system

Abstract

The system according to this embodiment aims to eliminate the complexities of file management and to achieve efficient file organization and searching. [Solution] The system according to the embodiment comprises a collection unit, a suggestion unit, an organization unit, a reception unit, a search unit, an analysis unit, and a learning unit. The collection unit collects files. The suggestion unit analyzes the files collected by the collection unit and suggests renaming and folder structures. The organization unit automatically organizes the files after approval of the content suggested by the suggestion unit. The reception unit receives search queries from users. The search unit searches for files based on the search queries received by the reception unit. The analysis unit analyzes the contents of the files. The learning unit learns the user's usage patterns.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there were problems such as unorganized management of files reducing productivity and manual file sorting taking a long time.

[0005] The system according to the embodiment aims to eliminate the complexity of file management and achieve efficient file sorting and searching.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a suggestion unit, an organization unit, a reception unit, a search unit, an analysis unit, and a learning unit. The collection unit collects files. The suggestion unit analyzes the files collected by the collection unit and suggests renaming and folder structures. The organization unit automatically organizes the files after the user approves the suggestions made by the suggestion unit. The reception unit receives search queries from the user. The search unit searches for files based on the search queries received by the reception unit. The analysis unit analyzes the contents of the files. The learning unit learns the user's usage patterns. [Effects of the Invention]

[0007] The system according to this embodiment eliminates the complexities of file management and enables efficient file organization and searching. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The file management system according to an embodiment of the present invention is an AI agent for eliminating the complexities of file management and improving business efficiency. This file management system has the function of collecting files, proposing renames and folder structures, and automatically organizing them after approval. Furthermore, it provides a function to search for files in an interactive format based on file names and content. When a file is uploaded, it proposes the optimal storage location and improves the accuracy of the proposal by learning the user's usage patterns. For example, the file management system collects files. For example, the file management system can collect document files, image files, audio files, etc. Next, the file management system analyzes the collected files and proposes renames and folder structures. For example, the file management system analyzes the content of the files and proposes appropriate naming conventions and folder hierarchies. When the user approves the proposal, the file management system automatically organizes the files. For example, the file management system moves files to appropriate folders and creates folders. Next, the file management system accepts search queries from the user. For example, the file management system accepts search queries entered in natural language. Next, the file management system searches for files based on the accepted search query. For example, the file management system performs full-text searches and metadata searches. Next, the file management system analyzes the content of the files. For example, a file management system performs text and image analysis. Next, based on the analyzed content, the system suggests storage locations. For instance, it suggests appropriate folder structures and storage solutions. Finally, the system learns user usage patterns. For example, it uses machine learning algorithms to learn user operation history and file usage patterns. This allows the system to optimize future suggestions. As a result, the system can eliminate the complexities of file management and improve operational efficiency.

[0029] The file management system according to this embodiment comprises a collection unit, a suggestion unit, an organization unit, a reception unit, a search unit, an analysis unit, another suggestion unit, and a learning unit. The collection unit collects files. The collection unit can collect, for example, document files, image files, audio files, etc. The collection unit can also collect files, for example, via a network. The suggestion unit analyzes the collected files and suggests renames and folder structures. The suggestion unit can, for example, analyze the contents of files and suggest appropriate naming conventions and folder hierarchies. The suggestion unit can also, for example, analyze the metadata of files and suggest appropriate folder structures. The organization unit automatically organizes the suggested contents after approval. The organization unit can, for example, move files to appropriate folders and create folders. The organization unit can also, for example, automatically rename files. The reception unit receives search queries from users. The reception unit can, for example, receive search queries entered in natural language. The reception unit can also, for example, receive search queries via voice input. The search unit searches for files based on the received search queries. The search unit performs full-text searches and metadata searches, for example. The search unit can also perform searches based on file names or content, for example. The analysis unit analyzes the content of files. The analysis unit performs text analysis and image analysis, for example. The analysis unit can also analyze the content of audio files, for example. The suggestion unit proposes storage locations based on the analyzed content. The suggestion unit proposes appropriate folder structures and storage, for example. The suggestion unit can also propose the optimal storage location based on the content of the files, for example. The learning unit learns the user's usage patterns. The learning unit learns the user's operation history and file usage status, for example, using machine learning algorithms. The learning unit can also analyze the user's usage patterns and optimize suggestions for future use, for example. As a result, the file management system according to this embodiment can eliminate the complexity of file management and improve work efficiency.

[0030] The collection unit collects files. For example, it can collect document files, image files, audio files, and more. Specifically, it collects files from various devices and storage devices via the network. For instance, it can retrieve files from cloud storage services or shared folders on a local network. The collection unit periodically scans the network to detect and collect new and updated files. It can also collect files and folders manually specified by the user. The collection unit retrieves data using appropriate methods depending on the file type and format and stores it in a central database. This allows the collection unit to centrally manage files across the entire system and quickly retrieve necessary information. Furthermore, the collection unit has the capability to monitor file collection status in real time and report the progress and errors of the collection process. This enables the collection unit to achieve efficient and reliable file collection, improving the overall system performance.

[0031] The suggestion department analyzes collected files and proposes renames and folder structures. For example, it analyzes file contents and proposes appropriate naming conventions and folder hierarchies. Specifically, the suggestion department analyzes file metadata and contents and generates optimal naming conventions based on the file type and content. For example, for document files, it can propose file names based on creation date, author name, keywords, etc. For image files, it can propose folder structures based on shooting date, location, and subject information. The suggestion department can make more accurate suggestions by using AI to analyze file contents in detail and learning user usage patterns and past organization methods. Furthermore, the suggestion department can collect user feedback and continuously improve the accuracy of its suggestions. This allows the suggestion department to make flexible suggestions that meet user needs and improve the efficiency of file management.

[0032] The organization unit automatically organizes files after the proposed content is approved. For example, the organization unit moves files to appropriate folders and creates folders. Specifically, the organization unit automatically organizes files based on the naming conventions and folder structure provided by the proposal unit. For example, document files are moved to the proposed folder and renamed to appropriate file names. Image files are organized into folders based on the date and location of shooting, and audio files are categorized according to their content. Because the organization unit automatically moves and renames files according to the proposal approved by the user, it significantly reduces the user's effort. Furthermore, the organization unit also has the function to monitor the file organization status in real time and report the progress of the organization process and any errors. This enables the organization unit to achieve efficient and reliable file organization, improving the overall system performance.

[0033] The reception desk receives search queries from users. For example, it accepts search queries entered in natural language. Specifically, the reception desk analyzes the search query entered by the user and converts it into appropriate search conditions. For example, if a user enters "search for meeting materials from 2023," the reception desk extracts the keywords "2023" and "meeting materials" and sends them to the search department. The reception desk can also accept search queries via voice input. In the case of voice input, the reception desk uses speech recognition technology to convert the speech into text and processes it as a search query. This allows the reception desk to enable users to enter search queries in natural language, improving the convenience of searching. Furthermore, the reception desk can record the user's search history and optimize future searches by referring to past search queries. This allows the reception desk to improve the user's search experience and support efficient information retrieval.

[0034] The search unit searches for files based on the received search query. For example, the search unit performs full-text searches and metadata searches. Specifically, it searches for file names, content, and metadata based on the search query entered by the user. In the case of a full-text search, the search unit searches the entire content of the file and highlights the parts that match the query. In the case of a metadata search, the search unit searches metadata such as the file creation date, author name, and keywords. The search unit displays search results quickly, allowing users to find the information they need immediately. Furthermore, the search unit also has a function to rank search results, displaying highly relevant files at the top. This allows the search unit to enable users to search for information efficiently, improving work efficiency.

[0035] The analysis unit analyzes the contents of files. For example, the analysis unit performs text analysis and image analysis. Specifically, the analysis unit analyzes the contents of files in detail and extracts important information. In the case of text analysis, the analysis unit uses natural language processing technology to analyze the content of documents and extract keywords and summaries. In the case of image analysis, the analysis unit uses image recognition technology to analyze the content of images and identify subjects and scenes. In the case of audio file analysis, the analysis unit uses speech recognition technology to convert audio into text and analyze the content. This allows the analysis unit to grasp the contents of files in detail and enable users to quickly obtain the information they need. Furthermore, the analysis unit also has functions to support file classification and organization based on the analysis results. This allows the analysis unit to improve the efficiency of file management and support the user's work.

[0036] The suggestion unit proposes storage locations based on the analyzed content. For example, the suggestion unit proposes appropriate folder structures and storage solutions. Specifically, based on the analysis results provided by the analysis unit, the suggestion unit proposes the optimal storage location according to the file content and metadata. For example, it suggests storing project-related document files in folders organized by project. It also suggests organizing image files into folders based on the date and location of shooting. The suggestion unit can learn user usage patterns and past organization methods to make more accurate suggestions. Furthermore, the suggestion unit can collect user feedback and continuously improve the accuracy of its suggestions. This allows the suggestion unit to make flexible suggestions that meet user needs and improve the efficiency of file management.

[0037] The learning unit learns user usage patterns. For example, it uses machine learning algorithms to learn user operation history and file usage. Specifically, the learning unit learns what kinds of files users frequently use and what organization methods they prefer, and optimizes suggestions for future use. For example, a user who frequently uses files related to a specific project will be given suggestions to prioritize displaying files related to that project. It also learns how users have organized files in the past and suggests similar organization methods. The learning unit continuously collects user operation history and feedback and updates its learning model to improve the accuracy of its suggestions. This allows the learning unit to provide flexible suggestions tailored to user needs and improve the efficiency of file management. Furthermore, the learning unit also has the function to monitor the overall system performance and optimize resources as needed. This allows the learning unit to improve the overall system efficiency and support users' work.

[0038] The collection unit can analyze the user's past file collection history and select the optimal collection method. For example, the collection unit can analyze the types and frequency of files the user has collected in the past and propose the optimal collection method. The collection unit can also determine the priority of files to collect during specific time periods based on the user's past collection history. The collection unit can also adjust the types and quantities of files to collect based on the user's past collection history. This allows the optimal collection method to be selected by analyzing the user's past collection history. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's past collection history data into a generating AI and have the generating AI select the optimal collection method.

[0039] The collection unit can filter files based on the user's current projects and areas of interest during collection. For example, the collection unit can collect only files related to the project the user is currently working on. The collection unit can also prioritize the collection of highly relevant files based on the user's areas of interest. For example, the collection unit can collect necessary files at the appropriate time according to the progress of the user's project. This allows for the collection of highly relevant files by filtering them based on the user's current projects and areas of interest. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's project data into a generating AI and have the generating AI perform the filtering.

[0040] The collection unit can prioritize the collection of highly relevant files by considering the user's geographical location information when collecting files. For example, if the user is in a specific location, the collection unit will prioritize the collection of files related to that location. The collection unit can also collect files related to locations close to the user's current location. For example, if the user is on the move, the collection unit can collect files related to their destination. This allows for the priority collection of highly relevant files by considering the user's geographical location information. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's geographical location data into a generating AI and have the generating AI select highly relevant files.

[0041] The collection unit can analyze the user's social media activity and collect relevant files when collecting files. For example, the collection unit can collect relevant files based on information shared by the user on social media. The collection unit can also collect relevant files based on information shared by the user's social media followers and friends. The collection unit can also analyze the user's social media activity history and collect relevant files. In this way, relevant files can be collected by analyzing the user's social media activity. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's social media data into a generating AI and have the generating AI select relevant files.

[0042] The suggestion unit can adjust the level of detail of its suggestions based on the importance of the files. For example, it might suggest detailed renaming and folder structures for high-importance files. For example, it might suggest simpler renaming and folder structures for low-importance files. The suggestion unit can also adjust the level of detail of its suggestions in stages according to importance. This allows for detailed suggestions for important files by adjusting the level of detail based on the importance of the files. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input file importance data into a generating AI and have the generating AI adjust the level of detail of its suggestions.

[0043] The suggestion unit can apply different suggestion algorithms depending on the file category during the suggestion process. For example, the suggestion unit can apply an algorithm that suggests a specific rename or folder structure to document files. For example, the suggestion unit can also apply an algorithm that suggests a different rename or folder structure to image files. For example, the suggestion unit can apply an algorithm that suggests yet another different rename or folder structure to video files. By applying different suggestion algorithms depending on the file category, more appropriate suggestions become possible. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input file category data into a generating AI and have the generating AI execute the application of the suggestion algorithm.

[0044] The proposal unit can determine the priority of proposals based on the file creation date when making a proposal. For example, the proposal unit may prioritize recently created files. The proposal unit may also postpone proposing older files. The proposal unit may also adjust the priority of proposals in stages according to the creation date. This allows for more appropriate proposals by determining the priority of proposals based on the file creation date. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input file creation date data into a generating AI and have the generating AI perform the determination of proposal priority.

[0045] The suggestion unit can adjust the order of suggestions based on the relevance of the files during the suggestion process. For example, the suggestion unit may prioritize suggesting files with high relevance. For example, the suggestion unit may also postpone suggesting files with low relevance. For example, the suggestion unit may also adjust the order of suggestions in stages according to relevance. This allows for more appropriate suggestions by adjusting the order of suggestions based on the relevance of the files. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input file relevance data into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0046] The organization unit can analyze the user's past organization history to select the optimal organization method during organization. For example, the organization unit can propose the optimal organization method based on the organization methods the user has used in the past. For example, the organization unit can also preferentially apply a specific organization method based on the user's past organization history. For example, the organization unit can analyze the user's past organization history and select the most efficient organization method. In this way, the optimal organization method can be selected by analyzing the user's past organization history. Some or all of the above processes in the organization unit may be performed using AI, for example, or without AI. For example, the organization unit can input the user's past organization history data into a generating AI and have the generating AI select the optimal organization method.

[0047] The organization unit can customize its organization methods based on the user's current project during the organization process. For example, the organization unit may prioritize organizing files related to the project the user is currently working on. The organization unit may also adjust its organization methods according to the progress of the user's project. The organization unit may also apply the most suitable organization method depending on the type of project the user is working on. This allows for more appropriate organization by customizing the organization methods based on the user's current project. Some or all of the above processes in the organization unit may be performed using AI, for example, or without AI. For example, the organization unit can input the user's project data into a generating AI and have the generating AI perform the customization of the organization methods.

[0048] The sorting unit can select the optimal sorting method by considering the user's geographical location information during sorting. For example, if the user is in a specific location, the sorting unit will prioritize sorting files related to that location. The sorting unit can also sort files related to locations close to the user's current location. For example, if the user is on the move, the sorting unit can sort files related to their destination. This allows the sorting unit to select the optimal sorting method by considering the user's geographical location information. Some or all of the above processing in the sorting unit may be performed using AI, for example, or without AI. For example, the sorting unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal sorting method.

[0049] The organization unit can analyze the user's social media activity and propose organization methods during the organization process. For example, the organization unit can organize relevant files based on information shared by the user on social media. The organization unit can also organize relevant files based on information shared by the user's social media followers and friends. The organization unit can also analyze the user's social media activity history and organize relevant files. This allows for the organization of relevant files by analyzing the user's social media activity. Some or all of the above processing in the organization unit may be performed using AI, for example, or without AI. For example, the organization unit can input the user's social media data into a generating AI and have the generating AI propose organization methods.

[0050] The reception desk can select the optimal reception method by referring to the user's past search history at the time of reception. For example, the reception desk can automatically display search queries that the user has frequently entered in the past as suggestions. For example, the reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest search queries to be used during a specific time period based on the user's past search history. This allows the reception desk to select the optimal reception method by referring to the user's past search history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past search history data into a generating AI and have the generating AI select the optimal reception method.

[0051] The reception unit can filter search queries based on the user's current project upon receipt. For example, the reception unit will only accept search queries related to the project the user is currently working on. The reception unit can also filter search queries according to the progress of the user's project. The reception unit can also suggest the most relevant search queries according to the type of project the user is working on. This allows for more relevant searches by filtering search queries based on the user's current project. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's project data into a generating AI and have the generating AI perform the filtering of search queries.

[0052] The reception unit can prioritize receiving highly relevant search queries by considering the user's geographical location information at the time of reception. For example, if the user is in a specific location, the reception unit will prioritize receiving search queries related to that location. The reception unit can also, for example, receive search queries related to locations close to the user's current location. For example, if the user is on the move, the reception unit can also receive search queries related to their destination. In this way, by considering the user's geographical location information, highly relevant search queries can be prioritized. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information data into a generating AI and have the generating AI select highly relevant search queries.

[0053] The reception unit can analyze the user's social media activity and accept relevant search queries upon receiving a request. For example, the reception unit can accept relevant search queries based on information shared by the user on social media. The reception unit can also accept relevant search queries based on information shared by the user's social media followers and friends. The reception unit can also analyze the user's social media activity history and accept relevant search queries. This allows the reception unit to accept relevant search queries by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media data into a generating AI and have the generating AI select relevant search queries.

[0054] The search unit can adjust the level of detail in search results based on the importance of the files during a search. For example, the search unit can display detailed search results for files of high importance. For example, the search unit can also display concise search results for files of low importance. The search unit can also adjust the level of detail in search results in stages according to importance. This allows for detailed search results for important files by adjusting the level of detail in search results based on the importance of the files. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input file importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in search results.

[0055] The search unit can apply different search algorithms depending on the file category during a search. For example, the search unit can apply a specific search algorithm to document files. The search unit can also apply a different search algorithm to image files. The search unit can also apply yet another different search algorithm to video files. By applying different search algorithms depending on the file category, more appropriate search results can be obtained. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input file category data into a generating AI and have the generating AI execute the application of the search algorithm.

[0056] The search unit can determine the priority of search results based on the file creation date during a search. For example, the search unit can prioritize recently created files in the search results. The search unit can also, for example, delay the display of older files in the search results. The search unit can also, for example, adjust the priority of search results in stages according to the creation date. This allows for more appropriate search results by determining the priority of search results based on the file creation date. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input file creation date data into a generating AI and have the generating AI perform the determination of the search result priority.

[0057] The search unit can adjust the order of search results based on the relevance of the files during a search. For example, the search unit can prioritize displaying highly relevant files in the search results. For example, the search unit can also display less relevant files later in the search results. For example, the search unit can also adjust the order of search results in stages according to relevance. By adjusting the order of search results based on the relevance of the files, more appropriate search results can be obtained. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input file relevance data into a generating AI and have the generating AI perform the adjustment of the order of search results.

[0058] The analysis unit can adjust the level of detail of the analysis based on the importance of the files during the analysis. For example, the analysis unit can perform a detailed analysis on files with high importance. For example, the analysis unit can also perform a simplified analysis on files with low importance. For example, the analysis unit can adjust the level of detail of the analysis in stages according to importance. This allows for detailed analysis of important files by adjusting the level of detail of the analysis based on the importance of the files. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input file importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0059] The analysis unit can apply different analysis algorithms depending on the file category during analysis. For example, the analysis unit applies a specific analysis algorithm to document files. The analysis unit can also apply a different analysis algorithm to image files. The analysis unit can also apply yet another different analysis algorithm to video files. By applying different analysis algorithms depending on the file category, more appropriate analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input file category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0060] The analysis unit can determine the priority of analysis results based on the file creation date during analysis. For example, the analysis unit may prioritize the analysis of recently created files. The analysis unit may also postpone the analysis of older files. The analysis unit may also adjust the priority of analysis results in stages according to the creation date. This allows for more appropriate analysis results by determining the priority of analysis results based on the file creation date. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input file creation date data into a generating AI and have the generating AI determine the priority of analysis results.

[0061] The analysis unit can adjust the order of analysis results based on the relationships between files during analysis. For example, the analysis unit may prioritize the analysis of highly relevant files. For example, the analysis unit may postpone the analysis of less relevant files. For example, the analysis unit may also adjust the order of analysis results in stages according to their relevance. By adjusting the order of analysis results based on the relationships between files, more appropriate analysis results can be obtained. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input file relationship data into a generating AI and have the generating AI perform the adjustment of the order of analysis results.

[0062] The suggestion unit can adjust the level of detail in the suggested storage location based on the importance of the file during the suggestion process. For example, the suggestion unit will suggest a detailed storage location for high-importance files. For example, the suggestion unit may also suggest a concise storage location for low-importance files. The suggestion unit can also adjust the level of detail in the suggested storage location in stages according to importance. This allows for detailed suggestions for important files by adjusting the level of detail in the suggested storage location based on the importance of the file. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input file importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the suggested storage location.

[0063] The suggestion unit can apply different storage location suggestion algorithms depending on the file category during the suggestion process. For example, the suggestion unit can apply a specific storage location suggestion algorithm to document files. The suggestion unit can also apply a different storage location suggestion algorithm to image files. The suggestion unit can also apply yet another different storage location suggestion algorithm to video files. By applying different storage location suggestion algorithms depending on the file category, more appropriate suggestions become possible. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input file category data into a generating AI and have the generating AI execute the application of a storage location suggestion algorithm.

[0064] The suggestion unit can determine the priority of storage location suggestions based on the file creation date when making a suggestion. For example, the suggestion unit may suggest recently created files as storage locations first. The suggestion unit may also suggest older files later. The suggestion unit may also adjust the priority of storage location suggestions in stages according to the creation date. This allows for more appropriate suggestions by determining the priority of storage location suggestions based on the file creation date. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input file creation date data into a generating AI and have the generating AI determine the priority of storage location suggestions.

[0065] The suggestion unit can adjust the order of suggested storage locations based on the relevance of the files during the suggestion process. For example, the suggestion unit may prioritize suggesting highly relevant files as storage locations. For example, the suggestion unit may also postpone suggesting less relevant files as storage locations. For example, the suggestion unit may also adjust the order of suggested storage locations in stages according to relevance. This allows for more appropriate suggestions by adjusting the order of suggested storage locations based on the relevance of the files. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input file relevance data into a generating AI and have the generating AI perform the adjustment of the order of suggested storage locations.

[0066] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. The learning unit can also preferentially apply a specific learning algorithm based on past learning data. For example, the learning unit can analyze past learning data and select the most efficient learning algorithm. This allows the learning algorithm to be optimized by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the optimization of the learning algorithm.

[0067] The learning unit can weight the training data based on the creation date of the files during training. For example, the learning unit can weight the training data of recently created files. The learning unit can also reduce the weight of the training data of older files. The learning unit can also adjust the weighting of the training data in stages according to the creation date. This allows for more appropriate training by weighting the training data based on the creation date of the files. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input file creation date data into a generating AI and have the generating AI perform the weighting of the training data.

[0068] The learning unit can adjust the order of training data based on the relationships between files during training. For example, the learning unit can prioritize using highly relevant files as training data. The learning unit can also postpone the use of less relevant files as training data. The learning unit can also adjust the order of training data stepwise according to the relationships between files. This allows for more appropriate training by adjusting the order of training data based on the relationships between files. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input file relationship data into a generating AI and have the generating AI perform the adjustment of the order of training data.

[0069] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0070] The collection unit can analyze the user's past file collection history and select the optimal collection method. For example, it can analyze the types and frequency of files the user has collected in the past and suggest the most suitable collection method. It can also determine the priority of files to collect during specific time periods based on the user's past collection history. Based on the user's past collection history, it can also adjust the types and quantities of files to collect. In this way, the optimal collection method can be selected by analyzing the user's past collection history.

[0071] The collection unit can filter files based on the user's current projects and areas of interest during collection. For example, it can collect only files related to the project the user is currently working on. It can also prioritize the collection of highly relevant files based on the user's areas of interest. Furthermore, it can collect necessary files at the appropriate time according to the progress of the user's project. This allows for the collection of highly relevant files by filtering them based on the user's current projects and areas of interest.

[0072] The collection unit can prioritize the collection of highly relevant files by considering the user's geographical location during file collection. For example, if the user is in a specific location, it can prioritize the collection of files related to that location. It can also collect files related to locations close to the user's current location. If the user is on the move, it can collect files related to their destination. In this way, by considering the user's geographical location, it can prioritize the collection of highly relevant files.

[0073] The collection unit can analyze a user's social media activity and collect relevant files when collecting files. For example, it can collect relevant files based on information shared by the user on social media. It can also collect relevant files based on information shared by the user's social media followers and friends. It can also collect relevant files by analyzing the user's social media activity history. In this way, relevant files can be collected by analyzing the user's social media activity.

[0074] The proposal function can adjust the level of detail in its proposals based on the importance of the files. For example, it can suggest detailed renaming and folder structures for high-importance files, and simpler renaming and folder structures for low-importance files. It can also adjust the level of detail in proposals in stages according to importance. This allows for detailed proposals for important files by adjusting the level of detail based on the file's importance.

[0075] The following briefly describes the processing flow for example form 1.

[0076] Step 1: The collection unit collects files. The collection unit can collect, for example, document files, image files, audio files, etc. It can also collect files via a network. Step 2: The proposal team analyzes the collected files and suggests renaming and folder structures. For example, the proposal team analyzes the file contents and metadata and suggests appropriate naming conventions and folder hierarchies. Step 3: The organization unit automatically organizes the content proposed by the proposal unit after approval. For example, the organization unit moves files to the appropriate folders and creates folders. It can also automatically rename files. Step 4: The reception desk receives search queries from users. The reception desk can accept search queries entered in natural language or via voice input, for example. Step 5: The search unit searches for files based on the search query received by the reception unit. The search unit can perform full-text searches, metadata searches, and searches based on file names or content, for example. Step 6: The analysis unit analyzes the contents of the file. The analysis unit can perform, for example, text analysis, image analysis, or audio file content analysis. Step 7: The proposal unit proposes a storage location based on the analysis performed by the analysis unit. For example, the proposal unit can propose an appropriate folder structure and storage, and suggest the optimal storage location based on the file content. Step 8: The learning unit learns the user's usage patterns. The learning unit uses machine learning algorithms, for example, to learn the user's operation history and file usage, and optimizes suggestions for future use.

[0077] (Example of form 2) The file management system according to an embodiment of the present invention is an AI agent for eliminating the complexities of file management and improving business efficiency. This file management system has the function of collecting files, proposing renames and folder structures, and automatically organizing them after approval. Furthermore, it provides a function to search for files in an interactive format based on file names and content. When a file is uploaded, it proposes the optimal storage location and improves the accuracy of the proposal by learning the user's usage patterns. For example, the file management system collects files. For example, the file management system can collect document files, image files, audio files, etc. Next, the file management system analyzes the collected files and proposes renames and folder structures. For example, the file management system analyzes the content of the files and proposes appropriate naming conventions and folder hierarchies. When the user approves the proposal, the file management system automatically organizes the files. For example, the file management system moves files to appropriate folders and creates folders. Next, the file management system accepts search queries from the user. For example, the file management system accepts search queries entered in natural language. Next, the file management system searches for files based on the accepted search query. For example, the file management system performs full-text searches and metadata searches. Next, the file management system analyzes the content of the files. For example, a file management system performs text and image analysis. Next, based on the analyzed content, the system suggests storage locations. For instance, it suggests appropriate folder structures and storage solutions. Finally, the system learns user usage patterns. For example, it uses machine learning algorithms to learn user operation history and file usage patterns. This allows the system to optimize future suggestions. As a result, the system can eliminate the complexities of file management and improve operational efficiency.

[0078] The file management system according to this embodiment comprises a collection unit, a suggestion unit, an organization unit, a reception unit, a search unit, an analysis unit, another suggestion unit, and a learning unit. The collection unit collects files. The collection unit can collect, for example, document files, image files, audio files, etc. The collection unit can also collect files, for example, via a network. The suggestion unit analyzes the collected files and suggests renames and folder structures. The suggestion unit can, for example, analyze the contents of files and suggest appropriate naming conventions and folder hierarchies. The suggestion unit can also, for example, analyze the metadata of files and suggest appropriate folder structures. The organization unit automatically organizes the suggested contents after approval. The organization unit can, for example, move files to appropriate folders and create folders. The organization unit can also, for example, automatically rename files. The reception unit receives search queries from users. The reception unit can, for example, receive search queries entered in natural language. The reception unit can also, for example, receive search queries via voice input. The search unit searches for files based on the received search queries. The search unit performs full-text searches and metadata searches, for example. The search unit can also perform searches based on file names or content, for example. The analysis unit analyzes the content of files. The analysis unit performs text analysis and image analysis, for example. The analysis unit can also analyze the content of audio files, for example. The suggestion unit proposes storage locations based on the analyzed content. The suggestion unit proposes appropriate folder structures and storage, for example. The suggestion unit can also propose the optimal storage location based on the content of the files, for example. The learning unit learns the user's usage patterns. The learning unit learns the user's operation history and file usage status, for example, using machine learning algorithms. The learning unit can also analyze the user's usage patterns and optimize suggestions for future use, for example. As a result, the file management system according to this embodiment can eliminate the complexity of file management and improve work efficiency.

[0079] The collection unit collects files. For example, it can collect document files, image files, audio files, and more. Specifically, it collects files from various devices and storage devices via the network. For instance, it can retrieve files from cloud storage services or shared folders on a local network. The collection unit periodically scans the network to detect and collect new and updated files. It can also collect files and folders manually specified by the user. The collection unit retrieves data using appropriate methods depending on the file type and format and stores it in a central database. This allows the collection unit to centrally manage files across the entire system and quickly retrieve necessary information. Furthermore, the collection unit has the capability to monitor file collection status in real time and report the progress and errors of the collection process. This enables the collection unit to achieve efficient and reliable file collection, improving the overall system performance.

[0080] The suggestion department analyzes collected files and proposes renames and folder structures. For example, it analyzes file contents and proposes appropriate naming conventions and folder hierarchies. Specifically, the suggestion department analyzes file metadata and contents and generates optimal naming conventions based on the file type and content. For example, for document files, it can propose file names based on creation date, author name, keywords, etc. For image files, it can propose folder structures based on shooting date, location, and subject information. The suggestion department can make more accurate suggestions by using AI to analyze file contents in detail and learning user usage patterns and past organization methods. Furthermore, the suggestion department can collect user feedback and continuously improve the accuracy of its suggestions. This allows the suggestion department to make flexible suggestions that meet user needs and improve the efficiency of file management.

[0081] The organization unit automatically organizes files after the proposed content is approved. For example, the organization unit moves files to appropriate folders and creates folders. Specifically, the organization unit automatically organizes files based on the naming conventions and folder structure provided by the proposal unit. For example, document files are moved to the proposed folder and renamed to appropriate file names. Image files are organized into folders based on the date and location of shooting, and audio files are categorized according to their content. Because the organization unit automatically moves and renames files according to the proposal approved by the user, it significantly reduces the user's effort. Furthermore, the organization unit also has the function to monitor the file organization status in real time and report the progress of the organization process and any errors. This enables the organization unit to achieve efficient and reliable file organization, improving the overall system performance.

[0082] The reception desk receives search queries from users. For example, it accepts search queries entered in natural language. Specifically, the reception desk analyzes the search query entered by the user and converts it into appropriate search conditions. For example, if a user enters "search for meeting materials from 2023," the reception desk extracts the keywords "2023" and "meeting materials" and sends them to the search department. The reception desk can also accept search queries via voice input. In the case of voice input, the reception desk uses speech recognition technology to convert the speech into text and processes it as a search query. This allows the reception desk to enable users to enter search queries in natural language, improving the convenience of searching. Furthermore, the reception desk can record the user's search history and optimize future searches by referring to past search queries. This allows the reception desk to improve the user's search experience and support efficient information retrieval.

[0083] The search unit searches for files based on the received search query. For example, the search unit performs full-text searches and metadata searches. Specifically, it searches for file names, content, and metadata based on the search query entered by the user. In the case of a full-text search, the search unit searches the entire content of the file and highlights the parts that match the query. In the case of a metadata search, the search unit searches metadata such as the file creation date, author name, and keywords. The search unit displays search results quickly, allowing users to find the information they need immediately. Furthermore, the search unit also has a function to rank search results, displaying highly relevant files at the top. This allows the search unit to enable users to search for information efficiently, improving work efficiency.

[0084] The analysis unit analyzes the contents of files. For example, the analysis unit performs text analysis and image analysis. Specifically, the analysis unit analyzes the contents of files in detail and extracts important information. In the case of text analysis, the analysis unit uses natural language processing technology to analyze the content of documents and extract keywords and summaries. In the case of image analysis, the analysis unit uses image recognition technology to analyze the content of images and identify subjects and scenes. In the case of audio file analysis, the analysis unit uses speech recognition technology to convert audio into text and analyze the content. This allows the analysis unit to grasp the contents of files in detail and enable users to quickly obtain the information they need. Furthermore, the analysis unit also has functions to support file classification and organization based on the analysis results. This allows the analysis unit to improve the efficiency of file management and support the user's work.

[0085] The suggestion unit proposes storage locations based on the analyzed content. For example, the suggestion unit proposes appropriate folder structures and storage solutions. Specifically, based on the analysis results provided by the analysis unit, the suggestion unit proposes the optimal storage location according to the file content and metadata. For example, it suggests storing project-related document files in folders organized by project. It also suggests organizing image files into folders based on the date and location of shooting. The suggestion unit can learn user usage patterns and past organization methods to make more accurate suggestions. Furthermore, the suggestion unit can collect user feedback and continuously improve the accuracy of its suggestions. This allows the suggestion unit to make flexible suggestions that meet user needs and improve the efficiency of file management.

[0086] The learning unit learns user usage patterns. For example, it uses machine learning algorithms to learn user operation history and file usage. Specifically, the learning unit learns what kinds of files users frequently use and what organization methods they prefer, and optimizes suggestions for future use. For example, a user who frequently uses files related to a specific project will be given suggestions to prioritize displaying files related to that project. It also learns how users have organized files in the past and suggests similar organization methods. The learning unit continuously collects user operation history and feedback and updates its learning model to improve the accuracy of its suggestions. This allows the learning unit to provide flexible suggestions tailored to user needs and improve the efficiency of file management. Furthermore, the learning unit also has the function to monitor the overall system performance and optimize resources as needed. This allows the learning unit to improve the overall system efficiency and support users' work.

[0087] The collection unit can estimate the user's emotions and adjust the timing of file collection based on the estimated emotions. For example, if the user is stressed, the collection unit can delay the collection timing and collect files when the user is relaxed. For example, if the user is concentrating, the collection unit can advance the collection timing to avoid disrupting the workflow. For example, if the user is tired, the collection unit can adjust the collection timing and collect files while the user is resting. This allows for more appropriate file collection timing by adjusting the timing of file collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0088] The collection unit can analyze the user's past file collection history and select the optimal collection method. For example, the collection unit can analyze the types and frequency of files the user has collected in the past and propose the optimal collection method. The collection unit can also determine the priority of files to collect during specific time periods based on the user's past collection history. The collection unit can also adjust the types and quantities of files to collect based on the user's past collection history. This allows the optimal collection method to be selected by analyzing the user's past collection history. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's past collection history data into a generating AI and have the generating AI select the optimal collection method.

[0089] The collection unit can filter files based on the user's current projects and areas of interest during collection. For example, the collection unit can collect only files related to the project the user is currently working on. The collection unit can also prioritize the collection of highly relevant files based on the user's areas of interest. For example, the collection unit can collect necessary files at the appropriate time according to the progress of the user's project. This allows for the collection of highly relevant files by filtering them based on the user's current projects and areas of interest. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's project data into a generating AI and have the generating AI perform the filtering.

[0090] The data collection unit can estimate the user's emotions and determine the priority of files to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit may postpone collecting less important files. For example, if the user is relaxed, the data collection unit may prioritize collecting more important files. For example, if the user is in a hurry, the data collection unit may quickly collect the most necessary files. This allows for the priority collection of important files by determining the priority of files to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0091] The collection unit can prioritize the collection of highly relevant files by considering the user's geographical location information when collecting files. For example, if the user is in a specific location, the collection unit will prioritize the collection of files related to that location. The collection unit can also collect files related to locations close to the user's current location. For example, if the user is on the move, the collection unit can collect files related to their destination. This allows for the priority collection of highly relevant files by considering the user's geographical location information. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's geographical location data into a generating AI and have the generating AI select highly relevant files.

[0092] The collection unit can analyze the user's social media activity and collect relevant files when collecting files. For example, the collection unit can collect relevant files based on information shared by the user on social media. The collection unit can also collect relevant files based on information shared by the user's social media followers and friends. The collection unit can also analyze the user's social media activity history and collect relevant files. In this way, relevant files can be collected by analyzing the user's social media activity. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's social media data into a generating AI and have the generating AI select relevant files.

[0093] The suggestion unit can estimate the user's emotions and adjust its renaming and folder structure suggestions based on the estimated emotions. For example, if the user is stressed, the suggestion unit might suggest simple renaming and folder structures. If the user is relaxed, for example, the suggestion unit might suggest more detailed renaming and folder structures. If the user is in a hurry, for example, the suggestion unit might suggest quick renaming and folder structures. By adjusting the renaming and folder structure suggestions according to the user's emotions, more appropriate suggestions become possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0094] The suggestion unit can adjust the level of detail of its suggestions based on the importance of the files. For example, it might suggest detailed renaming and folder structures for high-importance files. For example, it might suggest simpler renaming and folder structures for low-importance files. The suggestion unit can also adjust the level of detail of its suggestions in stages according to importance. This allows for detailed suggestions for important files by adjusting the level of detail based on the importance of the files. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input file importance data into a generating AI and have the generating AI adjust the level of detail of its suggestions.

[0095] The suggestion unit can apply different suggestion algorithms depending on the file category during the suggestion process. For example, the suggestion unit can apply an algorithm that suggests a specific rename or folder structure to document files. For example, the suggestion unit can also apply an algorithm that suggests a different rename or folder structure to image files. For example, the suggestion unit can apply an algorithm that suggests yet another different rename or folder structure to video files. By applying different suggestion algorithms depending on the file category, more appropriate suggestions become possible. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input file category data into a generating AI and have the generating AI execute the application of the suggestion algorithm.

[0096] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is stressed, the suggestion unit will make short and concise suggestions. If the user is relaxed, the suggestion unit may also make detailed suggestions. If the user is in a hurry, the suggestion unit may also make quick suggestions. By adjusting the length of suggestions according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0097] The proposal unit can determine the priority of proposals based on the file creation date when making a proposal. For example, the proposal unit may prioritize recently created files. The proposal unit may also postpone proposing older files. The proposal unit may also adjust the priority of proposals in stages according to the creation date. This allows for more appropriate proposals by determining the priority of proposals based on the file creation date. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input file creation date data into a generating AI and have the generating AI perform the determination of proposal priority.

[0098] The suggestion unit can adjust the order of suggestions based on the relevance of the files during the suggestion process. For example, the suggestion unit may prioritize suggesting files with high relevance. For example, the suggestion unit may also postpone suggesting files with low relevance. For example, the suggestion unit may also adjust the order of suggestions in stages according to relevance. This allows for more appropriate suggestions by adjusting the order of suggestions based on the relevance of the files. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input file relevance data into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0099] The sorting unit can estimate the user's emotions and adjust the automatic sorting method based on the estimated emotions. For example, if the user is stressed, the sorting unit can apply a simple sorting method. For example, if the user is relaxed, the sorting unit can apply a more detailed sorting method. For example, if the user is in a hurry, the sorting unit can perform sorting quickly. This allows for more appropriate sorting by adjusting the automatic sorting method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sorting unit may be performed using AI or not using AI. For example, the sorting unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0100] The organization unit can analyze the user's past organization history to select the optimal organization method during organization. For example, the organization unit can propose the optimal organization method based on the organization methods the user has used in the past. For example, the organization unit can also preferentially apply a specific organization method based on the user's past organization history. For example, the organization unit can analyze the user's past organization history and select the most efficient organization method. In this way, the optimal organization method can be selected by analyzing the user's past organization history. Some or all of the above processes in the organization unit may be performed using AI, for example, or without AI. For example, the organization unit can input the user's past organization history data into a generating AI and have the generating AI select the optimal organization method.

[0101] The organization unit can customize its organization methods based on the user's current project during the organization process. For example, the organization unit may prioritize organizing files related to the project the user is currently working on. The organization unit may also adjust its organization methods according to the progress of the user's project. The organization unit may also apply the most suitable organization method depending on the type of project the user is working on. This allows for more appropriate organization by customizing the organization methods based on the user's current project. Some or all of the above processes in the organization unit may be performed using AI, for example, or without AI. For example, the organization unit can input the user's project data into a generating AI and have the generating AI perform the customization of the organization methods.

[0102] The sorting unit can estimate the user's emotions and determine sorting priorities based on the estimated emotions. For example, if the user is stressed, the sorting unit may postpone sorting less important files. For example, if the user is relaxed, the sorting unit may prioritize sorting more important files. For example, if the user is in a hurry, the sorting unit may quickly sort the most necessary files. This allows for prioritizing important files by determining sorting priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sorting unit may be performed using AI or not. For example, the sorting unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0103] The sorting unit can select the optimal sorting method by considering the user's geographical location information during sorting. For example, if the user is in a specific location, the sorting unit will prioritize sorting files related to that location. The sorting unit can also sort files related to locations close to the user's current location. For example, if the user is on the move, the sorting unit can sort files related to their destination. This allows the sorting unit to select the optimal sorting method by considering the user's geographical location information. Some or all of the above processing in the sorting unit may be performed using AI, for example, or without AI. For example, the sorting unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal sorting method.

[0104] The organization unit can analyze the user's social media activity and propose organization methods during the organization process. For example, the organization unit can organize relevant files based on information shared by the user on social media. The organization unit can also organize relevant files based on information shared by the user's social media followers and friends. The organization unit can also analyze the user's social media activity history and organize relevant files. This allows for the organization of relevant files by analyzing the user's social media activity. Some or all of the above processing in the organization unit may be performed using AI, for example, or without AI. For example, the organization unit can input the user's social media data into a generating AI and have the generating AI propose organization methods.

[0105] The reception unit can estimate the user's emotions and adjust how search queries are received based on the estimated emotions. For example, if the user is stressed, the reception unit can provide a simple interface and minimize the input steps. If the user is relaxed, the reception unit can also provide detailed input options and suggest customizable input methods. If the user is in a hurry, the reception unit can prioritize voice input to allow for quick entry of search queries. This allows for more appropriate searches by adjusting how search queries are received according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0106] The reception desk can select the optimal reception method by referring to the user's past search history at the time of reception. For example, the reception desk can automatically display search queries that the user has frequently entered in the past as suggestions. For example, the reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest search queries to be used during a specific time period based on the user's past search history. This allows the reception desk to select the optimal reception method by referring to the user's past search history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past search history data into a generating AI and have the generating AI select the optimal reception method.

[0107] The reception unit can filter search queries based on the user's current project upon receipt. For example, the reception unit will only accept search queries related to the project the user is currently working on. The reception unit can also filter search queries according to the progress of the user's project. The reception unit can also suggest the most relevant search queries according to the type of project the user is working on. This allows for more relevant searches by filtering search queries based on the user's current project. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's project data into a generating AI and have the generating AI perform the filtering of search queries.

[0108] The reception unit can estimate the user's emotions and determine the priority of search queries to be received based on the estimated emotions. For example, if the user is stressed, the reception unit may postpone less important search queries. For example, if the user is relaxed, the reception unit may prioritize receiving more important search queries. For example, if the user is in a hurry, the reception unit may quickly receive the most necessary search queries. This allows for the priority of important search queries by determining the priority of search queries according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not using AI. For example, the reception unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0109] The reception unit can prioritize receiving highly relevant search queries by considering the user's geographical location information at the time of reception. For example, if the user is in a specific location, the reception unit will prioritize receiving search queries related to that location. The reception unit can also, for example, receive search queries related to locations close to the user's current location. For example, if the user is on the move, the reception unit can also receive search queries related to their destination. In this way, by considering the user's geographical location information, highly relevant search queries can be prioritized. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information data into a generating AI and have the generating AI select highly relevant search queries.

[0110] The reception unit can analyze the user's social media activity and accept relevant search queries upon receiving a request. For example, the reception unit can accept relevant search queries based on information shared by the user on social media. The reception unit can also accept relevant search queries based on information shared by the user's social media followers and friends. The reception unit can also analyze the user's social media activity history and accept relevant search queries. This allows the reception unit to accept relevant search queries by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media data into a generating AI and have the generating AI select relevant search queries.

[0111] The search unit can estimate the user's emotions and adjust how search results are displayed based on the estimated emotions. For example, if the user is stressed, the search unit can provide a simple and highly visible display. For example, if the user is relaxed, the search unit can also provide a display that includes detailed information. For example, if the user is in a hurry, the search unit can also provide a display that gets straight to the point. By adjusting how search results are displayed according to the user's emotions, more appropriate search results can be obtained. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using AI, for example, or not using AI. For example, the search unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0112] The search unit can adjust the level of detail in search results based on the importance of the files during a search. For example, the search unit can display detailed search results for files of high importance. For example, the search unit can also display concise search results for files of low importance. The search unit can also adjust the level of detail in search results in stages according to importance. This allows for detailed search results for important files by adjusting the level of detail in search results based on the importance of the files. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input file importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in search results.

[0113] The search unit can apply different search algorithms depending on the file category during a search. For example, the search unit can apply a specific search algorithm to document files. The search unit can also apply a different search algorithm to image files. The search unit can also apply yet another different search algorithm to video files. By applying different search algorithms depending on the file category, more appropriate search results can be obtained. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input file category data into a generating AI and have the generating AI execute the application of the search algorithm.

[0114] The search unit can estimate the user's emotions and adjust the length of search results based on the estimated emotions. For example, if the user is stressed, the search unit can display short and concise search results. For example, if the user is relaxed, the search unit can also display detailed search results. For example, if the user is in a hurry, the search unit can display search results quickly. By adjusting the length of search results according to the user's emotions, more appropriate search results can be obtained. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using AI, for example, or not using AI. For example, the search unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0115] The search unit can determine the priority of search results based on the file creation date during a search. For example, the search unit can prioritize recently created files in the search results. The search unit can also, for example, delay the display of older files in the search results. The search unit can also, for example, adjust the priority of search results in stages according to the creation date. This allows for more appropriate search results by determining the priority of search results based on the file creation date. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input file creation date data into a generating AI and have the generating AI perform the determination of the search result priority.

[0116] The search unit can adjust the order of search results based on the relevance of the files during a search. For example, the search unit can prioritize displaying highly relevant files in the search results. For example, the search unit can also display less relevant files later in the search results. For example, the search unit can also adjust the order of search results in stages according to relevance. By adjusting the order of search results based on the relevance of the files, more appropriate search results can be obtained. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input file relevance data into a generating AI and have the generating AI perform the adjustment of the order of search results.

[0117] The analysis unit can estimate the user's emotions and adjust the method of analyzing the file contents based on the estimated emotions. For example, if the user is stressed, the analysis unit can apply a simple analysis method. For example, if the user is relaxed, the analysis unit can also apply a detailed analysis method. For example, if the user is in a hurry, the analysis unit can perform a rapid analysis. This allows for more appropriate analysis by adjusting the method of analyzing the file contents according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0118] The analysis unit can adjust the level of detail of the analysis based on the importance of the files during the analysis. For example, the analysis unit can perform a detailed analysis on files with high importance. For example, the analysis unit can also perform a simplified analysis on files with low importance. For example, the analysis unit can adjust the level of detail of the analysis in stages according to importance. This allows for detailed analysis of important files by adjusting the level of detail of the analysis based on the importance of the files. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input file importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0119] The analysis unit can apply different analysis algorithms depending on the file category during analysis. For example, the analysis unit applies a specific analysis algorithm to document files. The analysis unit can also apply a different analysis algorithm to image files. The analysis unit can also apply yet another different analysis algorithm to video files. By applying different analysis algorithms depending on the file category, more appropriate analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input file category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0120] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, more appropriate analysis results can be obtained. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0121] The analysis unit can determine the priority of analysis results based on the file creation date during analysis. For example, the analysis unit may prioritize the analysis of recently created files. The analysis unit may also postpone the analysis of older files. The analysis unit may also adjust the priority of analysis results in stages according to the creation date. This allows for more appropriate analysis results by determining the priority of analysis results based on the file creation date. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input file creation date data into a generating AI and have the generating AI determine the priority of analysis results.

[0122] The analysis unit can adjust the order of analysis results based on the relationships between files during analysis. For example, the analysis unit may prioritize the analysis of highly relevant files. For example, the analysis unit may postpone the analysis of less relevant files. For example, the analysis unit may also adjust the order of analysis results in stages according to their relevance. By adjusting the order of analysis results based on the relationships between files, more appropriate analysis results can be obtained. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input file relationship data into a generating AI and have the generating AI perform the adjustment of the order of analysis results.

[0123] The suggestion unit can estimate the user's emotions and adjust the method of suggesting storage locations based on the estimated emotions. For example, if the user is stressed, the suggestion unit may suggest a simple storage location. If the user is relaxed, the suggestion unit may also suggest a detailed storage location. If the user is in a hurry, the suggestion unit may also suggest a quick storage location. By adjusting the method of suggesting storage locations according to the user's emotions, more appropriate suggestions become possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0124] The suggestion unit can adjust the level of detail in the suggested storage location based on the importance of the file during the suggestion process. For example, the suggestion unit will suggest a detailed storage location for high-importance files. For example, the suggestion unit may also suggest a concise storage location for low-importance files. The suggestion unit can also adjust the level of detail in the suggested storage location in stages according to importance. This allows for detailed suggestions for important files by adjusting the level of detail in the suggested storage location based on the importance of the file. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input file importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the suggested storage location.

[0125] The suggestion unit can apply different storage location suggestion algorithms depending on the file category during the suggestion process. For example, the suggestion unit can apply a specific storage location suggestion algorithm to document files. The suggestion unit can also apply a different storage location suggestion algorithm to image files. The suggestion unit can also apply yet another different storage location suggestion algorithm to video files. By applying different storage location suggestion algorithms depending on the file category, more appropriate suggestions become possible. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input file category data into a generating AI and have the generating AI execute the application of a storage location suggestion algorithm.

[0126] The suggestion unit can estimate the user's emotions and adjust the length of the suggested storage locations based on the estimated emotions. For example, if the user is stressed, the suggestion unit will suggest short and concise storage locations. If the user is relaxed, the suggestion unit may also suggest detailed storage locations. If the user is in a hurry, the suggestion unit may also suggest storage locations quickly. By adjusting the length of the suggested storage locations according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0127] The suggestion unit can determine the priority of storage location suggestions based on the file creation date when making a suggestion. For example, the suggestion unit may suggest recently created files as storage locations first. The suggestion unit may also suggest older files later. The suggestion unit may also adjust the priority of storage location suggestions in stages according to the creation date. This allows for more appropriate suggestions by determining the priority of storage location suggestions based on the file creation date. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input file creation date data into a generating AI and have the generating AI determine the priority of storage location suggestions.

[0128] The suggestion unit can adjust the order of suggested storage locations based on the relevance of the files during the suggestion process. For example, the suggestion unit may prioritize suggesting highly relevant files as storage locations. For example, the suggestion unit may also postpone suggesting less relevant files as storage locations. For example, the suggestion unit may also adjust the order of suggested storage locations in stages according to relevance. This allows for more appropriate suggestions by adjusting the order of suggested storage locations based on the relevance of the files. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input file relevance data into a generating AI and have the generating AI perform the adjustment of the order of suggested storage locations.

[0129] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is stressed, the learning unit can select simple training data. If the user is relaxed, for example, the learning unit can also select detailed training data. If the user is in a hurry, for example, the learning unit can also quickly select training data. This allows for more appropriate learning by selecting training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0130] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. The learning unit can also preferentially apply a specific learning algorithm based on past learning data. For example, the learning unit can analyze past learning data and select the most efficient learning algorithm. This allows the learning algorithm to be optimized by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the optimization of the learning algorithm.

[0131] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, if the user is stressed, the learning unit can reduce the learning frequency. For example, if the user is relaxed, the learning unit can increase the learning frequency. For example, if the user is in a hurry, the learning unit can perform learning quickly. This allows for more appropriate learning by adjusting the learning frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0132] The learning unit can weight the training data based on the creation date of the files during training. For example, the learning unit can weight the training data of recently created files. The learning unit can also reduce the weight of the training data of older files. The learning unit can also adjust the weighting of the training data in stages according to the creation date. This allows for more appropriate training by weighting the training data based on the creation date of the files. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input file creation date data into a generating AI and have the generating AI perform the weighting of the training data.

[0133] The learning unit can adjust the order of training data based on the relationships between files during training. For example, the learning unit can prioritize using highly relevant files as training data. The learning unit can also postpone the use of less relevant files as training data. The learning unit can also adjust the order of training data stepwise according to the relationships between files. This allows for more appropriate training by adjusting the order of training data based on the relationships between files. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input file relationship data into a generating AI and have the generating AI perform the adjustment of the order of training data.

[0134] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0135] The collection unit can estimate the user's emotions and adjust the timing of file collection based on those emotions. For example, if the user is stressed, the collection timing can be delayed to allow collection when the user is relaxed. If the user is concentrating, the collection timing can be advanced to avoid disrupting the workflow. If the user is tired, the collection timing can be adjusted to allow collection while the user is resting. By adjusting the timing of file collection according to the user's emotions, files can be collected at a more appropriate time.

[0136] The suggestion function can estimate the user's emotions and adjust the renaming and folder structure suggestions based on those emotions. For example, if the user is stressed, it can suggest simple renaming and folder structures. If the user is relaxed, it can suggest detailed renaming and folder structures. If the user is in a hurry, it can suggest quick renaming and folder structures. By adjusting the renaming and folder structure suggestions according to the user's emotions, more appropriate suggestions can be made.

[0137] The organization unit can estimate the user's emotions and adjust the automatic organization method based on those emotions. For example, if the user is stressed, a simple organization method can be applied. If the user is relaxed, a more detailed organization method can be applied. If the user is in a hurry, the organization can be performed quickly. By adjusting the automatic organization method according to the user's emotions, more appropriate organization becomes possible.

[0138] The reception system can estimate the user's emotions and adjust how search queries are processed based on that estimation. For example, if the user is stressed, a simple interface can be provided, minimizing the input steps. If the user is relaxed, detailed input options can be offered, and customizable input methods can be suggested. If the user is in a hurry, voice input can be prioritized to allow for quick entry of search queries. By adjusting how search queries are processed according to the user's emotions, more relevant searches can be achieved.

[0139] The search engine can estimate the user's emotions and adjust how search results are displayed based on that estimation. For example, if a user is stressed, it can provide a simple and highly visible display. If a user is relaxed, it can provide a display that includes detailed information. If a user is in a hurry, it can provide a display that gets straight to the point. By adjusting how search results are displayed according to the user's emotions, more relevant search results can be obtained.

[0140] The collection unit can analyze the user's past file collection history and select the optimal collection method. For example, it can analyze the types and frequency of files the user has collected in the past and suggest the most suitable collection method. It can also determine the priority of files to collect during specific time periods based on the user's past collection history. Based on the user's past collection history, it can also adjust the types and quantities of files to collect. In this way, the optimal collection method can be selected by analyzing the user's past collection history.

[0141] The collection unit can filter files based on the user's current projects and areas of interest during collection. For example, it can collect only files related to the project the user is currently working on. It can also prioritize the collection of highly relevant files based on the user's areas of interest. Furthermore, it can collect necessary files at the appropriate time according to the progress of the user's project. This allows for the collection of highly relevant files by filtering them based on the user's current projects and areas of interest.

[0142] The collection unit can prioritize the collection of highly relevant files by considering the user's geographical location during file collection. For example, if the user is in a specific location, it can prioritize the collection of files related to that location. It can also collect files related to locations close to the user's current location. If the user is on the move, it can collect files related to their destination. In this way, by considering the user's geographical location, it can prioritize the collection of highly relevant files.

[0143] The collection unit can analyze a user's social media activity and collect relevant files when collecting files. For example, it can collect relevant files based on information shared by the user on social media. It can also collect relevant files based on information shared by the user's social media followers and friends. It can also collect relevant files by analyzing the user's social media activity history. In this way, relevant files can be collected by analyzing the user's social media activity.

[0144] The proposal function can adjust the level of detail in its proposals based on the importance of the files. For example, it can suggest detailed renaming and folder structures for high-importance files, and simpler renaming and folder structures for low-importance files. It can also adjust the level of detail in proposals in stages according to importance. This allows for detailed proposals for important files by adjusting the level of detail based on the file's importance.

[0145] The following briefly describes the processing flow for example form 2.

[0146] Step 1: The collection unit collects files. The collection unit can collect, for example, document files, image files, audio files, etc. It can also collect files via a network. Step 2: The proposal team analyzes the collected files and suggests renaming and folder structures. For example, the proposal team analyzes the file contents and metadata and suggests appropriate naming conventions and folder hierarchies. Step 3: The organization unit automatically organizes the content proposed by the proposal unit after approval. For example, the organization unit moves files to the appropriate folders and creates folders. It can also automatically rename files. Step 4: The reception desk receives search queries from users. The reception desk can accept search queries entered in natural language or via voice input, for example. Step 5: The search unit searches for files based on the search query received by the reception unit. The search unit can perform full-text searches, metadata searches, and searches based on file names or content, for example. Step 6: The analysis unit analyzes the contents of the file. The analysis unit can perform, for example, text analysis, image analysis, or audio file content analysis. Step 7: The proposal unit proposes a storage location based on the analysis performed by the analysis unit. For example, the proposal unit can propose an appropriate folder structure and storage, and suggest the optimal storage location based on the file content. Step 8: The learning unit learns the user's usage patterns. The learning unit uses machine learning algorithms, for example, to learn the user's operation history and file usage, and optimizes suggestions for future use.

[0147] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0148] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0149] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0150] Each of the multiple elements described above, including the collection unit, proposal unit, organization unit, reception unit, search unit, analysis unit, proposal unit, and learning unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects document files, image files, audio files, etc. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected files and proposes renaming and folder structures. The organization unit is implemented by the control unit 46A of the smart device 14 and automatically organizes the proposed content after approval. The reception unit is implemented by the control unit 46A of the smart device 14 and receives search queries from the user. The search unit is implemented by the specific processing unit 290 of the data processing unit 12 and searches for files based on the received search queries. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the contents of the files. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and proposes a storage location based on the analyzed content. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and learns the user's usage patterns. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0151] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0152] As shown in Figure 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.

[0153] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0154] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0155] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0157] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0158] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0159] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0160] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0161] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0162] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0163] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0164] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0165] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0166] Each of the multiple elements described above, including the collection unit, proposal unit, organization unit, reception unit, search unit, analysis unit, proposal unit, and learning unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects document files, image files, audio files, etc. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected files and proposes renames and folder structures. The organization unit is implemented by the control unit 46A of the smart glasses 214 and automatically organizes the proposed content after approval. The reception unit is implemented by the control unit 46A of the smart glasses 214 and receives search queries from the user. The search unit is implemented by the specific processing unit 290 of the data processing unit 12 and searches for files based on the received search queries. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the contents of the files. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and proposes a storage location based on the analyzed content. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and learns the user's usage patterns. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0167] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0168] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0169] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0170] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0171] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0172] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0173] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0174] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0175] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0176] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0177] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0178] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0179] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0180] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0181] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0182] Each of the multiple elements described above, including the collection unit, proposal unit, organization unit, reception unit, search unit, analysis unit, proposal unit, and learning unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects document files, image files, audio files, etc. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected files and proposes renames and folder structures. The organization unit is implemented by the control unit 46A of the headset terminal 314 and automatically organizes the proposed content after approval. The reception unit is implemented by the control unit 46A of the headset terminal 314 and receives search queries from the user. The search unit is implemented by the specific processing unit 290 of the data processing unit 12 and searches for files based on the received search queries. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the contents of the files. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and proposes a storage location based on the analyzed content. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and learns the user's usage patterns. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0183] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0184] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0185] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0186] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0187] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0188] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0189] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0190] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0191] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0192] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0193] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0194] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0195] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0196] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0197] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0198] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0199] Each of the multiple elements described above, including the collection unit, proposal unit, organization unit, reception unit, search unit, analysis unit, proposal unit, and learning unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects document files, image files, audio files, etc. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected files and proposes renaming and folder structures. The organization unit is implemented by, for example, the control unit 46A of the robot 414 and automatically organizes the proposed content after approval. The reception unit is implemented by, for example, the control unit 46A of the robot 414 and receives search queries from users. The search unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and searches for files based on the received search queries. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the contents of files. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and proposes a storage location based on the analyzed content. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and learns the user's usage patterns. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0200] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0201] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0202] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0203] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0204] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0205] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0206] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0207] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0208] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0209] 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.

[0210] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0211] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0212] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0213] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0214] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0215] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0216] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0217] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0218] (Note 1) A collection unit that collects files, The aforementioned collection unit analyzes the files collected and proposes renames and folder structures, and A sorting unit automatically organizes the content proposed by the aforementioned proposal unit after approval, A reception area that receives search queries from users, A search unit that searches for files based on the search query received by the reception unit, An analysis unit that analyzes the contents of the file, A proposal unit proposes a storage location based on the analysis performed by the aforementioned analysis unit, It includes a learning unit that learns the user's usage patterns. A system characterized by the following features. (Note 2) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of file collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Analyze the user's past file collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is When collecting files, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is It estimates the user's emotions and determines the priority of files to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is When collecting files, the system prioritizes collecting files that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting files, the system analyzes the user's social media activity and collects relevant files. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned proposal section is, It estimates the user's emotions and adjusts the renaming and folder structure suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the file. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the file category. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned proposal section is, When submitting a proposal, prioritize it based on when the file was created. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, When submitting proposals, adjust the order of proposals based on the relevance of the files. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned editing unit, It estimates the user's emotions and adjusts the automated sorting method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned editing unit, During organization, the system analyzes the user's past organization history to select the optimal organization method. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned editing unit, During organization, customize the organization method based on the user's current project. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned editing unit, It estimates the user's emotions and determines the priority of sorting based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned editing unit, During organization, the optimal organization method is selected considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned editing unit, During organization, the optimal organization method is selected considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned editing unit, During the organization process, we analyze users' social media activity and propose methods for organization. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned reception unit is It estimates the user's sentiment and adjusts how search queries are accepted based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned reception unit is During registration, the system will refer to the user's past search history to select the most suitable registration method. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned reception unit is At the time of registration, the search query is filtered based on the user's current project. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned reception unit is It estimates the user's sentiment and determines the priority of search queries to accept based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned reception unit is When a user submits a request, the system prioritizes accepting highly relevant search queries, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned reception unit is Upon registration, the system analyzes the user's social media activity and accepts relevant search queries. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned search unit, It estimates the user's sentiment and adjusts how search results are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned search unit, When searching, adjust the level of detail in search results based on the importance of the files. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned search unit, When searching, different search algorithms are applied depending on the file category. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned search unit, It estimates the user's sentiment and adjusts the length of search results based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned search unit, When searching, search results are prioritized based on when the files were created. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned search unit, When searching, the order of search results is adjusted based on the relevance of the files. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned analysis unit, It estimates the user's emotions and adjusts the file content analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the files. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the file category. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned analysis unit, During analysis, the priority of analysis results is determined based on when the files were created. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned analysis unit, During analysis, the order of analysis results is adjusted based on the relevance of the files. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned proposal section is, It estimates the user's emotions and adjusts the method of suggesting storage locations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned proposal section is, At the time of proposal, adjust the detail level of the storage location proposal based on the importance of the file. The system according to appended note 1, characterized in that. (Appended note 41) The proposal unit At the time of proposal, apply different storage location proposal algorithms according to the category of the file. The system according to appended note 1, characterized in that. (Appended note 42) The proposal unit Estimate the user's emotion and adjust the length of the storage location proposal based on the estimated user's emotion. The system according to appended note 1, characterized in that. (Appended note 43) The proposal unit At the time of proposal, determine the priority of the storage location proposal based on the creation time of the file. The system according to appended note 1, characterized in that. (Appended note 44) The proposal unit At the time of proposal, adjust the order of the storage location proposal based on the relevance of the file. The system according to appended note 1, characterized in that. (Appended note 45) The learning unit Estimate the user's emotion and select learning data based on the estimated user's emotion. The system according to appended note 1, characterized in that. (Appended note 46) The learning unit At the time of learning, optimize the learning algorithm by referring to past learning data. The system according to appended note 1, characterized in that. (Appended note 47) The learning unit Estimate the user's emotion and adjust the learning frequency based on the estimated user's emotion. The system according to appended note 1, characterized in that. (Appended note 48) The learning unit At the time of learning, perform weighting of learning data based on the creation time of the file. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0219] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects files, The aforementioned collection unit analyzes the files collected and proposes renames and folder structures, and A sorting unit automatically organizes the content proposed by the aforementioned proposal unit after approval, A reception area that receives search queries from users, A search unit that searches for files based on the search query received by the reception unit, An analysis unit that analyzes the contents of the file, A proposal unit proposes a storage location based on the analysis performed by the aforementioned analysis unit, It includes a learning unit that learns the user's usage patterns. A system characterized by the following features.

2. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of file collection based on the estimated user emotions. The system according to feature 1.

3. The aforementioned collection unit is Analyze the user's past file collection history and select the optimal collection method. The system according to feature 1.

4. The aforementioned collection unit is When collecting files, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

5. The aforementioned collection unit is It estimates the user's emotions and determines the priority of files to collect based on the estimated user emotions. The system according to feature 1.

6. The aforementioned collection unit is When collecting files, the system prioritizes collecting files that are highly relevant, taking into account the user's geographical location. The system according to feature 1.

7. The aforementioned collection unit is When collecting files, the system analyzes the user's social media activity and collects relevant files. The system according to feature 1.

8. The aforementioned proposal section is, It estimates the user's emotions and adjusts the renaming and folder structure suggestions based on those estimated emotions. The system according to feature 1.

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