System

Abstract

An object of a system according to an embodiment is to automatically organize local and cloud files in consideration of file contents and a folder management structure.SOLUTION: A system according to an embodiment includes an organizing condition designating unit, a file analyzing unit, a folder management optimizing unit, a file organizing unit, and a regular organizing unit. The organizing condition specifying unit receives an organizing condition specified by a user. The file analysis unit analyzes the content of the file based on the organizing condition received by the organizing condition specifying unit. The folder management optimizing unit optimizes the folder management structure based on the contents of the file analyzed by the file analyzing unit. The file organizing unit organizes local and cloud files based on the folder management structure optimized by the folder management optimizing unit. The regular organization unit regularly organizes the files organized by the file organization unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, organizing files was often done manually, making efficient management difficult.

[0005] The system according to the embodiment aims to automatically organize local and cloud files, taking into account the file content and folder management structure. [Means for solving the problem]

[0006] The system according to the embodiment includes an organization condition specification unit, a file analysis unit, a folder management optimization unit, a file organization unit, and a periodic organization unit. The organization condition specification unit accepts organization conditions specified by a user. The file analysis unit analyzes the contents of files based on the organization conditions accepted by the organization condition specification unit. The folder management optimization unit optimizes the folder management structure based on the contents of files analyzed by the file analysis unit. The file organization unit organizes local and cloud files based on the folder management structure optimized by the folder management optimization unit. The periodic organization unit periodically organizes the files organized by the file organization unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically organize local and cloud files taking into account the file content and folder management structure. [Brief explanation of the drawings]

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

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

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

[0028] (Example 1) The file organization system according to an embodiment of the present invention is a system in which a generation AI automatically sorts and organizes files periodically both locally and in the cloud, taking into account the file contents and folder management structure based on the organization conditions specified by the user. This significantly reduces the user's workload and enables efficient file management.

[0029] A file organization system according to an embodiment includes an organization condition specification unit, a file analysis unit, a folder management optimization unit, a file organization unit, and a periodic organization unit. The organization condition specification unit accepts organization conditions specified by a user. For example, the user can set conditions such as file type, creation date, update date, file size, and keywords. The file analysis unit analyzes the contents of files based on the organization conditions accepted by the organization condition specification unit. For example, in the case of document files, the text content is analyzed and classified based on keywords or themes. In the case of image files, the content is analyzed using image recognition technology and sorted into appropriate categories. The folder management optimization unit optimizes the folder management structure based on the file content analyzed by the file analysis unit. For example, related files are placed in one folder and subfolders are created to organize the hierarchical structure. The file organization unit organizes local and cloud files based on the folder management structure optimized by the folder management optimization unit. For example, files stored on a local hard disk and files stored in cloud storage are simultaneously analyzed and sorted into appropriate folders based on the specified organization conditions. The periodic organization unit periodically organizes the files organized by the file organization unit. For example, the file contents are analyzed at a frequency specified by the user, such as daily, weekly, or monthly, and the folder management structure is optimized. As a result, the file organization system according to the embodiment can significantly reduce the user's workload and achieve efficient file management.

[0030] In the organizing condition specification unit, the generation AI learns past organizing history and proposes optimal conditions for the organizing conditions specified by the user. In the organizing condition specification unit, for example, the generation AI analyzes past organizing history based on the organizing conditions specified by the user and proposes optimal organizing conditions. For example, it prioritizes proposing conditions that have been frequently used in the past. The generation AI also learns the user's past organizing history and analyzes the user's organizing patterns. This proposes the optimal organizing method for the conditions specified by the user. Furthermore, for the organizing conditions specified by the user, the generation AI automatically generates optimal conditions based on past organizing history. For example, it proposes conditions by referring to past success stories. In this way, the generation AI learns past organizing history and proposes optimal conditions, thereby making the user's organizing work more efficient.

[0031] The organizing condition specification unit allows the user to specify organizing conditions by voice input, and the generation AI analyzes the voice to set the organizing conditions. The organizing condition specification unit, for example, builds a system in which the user specifies organizing conditions by voice input, and the generation AI analyzes the voice to set the conditions. For example, it converts the voice into text using voice recognition technology. The generation AI also analyzes the organizing conditions specified by voice input and proposes an appropriate organizing method. For example, it sets file types and keywords based on voice commands. Furthermore, the user specifies organizing conditions by voice input, and the generation AI analyzes the voice in real time to set the conditions. For example, it uses voice recognition technology to instantly reflect the conditions. This allows the user to specify organizing conditions by voice input, and the generation AI analyzes the voice to set the conditions, thereby improving user convenience.

[0032] The organizing condition specification unit allows the user to specify organizing conditions from a mobile device such as a smartphone or tablet. The organizing condition specification unit builds a system that allows the user to specify organizing conditions from, for example, a smartphone or tablet. For example, a mobile app is developed to make it easy to set conditions. Furthermore, when organizing conditions are specified from a mobile device, a generation AI analyzes the conditions in real time and suggests the optimal organizing method. For example, the conditions are entered through a mobile app. Furthermore, by allowing the user to specify organizing conditions from a smartphone or tablet, the user can easily set the conditions from anywhere. For example, the voice input function of the mobile device is utilized. This allows the user to specify organizing conditions from a mobile device such as a smartphone or tablet, thereby improving user convenience.

[0033] The organizing condition specification unit adds a function for sharing organizing conditions between different users and referring to the conditions of other users. The organizing condition specification unit, for example, builds a system that allows different users to share organizing conditions and adds a function for referring to the conditions of other users. For example, it creates a shared folder and saves the conditions. It also allows users to set optimal conditions by referring to the organizing conditions of other users. For example, it adds a condition ranking function and displays popular conditions. Furthermore, it develops a system that shares organizing conditions between different users and suggests optimal organizing methods based on the conditions of other users. For example, it builds a condition sharing platform. This allows different users to share organizing conditions and refer to the conditions of other users to suggest optimal organizing methods.

[0034] The file analysis unit classifies files by taking into account their metadata when analyzing their contents. For example, when the generation AI analyzes the contents of a file, the file analysis unit constructs a system that takes into account file metadata (creator, creation date, etc.) to classify the file. For example, it evaluates the importance of the file based on the metadata. It also analyzes the file's metadata and classifies the file into an appropriate category. For example, it groups files based on the creator and creation date. Furthermore, by having the generation AI classify files by taking into account their metadata, it achieves more accurate file organization. For example, it evaluates the relevance of files based on their metadata. As a result, by taking file metadata into account in classification, it is possible to achieve more accurate file organization.

[0035] The file analysis unit supports multiple languages ​​when analyzing file contents, and appropriately classifies files in different languages. For example, the file analysis unit builds a system in which the generation AI supports multiple languages ​​and appropriately classifies files in different languages. For example, it identifies the language of the file using language identification technology. It also analyzes files in different languages, and the generation AI classifies them into the appropriate category. For example, it applies different classification rules to each language. Furthermore, by having the generation AI support multiple languages, it achieves international file organization. For example, it appropriately classifies files in English, Japanese, Chinese, etc. This makes it possible to support multiple languages ​​and appropriately classify files in different languages, thereby achieving international file organization.

[0036] The file analysis unit visually displays the analysis results of the file contents to the user, allowing the user to check the analysis results. The file analysis unit, for example, builds a system that visually displays the analysis results of the file contents, allowing the user to check the analysis results. For example, the analysis results are displayed using graphs or charts. In addition, the generation AI analyzes the file contents and visually displays the results to the user. For example, the analysis results are displayed in dashboard format. Furthermore, by visually displaying the analysis results of the file contents, the user can easily check the analysis results. For example, the analysis results are displayed in an interactive graph. In this way, by visually displaying the analysis results of the file contents, the user can easily check the analysis results.

[0037] When analyzing file contents, the file analysis unit improves classification accuracy by referring to related external databases. For example, the file analysis unit builds a system in which, when the generation AI analyzes file contents, it refers to related external databases (e.g., Wikipedia) to improve classification accuracy. For example, it classifies files based on information from external databases. Furthermore, the generation AI analyzes file contents by referring to external databases to improve classification accuracy. For example, it obtains related keywords and themes from external databases. Furthermore, the generation AI analyzes file contents by referring to external databases to improve classification accuracy. For example, it evaluates the relevance of files based on information from external databases. In this way, more accurate file organization can be achieved by improving classification accuracy by referring to related external databases.

[0038] When optimizing the folder management structure, the folder management optimization unit takes file access frequency into consideration and places frequently used files on a priority basis. In the folder management optimization unit, for example, the generation AI analyzes file access frequency and proposes a folder management structure that places frequently used files on a priority basis. For example, frequently accessed files are placed at the top level. In addition, a system is constructed in which the generation AI proposes an optimal folder management structure based on file access frequency. For example, frequently used files are placed on a priority basis. Furthermore, the generation AI optimizes the folder management structure by taking file access frequency into consideration. For example, frequently used files are placed in a location that can be accessed easily. In this way, user convenience can be improved by optimizing the folder management structure by taking file access frequency into consideration.

[0039] The folder management optimization unit visually displays the optimization results of the folder management structure to the user, allowing the user to check them. The folder management optimization unit, for example, builds a system that visually displays the optimization results of the folder management structure, allowing the user to check them. For example, it displays the folder structure in a graph or chart. In addition, the generation AI optimizes the folder management structure and visually displays the results to the user. For example, it displays the optimized folder structure in a dashboard format. Furthermore, by visually displaying the optimization results of the folder management structure, the user can easily check them. For example, it displays the optimization results in an interactive graph. In this way, by visually displaying the optimization results of the folder management structure, the user can easily check them.

[0040] The folder management optimization unit adds a function to perform optimization by referring to the folder structures of other users. The folder management optimization unit, for example, builds a system in which the generation AI performs optimization by referring to the folder structures of other users. For example, it proposes a folder structure based on success stories of other users. Also, by referring to the folder structures of other users, the generation AI proposes the optimal folder management structure. For example, it performs optimization based on the structure of a shared folder. Furthermore, it adds a function in which the generation AI performs optimization by referring to the folder structures of other users. For example, it learns the folder structures of other users and proposes the optimal structure. As a result, by performing optimization by referring to the folder structures of other users, it is possible to propose a more effective folder management structure.

[0041] The file organization unit also takes into account the backup status of files when organizing local and cloud files. For example, the file organization unit constructs a system in which the generation AI organizes local and cloud files while taking into account the backup status of files. For example, it prioritizes organizing files that have not been backed up. The generation AI also analyzes the backup status of files and proposes an appropriate organization method. For example, it moves files that need to be backed up to the cloud. Furthermore, the generation AI organizes files while taking into account the backup status of files, thereby ensuring the safety of data. For example, it automatically backs up files that have not been backed up. In this way, the safety of data can be ensured by organizing files while taking into account the backup status of files.

[0042] The file organization unit organizes local and cloud files taking into account the file's security level. For example, the file organization unit builds a system in which the generation AI organizes local and cloud files taking into account the file's security level. For example, highly confidential files are encrypted and stored. The generation AI also analyzes the file's security level and suggests an appropriate organization method. For example, files are sorted into different folders depending on their security level. Furthermore, the generation AI organizes files taking into account their security level, ensuring data safety. For example, files with a high security level are moved to the cloud. In this way, data safety can be ensured by organizing files taking into account their security level.

[0043] The file organization unit visually displays the results of local and cloud file organization to the user, allowing the user to check them. The file organization unit, for example, builds a system that visually displays the results of local and cloud file organization, allowing the user to check them. For example, the organization results are displayed in graphs or charts. In addition, the generation AI organizes local and cloud files and visually displays the results to the user. For example, the organization results are displayed in dashboard format. Furthermore, by visually displaying the results of local and cloud file organization, the user can easily check them. For example, the organization results are displayed in an interactive graph. This visually displays the results of local and cloud file organization, allowing the user to easily check them.

[0044] The file organization unit adds a function to organize local and cloud files by referring to the organization methods of other users. The file organization unit, for example, builds a system in which the generation AI organizes local and cloud files by referring to the organization methods of other users. For example, it proposes an organization method based on the success stories of other users. Furthermore, by referring to the organization methods of other users, the generation AI proposes the optimal file organization method. For example, it organizes based on the structure of shared folders. Furthermore, a function is added in which the generation AI organizes local and cloud files by referring to the organization methods of other users. For example, it learns the organization methods of other users and proposes the optimal method. As a result, by organizing local and cloud files by referring to the organization methods of other users, it is possible to propose a more effective organization method.

[0045] The regular organization unit considers the user's schedule when the generation AI periodically organizes files and suggests the optimal organization timing. For example, the regular organization unit constructs a system in which the generation AI analyzes the user's schedule and suggests the optimal organization timing. For example, the organization timing is set based on the user's calendar information. The generation AI also considers the user's schedule and suggests the optimal timing for regular file organization. For example, organization is performed during times when the user is not busy. Furthermore, the generation AI analyzes the user's schedule and automatically generates the optimal organization timing. For example, the organization timing is adjusted to match the user's schedule. This makes it possible to improve user convenience by considering the user's schedule and suggesting the optimal organization timing.

[0046] The periodic organization unit takes file update history into consideration when the generation AI periodically organizes files. For example, the periodic organization unit constructs a system in which the generation AI analyzes file update history and takes this into consideration when periodically organizing files. For example, it prioritizes organizing recently updated files. Furthermore, based on the file update history, the generation AI proposes the optimal method for periodic file organization. For example, it places frequently updated files in a specific folder. Furthermore, the generation AI performs periodic file organization by taking file update history into consideration. For example, it evaluates the importance of files based on the update history and adjusts the organization method. In this way, by organizing files taking file update history into consideration, it is possible to organize files based on the latest information.

[0047] The periodic organization unit visually displays the results of periodic file organization to the user, allowing the user to check them. The periodic organization unit, for example, builds a system that visually displays the results of periodic file organization, allowing the user to check them. For example, it displays the organization results in graphs or charts. In addition, the generation AI periodically organizes files and visually displays the results to the user. For example, it displays the organization results in dashboard format. Furthermore, by visually displaying the results of periodic file organization, the user can easily check them. For example, it displays the organization results in an interactive graph. In this way, by visually displaying the results of periodic file organization, the user can easily check them.

[0048] The periodic organization unit adds a function to perform periodic file organization by referring to the organization frequency of other users. The periodic organization unit, for example, builds a system in which the generation AI performs periodic file organization by referring to the organization frequency of other users. For example, the organization frequency is suggested based on the success stories of other users. Furthermore, by referring to the organization frequency of other users, the generation AI suggests the optimal file organization frequency. For example, organization is performed based on the organization frequency of shared folders. Furthermore, a function is added in which the generation AI performs periodic file organization by referring to the organization frequency of other users. For example, it learns the organization frequency of other users and suggests the optimal frequency. As a result, by performing periodic file organization by referring to the organization frequency of other users, a more effective organization method can be suggested.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The organization condition specification unit accepts organization conditions specified by the user. For example, the user can set conditions such as file type, creation date, update date, file size, and keywords. The file analysis unit analyzes the contents of files based on the organization conditions accepted by the organization condition specification unit. For example, in the case of document files, the text content is analyzed and classified based on keywords or themes. In the case of image files, the content is analyzed using image recognition technology and sorted into appropriate categories. The folder management optimization unit optimizes the folder management structure based on the file content analyzed by the file analysis unit. For example, related files are grouped together in one folder, and subfolders are created to organize the hierarchical structure. The file organization unit organizes local and cloud files based on the folder management structure optimized by the folder management optimization unit. For example, files stored on a local hard disk and files stored in cloud storage are simultaneously analyzed and sorted into appropriate folders based on the specified organization conditions. The periodic organization unit periodically organizes the files organized by the file organization unit. For example, the file content is analyzed at a frequency specified by the user, such as daily, weekly, or monthly, and the folder management structure is optimized. As a result, the file organization system according to the embodiment significantly reduces the user's effort and enables efficient file management.

[0051] In the organizing condition specification unit, the generation AI learns past organizing history and proposes the optimal conditions for the organizing conditions specified by the user. For example, based on the organizing conditions specified by the user, the generation AI analyzes past organizing history and proposes the optimal organizing conditions. For example, it may prioritize proposals for conditions that have been frequently used in the past. The generation AI also learns the user's past organizing history and analyzes the user's organizing patterns. This proposes the optimal organizing method for the conditions specified by the user. Furthermore, for the organizing conditions specified by the user, the generation AI automatically generates the optimal conditions based on past organizing history. For example, it proposes conditions by referring to past success stories. In this way, the generation AI learns past organizing history and proposes the optimal conditions, thereby making the user's organizing work more efficient.

[0052] The organizing condition specification unit allows the user to specify organizing conditions by voice input, and the generation AI analyzes the voice to set the organizing conditions. For example, a system is constructed in which the user specifies organizing conditions by voice input, and the generation AI analyzes the voice to set the conditions. For example, speech recognition technology is used to convert speech to text. The generation AI also analyzes the organizing conditions specified by voice input and proposes an appropriate organizing method. For example, file types and keywords are set based on voice commands. Furthermore, the user specifies organizing conditions by voice input, and the generation AI analyzes the voice in real time to set the conditions. For example, speech recognition technology is used to instantly reflect the conditions. This allows the user to specify organizing conditions by voice input, and the generation AI analyzes the voice to set the conditions, thereby improving user convenience.

[0053] The organizing condition specification unit allows users to specify organizing conditions from mobile devices such as smartphones or tablets. For example, a system is constructed that allows users to specify organizing conditions from smartphones or tablets. For example, a mobile app is developed to make it easy to set conditions. Furthermore, when organizing conditions are specified from a mobile device, the generation AI analyzes the conditions in real time and suggests the optimal organizing method. For example, the conditions are entered through a mobile app. Furthermore, by allowing users to specify organizing conditions from smartphones or tablets, users can easily set conditions from anywhere. For example, the voice input function of the mobile device is utilized. This allows users to specify organizing conditions from mobile devices such as smartphones and tablets, thereby improving user convenience.

[0054] The organizing condition specification unit adds a function to share organizing conditions between different users and refer to other users' conditions. For example, a system is constructed that allows different users to share organizing conditions and a function to refer to other users' conditions is added. For example, a shared folder is created and conditions are saved. Also, by referring to other users' organizing conditions, users can set optimal conditions. For example, a condition ranking function is added and popular conditions are displayed. Furthermore, a system is developed that allows different users to share organizing conditions and propose optimal organizing methods based on other users' conditions. For example, a condition sharing platform is constructed. This allows different users to share organizing conditions and refer to other users' conditions to propose optimal organizing methods.

[0055] When analyzing the contents of a file, the file analysis unit takes the file's metadata into consideration for classification. For example, a system can be constructed in which the generation AI takes the file's metadata (creator, creation date, etc.) into consideration for classification when analyzing the contents of a file. For example, the importance of a file can be evaluated based on the metadata. The generation AI can also analyze the file's metadata and classify it into an appropriate category. For example, files can be grouped based on the creator or creation date. Furthermore, by having the generation AI take the file's metadata into consideration for classification, more accurate file organization can be achieved. For example, the relevance of files can be evaluated based on the metadata. In this way, more accurate file organization can be achieved by taking the file's metadata into consideration for classification.

[0056] When analyzing file contents, the file analysis unit supports multiple languages ​​and appropriately classifies files in different languages. For example, a system can be built in which the generation AI supports multiple languages ​​and appropriately classifies files in different languages. For example, language identification technology can be used to identify the language of the file. Files in different languages ​​can also be analyzed, and the generation AI can classify them into the appropriate category. For example, different classification rules can be applied for each language. Furthermore, by having the generation AI support multiple languages, international file organization can be achieved. For example, files in English, Japanese, Chinese, etc. can be appropriately classified. This makes it possible to support multiple languages ​​and appropriately classify files in different languages, thereby achieving international file organization.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The organizing condition specification unit accepts organizing conditions specified by the user. For example, the user can set conditions such as file type, creation date, update date, file size, and keywords. Step 2: The file analysis unit analyzes the contents of the file based on the organization conditions accepted by the organization condition specification unit. For example, in the case of document files, the text content is analyzed and classified based on keywords and themes. In the case of image files, the content is analyzed using image recognition technology and sorted into appropriate categories. Step 3: The folder management optimization unit optimizes the folder management structure based on the file contents analyzed by the file analysis unit. For example, related files are grouped together in one folder, and subfolders are created to organize the hierarchical structure. Step 4: The file organizer organizes local and cloud files based on the folder management structure optimized by the folder management optimization unit. For example, it simultaneously analyzes files stored on the local hard disk and files stored in cloud storage and sorts them into appropriate folders based on the specified organization conditions. Step 5: The periodic organizer periodically organizes the files organized by the file organizer. For example, the periodic organizer analyzes the contents of the files at a frequency specified by the user, such as daily, weekly, or monthly, and optimizes the folder management structure.

[0059] (Example 2) The file organization system according to an embodiment of the present invention is a system in which a generation AI automatically sorts and organizes files periodically both locally and in the cloud, taking into account the file contents and folder management structure based on the organization conditions specified by the user. This significantly reduces the user's workload and enables efficient file management.

[0060] A file organization system according to an embodiment includes an organization condition specification unit, a file analysis unit, a folder management optimization unit, a file organization unit, and a periodic organization unit. The organization condition specification unit accepts organization conditions specified by a user. For example, the user can set conditions such as file type, creation date, update date, file size, and keywords. The file analysis unit analyzes the contents of files based on the organization conditions accepted by the organization condition specification unit. For example, in the case of document files, the text content is analyzed and classified based on keywords or themes. In the case of image files, the content is analyzed using image recognition technology and sorted into appropriate categories. The folder management optimization unit optimizes the folder management structure based on the file content analyzed by the file analysis unit. For example, related files are placed in one folder and subfolders are created to organize the hierarchical structure. The file organization unit organizes local and cloud files based on the folder management structure optimized by the folder management optimization unit. For example, files stored on a local hard disk and files stored in cloud storage are simultaneously analyzed and sorted into appropriate folders based on the specified organization conditions. The periodic organization unit periodically organizes the files organized by the file organization unit. For example, the file contents are analyzed at a frequency specified by the user, such as daily, weekly, or monthly, and the folder management structure is optimized. As a result, the file organization system according to the embodiment can significantly reduce the user's workload and achieve efficient file management.

[0061] In the organizing condition specification unit, the generation AI learns past organizing history and proposes optimal conditions for the organizing conditions specified by the user. In the organizing condition specification unit, for example, the generation AI analyzes past organizing history based on the organizing conditions specified by the user and proposes optimal organizing conditions. For example, it prioritizes proposing conditions that have been frequently used in the past. The generation AI also learns the user's past organizing history and analyzes the user's organizing patterns. This proposes the optimal organizing method for the conditions specified by the user. Furthermore, for the organizing conditions specified by the user, the generation AI automatically generates optimal conditions based on past organizing history. For example, it proposes conditions by referring to past success stories. In this way, the generation AI learns past organizing history and proposes optimal conditions, thereby making the user's organizing work more efficient.

[0062] The organizing condition specification unit allows the user to specify organizing conditions by voice input, and the generation AI analyzes the voice to set the organizing conditions. The organizing condition specification unit, for example, builds a system in which the user specifies organizing conditions by voice input, and the generation AI analyzes the voice to set the conditions. For example, it converts the voice into text using voice recognition technology. The generation AI also analyzes the organizing conditions specified by voice input and proposes an appropriate organizing method. For example, it sets file types and keywords based on voice commands. Furthermore, the user specifies organizing conditions by voice input, and the generation AI analyzes the voice in real time to set the conditions. For example, it uses voice recognition technology to instantly reflect the conditions. This allows the user to specify organizing conditions by voice input, and the generation AI analyzes the voice to set the conditions, thereby improving user convenience.

[0063] The organizing condition specification unit uses the emotion estimation function to suggest organizing conditions according to the user's emotional state. For example, the organizing condition specification unit uses the emotion estimation function to analyze the user's emotional state and suggest a simple organizing method if stress is high. For example, it adjusts the organizing conditions based on the emotion score. In addition, a system is constructed in which the generative AI suggests optimal organizing conditions according to the user's emotional state. For example, it suggests a detailed organizing method if the user is relaxed. Furthermore, it uses the emotion estimation function to monitor the user's emotional state in real time and suggests organizing conditions based on the results. For example, it suggests a simple organizing method if the emotion score is low. In this way, the emotion estimation function can be used to suggest organizing conditions according to the user's emotional state, thereby reducing the user's stress.

[0064] The organizing condition specification unit allows the user to specify organizing conditions from a mobile device such as a smartphone or tablet. The organizing condition specification unit builds a system that allows the user to specify organizing conditions from, for example, a smartphone or tablet. For example, a mobile app is developed to make it easy to set conditions. Furthermore, when organizing conditions are specified from a mobile device, a generation AI analyzes the conditions in real time and suggests the optimal organizing method. For example, the conditions are entered through a mobile app. Furthermore, by allowing the user to specify organizing conditions from a smartphone or tablet, the user can easily set the conditions from anywhere. For example, the voice input function of the mobile device is utilized. This allows the user to specify organizing conditions from a mobile device such as a smartphone or tablet, thereby improving user convenience.

[0065] The organizing condition specification unit adds a function for sharing organizing conditions between different users and referring to the conditions of other users. The organizing condition specification unit, for example, builds a system that allows different users to share organizing conditions and adds a function for referring to the conditions of other users. For example, it creates a shared folder and saves the conditions. It also allows users to set optimal conditions by referring to the organizing conditions of other users. For example, it adds a condition ranking function and displays popular conditions. Furthermore, it develops a system that shares organizing conditions between different users and suggests optimal organizing methods based on the conditions of other users. For example, it builds a condition sharing platform. This allows different users to share organizing conditions and refer to the conditions of other users to suggest optimal organizing methods.

[0066] The organizing condition specification unit uses the emotion estimation function to monitor the emotions of the user when specifying organizing conditions in real time and make suggestions that will elicit positive emotions. The organizing condition specification unit, for example, uses the emotion estimation function to build a system that monitors the emotions of the user when specifying organizing conditions in real time and makes suggestions that will elicit positive emotions. For example, the specification unit adjusts suggestions based on the emotion score. The specification unit also analyzes the user's emotional state in real time and suggests organizing conditions that will elicit positive emotions. For example, if the emotion score is low, it displays an encouraging message. Furthermore, the specification unit uses the emotion estimation function to provide an interface that elicits positive emotions when the user specifies organizing conditions. For example, it makes suggestions based on the emotion score. In this way, the specification unit can improve user satisfaction by using the emotion estimation function to monitor the emotions of the user when specifying organizing conditions in real time and make suggestions that will elicit positive emotions.

[0067] The file analysis unit classifies files by taking into account their metadata when analyzing their contents. For example, when the generation AI analyzes the contents of a file, the file analysis unit constructs a system that takes into account file metadata (creator, creation date, etc.) to classify the file. For example, it evaluates the importance of the file based on the metadata. It also analyzes the file's metadata and classifies the file into an appropriate category. For example, it groups files based on the creator and creation date. Furthermore, by having the generation AI classify files by taking into account their metadata, it achieves more accurate file organization. For example, it evaluates the relevance of files based on their metadata. As a result, by taking file metadata into account in classification, it is possible to achieve more accurate file organization.

[0068] The file analysis unit supports multiple languages ​​when analyzing file contents, and appropriately classifies files in different languages. For example, the file analysis unit builds a system in which the generation AI supports multiple languages ​​and appropriately classifies files in different languages. For example, it identifies the language of the file using language identification technology. It also analyzes files in different languages, and the generation AI classifies them into the appropriate category. For example, it applies different classification rules to each language. Furthermore, by having the generation AI support multiple languages, it achieves international file organization. For example, it appropriately classifies files in English, Japanese, Chinese, etc. This makes it possible to support multiple languages ​​and appropriately classify files in different languages, thereby achieving international file organization.

[0069] The file analysis unit uses the emotion estimation function to analyze the emotional tone of the file contents and classify the files based on the emotion. The file analysis unit, for example, uses the emotion estimation function to analyze the emotional tone of the file contents and build a system that classifies files based on emotion. For example, files with positive emotions are prioritized for classification. The emotional tone of the file contents is also analyzed, and the generation AI classifies the files into appropriate categories based on the emotion. For example, files are grouped based on the emotion score. Furthermore, the emotion estimation function is used to analyze the emotional tone of the file contents in real time and classify the files based on emotion. For example, files with negative emotions are sorted into a separate folder. In this way, by using the emotion estimation function to analyze the emotional tone of the file contents and classify files based on emotion, file organization that takes the user's emotions into consideration can be realized.

[0070] The file analysis unit visually displays the analysis results of the file contents to the user, allowing the user to check the analysis results. The file analysis unit, for example, builds a system that visually displays the analysis results of the file contents, allowing the user to check the analysis results. For example, the analysis results are displayed using graphs or charts. In addition, the generation AI analyzes the file contents and visually displays the results to the user. For example, the analysis results are displayed in dashboard format. Furthermore, by visually displaying the analysis results of the file contents, the user can easily check the analysis results. For example, the analysis results are displayed in an interactive graph. In this way, by visually displaying the analysis results of the file contents, the user can easily check the analysis results.

[0071] When analyzing file contents, the file analysis unit improves classification accuracy by referring to related external databases. For example, the file analysis unit builds a system in which, when the generation AI analyzes file contents, it refers to related external databases (e.g., Wikipedia) to improve classification accuracy. For example, it classifies files based on information from external databases. Furthermore, the generation AI analyzes file contents by referring to external databases to improve classification accuracy. For example, it obtains related keywords and themes from external databases. Furthermore, the generation AI analyzes file contents by referring to external databases to improve classification accuracy. For example, it evaluates the relevance of files based on information from external databases. In this way, more accurate file organization can be achieved by improving classification accuracy by referring to related external databases.

[0072] The file analysis unit uses the emotion estimation function to collect the user's emotional responses to the file content analysis results and improve the accuracy of the analysis algorithm. The file analysis unit, for example, uses the emotion estimation function to collect the user's emotional responses to the file content analysis results and builds a system that improves the accuracy of the analysis algorithm based on the data. For example, the file analysis unit adjusts the algorithm based on the emotion score. The file analysis unit also collects the user's emotional responses in real time and improves the accuracy of the file content analysis algorithm. For example, analysis results with a high number of positive emotional responses are preferentially adopted. Furthermore, the emotion estimation function is used to collect the user's emotional responses to the file content analysis results and improves the analysis algorithm based on the data. For example, the analysis results are re-evaluated based on the emotion score. In this way, by using the emotion estimation function to collect the user's emotional responses to the file content analysis results and improving the accuracy of the analysis algorithm, it is possible to realize file organization that is more suitable for the user.

[0073] When optimizing the folder management structure, the folder management optimization unit takes file access frequency into consideration and places frequently used files on a priority basis. In the folder management optimization unit, for example, the generation AI analyzes file access frequency and proposes a folder management structure that places frequently used files on a priority basis. For example, frequently accessed files are placed at the top level. In addition, a system is constructed in which the generation AI proposes an optimal folder management structure based on file access frequency. For example, frequently used files are placed on a priority basis. Furthermore, the generation AI optimizes the folder management structure by taking file access frequency into consideration. For example, frequently used files are placed in a location that can be accessed easily. In this way, user convenience can be improved by optimizing the folder management structure by taking file access frequency into consideration.

[0074] The folder management optimization unit uses the emotion estimation function to propose a folder management structure that corresponds to the user's emotional state. For example, the folder management optimization unit uses the emotion estimation function to analyze the user's emotional state and propose a simple folder management structure if stress is high. For example, the folder structure is adjusted based on the emotion score. In addition, a system is constructed in which the generative AI proposes an optimal folder management structure according to the user's emotional state. For example, a detailed folder structure is proposed if the user is relaxed. Furthermore, the emotion estimation function is used to monitor the user's emotional state in real time and propose a folder management structure based on the results. For example, a simple folder structure is proposed if the emotion score is low. In this way, the emotion estimation function can be used to propose a folder management structure that corresponds to the user's emotional state, thereby reducing the user's stress.

[0075] The folder management optimization unit visually displays the optimization results of the folder management structure to the user, allowing the user to check them. The folder management optimization unit, for example, builds a system that visually displays the optimization results of the folder management structure, allowing the user to check them. For example, it displays the folder structure in a graph or chart. In addition, the generation AI optimizes the folder management structure and visually displays the results to the user. For example, it displays the optimized folder structure in a dashboard format. Furthermore, by visually displaying the optimization results of the folder management structure, the user can easily check them. For example, it displays the optimization results in an interactive graph. In this way, by visually displaying the optimization results of the folder management structure, the user can easily check them.

[0076] The folder management optimization unit adds a function to perform optimization by referring to the folder structures of other users. The folder management optimization unit, for example, builds a system in which the generation AI performs optimization by referring to the folder structures of other users. For example, it proposes a folder structure based on success stories of other users. Also, by referring to the folder structures of other users, the generation AI proposes the optimal folder management structure. For example, it performs optimization based on the structure of a shared folder. Furthermore, it adds a function in which the generation AI performs optimization by referring to the folder structures of other users. For example, it learns the folder structures of other users and proposes the optimal structure. As a result, by performing optimization by referring to the folder structures of other users, it is possible to propose a more effective folder management structure.

[0077] The folder management optimization unit uses an emotion estimation function to collect users' emotional reactions to the optimization results of the folder management structure, thereby improving the accuracy of the optimization algorithm. The folder management optimization unit, for example, uses the emotion estimation function to collect users' emotional reactions to the optimization results of the folder management structure, and builds a system that improves the accuracy of the optimization algorithm based on the data. For example, the folder management optimization unit adjusts the algorithm based on the emotion score. The folder management optimization unit also collects users' emotional reactions in real time, thereby improving the accuracy of the optimization algorithm for the folder management structure. For example, it preferentially adopts optimization results with a high number of positive emotional reactions. Furthermore, the emotion estimation function is used to collect users' emotional reactions to the optimization results of the folder management structure, and the optimization algorithm is improved based on the data. For example, it reevaluates the optimization results based on the emotion score. In this way, by using the emotion estimation function to collect users' emotional reactions to the optimization results of the folder management structure and improving the accuracy of the optimization algorithm, it is possible to realize a folder management structure that is more suitable for the user.

[0078] The file organization unit also takes into account the backup status of files when organizing local and cloud files. For example, the file organization unit constructs a system in which the generation AI organizes local and cloud files while taking into account the backup status of files. For example, it prioritizes organizing files that have not been backed up. The generation AI also analyzes the backup status of files and proposes an appropriate organization method. For example, it moves files that need to be backed up to the cloud. Furthermore, the generation AI organizes files while taking into account the backup status of files, thereby ensuring the safety of data. For example, it automatically backs up files that have not been backed up. In this way, the safety of data can be ensured by organizing files while taking into account the backup status of files.

[0079] The file organization unit organizes local and cloud files taking into account the file's security level. For example, the file organization unit builds a system in which the generation AI organizes local and cloud files taking into account the file's security level. For example, highly confidential files are encrypted and stored. The generation AI also analyzes the file's security level and suggests an appropriate organization method. For example, files are sorted into different folders depending on their security level. Furthermore, the generation AI organizes files taking into account their security level, ensuring data safety. For example, files with a high security level are moved to the cloud. In this way, data safety can be ensured by organizing files taking into account their security level.

[0080] The file organization unit uses the emotion estimation function to suggest a file organization method that corresponds to the user's emotional state. For example, the file organization unit uses the emotion estimation function to analyze the user's emotional state and suggest a simple file organization method if the user is highly stressed. For example, the organization method is adjusted based on the emotion score. In addition, a system is constructed in which the generative AI suggests the optimal file organization method according to the user's emotional state. For example, a detailed organization method is suggested if the user is relaxed. Furthermore, the emotion estimation function is used to monitor the user's emotional state in real time and suggest a file organization method based on the results. For example, a simple organization method is suggested if the emotion score is low. In this way, the emotion estimation function can be used to suggest a file organization method that corresponds to the user's emotional state, thereby reducing the user's stress.

[0081] The file organization unit visually displays the results of local and cloud file organization to the user, allowing the user to check them. The file organization unit, for example, builds a system that visually displays the results of local and cloud file organization, allowing the user to check them. For example, the organization results are displayed in graphs or charts. In addition, the generation AI organizes local and cloud files and visually displays the results to the user. For example, the organization results are displayed in dashboard format. Furthermore, by visually displaying the results of local and cloud file organization, the user can easily check them. For example, the organization results are displayed in an interactive graph. This visually displays the results of local and cloud file organization, allowing the user to easily check them.

[0082] The file organization unit adds a function to organize local and cloud files by referring to the organization methods of other users. The file organization unit, for example, builds a system in which the generation AI organizes local and cloud files by referring to the organization methods of other users. For example, it proposes an organization method based on the success stories of other users. Furthermore, by referring to the organization methods of other users, the generation AI proposes the optimal file organization method. For example, it organizes based on the structure of shared folders. Furthermore, a function is added in which the generation AI organizes local and cloud files by referring to the organization methods of other users. For example, it learns the organization methods of other users and proposes the optimal method. As a result, by organizing local and cloud files by referring to the organization methods of other users, it is possible to propose a more effective organization method.

[0083] The file organization unit uses an emotion estimation function to collect users' emotional responses to local and cloud file organization results and improve the accuracy of the organization algorithm. The file organization unit, for example, uses the emotion estimation function to collect users' emotional responses to local and cloud file organization results and builds a system that improves the accuracy of the organization algorithm based on the data. For example, the file organization unit adjusts the algorithm based on the emotion score. The file organization unit also collects users' emotional responses in real time and improves the accuracy of the local and cloud file organization algorithm. For example, organization results with a high number of positive emotional responses are preferentially adopted. Furthermore, the emotion estimation function is used to collect users' emotional responses to local and cloud file organization results and improves the organization algorithm based on the data. For example, the organization results are re-evaluated based on the emotion score. In this way, by using the emotion estimation function to collect users' emotional responses to local and cloud file organization results and improving the accuracy of the organization algorithm, it is possible to realize file organization that is more suitable for the user.

[0084] The regular organization unit considers the user's schedule when the generation AI periodically organizes files and suggests the optimal organization timing. For example, the regular organization unit constructs a system in which the generation AI analyzes the user's schedule and suggests the optimal organization timing. For example, the organization timing is set based on the user's calendar information. The generation AI also considers the user's schedule and suggests the optimal timing for regular file organization. For example, organization is performed during times when the user is not busy. Furthermore, the generation AI analyzes the user's schedule and automatically generates the optimal organization timing. For example, the organization timing is adjusted to match the user's schedule. This makes it possible to improve user convenience by considering the user's schedule and suggesting the optimal organization timing.

[0085] The periodic organization unit takes file update history into consideration when the generation AI periodically organizes files. For example, the periodic organization unit constructs a system in which the generation AI analyzes file update history and takes this into consideration when periodically organizing files. For example, it prioritizes organizing recently updated files. Furthermore, based on the file update history, the generation AI proposes the optimal method for periodic file organization. For example, it places frequently updated files in a specific folder. Furthermore, the generation AI performs periodic file organization by taking file update history into consideration. For example, it evaluates the importance of files based on the update history and adjusts the organization method. In this way, by organizing files taking file update history into consideration, it is possible to organize files based on the latest information.

[0086] The regular sorting unit uses the emotion estimation function to suggest a sorting frequency according to the user's emotional state. The regular sorting unit, for example, uses the emotion estimation function to build a system that analyzes the user's emotional state and suggests reducing the sorting frequency if stress is high. For example, the sorting frequency is adjusted based on the emotion score. Also, a system is built in which the generation AI suggests the optimal sorting frequency according to the user's emotional state. For example, if the user is relaxed, the sorting frequency is increased. Furthermore, the emotion estimation function is used to monitor the user's emotional state in real time and suggests a sorting frequency based on the results. For example, if the emotion score is low, the sorting frequency is reduced. In this way, the emotion estimation function can be used to suggest a sorting frequency according to the user's emotional state, thereby reducing the user's stress.

[0087] The periodic organization unit visually displays the results of periodic file organization to the user, allowing the user to check them. The periodic organization unit, for example, builds a system that visually displays the results of periodic file organization, allowing the user to check them. For example, it displays the organization results in graphs or charts. In addition, the generation AI periodically organizes files and visually displays the results to the user. For example, it displays the organization results in dashboard format. Furthermore, by visually displaying the results of periodic file organization, the user can easily check them. For example, it displays the organization results in an interactive graph. In this way, by visually displaying the results of periodic file organization, the user can easily check them.

[0088] The periodic organization unit adds a function to perform periodic file organization by referring to the organization frequency of other users. The periodic organization unit, for example, builds a system in which the generation AI performs periodic file organization by referring to the organization frequency of other users. For example, the organization frequency is suggested based on the success stories of other users. Furthermore, by referring to the organization frequency of other users, the generation AI suggests the optimal file organization frequency. For example, organization is performed based on the organization frequency of shared folders. Furthermore, a function is added in which the generation AI performs periodic file organization by referring to the organization frequency of other users. For example, it learns the organization frequency of other users and suggests the optimal frequency. As a result, by performing periodic file organization by referring to the organization frequency of other users, a more effective organization method can be suggested.

[0089] The periodic organization unit uses the emotion estimation function to collect users' emotional responses to the results of periodic file organization, thereby improving the accuracy of the organization algorithm. The periodic organization unit, for example, uses the emotion estimation function to collect users' emotional responses to the results of periodic file organization, and builds a system that improves the accuracy of the organization algorithm based on that data. For example, the algorithm is adjusted based on the emotion score. The periodic organization unit also collects users' emotional responses in real time, thereby improving the accuracy of the periodic file organization algorithm. For example, organization results with a high number of positive emotional responses are preferentially adopted. Furthermore, the emotion estimation function is used to collect users' emotional responses to the results of periodic file organization, and the organization algorithm is improved based on that data. For example, the organization results are re-evaluated based on the emotion score. In this way, by using the emotion estimation function to collect users' emotional responses to the results of periodic file organization and improving the accuracy of the organization algorithm, it is possible to realize file organization that is more suitable for the user.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The organization condition specification unit accepts organization conditions specified by the user. For example, the user can set conditions such as file type, creation date, update date, file size, and keywords. The file analysis unit analyzes the contents of files based on the organization conditions accepted by the organization condition specification unit. For example, in the case of document files, the text content is analyzed and classified based on keywords or themes. In the case of image files, the content is analyzed using image recognition technology and sorted into appropriate categories. The folder management optimization unit optimizes the folder management structure based on the file content analyzed by the file analysis unit. For example, related files are grouped together in one folder, and subfolders are created to organize the hierarchical structure. The file organization unit organizes local and cloud files based on the folder management structure optimized by the folder management optimization unit. For example, files stored on a local hard disk and files stored in cloud storage are simultaneously analyzed and sorted into appropriate folders based on the specified organization conditions. The periodic organization unit periodically organizes the files organized by the file organization unit. For example, the file content is analyzed at a frequency specified by the user, such as daily, weekly, or monthly, and the folder management structure is optimized. As a result, the file organization system according to the embodiment significantly reduces the user's effort and enables efficient file management.

[0092] In the organizing condition specification unit, the generation AI learns past organizing history and proposes the optimal conditions for the organizing conditions specified by the user. For example, based on the organizing conditions specified by the user, the generation AI analyzes past organizing history and proposes the optimal organizing conditions. For example, it may prioritize proposals for conditions that have been frequently used in the past. The generation AI also learns the user's past organizing history and analyzes the user's organizing patterns. This proposes the optimal organizing method for the conditions specified by the user. Furthermore, for the organizing conditions specified by the user, the generation AI automatically generates the optimal conditions based on past organizing history. For example, it proposes conditions by referring to past success stories. In this way, the generation AI learns past organizing history and proposes the optimal conditions, thereby making the user's organizing work more efficient.

[0093] The organizing condition specification unit allows the user to specify organizing conditions by voice input, and the generation AI analyzes the voice to set the organizing conditions. For example, a system is constructed in which the user specifies organizing conditions by voice input, and the generation AI analyzes the voice to set the conditions. For example, speech recognition technology is used to convert speech to text. The generation AI also analyzes the organizing conditions specified by voice input and proposes an appropriate organizing method. For example, file types and keywords are set based on voice commands. Furthermore, the user specifies organizing conditions by voice input, and the generation AI analyzes the voice in real time to set the conditions. For example, speech recognition technology is used to instantly reflect the conditions. This allows the user to specify organizing conditions by voice input, and the generation AI analyzes the voice to set the conditions, thereby improving user convenience.

[0094] The organizing condition specification unit uses the emotion estimation function to suggest organizing conditions according to the user's emotional state. For example, the emotion estimation function is used to analyze the user's emotional state, and if stress is high, a simple organizing method is suggested. For example, the organizing conditions are adjusted based on the emotion score. In addition, a system is constructed in which the generation AI suggests optimal organizing conditions according to the user's emotional state. For example, if the user is relaxed, a detailed organizing method is suggested. Furthermore, the emotion estimation function is used to monitor the user's emotional state in real time, and organizing conditions are suggested based on the results. For example, if the emotion score is low, a simple organizing method is suggested. In this way, the emotion estimation function can be used to suggest organizing conditions according to the user's emotional state, thereby reducing the user's stress.

[0095] The organizing condition specification unit allows users to specify organizing conditions from mobile devices such as smartphones or tablets. For example, a system is constructed that allows users to specify organizing conditions from smartphones or tablets. For example, a mobile app is developed to make it easy to set conditions. Furthermore, when organizing conditions are specified from a mobile device, the generation AI analyzes the conditions in real time and suggests the optimal organizing method. For example, the conditions are entered through a mobile app. Furthermore, by allowing users to specify organizing conditions from smartphones or tablets, users can easily set conditions from anywhere. For example, the voice input function of the mobile device is utilized. This allows users to specify organizing conditions from mobile devices such as smartphones and tablets, thereby improving user convenience.

[0096] The organizing condition specification unit adds a function to share organizing conditions between different users and refer to other users' conditions. For example, a system is constructed that allows different users to share organizing conditions and a function to refer to other users' conditions is added. For example, a shared folder is created and conditions are saved. Also, by referring to other users' organizing conditions, users can set optimal conditions. For example, a condition ranking function is added and popular conditions are displayed. Furthermore, a system is developed that allows different users to share organizing conditions and propose optimal organizing methods based on other users' conditions. For example, a condition sharing platform is constructed. This allows different users to share organizing conditions and refer to other users' conditions to propose optimal organizing methods.

[0097] The organizing condition specification unit uses the emotion estimation function to monitor the emotions of the user when specifying organizing conditions in real time and make suggestions that will elicit positive emotions. For example, a system is constructed that uses the emotion estimation function to monitor the emotions of the user when specifying organizing conditions in real time and make suggestions that will elicit positive emotions. For example, the system adjusts suggestions based on the emotion score. The system also analyzes the user's emotional state in real time and suggests organizing conditions that will elicit positive emotions. For example, if the emotion score is low, it displays an encouraging message. Furthermore, the emotion estimation function is used to provide an interface that elicits positive emotions when the user specifies organizing conditions. For example, it makes suggestions based on the emotion score. In this way, the emotion estimation function is used to monitor the emotions of the user when specifying organizing conditions in real time and make suggestions that will elicit positive emotions, thereby improving user satisfaction.

[0098] When analyzing the contents of a file, the file analysis unit takes the file's metadata into consideration for classification. For example, a system can be constructed in which the generation AI takes the file's metadata (creator, creation date, etc.) into consideration for classification when analyzing the contents of a file. For example, the importance of a file can be evaluated based on the metadata. The generation AI can also analyze the file's metadata and classify it into an appropriate category. For example, files can be grouped based on the creator or creation date. Furthermore, by having the generation AI take the file's metadata into consideration for classification, more accurate file organization can be achieved. For example, the relevance of files can be evaluated based on the metadata. In this way, more accurate file organization can be achieved by taking the file's metadata into consideration for classification.

[0099] When analyzing file contents, the file analysis unit supports multiple languages ​​and appropriately classifies files in different languages. For example, a system can be built in which the generation AI supports multiple languages ​​and appropriately classifies files in different languages. For example, language identification technology can be used to identify the language of the file. Files in different languages ​​can also be analyzed, and the generation AI can classify them into the appropriate category. For example, different classification rules can be applied for each language. Furthermore, by having the generation AI support multiple languages, international file organization can be achieved. For example, files in English, Japanese, Chinese, etc. can be appropriately classified. This makes it possible to support multiple languages ​​and appropriately classify files in different languages, thereby achieving international file organization.

[0100] The file analysis unit uses the emotion estimation function to analyze the emotional tone of the file contents and classify the files based on the emotion. For example, the emotion estimation function is used to analyze the emotional tone of the file contents and build a system that classifies files based on emotion. For example, files with positive emotions are prioritized for classification. The emotional tone of the file contents is also analyzed, and the generation AI classifies the files into appropriate categories based on emotion. For example, files are grouped based on the emotion score. Furthermore, the emotion estimation function is used to analyze the emotional tone of the file contents in real time and classify the files based on emotion. For example, files with negative emotions are sorted into a separate folder. In this way, by using the emotion estimation function to analyze the emotional tone of the file contents and classify files based on emotion, it is possible to realize file organization that takes the user's emotions into consideration.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The organizing condition specification unit accepts organizing conditions specified by the user. For example, the user can set conditions such as file type, creation date, update date, file size, and keywords. Step 2: The file analysis unit analyzes the contents of the file based on the organization conditions accepted by the organization condition specification unit. For example, in the case of document files, the text content is analyzed and classified based on keywords and themes. In the case of image files, the content is analyzed using image recognition technology and sorted into appropriate categories. Step 3: The folder management optimization unit optimizes the folder management structure based on the file contents analyzed by the file analysis unit. For example, related files are grouped together in one folder, and subfolders are created to organize the hierarchical structure. Step 4: The file organizer organizes local and cloud files based on the folder management structure optimized by the folder management optimization unit. For example, it simultaneously analyzes files stored on the local hard disk and files stored in cloud storage and sorts them into appropriate folders based on the specified organization conditions. Step 5: The periodic organizer periodically organizes the files organized by the file organizer. For example, the periodic organizer analyzes the contents of the files at a frequency specified by the user, such as daily, weekly, or monthly, and optimizes the folder management structure.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0144] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0155] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0160] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0161] 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.

[0162] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0163] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0164] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0165] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a sorting condition specification unit that accepts sorting conditions specified by a user; a file analysis unit that analyzes the contents of a file based on the reorganization conditions accepted by the reorganization condition designation unit; a folder management optimization unit that optimizes a folder management structure based on the contents of the file analyzed by the file analysis unit; a file organizer that organizes local and cloud files based on the folder management structure optimized by the folder management optimization unit; a periodic organizing unit that periodically organizes the files organized by the file organizing unit; A system characterized by:

2. The organizing condition designation unit The generation AI learns past organization history and proposes optimal conditions for the organization conditions specified by the user.

2. The system of claim 1.

3. The organizing condition designation unit The user can specify the sorting conditions by voice input, and the generation AI analyzes the voice and sets the sorting conditions.

2. The system of claim 1.

4. The organizing condition designation unit Suggesting the sorting conditions according to the user's emotional state 2. The system of claim 1.

5. The organizing condition designation unit The above-mentioned sorting conditions can be specified from a mobile device such as a smartphone or tablet.

2. The system of claim 1.

6. The organizing condition designation unit Add a function to share the above sorting conditions between different users and refer to other users' conditions.

2. The system of claim 1.

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