system

Abstract

The system according to this embodiment aims to reduce file search time and improve business efficiency. [Solution] The system according to the embodiment comprises a metadata assignment unit, a storage unit, and a search unit. The metadata assignment unit automatically assigns metadata when a file is created. The storage unit saves the file to the cloud along with the metadata assigned by the metadata assignment unit. The search unit searches the files saved by the storage unit and presents the most suitable file.

Description

Technical Field

[0004] ,

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

Background Art

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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it takes a lot of time to search for files and the work efficiency decreases.

[0005] The system according to the embodiment aims to shorten the file search time and improve the work efficiency.

Means for Solving the Problems

[0006] The system according to the embodiment includes a metadata adding unit, a storage unit, and a search unit. The metadata adding unit automatically adds metadata when a file is created. The storage unit stores the file in the cloud together with the metadata added by the metadata adding unit. The search unit searches for the files stored by the storage unit and presents the optimal files. [Effects of the Invention]

[0007] The system according to this embodiment can reduce file search time and improve work efficiency. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​file management system according to an embodiment of the present invention is a system designed to reduce file search time by as much as 150 hours per year. In this system, an AI agent interacts with the user when a file is created and automatically adds metadata based on the content and purpose. This allows the system to instantly present the most suitable file later, improving work efficiency and maintaining concentration. Specifically, it consists of the following steps. First, when a user creates a file, the AI ​​agent interacts with the user and automatically adds metadata based on the file's content and purpose. For example, if a user creates a file as "meeting materials," the AI ​​agent adds metadata such as "meeting," "materials," and "date" based on its content and purpose. Next, the file is saved to the cloud. Files created by all employees are saved on the cloud and managed along with their metadata. This enables centralized file management. Furthermore, when a user searches for a file, the AI ​​agent automatically presents the most suitable file. For example, if a user enters "I'm looking for last month's meeting materials," the AI ​​agent instantly presents the most suitable file based on the metadata. This significantly reduces the time required for file searches. This system improves work efficiency and helps maintain concentration. Furthermore, by significantly reducing the time required for file searches, it enables a stress-free work environment, allowing employees to make more effective use of their valuable time. In this way, the AI ​​file management system can streamline user file management and improve work productivity.

[0029] The AI ​​file management system according to this embodiment comprises a metadata assignment unit, a storage unit, and a search unit. The metadata assignment unit automatically assigns metadata when a file is created. For example, when a user creates a file, the metadata assignment unit automatically assigns metadata based on the file's content and purpose while an AI agent interacts with the user. For example, if a user creates a file as "meeting materials," the metadata assignment unit assigns metadata such as "meeting," "materials," and "date" based on its content and purpose. The metadata assignment unit can assign metadata by interacting with the user using, for example, a chatbot or speech recognition technology. The storage unit saves the file to the cloud along with the metadata assigned by the metadata assignment unit. For example, the storage unit saves files created by all employees to the cloud and manages them along with their metadata. For example, the storage unit can save files using cloud services. The search unit searches for files saved by the storage unit and presents the most suitable file. For example, the search unit presents the most suitable file based on search conditions entered by the user. For example, the search unit can instantly present the most suitable file based on metadata. The search unit can search for files using, for example, keyword searches or filtering conditions, and present the most suitable files. This allows the AI ​​file management system according to this embodiment to automatically add metadata when files are created and efficiently search files stored in the cloud. As a result, users can significantly reduce the time spent searching for files and improve their work efficiency.

[0030] The metadata assignment unit automatically assigns metadata when a file is created. For example, when a user creates a file, the metadata assignment unit automatically assigns metadata based on the file's content and purpose while interacting with the user through an AI agent. Specifically, when a user creates a new file, the AI ​​agent asks the user questions using chatbot or speech recognition technology. For example, it might ask questions such as, "What will this file be used for?" or "What is the main content of this file?" and generate appropriate metadata based on the user's answers. If a user creates a file as "meeting materials," the AI ​​agent automatically assigns metadata such as "meeting," "materials," and "date." Furthermore, the AI ​​agent can analyze the file's content and add relevant keywords and tags. For example, if the meeting materials include "project plan" or "budget estimate," these keywords will be added as metadata. This allows the metadata assignment unit to save the user time and streamline file management. In addition, the metadata assignment unit can learn the user's past file creation history and usage patterns to assign more accurate metadata. For example, it can learn the types and content of files that a particular user frequently creates and suggest the most suitable metadata for that user. This allows the metadata assignment unit to respond flexibly to user needs, further improving the efficiency of file management.

[0031] The storage unit saves files to the cloud along with the metadata assigned by the metadata assignment unit. For example, the storage unit can save files created by all employees to the cloud and manage them along with their metadata. Specifically, the storage unit can use cloud services to save files. This ensures the security and availability of files, allowing employees to access them from anywhere. The storage unit centrally manages metadata when files are saved, facilitating file searching and organization. For example, when a file is saved to the cloud, metadata is automatically assigned and stored as file attribute information. This allows users to easily search for files based on their content and purpose. Furthermore, the storage unit has a file version control function, allowing it to track the file's change history. This allows users to revert to previous versions, enabling them to work with peace of mind even if they accidentally make changes. The storage unit also has a file backup function, preventing data loss by regularly creating backups. This enhances the security and reliability of files, providing users with an environment where they can manage their files with confidence.

[0032] The search unit searches for files stored by the storage unit and presents the most relevant files. For example, the search unit presents the most relevant files based on search criteria entered by the user. Specifically, the search unit can instantly present the most relevant files based on metadata. For example, if a user searches for "meeting materials," the search unit will prioritize displaying files whose metadata includes "meeting" or "materials." Furthermore, the search unit can search for files using keyword searches and filtering conditions and present the most relevant files. For example, if a user searches for "2023 budget plan," the search unit will display files whose metadata includes "2023," "budget," and "plan." In addition, the search unit can use AI to analyze the user's search intent and suggest highly relevant files. For example, even if a user searches with vague keywords, the AI ​​learns past search history and user behavior patterns to suggest the most relevant files. This allows the search unit to quickly and accurately find the files the user needs, improving work efficiency. Furthermore, the search unit can update search results in real time, providing files based on the latest information. This allows the search engine to always provide highly accurate search results based on the latest information, enabling flexible responses to meet user needs.

[0033] The metadata assignment unit can automatically assign metadata based on the content and purpose of a file while interacting with the user. For example, the metadata assignment unit can interact with the user using a chatbot and assign metadata based on the content and purpose of the file. For example, if a user creates a file as "meeting materials," the metadata assignment unit will use a chatbot to assign metadata such as "meeting," "materials," and "date." The metadata assignment unit can also interact with the user using speech recognition technology and assign metadata. For example, if a user inputs "meeting materials" by voice, the metadata assignment unit will assign metadata such as "meeting," "materials," and "date" based on the content and purpose. This improves the accuracy of metadata by automatically assigning metadata while interacting with the user. Some or all of the above processing in the metadata assignment unit may be performed using AI, for example, or without AI. For example, the metadata assignment unit can input user interaction data into a generating AI and have the generating AI perform the metadata assignment.

[0034] The search unit can present the most suitable files based on the search criteria entered by the user. For example, if the user enters "I'm looking for last month's meeting materials," the search unit will present the most suitable files based on metadata. The search unit can also search for files using keyword searches and present the most suitable files. Furthermore, the search unit can search for files using filtering conditions and present the most suitable files. For example, if the user enters filtering conditions such as "meeting materials," "last month," and "Project A," the search unit will present the most suitable files based on these conditions. This improves search efficiency by presenting the most suitable files based on the search criteria entered by the user. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the search criteria entered by the user into a generating AI and have the generating AI perform the task of presenting the most suitable files.

[0035] The storage unit can store files created by all employees on the cloud and manage them along with metadata. For example, the storage unit can store files created by all employees on the cloud and manage them along with metadata. The storage unit can, for example, use cloud services to store files. The storage unit can, for example, centrally manage files created by all employees and manage them on the cloud along with metadata. This enables centralized file management by storing files created by all employees on the cloud and managing them along with metadata. Some or all of the above-described processes in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input files created by all employees into a generating AI and have the generating AI perform cloud management.

[0036] The metadata assignment unit can assign metadata such as "meeting," "document," and "date" based on the content and purpose of the file. For example, if a user creates a file as "meeting materials," the metadata assignment unit will assign metadata such as "meeting," "document," and "date" based on its content and purpose. The metadata assignment unit can also automatically assign metadata such as "meeting," "document," and "date" based on the content and purpose of the file. This improves search accuracy by assigning metadata based on the content and purpose of the file. Some or all of the above processing in the metadata assignment unit may be performed using AI, for example, or without AI. For example, the metadata assignment unit can input data based on the content and purpose of the file into a generating AI and have the generating AI perform the metadata assignment.

[0037] The search unit can instantly present the most suitable files based on metadata. For example, if a user enters "I'm looking for last month's meeting materials," the search unit will instantly present the most suitable files based on the metadata. The search unit can also search for files using keyword searches and instantly present the most suitable files. Furthermore, the search unit can search for files using filtering conditions and instantly present the most suitable files. For example, if a user enters filtering conditions such as "meeting materials," "last month," and "Project A," the search unit will instantly present the most suitable files based on these conditions. This reduces search time by instantly presenting the most suitable files based on metadata. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input metadata-based data into a generating AI and have the generating AI perform the task of presenting the most suitable files.

[0038] The metadata assignment unit can analyze the user's past file creation history when a file is created and automatically suggest the most suitable metadata. For example, the metadata assignment unit can suggest the most suitable metadata for similar files based on the metadata of files the user has created in the past. For example, the metadata assignment unit can automatically suggest metadata related to a specific project from the user's past file creation history. For example, the metadata assignment unit can analyze patterns of metadata used by the user in the past and suggest the most suitable metadata. In this way, by analyzing the user's past file creation history, the most suitable metadata can be automatically suggested. Some or all of the above processing in the metadata assignment unit may be performed using AI, for example, or without AI. For example, the metadata assignment unit can input the user's past file creation history data into a generating AI and have the generating AI suggest the most suitable metadata.

[0039] The metadata assignment unit can adjust the level of metadata detail based on the file's content and purpose. For example, in the case of important meeting materials, the metadata assignment unit can assign detailed metadata to make them easier to search. For example, in the case of simple notes or temporary files, the metadata assignment unit can assign only basic metadata. For example, the metadata assignment unit can adjust the level of metadata detail required according to the progress of a project. This improves search accuracy by adjusting the level of metadata detail based on the file's content and purpose. Some or all of the above processing in the metadata assignment unit may be performed using AI, for example, or without AI. For example, the metadata assignment unit can input data based on the file's content and purpose into a generating AI and have the generating AI perform the adjustment of the level of metadata detail.

[0040] The metadata assignment unit can assign highly relevant metadata by considering the user's geographical location information when assigning metadata. For example, if a user creates a file in a specific location, the metadata assignment unit automatically assigns metadata related to that location. For example, if a user creates a file while on a business trip, the metadata assignment unit assigns information about the business trip destination as metadata. For example, if a user creates a file related to a project in a specific region, the metadata assignment unit assigns metadata related to that region. This allows for the assignment of highly relevant metadata by considering the user's geographical location information. Some or all of the above processing in the metadata assignment unit may be performed using AI, for example, or without AI. For example, the metadata assignment unit can input the user's geographical location information data into a generating AI and have the generating AI perform the assignment of highly relevant metadata.

[0041] The metadata assignment unit can analyze the user's social media activity and assign relevant metadata when assigning metadata. For example, the metadata assignment unit can assign relevant metadata based on information shared by the user on social media. For example, the metadata assignment unit can assign metadata related to events the user participated in on social media. For example, the metadata assignment unit can analyze the user's social media activity history and suggest relevant metadata. In this way, relevant metadata can be assigned by analyzing the user's social media activity. Some or all of the above processing in the metadata assignment unit may be performed using AI, for example, or without AI. For example, the metadata assignment unit can input the user's social media activity data into a generating AI and have the generating AI perform the assignment of relevant metadata.

[0042] The storage unit can analyze the user's past saving history when saving a file and suggest the optimal saving method. For example, the storage unit can suggest the optimal saving method for similar files based on the saving methods the user has used to save files in the past. For example, the storage unit can automatically suggest saving methods related to a specific project based on the user's past saving history. For example, the storage unit can analyze patterns of saving methods the user has used in the past and suggest the optimal saving method. In this way, the optimal saving method can be suggested by analyzing the user's past saving history. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the user's past saving history data into a generating AI and have the generating AI suggest the optimal saving method.

[0043] The storage unit can automatically optimize file names and folder structures when saving files, based on their content and purpose. For example, for important meeting materials, the storage unit automatically sets detailed file names and folder structures. For simple notes or temporary files, the storage unit sets only basic file names and folder structures. The storage unit can automatically optimize necessary file names and folder structures according to the progress of a project, for example. This makes file management easier by automatically optimizing file names and folder structures when saving files based on their content and purpose. Some or all of the above processes in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input data based on the content and purpose of files into a generating AI and have the generating AI perform the optimization of file names and folder structures when saving.

[0044] The storage unit can select a highly relevant storage method when saving a file, taking into account the user's geographical location information. For example, if the user saves a file in a specific location, the storage unit automatically selects a storage method related to that location. For example, if the user saves a file while on a business trip, the storage unit selects a storage method that takes into account the information of the business trip destination. For example, if the user saves a file related to a project in a specific region, the storage unit selects a storage method related to that region. In this way, a highly relevant storage method can be selected by taking into account the user's geographical location information. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the user's geographical location information data into a generating AI and have the generating AI perform the selection of a highly relevant storage method.

[0045] The storage unit can analyze the user's social media activity when saving files and suggest relevant storage methods. For example, the storage unit can suggest relevant storage methods based on information shared by the user on social media. For example, the storage unit can suggest relevant storage methods related to events the user participated in on social media. For example, the storage unit can analyze the user's social media activity history and suggest relevant storage methods. In this way, relevant storage methods can be suggested by analyzing the user's social media activity. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the user's social media activity data into a generating AI and have the generating AI perform the task of suggesting relevant storage methods.

[0046] The search unit can analyze the user's past search history during a search and automatically suggest the optimal search criteria. For example, the search unit can automatically suggest similar search criteria based on keywords the user has searched for in the past. For example, the search unit can automatically suggest search criteria related to a specific project based on the user's past search history. For example, the search unit can analyze patterns of search criteria used by the user in the past and suggest the optimal search criteria. In this way, by analyzing the user's past search history, the optimal search criteria can be automatically suggested. Some or all of the above processes in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the user's past search history data into a generating AI and have the generating AI suggest the optimal search criteria.

[0047] The search unit can adjust the level of detail in search results based on the content and purpose of the files during a search. For example, in the case of important meeting materials, the search unit will display detailed search results to make it easier to find related files. For example, in the case of simple notes or temporary files, the search unit will display only basic search results. The search unit can adjust the level of detail in search results as needed, for example, according to the progress of a project. This improves search accuracy by adjusting the level of detail in search results based on the content and purpose of the files. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input data based on the content and purpose of the files into a generating AI and have the generating AI perform the adjustment of the level of detail in the search results.

[0048] The search unit can display highly relevant search results by considering the user's geographical location during a search. For example, if the user is searching in a specific location, the search unit will prioritize displaying search results related to that location. For example, if the user is searching while on a business trip, the search unit will display search results that take into account information about the destination. For example, if the user is searching for files related to a project in a specific region, the search unit will display search results related to that region. In this way, highly relevant search results can be displayed by considering the user's geographical location. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the user's geographical location data into a generating AI and have the generating AI perform the display of highly relevant search results.

[0049] The search unit can analyze the user's social media activity during a search and display relevant search results. For example, the search unit can display relevant search results based on information shared by the user on social media. For example, the search unit can display search results related to events the user participated in on social media. For example, the search unit can analyze the user's social media activity history and suggest relevant search results. In this way, relevant search results can be displayed by analyzing the user's social media activity. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the user's social media activity data into a generating AI and have the generating AI display relevant search results.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The metadata assignment unit can adjust the level of metadata detail based on the file's content and purpose. For example, for important meeting materials, detailed metadata can be assigned to make them easier to search. For simple notes or temporary files, only basic metadata can be assigned. The level of metadata detail can be adjusted according to the project's progress. This improves search accuracy by adjusting the level of metadata detail based on the file's content and purpose.

[0052] The storage unit can analyze the user's past saving history when saving a file and suggest the optimal saving method. For example, it can suggest the best saving method for similar files based on the saving methods the user has used in the past. It can also automatically suggest saving methods related to a specific project based on the user's past saving history. By analyzing patterns in the saving methods the user has used in the past, it can suggest the optimal saving method. In this way, it can suggest the best saving method by analyzing the user's past saving history.

[0053] The search function can analyze a user's past search history and automatically suggest the most suitable search criteria. For example, it can automatically suggest similar search criteria based on keywords the user has previously searched for. It can also automatically suggest search criteria related to a specific project based on the user's past search history. By analyzing patterns in the search criteria the user has used in the past, it can suggest the most suitable search criteria. In this way, by analyzing the user's past search history, it can automatically suggest the most suitable search criteria.

[0054] The storage unit can select the most relevant storage method when saving files, taking into account the user's geographical location. For example, if a user saves a file in a specific location, it automatically selects a storage method relevant to that location. If a user saves a file while on a business trip, it selects a storage method that takes into account the information of the business trip destination. If a user saves files related to a project in a specific region, it selects a storage method relevant to that region. In this way, by considering the user's geographical location, it can select the most relevant storage method.

[0055] The metadata assignment unit can analyze a user's social media activity and assign relevant metadata when assigning metadata. For example, it can assign relevant metadata based on information shared by the user on social media. It can also assign metadata related to events the user participated in on social media. It can analyze a user's social media activity history and suggest relevant metadata. In this way, relevant metadata can be assigned by analyzing the user's social media activity.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The metadata assignment unit automatically assigns metadata when a file is created. For example, when a user creates a file, the AI ​​agent interacts with the user and automatically assigns metadata based on the file's content and purpose. Specifically, if a user creates a file as "meeting materials," the metadata assignment unit will assign metadata such as "meeting," "materials," and "date" based on its content and purpose. The metadata assignment unit can interact with the user using chatbots or speech recognition technology and assign metadata accordingly. Step 2: The storage unit saves the files to the cloud along with the metadata assigned by the metadata assignment unit. For example, all files created by employees are saved to the cloud and managed along with their metadata. The storage unit can use cloud services to save files. Step 3: The search unit searches the files saved by the storage unit and presents the most suitable file. For example, it can present the most suitable file based on the search criteria entered by the user. The search unit can instantly present the most suitable file based on metadata. The search unit can search for files using keyword searches and filtering conditions and present the most suitable file.

[0058] (Example of form 2) The AI ​​file management system according to an embodiment of the present invention is a system designed to reduce file search time by as much as 150 hours per year. In this system, an AI agent interacts with the user when a file is created and automatically adds metadata based on the content and purpose. This allows the system to instantly present the most suitable file later, improving work efficiency and maintaining concentration. Specifically, it consists of the following steps. First, when a user creates a file, the AI ​​agent interacts with the user and automatically adds metadata based on the file's content and purpose. For example, if a user creates a file as "meeting materials," the AI ​​agent adds metadata such as "meeting," "materials," and "date" based on its content and purpose. Next, the file is saved to the cloud. Files created by all employees are saved on the cloud and managed along with their metadata. This enables centralized file management. Furthermore, when a user searches for a file, the AI ​​agent automatically presents the most suitable file. For example, if a user enters "I'm looking for last month's meeting materials," the AI ​​agent instantly presents the most suitable file based on the metadata. This significantly reduces the time required for file searches. This system improves work efficiency and helps maintain concentration. Furthermore, by significantly reducing the time required for file searches, it enables a stress-free work environment, allowing employees to make more effective use of their valuable time. In this way, the AI ​​file management system can streamline user file management and improve work productivity.

[0059] The AI ​​file management system according to this embodiment comprises a metadata assignment unit, a storage unit, and a search unit. The metadata assignment unit automatically assigns metadata when a file is created. For example, when a user creates a file, the metadata assignment unit automatically assigns metadata based on the file's content and purpose while an AI agent interacts with the user. For example, if a user creates a file as "meeting materials," the metadata assignment unit assigns metadata such as "meeting," "materials," and "date" based on its content and purpose. The metadata assignment unit can assign metadata by interacting with the user using, for example, a chatbot or speech recognition technology. The storage unit saves the file to the cloud along with the metadata assigned by the metadata assignment unit. For example, the storage unit saves files created by all employees to the cloud and manages them along with their metadata. For example, the storage unit can save files using cloud services. The search unit searches for files saved by the storage unit and presents the most suitable file. For example, the search unit presents the most suitable file based on search conditions entered by the user. For example, the search unit can instantly present the most suitable file based on metadata. The search unit can search for files using, for example, keyword searches or filtering conditions, and present the most suitable files. This allows the AI ​​file management system according to this embodiment to automatically add metadata when files are created and efficiently search files stored in the cloud. As a result, users can significantly reduce the time spent searching for files and improve their work efficiency.

[0060] The metadata assignment unit automatically assigns metadata when a file is created. For example, when a user creates a file, the metadata assignment unit automatically assigns metadata based on the file's content and purpose while interacting with the user through an AI agent. Specifically, when a user creates a new file, the AI ​​agent asks the user questions using chatbot or speech recognition technology. For example, it might ask questions such as, "What will this file be used for?" or "What is the main content of this file?" and generate appropriate metadata based on the user's answers. If a user creates a file as "meeting materials," the AI ​​agent automatically assigns metadata such as "meeting," "materials," and "date." Furthermore, the AI ​​agent can analyze the file's content and add relevant keywords and tags. For example, if the meeting materials include "project plan" or "budget estimate," these keywords will be added as metadata. This allows the metadata assignment unit to save the user time and streamline file management. In addition, the metadata assignment unit can learn the user's past file creation history and usage patterns to assign more accurate metadata. For example, it can learn the types and content of files that a particular user frequently creates and suggest the most suitable metadata for that user. This allows the metadata assignment unit to respond flexibly to user needs, further improving the efficiency of file management.

[0061] The storage unit saves files to the cloud along with the metadata assigned by the metadata assignment unit. For example, the storage unit can save files created by all employees to the cloud and manage them along with their metadata. Specifically, the storage unit can use cloud services to save files. This ensures the security and availability of files, allowing employees to access them from anywhere. The storage unit centrally manages metadata when files are saved, facilitating file searching and organization. For example, when a file is saved to the cloud, metadata is automatically assigned and stored as file attribute information. This allows users to easily search for files based on their content and purpose. Furthermore, the storage unit has a file version control function, allowing it to track the file's change history. This allows users to revert to previous versions, enabling them to work with peace of mind even if they accidentally make changes. The storage unit also has a file backup function, preventing data loss by regularly creating backups. This enhances the security and reliability of files, providing users with an environment where they can manage their files with confidence.

[0062] The search unit searches for files stored by the storage unit and presents the most relevant files. For example, the search unit presents the most relevant files based on search criteria entered by the user. Specifically, the search unit can instantly present the most relevant files based on metadata. For example, if a user searches for "meeting materials," the search unit will prioritize displaying files whose metadata includes "meeting" or "materials." Furthermore, the search unit can search for files using keyword searches and filtering conditions and present the most relevant files. For example, if a user searches for "2023 budget plan," the search unit will display files whose metadata includes "2023," "budget," and "plan." In addition, the search unit can use AI to analyze the user's search intent and suggest highly relevant files. For example, even if a user searches with vague keywords, the AI ​​learns past search history and user behavior patterns to suggest the most relevant files. This allows the search unit to quickly and accurately find the files the user needs, improving work efficiency. Furthermore, the search unit can update search results in real time, providing files based on the latest information. This allows the search engine to always provide highly accurate search results based on the latest information, enabling flexible responses to meet user needs.

[0063] The metadata assignment unit can automatically assign metadata based on the content and purpose of a file while interacting with the user. For example, the metadata assignment unit can interact with the user using a chatbot and assign metadata based on the content and purpose of the file. For example, if a user creates a file as "meeting materials," the metadata assignment unit will use a chatbot to assign metadata such as "meeting," "materials," and "date." The metadata assignment unit can also interact with the user using speech recognition technology and assign metadata. For example, if a user inputs "meeting materials" by voice, the metadata assignment unit will assign metadata such as "meeting," "materials," and "date" based on the content and purpose. This improves the accuracy of metadata by automatically assigning metadata while interacting with the user. Some or all of the above processing in the metadata assignment unit may be performed using AI, for example, or without AI. For example, the metadata assignment unit can input user interaction data into a generating AI and have the generating AI perform the metadata assignment.

[0064] The search unit can present the most suitable files based on the search criteria entered by the user. For example, if the user enters "I'm looking for last month's meeting materials," the search unit will present the most suitable files based on metadata. The search unit can also search for files using keyword searches and present the most suitable files. Furthermore, the search unit can search for files using filtering conditions and present the most suitable files. For example, if the user enters filtering conditions such as "meeting materials," "last month," and "Project A," the search unit will present the most suitable files based on these conditions. This improves search efficiency by presenting the most suitable files based on the search criteria entered by the user. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the search criteria entered by the user into a generating AI and have the generating AI perform the task of presenting the most suitable files.

[0065] The storage unit can store files created by all employees on the cloud and manage them along with metadata. For example, the storage unit can store files created by all employees on the cloud and manage them along with metadata. The storage unit can, for example, use cloud services to store files. The storage unit can, for example, centrally manage files created by all employees and manage them on the cloud along with metadata. This enables centralized file management by storing files created by all employees on the cloud and managing them along with metadata. Some or all of the above-described processes in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input files created by all employees into a generating AI and have the generating AI perform cloud management.

[0066] The metadata assignment unit can assign metadata such as "meeting," "document," and "date" based on the content and purpose of the file. For example, if a user creates a file as "meeting materials," the metadata assignment unit will assign metadata such as "meeting," "document," and "date" based on its content and purpose. The metadata assignment unit can also automatically assign metadata such as "meeting," "document," and "date" based on the content and purpose of the file. This improves search accuracy by assigning metadata based on the content and purpose of the file. Some or all of the above processing in the metadata assignment unit may be performed using AI, for example, or without AI. For example, the metadata assignment unit can input data based on the content and purpose of the file into a generating AI and have the generating AI perform the metadata assignment.

[0067] The search unit can instantly present the most suitable files based on metadata. For example, if a user enters "I'm looking for last month's meeting materials," the search unit will instantly present the most suitable files based on the metadata. The search unit can also search for files using keyword searches and instantly present the most suitable files. Furthermore, the search unit can search for files using filtering conditions and instantly present the most suitable files. For example, if a user enters filtering conditions such as "meeting materials," "last month," and "Project A," the search unit will instantly present the most suitable files based on these conditions. This reduces search time by instantly presenting the most suitable files based on metadata. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input metadata-based data into a generating AI and have the generating AI perform the task of presenting the most suitable files.

[0068] The metadata assignment unit can estimate the user's emotions and adjust the metadata assignment method based on the estimated user emotions. For example, if the user is stressed, the metadata assignment unit can provide a simple metadata assignment interface and minimize input steps. For example, if the user is relaxed, the metadata assignment unit can provide detailed metadata input options and suggest a customizable metadata assignment method. For example, if the user is in a hurry, the metadata assignment unit can prioritize voice input to enable rapid metadata assignment. This reduces the user's burden by adjusting the metadata assignment method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the metadata assignment unit may be performed using AI or not using AI. For example, the metadata assignment unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the metadata assignment method.

[0069] The metadata assignment unit can analyze the user's past file creation history when a file is created and automatically suggest the most suitable metadata. For example, the metadata assignment unit can suggest the most suitable metadata for similar files based on the metadata of files the user has created in the past. For example, the metadata assignment unit can automatically suggest metadata related to a specific project from the user's past file creation history. For example, the metadata assignment unit can analyze patterns of metadata used by the user in the past and suggest the most suitable metadata. In this way, by analyzing the user's past file creation history, the most suitable metadata can be automatically suggested. Some or all of the above processing in the metadata assignment unit may be performed using AI, for example, or without AI. For example, the metadata assignment unit can input the user's past file creation history data into a generating AI and have the generating AI suggest the most suitable metadata.

[0070] The metadata assignment unit can adjust the level of metadata detail based on the file's content and purpose. For example, in the case of important meeting materials, the metadata assignment unit can assign detailed metadata to make them easier to search. For example, in the case of simple notes or temporary files, the metadata assignment unit can assign only basic metadata. For example, the metadata assignment unit can adjust the level of metadata detail required according to the progress of a project. This improves search accuracy by adjusting the level of metadata detail based on the file's content and purpose. Some or all of the above processing in the metadata assignment unit may be performed using AI, for example, or without AI. For example, the metadata assignment unit can input data based on the file's content and purpose into a generating AI and have the generating AI perform the adjustment of the level of metadata detail.

[0071] The metadata assignment unit can estimate the user's emotions and determine the priority of metadata based on the estimated emotions. For example, if the user is stressed, the metadata assignment unit will prioritize assigning important metadata and postpone other metadata. For example, if the user is relaxed, the metadata assignment unit will prioritize assigning detailed metadata to facilitate file management. For example, if the user is in a hurry, the metadata assignment unit will prioritize assigning only basic metadata to quickly save files. This reduces the user's burden by determining the priority of metadata based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the metadata assignment unit may be performed using AI or not using AI. For example, the metadata assignment unit can input user emotion data into a generative AI and have the generative AI perform the determination of metadata priority.

[0072] The metadata assignment unit can assign highly relevant metadata by considering the user's geographical location information when assigning metadata. For example, if a user creates a file in a specific location, the metadata assignment unit automatically assigns metadata related to that location. For example, if a user creates a file while on a business trip, the metadata assignment unit assigns information about the business trip destination as metadata. For example, if a user creates a file related to a project in a specific region, the metadata assignment unit assigns metadata related to that region. This allows for the assignment of highly relevant metadata by considering the user's geographical location information. Some or all of the above processing in the metadata assignment unit may be performed using AI, for example, or without AI. For example, the metadata assignment unit can input the user's geographical location information data into a generating AI and have the generating AI perform the assignment of highly relevant metadata.

[0073] The metadata assignment unit can analyze the user's social media activity and assign relevant metadata when assigning metadata. For example, the metadata assignment unit can assign relevant metadata based on information shared by the user on social media. For example, the metadata assignment unit can assign metadata related to events the user participated in on social media. For example, the metadata assignment unit can analyze the user's social media activity history and suggest relevant metadata. In this way, relevant metadata can be assigned by analyzing the user's social media activity. Some or all of the above processing in the metadata assignment unit may be performed using AI, for example, or without AI. For example, the metadata assignment unit can input the user's social media activity data into a generating AI and have the generating AI perform the assignment of relevant metadata.

[0074] The storage unit can estimate the user's emotions and adjust how files are saved based on those emotions. For example, if the user is stressed, the storage unit can provide a simple save interface and minimize the saving process. If the user is relaxed, for example, the storage unit can provide detailed save options and suggest a customizable save method. If the user is in a hurry, for example, the storage unit can prioritize voice input to allow for quick file saving. This reduces the user's burden by adjusting how files are saved based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the storage unit may be performed using AI or not. For example, the storage unit can input user emotion data into a generative AI and have the generative AI adjust how files are saved.

[0075] The storage unit can analyze the user's past saving history when saving a file and suggest the optimal saving method. For example, the storage unit can suggest the optimal saving method for similar files based on the saving methods the user has used to save files in the past. For example, the storage unit can automatically suggest saving methods related to a specific project based on the user's past saving history. For example, the storage unit can analyze patterns of saving methods the user has used in the past and suggest the optimal saving method. In this way, the optimal saving method can be suggested by analyzing the user's past saving history. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the user's past saving history data into a generating AI and have the generating AI suggest the optimal saving method.

[0076] The storage unit can automatically optimize file names and folder structures when saving files, based on their content and purpose. For example, for important meeting materials, the storage unit automatically sets detailed file names and folder structures. For simple notes or temporary files, the storage unit sets only basic file names and folder structures. The storage unit can automatically optimize necessary file names and folder structures according to the progress of a project, for example. This makes file management easier by automatically optimizing file names and folder structures when saving files based on their content and purpose. Some or all of the above processes in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input data based on the content and purpose of files into a generating AI and have the generating AI perform the optimization of file names and folder structures when saving.

[0077] The storage unit can estimate the user's emotions and determine the priority of files to save based on the estimated emotions. For example, if the user is stressed, the storage unit will prioritize saving important files and postpone saving other files. For example, if the user is relaxed, the storage unit will prioritize saving detailed files to make file management easier. For example, if the user is in a hurry, the storage unit will prioritize saving only basic files to save them quickly. This reduces the user's burden by determining the priority of files to save based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the storage unit may be performed using AI or not. For example, the storage unit can input user emotion data into a generative AI and have the generative AI determine the priority of files to save.

[0078] The storage unit can select a highly relevant storage method when saving a file, taking into account the user's geographical location information. For example, if the user saves a file in a specific location, the storage unit automatically selects a storage method related to that location. For example, if the user saves a file while on a business trip, the storage unit selects a storage method that takes into account the information of the business trip destination. For example, if the user saves a file related to a project in a specific region, the storage unit selects a storage method related to that region. In this way, a highly relevant storage method can be selected by taking into account the user's geographical location information. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the user's geographical location information data into a generating AI and have the generating AI perform the selection of a highly relevant storage method.

[0079] The storage unit can analyze the user's social media activity when saving files and suggest relevant storage methods. For example, the storage unit can suggest relevant storage methods based on information shared by the user on social media. For example, the storage unit can suggest relevant storage methods related to events the user participated in on social media. For example, the storage unit can analyze the user's social media activity history and suggest relevant storage methods. In this way, relevant storage methods can be suggested by analyzing the user's social media activity. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the user's social media activity data into a generating AI and have the generating AI perform the task of suggesting relevant storage methods.

[0080] The search unit can estimate the user's emotions and adjust how search results are displayed based on the estimated emotions. For example, if the user is stressed, the search unit provides a simple and highly visible display. If the user is relaxed, the search unit provides a display that includes detailed information. If the user is in a hurry, the search unit provides a display that gets straight to the point. By adjusting how search results are displayed based on the user's emotions, the user's burden can be reduced. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using AI, or not using AI. For example, the search unit can input user emotion data into a generative AI and have the generative AI adjust how search results are displayed.

[0081] The search unit can analyze the user's past search history during a search and automatically suggest the optimal search criteria. For example, the search unit can automatically suggest similar search criteria based on keywords the user has searched for in the past. For example, the search unit can automatically suggest search criteria related to a specific project based on the user's past search history. For example, the search unit can analyze patterns of search criteria used by the user in the past and suggest the optimal search criteria. In this way, by analyzing the user's past search history, the optimal search criteria can be automatically suggested. Some or all of the above processes in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the user's past search history data into a generating AI and have the generating AI suggest the optimal search criteria.

[0082] The search unit can adjust the level of detail in search results based on the content and purpose of the files during a search. For example, in the case of important meeting materials, the search unit will display detailed search results to make it easier to find related files. For example, in the case of simple notes or temporary files, the search unit will display only basic search results. The search unit can adjust the level of detail in search results as needed, for example, according to the progress of a project. This improves search accuracy by adjusting the level of detail in search results based on the content and purpose of the files. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input data based on the content and purpose of the files into a generating AI and have the generating AI perform the adjustment of the level of detail in the search results.

[0083] The search unit can estimate the user's emotions and prioritize search results based on those emotions. For example, if the user is stressed, the search unit will prioritize important search results and postpone other results. If the user is relaxed, the search unit will prioritize detailed search results to facilitate file management. If the user is in a hurry, the search unit will prioritize only basic search results to quickly find files. This reduces the user's burden by prioritizing search results based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using AI or not. For example, the search unit can input user emotion data into a generative AI and have the generative AI determine the priority of search results.

[0084] The search unit can display highly relevant search results by considering the user's geographical location during a search. For example, if the user is searching in a specific location, the search unit will prioritize displaying search results related to that location. For example, if the user is searching while on a business trip, the search unit will display search results that take into account information about the destination. For example, if the user is searching for files related to a project in a specific region, the search unit will display search results related to that region. In this way, highly relevant search results can be displayed by considering the user's geographical location. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the user's geographical location data into a generating AI and have the generating AI perform the display of highly relevant search results.

[0085] The search unit can analyze the user's social media activity during a search and display relevant search results. For example, the search unit can display relevant search results based on information shared by the user on social media. For example, the search unit can display search results related to events the user participated in on social media. For example, the search unit can analyze the user's social media activity history and suggest relevant search results. In this way, relevant search results can be displayed by analyzing the user's social media activity. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the user's social media activity data into a generating AI and have the generating AI display relevant search results.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The metadata assignment unit can estimate the user's emotions and adjust the metadata assignment method based on the estimated emotions. For example, if the user is stressed, it provides a simple metadata assignment interface and minimizes input steps. If the user is relaxed, it provides detailed metadata input options and suggests a customizable metadata assignment method. If the user is in a hurry, it prioritizes voice input to enable rapid metadata assignment. In this way, the burden on the user can be reduced by adjusting the metadata assignment method based on the user's emotions.

[0088] The metadata assignment unit can adjust the level of metadata detail based on the file's content and purpose. For example, for important meeting materials, detailed metadata can be assigned to make them easier to search. For simple notes or temporary files, only basic metadata can be assigned. The level of metadata detail can be adjusted according to the project's progress. This improves search accuracy by adjusting the level of metadata detail based on the file's content and purpose.

[0089] The search engine can estimate the user's emotions and adjust how search results are displayed based on that estimation. For example, if the user is stressed, it provides a simple and highly visible display. If the user is relaxed, it provides a display that includes detailed information. If the user is in a hurry, it provides a display that gets straight to the point. By adjusting how search results are displayed based on the user's emotions, the user's burden can be reduced.

[0090] The storage unit can analyze the user's past saving history when saving a file and suggest the optimal saving method. For example, it can suggest the best saving method for similar files based on the saving methods the user has used in the past. It can also automatically suggest saving methods related to a specific project based on the user's past saving history. By analyzing patterns in the saving methods the user has used in the past, it can suggest the optimal saving method. In this way, it can suggest the best saving method by analyzing the user's past saving history.

[0091] The search function can analyze a user's past search history and automatically suggest the most suitable search criteria. For example, it can automatically suggest similar search criteria based on keywords the user has previously searched for. It can also automatically suggest search criteria related to a specific project based on the user's past search history. By analyzing patterns in the search criteria the user has used in the past, it can suggest the most suitable search criteria. In this way, by analyzing the user's past search history, it can automatically suggest the most suitable search criteria.

[0092] The metadata assignment unit can estimate the user's emotions and prioritize metadata based on those emotions. For example, if the user is stressed, important metadata will be assigned first, while other metadata will be given lower priority. If the user is relaxed, detailed metadata will be assigned first to facilitate file management. If the user is in a hurry, only basic metadata will be assigned first to quickly save the file. In this way, prioritizing metadata based on the user's emotions can reduce the user's burden.

[0093] The storage unit can select the most relevant storage method when saving files, taking into account the user's geographical location. For example, if a user saves a file in a specific location, it automatically selects a storage method relevant to that location. If a user saves a file while on a business trip, it selects a storage method that takes into account the information of the business trip destination. If a user saves files related to a project in a specific region, it selects a storage method relevant to that region. In this way, by considering the user's geographical location, it can select the most relevant storage method.

[0094] The search engine can estimate the user's emotions and prioritize search results based on that estimation. For example, if the user is stressed, important search results will be displayed first, while other results will be prioritized. If the user is relaxed, detailed search results will be prioritized to make file management easier. If the user is in a hurry, only basic search results will be prioritized to allow them to find files quickly. This reduces the user's burden by prioritizing search results based on their emotions.

[0095] The metadata assignment unit can analyze a user's social media activity and assign relevant metadata when assigning metadata. For example, it can assign relevant metadata based on information shared by the user on social media. It can also assign metadata related to events the user participated in on social media. It can analyze a user's social media activity history and suggest relevant metadata. In this way, relevant metadata can be assigned by analyzing the user's social media activity.

[0096] The storage unit can estimate the user's emotions and determine the priority of files to save based on those emotions. For example, if the user is stressed, important files will be saved first, and other files will be saved later. If the user is relaxed, detailed files will be saved first to make file management easier. If the user is in a hurry, only basic files will be saved first to save them quickly. In this way, the user's burden can be reduced by determining the priority of files to save based on their emotions.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The metadata assignment unit automatically assigns metadata when a file is created. For example, when a user creates a file, the AI ​​agent interacts with the user and automatically assigns metadata based on the file's content and purpose. Specifically, if a user creates a file as "meeting materials," the metadata assignment unit will assign metadata such as "meeting," "materials," and "date" based on its content and purpose. The metadata assignment unit can interact with the user using chatbots or speech recognition technology and assign metadata accordingly. Step 2: The storage unit saves the files to the cloud along with the metadata assigned by the metadata assignment unit. For example, all files created by employees are saved to the cloud and managed along with their metadata. The storage unit can use cloud services to save files. Step 3: The search unit searches the files saved by the storage unit and presents the most suitable file. For example, it can present the most suitable file based on the search criteria entered by the user. The search unit can instantly present the most suitable file based on metadata. The search unit can search for files using keyword searches and filtering conditions and present the most suitable file.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0101] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0102] Each of the multiple elements described above, including the metadata assignment unit, storage unit, and search unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the metadata assignment unit is implemented by the control unit 46A of the smart device 14, where an AI agent interacts with the user to assign metadata when they create a file. The storage unit is implemented by the specific processing unit 290 of the data processing device 12, where the file is stored in the cloud along with the metadata. The search unit is implemented by the specific processing unit 290 of the data processing device 12, where the optimal file is presented based on the metadata. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

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

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0109] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0110] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0111] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0112] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0113] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0117] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the metadata assignment unit, storage unit, and search unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the metadata assignment unit is implemented by the control unit 46A of the smart glasses 214, where an AI agent interacts with the user to assign metadata when they create a file. The storage unit is implemented by the specific processing unit 290 of the data processing device 12, where the file is saved to the cloud along with the metadata. The search unit is implemented by the specific processing unit 290 of the data processing device 12, where the optimal file is presented based on the metadata. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the metadata assignment unit, storage unit, and search unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the metadata assignment unit is implemented by the control unit 46A of the headset terminal 314, where an AI agent interacts with the user to assign metadata when they create a file. The storage unit is implemented by the specific processing unit 290 of the data processing unit 12, where the file is saved to the cloud along with the metadata. The search unit is implemented by the specific processing unit 290 of the data processing unit 12, where the optimal file is presented based on the metadata. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0143] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements described above, including the metadata assignment unit, storage unit, and search unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the metadata assignment unit is implemented by the control unit 46A of the robot 414, where an AI agent interacts with the user to assign metadata when they create a file. The storage unit is implemented by the specific processing unit 290 of the data processing unit 12, where the file is stored in the cloud along with the metadata. The search unit is implemented by the specific processing unit 290 of the data processing unit 12, where the optimal file is presented based on the metadata. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

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

[0153] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0154] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0155] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0156] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

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

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

[0159] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

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

[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] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0163] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

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

[0165] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0167] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0168] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0169] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0170] (Note 1) A metadata assignment unit that automatically adds metadata when a file is created, A storage unit that saves the file to the cloud along with the metadata added by the metadata assignment unit, The system includes a search unit that searches for files stored by the storage unit and presents the most suitable file. A system characterized by the following features. (Note 2) The metadata assignment unit, Automatically assigns metadata based on the file's content and purpose while interacting with the user. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned search unit, The system presents the most suitable files based on the search criteria entered by the user. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned storage unit is All employee-created files are stored in the cloud and managed along with their metadata. The system described in Appendix 1, characterized by the features described herein. (Note 5) The metadata assignment unit, Metadata such as "meeting," "document," and "date" are added based on the file's content and purpose. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned search unit, Instantly presents the most suitable file based on metadata. The system described in Appendix 1, characterized by the features described herein. (Note 7) The metadata assignment unit, It estimates the user's emotions and adjusts how metadata is assigned based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The metadata assignment unit, When a file is created, the system analyzes the user's past file creation history and automatically suggests the most suitable metadata. The system described in Appendix 1, characterized by the features described herein. (Note 9) The metadata assignment unit, Adjust the level of metadata detail based on the file's content and purpose. The system described in Appendix 1, characterized by the features described herein. (Note 10) The metadata assignment unit, It estimates the user's sentiment and prioritizes metadata based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The metadata assignment unit, When assigning metadata, consider the user's geographical location to assign more relevant metadata. The system described in Appendix 1, characterized by the features described herein. (Note 12) The metadata assignment unit, When assigning metadata, the system analyzes the user's social media activity and assigns relevant metadata. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned storage unit is It estimates the user's emotions and adjusts how files are saved based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned storage unit is When saving a file, the system analyzes the user's past saving history and suggests the optimal saving method. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned storage unit is The system automatically optimizes file names and folder structures when saving files, based on their content and purpose. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned storage unit is It estimates the user's emotions and determines the priority of files to save based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned storage unit is When saving a file, the system selects the most relevant saving method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned storage unit is When saving files, the system analyzes the user's social media activity and suggests relevant saving methods. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned search unit, It estimates the user's sentiment and adjusts how search results are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned search unit, During a search, the system analyzes the user's past search history and automatically suggests the most suitable search criteria. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned search unit, When searching, adjust the level of detail in search results based on the file's content and purpose. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned search unit, It estimates the user's emotions and determines the priority of search results based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned search unit, When searching, the system displays more relevant search results by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned search unit, When you search, we analyze your social media activity and display relevant search results. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A metadata assignment unit that automatically adds metadata when a file is created, A storage unit that saves the file to the cloud along with the metadata added by the metadata assignment unit, The system includes a search unit that searches for files stored by the storage unit and presents the most suitable file. A system characterized by the following features.

2. The metadata assignment unit, Automatically assigns metadata based on the file's content and purpose while interacting with the user. The system according to feature 1.

3. The aforementioned search unit, The system presents the most suitable files based on the search criteria entered by the user. The system according to feature 1.

4. The aforementioned storage unit is All employee-created files are stored in the cloud and managed along with their metadata. The system according to feature 1.

5. The metadata assignment unit, Based on the file's content and purpose, metadata such as meeting details, documents, and dates are added. The system according to feature 1.

6. The aforementioned search unit, Instantly presents the most suitable file based on metadata. The system according to feature 1.

7. The metadata assignment unit, It estimates the user's emotions and adjusts how metadata is assigned based on those estimated emotions. The system according to feature 1.

8. The metadata assignment unit, When a file is created, the system analyzes the user's past file creation history and automatically suggests the most suitable metadata. The system according to feature 1.

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