if you enter a prompt such as "Create a presentation for a new product," the generation AI will reference related files in Google Drive and generate a presentation containing appropriate content.

[0085] 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.

[0086] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0090] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0091] The 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.

[0092] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0093] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0094] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0095] Fig. 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.

[0096] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0097] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0098] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0099] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0100] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0101] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] The data processing system 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 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.

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] The 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.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0110] 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.

[0111] 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.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0113] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0129] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0152] 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 file storage unit that stores company-related files in a cloud storage service; a file search unit that uses a generation AI to present a storage destination of the file stored by the file storage unit; a file creation unit that creates a new file by referring to the file stored in the cloud storage service. A system characterized by:

2. The file search unit The generating AI is used to search the cloud storage service and present the storage location of the corresponding file.

2. The system of claim 1.

3. The file storage unit It also works with cloud storage services to integrate file management across multiple platforms.

2. The system of claim 1.

4. The file creation unit When the generation AI creates the file, it learns the content of similar files from the past and generates a higher quality file.

2. The system of claim 1.

5. The file storage unit When storing the file, an emotion estimation function is used to analyze the emotion the user felt when uploading the file, and a folder structure is proposed to elicit positive emotions.

2. The system of claim 1.

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