System

Abstract

An object of the system according to the embodiment is to facilitate centralized management of data and to efficiently organize and manage data.SOLUTION: A system according to an embodiment includes a management unit, an assignment unit, a setting unit, and a sorting unit. The management unit manages a storage destination of the data. The assigning unit assigns metadata to the data managed by the managing unit. The setting unit sets the item of the metadata assigned by the assigning unit. The organizing unit organizes the data based on the metadata set by the setting unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not provided sufficient centralized management of data, making data organization and management cumbersome.

[0005] The system according to the embodiment aims to facilitate the centralized management of data and to efficiently organize and manage data. [Means for solving the problem]

[0006] The system according to the embodiment includes a management unit, an assignment unit, a setting unit, and an organization unit. The management unit manages the storage destination of data. The assignment unit assigns metadata to data managed by the management unit. The setting unit sets the items of the metadata assigned by the assignment unit. The organization unit organizes the data based on the metadata set by the setting unit. [Effects of the Invention]

[0007] The system according to the embodiment facilitates centralized management of data and enables efficient organization and management of data. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A data wrangling system according to an embodiment of the present invention is a system that uses generative AI to organize and cleanse data. The data wrangling system manages data storage destinations, assigns metadata, sets metadata items, and organizes the data based on the metadata. For example, the data wrangling system manages storage destinations for data aggregated in a data lake or data warehouse. Next, the data wrangling system assigns metadata items such as creation date, administrator, data type, data description, and update frequency, and assigns tags to the data. Furthermore, the data wrangling system assigns metadata items required for specific business operations to make data easier to manage. Finally, the data wrangling system classifies data based on specific conditions and organizes it for easy search. This improves data searchability and maintains data integrity. This improves data searchability and maintains data integrity. For example, when searching for data based on specific conditions, utilizing metadata allows for quick search results. Furthermore, maintaining data integrity improves data quality and improves business efficiency.

[0029] A data wrangling system according to an embodiment includes a management unit, an assignment unit, a setting unit, and an organization unit. The management unit manages data storage destinations. For example, the management unit manages storage destinations of data aggregated in a data lake or a data warehouse. The management unit can also perform database management and access control. For example, the management unit manages the storage location and access permissions of each piece of data. The assignment unit assigns metadata to the data. The assignment unit sets metadata items such as creation date, administrator, data type, data description, and update frequency, and assigns tags to the data. For example, the assignment unit assigns tags to the data to facilitate management. The setting unit sets metadata items. For example, the setting unit sets metadata items required for a specific business. For example, the setting unit sets metadata items required for a specific business to facilitate data management. The organization unit organizes the data based on the metadata. For example, the organization unit classifies data based on specific conditions and organizes it to make it easier to search. For example, the organization unit classifies data based on specific conditions and organizes it to make it easier to search. As a result, the data wrangling system according to the embodiment can efficiently manage data storage destinations, assign metadata, set items, and organize data. For example, the data wrangling system manages data storage destinations, assigns metadata, sets metadata items, and organizes data based on the metadata. This improves data searchability and maintains data integrity.

[0030] The management unit can manage the storage destination of the data aggregated in the data lake or data warehouse. A data lake stores, for example, structured data and unstructured data. A data warehouse includes, for example, a data aggregation method and an analysis function. The management unit, for example, manages the storage destination of the data aggregated in the data lake or data warehouse. For example, the management unit manages the storage location and access rights of each data. This allows efficient management of the data storage destination of the data lake or data warehouse. Some or all of the above-mentioned processing in the management unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the management unit can input the storage destination of the data aggregated in the data lake or data warehouse into the generation AI and have the generation AI manage the storage destination.

[0031] The tagging unit can set metadata items such as creation date, administrator, data type, data description, and update frequency, and tag the data. The creation date includes, for example, a timestamp and a date format. The administrator includes, for example, the data owner and access rights. The data type includes, for example, text data and image data. The data description includes, for example, an overview and a detailed description. The update frequency includes, for example, daily updates and weekly updates. The tagging unit sets metadata items such as creation date, administrator, data type, data description, and update frequency, and tag the data. For example, the tagging unit tags the data to facilitate management. By assigning metadata to the data, data management becomes easier. Some or all of the above-described processing in the tagging unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the tagging unit inputs metadata items to be assigned to the data into the generation AI and causes the generation AI to assign the metadata.

[0032] The setting unit can set metadata items required for a specific business. Examples of specific business operations include sales and manufacturing. The setting unit, for example, sets the metadata items required for the specific business. For example, the setting unit sets the metadata items required for the specific business to make data management easier. This makes data management more efficient by setting metadata items according to the business. Some or all of the above-described processing in the setting unit may be performed using, or without, a generation AI. For example, the setting unit inputs metadata items required for the specific business operation into the generation AI and causes the generation AI to set the metadata items.

[0033] The organizing unit can classify data based on specific conditions and organize it to make it easier to search. The specific conditions include, for example, the type of data and the creation date. The organizing unit, for example, classifies data based on specific conditions and organizes it to make it easier to search. For example, the organizing unit classifies data based on specific conditions and organizes it to make it easier to search. By classifying data and making it easier to search, the efficiency of data use is improved. Some or all of the above-mentioned processing in the organizing unit may be performed using, or without, the generation AI. For example, the organizing unit may input conditions for classifying data based on specific conditions into the generation AI and cause the generation AI to classify the data.

[0034] The organizing unit can maintain data consistency by utilizing metadata. Consistency includes, for example, consistency checks and data validation. The organizing unit, for example, maintains data consistency by utilizing metadata. For example, the organizing unit maintains data consistency by utilizing metadata. In this way, data consistency is maintained by utilizing the metadata. Some or all of the above-mentioned processing in the organizing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the organizing unit can input conditions for maintaining data consistency using metadata into the generation AI, and cause the generation AI to execute processing for maintaining data consistency.

[0035] The management unit can optimize the storage destination based on the importance of the data. The importance of the data includes, for example, business impact and access frequency. In the management unit, for example, the generation AI analyzes the importance of the data and places important data in a highly secure storage destination. The management unit can also have the generation AI place frequently accessed data in high-speed storage according to the importance of the data. The management unit can also have the generation AI evaluate the importance of the data and adjust the frequency of backups. In this way, optimizing the storage destination based on the importance of the data improves the efficiency of data management. Some or all of the above-mentioned processing in the management unit may be performed using, or without, the generation AI. For example, the management unit can input the importance of the data into the generation AI and have the generation AI optimize the storage destination.

[0036] The management unit can adjust the storage destination based on the access frequency of the data. The access frequency includes, for example, the number of daily accesses and the number of monthly accesses. For example, the management unit allows the generation AI to analyze the access frequency of the data and place frequently accessed data in high-speed storage. The management unit can also allow the generation AI to move infrequently accessed data to archive storage based on the access frequency of the data. The management unit can also allow the generation AI to learn the access patterns of the data and dynamically adjust the optimal storage destination. This improves data search efficiency by adjusting the storage destination based on the access frequency of the data. Some or all of the above-mentioned processing in the management unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the management unit can input the access frequency of the data to the generation AI and have the generation AI adjust the storage destination.

[0037] The management unit can apply different storage policies depending on the type of data. The storage policy may include, for example, a data retention period and a backup method. In the management unit, for example, the generation AI identifies the type of data and applies a strict security policy to confidential data. In addition, the management unit can also store image data in dedicated storage based on the type of data. In addition, the management unit can also have the generation AI analyze the type of data and apply a compression policy to text data. This improves the efficiency of data management by applying a storage policy according to the type of data. Some or all of the above-mentioned processing in the management unit may be performed using, or without, the generation AI. For example, the management unit can input the type of data to the generation AI and have the generation AI apply the storage policy.

[0038] The management unit can select a storage destination based on the geographic distribution of the data. Geographic distribution includes, for example, the location of data centers and the locations of users. In the management unit, for example, the generation AI analyzes the geographic distribution of the data and selects the optimal storage destination for each region. The management unit can also have the generation AI replicate the data based on the geographic distribution of the data to strengthen disaster prevention measures. The management unit can also have the generation AI optimize the data transfer speed by taking the geographic distribution of the data into consideration. In this way, selecting a storage destination based on the geographic distribution of the data improves the efficiency of data management. Some or all of the above-mentioned processing in the management unit may be performed using, or without, the generation AI. For example, the management unit can input the geographic distribution of the data into the generation AI and have the generation AI select a storage destination.

[0039] The management unit can group storage destinations based on data relevance. Relevance includes, for example, common tags and related metadata. For example, the management unit allows the generation AI to analyze data relevance and group related data into the same storage destination. The management unit can also allow the generation AI to group data based on data relevance to improve data search efficiency. The management unit can also allow the generation AI to evaluate data relevance and optimize a data backup strategy. As a result, data search efficiency is improved by grouping storage destinations based on data relevance. Some or all of the above-mentioned processing in the management unit can be performed using, or without, the generation AI. For example, the management unit can input data relevance to the generation AI and have the generation AI group the storage destinations.

[0040] The management unit can divide the storage destinations according to the security level of the data. Security levels include, for example, confidential data and public data. In the management unit, for example, the generation AI evaluates the security level of the data and places confidential data in a high-security storage destination. In addition, the management unit can also place general data in low-cost storage based on the security level of the data. In addition, the management unit can also have the generation AI analyze the security level of the data and apply an encryption policy to the data. In this way, data security is improved by dividing the storage destinations based on the security level of the data. Some or all of the above-mentioned processing in the management unit may be performed using, or without, the generation AI. For example, the management unit can input the security level of the data to the generation AI and have the generation AI divide the storage destinations.

[0041] The assigning unit can adjust the level of detail of the metadata based on the importance of the data. The level of detail includes, for example, a detailed description or a concise description. For example, the assigning unit allows the generation AI to analyze the importance of the data and assign detailed metadata to important data. The assigning unit can also assign simplified metadata to general data depending on the importance of the data. The assigning unit can also allow the generation AI to evaluate the importance of the data and adjust the frequency of metadata updates. This improves the efficiency of data management by adjusting the level of detail of the metadata based on the importance of the data. Some or all of the above-mentioned processing in the assigning unit may be performed using, or without, the generation AI. For example, the assigning unit can input the importance of the data to the generation AI and cause the generation AI to execute processing to adjust the level of detail of the metadata.

[0042] The assignment unit can apply different metadata items depending on the data category. Categories include, for example, business categories and data categories. In the assignment unit, for example, the generation AI identifies the data category and assigns appropriate metadata items for each category. The assignment unit can also set metadata items required for a specific business based on the data category. The assignment unit can also analyze the data category and apply different metadata assignment policies for each category. This improves the efficiency of data management by applying metadata items according to the data category. Some or all of the above-mentioned processing in the assignment unit can be performed using, or without, the generation AI. For example, the assignment unit can input the data category to the generation AI and have the generation AI apply the metadata items.

[0043] The assignment unit can improve accuracy by referring to the user's past metadata assignment history. The past metadata assignment history includes, for example, a method for storing and analyzing history data. For example, the assignment unit uses a generation AI to analyze the user's past metadata assignment history and propose optimal metadata items. The assignment unit can also provide an efficient metadata assignment method by having the generation AI learn the user's past metadata assignment patterns. The assignment unit can also maintain consistency of the metadata based on the user's past metadata assignment history. This maintains consistency of the metadata by referring to the user's past metadata assignment history. Some or all of the above-described processing in the assignment unit may be performed using, or without, the generation AI. For example, the assignment unit can input the user's past metadata assignment history into the generation AI and have the generation AI assign the metadata.

[0044] The assignment unit can adjust metadata items based on the time of data submission. The submission time includes, for example, a submission deadline and a submission date. For example, the assignment unit analyzes the time of data submission by the generation AI and assigns detailed metadata to the latest data. The assignment unit can also assign simplified metadata to older data based on the time of data submission by the generation AI. The assignment unit can also adjust the frequency of metadata updates taking into account the time of data submission by the generation AI. This improves the efficiency of data management by adjusting metadata items based on the time of data submission. Some or all of the above-mentioned processing in the assignment unit may be performed using, or without, the generation AI. For example, the assignment unit can input the time of data submission to the generation AI and cause the generation AI to execute processing to adjust the metadata items.

[0045] The assignment unit can adjust the order of metadata based on the relevance of the data. The order includes, for example, order of importance or order of relevance. For example, the assignment unit allows the generation AI to analyze the relevance of the data and assign related metadata items preferentially. The assignment unit can also optimize the order of metadata based on the relevance of the data. The assignment unit can also allow the generation AI to evaluate the relevance of the data and adjust the metadata assignment policy. This improves the efficiency of data management by adjusting the order of metadata based on the relevance of the data. Some or all of the above-mentioned processing in the assignment unit may be performed using, or without, the generation AI. For example, the assignment unit can input the relevance of the data to the generation AI and cause the generation AI to execute processing to adjust the order of the metadata.

[0046] The assigning unit can adjust the use of technical terms in the metadata according to the user's level of expertise. Expertise levels include, for example, beginner, intermediate, and advanced. For example, the assigning unit can have the generation AI evaluate the user's level of expertise and assign metadata using simple terms to beginners. The assigning unit can also assign metadata using detailed technical terms to experts based on the user's level of expertise. The assigning unit can also adjust the metadata description by having the generation AI take the user's level of expertise into consideration. This improves the efficiency of data management by adjusting the use of technical terms in the metadata according to the user's level of expertise. Some or all of the above-described processing in the assigning unit can be performed using, or without, the generation AI. For example, the assigning unit can input the user's level of expertise to the generation AI and cause the generation AI to perform a process of adjusting the use of technical terms in the metadata.

[0047] The setting unit can adjust the level of detail of an item based on the importance of a specific task. The importance includes, for example, business impact and access frequency. For example, the setting unit allows the generation AI to analyze the importance of a task and set detailed metadata items for important tasks. The setting unit can also allow the generation AI to set simplified metadata items for general tasks according to the importance of the task. The setting unit can also allow the generation AI to evaluate the importance of a task and adjust the update frequency of the metadata items. This improves the efficiency of data management by adjusting the level of detail of an item based on the importance of a specific task. Some or all of the above-described processing in the setting unit may be performed using, or without, the generation AI. For example, the setting unit can input the importance of a task to the generation AI and cause the generation AI to execute a process of adjusting the level of detail of an item.

[0048] The setting unit can apply different setting algorithms depending on the business category. Setting algorithms include, for example, rule-based and machine learning-based algorithms. For example, the setting unit allows the generation AI to identify the business category and set appropriate metadata items for each category. The setting unit can also allow the generation AI to set metadata items required for a specific business based on the business category. The setting unit can also allow the generation AI to analyze the business category and apply different metadata setting policies for each category. This improves the efficiency of data management by applying setting algorithms according to the business category. Some or all of the above-mentioned processing in the setting unit may be performed using, or without, the generation AI. For example, the setting unit can input the business category to the generation AI and cause the generation AI to apply the setting algorithm.

[0049] The setting unit can improve accuracy by referring to the user's past setting history. The past setting history includes, for example, a method for saving and analyzing history data. For example, the setting unit uses a generation AI to analyze the user's past setting history and suggest optimal metadata items. The setting unit can also have the generation AI learn the user's past setting patterns and provide an efficient metadata setting method. The setting unit can also have the generation AI maintain consistency of the metadata based on the user's past setting history. In this way, the consistency of the metadata is maintained by referring to the user's past setting history. Some or all of the above-mentioned processing in the setting unit may be performed using, or without, the generation AI. For example, the setting unit can input the user's past setting history into the generation AI and have the generation AI set the metadata.

[0050] The setting unit can adjust the priority of items based on the time of submission of the work. The submission time includes, for example, a submission deadline and a submission date. For example, the setting unit analyzes the time of submission of the work by the generation AI and sets detailed metadata items for the latest work. The setting unit can also set simplified metadata items for older work based on the time of submission of the work by the generation AI. The setting unit can also adjust the update frequency of the metadata items by the generation AI taking into account the time of submission of the work. This improves the efficiency of data management by adjusting the priority of items based on the time of submission of the work. Some or all of the above-mentioned processing in the setting unit may be performed using, or without, the generation AI. For example, the setting unit can input the time of submission of the work to the generation AI and cause the generation AI to execute a process of adjusting the priority of items.

[0051] The setting unit can adjust the order of the items based on the business relevance. The order includes, for example, order of importance or order of relevance. For example, the setting unit allows the generation AI to analyze the business relevance and prioritize setting related metadata items. The setting unit can also allow the generation AI to optimize the order of the metadata items based on the business relevance. The setting unit can also allow the generation AI to evaluate the business relevance and adjust the setting policy for the metadata items. This improves the efficiency of data management by adjusting the order of the items based on the business relevance. Some or all of the above-mentioned processing in the setting unit may be performed using, or without, the generation AI. For example, the setting unit can input the business relevance to the generation AI and cause the generation AI to execute processing to adjust the order of the items.

[0052] The setting unit can adjust the use of technical terms for an item according to the user's level of expertise. Expertise levels include, for example, beginner, intermediate, and advanced. For example, the setting unit allows the generation AI to evaluate the user's level of expertise and set metadata items using simple terms for beginners. The setting unit can also allow the generation AI to set metadata items using detailed technical terms for experts based on the user's level of expertise. The setting unit can also allow the generation AI to adjust the description of the metadata item taking the user's level of expertise into consideration. This improves the efficiency of data management by adjusting the use of technical terms for an item according to the user's level of expertise. Some or all of the above-described processing in the setting unit can be performed using, or without, the generation AI. For example, the setting unit can input the user's level of expertise into the generation AI and cause the generation AI to perform a process of adjusting the use of technical terms for an item.

[0053] The organizing unit can adjust the level of detail of the organizing based on the importance of the data. The level of detail includes, for example, a detailed explanation or a concise explanation. For example, the generating AI analyzes the importance of the data and applies a detailed organizing method to important data. The organizing unit can also apply a simplified organizing method to general data depending on the importance of the data. The organizing unit can also evaluate the importance of the data and adjust the frequency of organizing updates. This improves the efficiency of data management by adjusting the level of detail of the organizing based on the importance of the data. Some or all of the above-mentioned processing in the organizing unit may be performed using, or without, the generating AI. For example, the organizing unit can input the importance of the data to the generating AI and cause the generating AI to execute processing to adjust the level of detail of the organizing.

[0054] The sorting unit can apply different sorting algorithms depending on the data category. Examples of sorting algorithms include rule-based and machine learning-based algorithms. For example, the sorting unit uses a generation AI to identify the data category and apply an appropriate sorting algorithm for each category. The sorting unit can also use a generation AI to apply a sorting method required for a specific task based on the data category. The sorting unit can also use a generation AI to analyze the data category and apply different sorting policies for each category. This improves the efficiency of data management by applying a sorting algorithm according to the data category. Some or all of the above-described processing in the sorting unit can be performed using, or without, the generation AI. For example, the sorting unit can input the data category to the generation AI and have the generation AI apply the sorting algorithm.

[0055] The organizing unit can improve accuracy by referring to the user's past organizing history. The past organizing history includes, for example, a method for saving and analyzing history data. In the organizing unit, for example, the generation AI analyzes the user's past organizing history and proposes an optimal organizing method. In addition, the organizing unit can also provide an efficient organizing method by having the generation AI learn the user's past organizing patterns. In addition, the organizing unit can also maintain data consistency based on the user's past organizing history. In this way, data consistency is maintained by referring to the user's past organizing history. Some or all of the above-mentioned processing in the organizing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the organizing unit can input the user's past organizing history into the generation AI and cause the generation AI to execute processing to improve the accuracy of the organization.

[0056] The organizing unit can adjust the priority of organizing based on the time of data submission. The submission time includes, for example, a submission deadline and a submission date. For example, the organizing unit analyzes the time of data submission by the generation AI and prioritizes organizing the latest data. The organizing unit can also postpone older data based on the time of data submission by the generation AI. The organizing unit can also adjust the frequency of organizing updates taking into account the time of data submission by the generation AI. This improves the efficiency of data management by adjusting the priority of organizing based on the time of data submission. Some or all of the above-mentioned processing in the organizing unit may be performed using, or without, the generation AI. For example, the organizing unit can input the time of data submission to the generation AI and cause the generation AI to execute processing to adjust the priority of organizing.

[0057] The organizing unit can adjust the order of organizing based on the relevance of the data. The order includes, for example, order of importance or order of relevance. For example, the organizing unit uses a generation AI to analyze the relevance of the data and prioritize organizing related data. The organizing unit can also optimize the order of organizing based on the relevance of the data. The organizing unit can also evaluate the relevance of the data and adjust the organizing policy. This improves the efficiency of data management by adjusting the order of organizing based on the relevance of the data. Some or all of the above-mentioned processing in the organizing unit may be performed using, or without, the generation AI. For example, the organizing unit can input the relevance of the data to the generation AI and cause the generation AI to execute processing to adjust the order of organizing.

[0058] The organizing unit can adjust the use of specialized terminology for organization according to the user's level of expertise. Expertise levels include, for example, beginner, intermediate, and advanced. For example, the generating AI can evaluate the user's level of expertise and organize data using simple terms for beginners. The organizing unit can also organize data using detailed specialized terminology for experts based on the user's level of expertise. The organizing unit can also adjust the data description by having the generating AI take the user's level of expertise into consideration. This improves the efficiency of data management by adjusting the use of specialized terminology for organization according to the user's level of expertise. Some or all of the above-described processing in the organizing unit can be performed, for example, using the generating AI, or can be performed without using the generating AI. For example, the organizing unit can input the user's level of expertise into the generating AI and cause the generating AI to perform a process of adjusting the use of specialized terminology for organization.

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

[0060] The management unit can select a storage destination based on the geographic distribution of data. For example, it can select the optimal storage destination for each region, taking into account the location of the data center and the location of the user. It can also replicate data based on the geographic distribution of data to strengthen disaster prevention measures. It can also optimize data transfer speeds by taking the geographic distribution of data into account. This improves the efficiency of data management by selecting a storage destination based on the geographic distribution of data.

[0061] The setting unit can adjust the priority of items based on the time of submission of work. For example, detailed metadata items can be set for the latest work, taking into account the submission deadline and submission date. Also, simplified metadata items can be set for older work. Furthermore, the update frequency of metadata items can be adjusted taking into account the time of submission of work. In this way, by adjusting the priority of items based on the time of submission of work, the efficiency of data management is improved.

[0062] The management unit can optimize storage destinations based on the importance of the data. For example, important data can be placed in highly secure storage destinations, taking into account business impact and access frequency. Frequently accessed data can also be placed in high-speed storage. Furthermore, the importance of data can be evaluated and the frequency of backups can be adjusted. This improves the efficiency of data management by optimizing storage destinations based on the importance of data.

[0063] The metadata assignment unit can apply different metadata items depending on the data category. For example, it can identify business categories and data categories and assign appropriate metadata items to each category. It can also set metadata items required for specific business operations. It can also apply different metadata assignment policies to each category. This improves the efficiency of data management by applying metadata items according to the data category.

[0064] The organizer can adjust the order of organization based on the relevance of data. For example, it prioritizes organizing related data by taking into account the order of importance or relevance. It can also optimize the order of organization based on the relevance of data. It can also evaluate the relevance of data and adjust the organization policy. This improves the efficiency of data management by adjusting the order of organization based on the relevance of data.

[0065] The assignment unit can improve accuracy by referring to the user's past metadata assignment history. For example, the assignment unit can analyze the user's past metadata assignment history using a method for storing and analyzing history data and propose optimal metadata items. The unit can also learn the user's past metadata assignment patterns and provide an efficient metadata assignment method. Furthermore, the unit can maintain consistency of metadata based on the user's past metadata assignment history. As a result, consistency of metadata is maintained by referring to the user's past metadata assignment history.

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

[0067] Step 1: The management unit manages the data storage destination. For example, the management unit manages the storage destination of data aggregated in a data lake or data warehouse. The management unit can also perform database management and access control. For example, the management unit manages the storage location and access rights of each data item. Step 2: The assigning unit assigns metadata to the data. The assigning unit sets metadata items such as creation date, administrator, data type, data description, and update frequency, and assigns tags to the data. For example, the assigning unit assigns tags to the data to make management easier. Step 3: The setting unit sets metadata items. For example, the setting unit sets metadata items required for a specific business. For example, the setting unit sets metadata items required for a specific business to make it easier to manage data. Step 4: The organizer organizes the data based on the metadata. For example, the organizer classifies the data based on specific conditions and organizes it to make it easier to search. For example, the organizer classifies the data based on specific conditions and organizes it to make it easier to search.

[0068] (Example 2) A data wrangling system according to an embodiment of the present invention is a system that uses generative AI to organize and cleanse data. The data wrangling system manages data storage destinations, assigns metadata, sets metadata items, and organizes the data based on the metadata. For example, the data wrangling system manages storage destinations for data aggregated in a data lake or data warehouse. Next, the data wrangling system assigns metadata items such as creation date, administrator, data type, data description, and update frequency, and assigns tags to the data. Furthermore, the data wrangling system assigns metadata items required for specific business operations to make data easier to manage. Finally, the data wrangling system classifies data based on specific conditions and organizes it for easy search. This improves data searchability and maintains data integrity. This improves data searchability and maintains data integrity. For example, when searching for data based on specific conditions, utilizing metadata allows for quick search results. Furthermore, maintaining data integrity improves data quality and improves business efficiency.

[0069] A data wrangling system according to an embodiment includes a management unit, an assignment unit, a setting unit, and an organization unit. The management unit manages data storage destinations. For example, the management unit manages storage destinations of data aggregated in a data lake or a data warehouse. The management unit can also perform database management and access control. For example, the management unit manages the storage location and access permissions of each piece of data. The assignment unit assigns metadata to the data. The assignment unit sets metadata items such as creation date, administrator, data type, data description, and update frequency, and assigns tags to the data. For example, the assignment unit assigns tags to the data to facilitate management. The setting unit sets metadata items. For example, the setting unit sets metadata items required for a specific business. For example, the setting unit sets metadata items required for a specific business to facilitate data management. The organization unit organizes the data based on the metadata. For example, the organization unit classifies data based on specific conditions and organizes it to make it easier to search. For example, the organization unit classifies data based on specific conditions and organizes it to make it easier to search. As a result, the data wrangling system according to the embodiment can efficiently manage data storage destinations, assign metadata, set items, and organize data. For example, the data wrangling system manages data storage destinations, assigns metadata, sets metadata items, and organizes data based on the metadata. This improves data searchability and maintains data integrity.

[0070] The management unit can manage the storage destination of the data aggregated in the data lake or data warehouse. A data lake stores, for example, structured data and unstructured data. A data warehouse includes, for example, a data aggregation method and an analysis function. The management unit, for example, manages the storage destination of the data aggregated in the data lake or data warehouse. For example, the management unit manages the storage location and access rights of each data. This allows efficient management of the data storage destination of the data lake or data warehouse. Some or all of the above-mentioned processing in the management unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the management unit can input the storage destination of the data aggregated in the data lake or data warehouse into the generation AI and have the generation AI manage the storage destination.

[0071] The tagging unit can set metadata items such as creation date, administrator, data type, data description, and update frequency, and tag the data. The creation date includes, for example, a timestamp and a date format. The administrator includes, for example, the data owner and access rights. The data type includes, for example, text data and image data. The data description includes, for example, an overview and a detailed description. The update frequency includes, for example, daily updates and weekly updates. The tagging unit sets metadata items such as creation date, administrator, data type, data description, and update frequency, and tag the data. For example, the tagging unit tags the data to facilitate management. By assigning metadata to the data, data management becomes easier. Some or all of the above-described processing in the tagging unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the tagging unit inputs metadata items to be assigned to the data into the generation AI and causes the generation AI to assign the metadata.

[0072] The setting unit can set metadata items required for a specific business. Examples of specific business operations include sales and manufacturing. The setting unit, for example, sets the metadata items required for the specific business. For example, the setting unit sets the metadata items required for the specific business to make data management easier. This makes data management more efficient by setting metadata items according to the business. Some or all of the above-described processing in the setting unit may be performed using, or without, a generation AI. For example, the setting unit inputs metadata items required for the specific business operation into the generation AI and causes the generation AI to set the metadata items.

[0073] The organizing unit can classify data based on specific conditions and organize it to make it easier to search. The specific conditions include, for example, the type of data and the creation date. The organizing unit, for example, classifies data based on specific conditions and organizes it to make it easier to search. For example, the organizing unit classifies data based on specific conditions and organizes it to make it easier to search. By classifying data and making it easier to search, the efficiency of data use is improved. Some or all of the above-mentioned processing in the organizing unit may be performed using, or without, the generation AI. For example, the organizing unit may input conditions for classifying data based on specific conditions into the generation AI and cause the generation AI to classify the data.

[0074] The organizing unit can maintain data consistency by utilizing metadata. Consistency includes, for example, consistency checks and data validation. The organizing unit, for example, maintains data consistency by utilizing metadata. For example, the organizing unit maintains data consistency by utilizing metadata. In this way, data consistency is maintained by utilizing the metadata. Some or all of the above-mentioned processing in the organizing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the organizing unit can input conditions for maintaining data consistency using metadata into the generation AI, and cause the generation AI to execute processing for maintaining data consistency.

[0075] The management unit can estimate the user's emotions and dynamically change the data storage destination based on the estimated user emotions. For example, when the user is feeling stressed, the management unit automatically optimizes the data storage destination, thereby reducing the user's burden. Furthermore, when the user is relaxed, the management unit can flexibly change the data storage destination to suit the user's preferences. Furthermore, when the user is in a hurry, the management unit can quickly change the data storage destination, thereby achieving efficient data management. This reduces the user's burden by dynamically changing the data storage destination according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the management unit may be performed using, for example, the generation AI. For example, the management unit can input the user's emotion data into the generation AI and cause the generation AI to execute a process of dynamically changing the data storage destination based on the emotion.

[0076] The management unit can optimize the storage destination based on the importance of the data. The importance of the data includes, for example, business impact and access frequency. In the management unit, for example, the generation AI analyzes the importance of the data and places important data in a highly secure storage destination. The management unit can also have the generation AI place frequently accessed data in high-speed storage according to the importance of the data. The management unit can also have the generation AI evaluate the importance of the data and adjust the frequency of backups. In this way, optimizing the storage destination based on the importance of the data improves the efficiency of data management. Some or all of the above-mentioned processing in the management unit may be performed using, or without, the generation AI. For example, the management unit can input the importance of the data into the generation AI and have the generation AI optimize the storage destination.

[0077] The management unit can adjust the storage destination based on the access frequency of the data. The access frequency includes, for example, the number of daily accesses and the number of monthly accesses. For example, the management unit allows the generation AI to analyze the access frequency of the data and place frequently accessed data in high-speed storage. The management unit can also allow the generation AI to move infrequently accessed data to archive storage based on the access frequency of the data. The management unit can also allow the generation AI to learn the access patterns of the data and dynamically adjust the optimal storage destination. This improves data search efficiency by adjusting the storage destination based on the access frequency of the data. Some or all of the above-mentioned processing in the management unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the management unit can input the access frequency of the data to the generation AI and have the generation AI adjust the storage destination.

[0078] The management unit can apply different storage policies depending on the type of data. The storage policy may include, for example, a data retention period and a backup method. In the management unit, for example, the generation AI identifies the type of data and applies a strict security policy to confidential data. In addition, the management unit can also store image data in dedicated storage based on the type of data. In addition, the management unit can also have the generation AI analyze the type of data and apply a compression policy to text data. This improves the efficiency of data management by applying a storage policy according to the type of data. Some or all of the above-mentioned processing in the management unit may be performed using, or without, the generation AI. For example, the management unit can input the type of data to the generation AI and have the generation AI apply the storage policy.

[0079] The management unit can estimate the user's emotions and prioritize data storage destinations based on the estimated user emotions. For example, when the user is feeling stressed, the management unit allows the generation AI to prioritize storing important data, thereby reducing the user's burden. Furthermore, when the user is relaxed, the management unit can flexibly adjust the data storage destinations to suit the user's preferences. Furthermore, when the user is in a hurry, the management unit can prioritize storing data that the generation AI can access quickly. This reduces the user's burden by prioritizing data storage destinations based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the management unit may be performed using, for example, the generation AI, or without the generation AI. For example, the management unit can input the user's emotion data into the generation AI and cause the generation AI to execute a process of prioritizing data storage destinations based on the emotion.

[0080] The management unit can select a storage destination based on the geographic distribution of the data. Geographic distribution includes, for example, the location of data centers and the locations of users. In the management unit, for example, the generation AI analyzes the geographic distribution of the data and selects the optimal storage destination for each region. The management unit can also have the generation AI replicate the data based on the geographic distribution of the data to strengthen disaster prevention measures. The management unit can also have the generation AI optimize the data transfer speed by taking the geographic distribution of the data into consideration. In this way, selecting a storage destination based on the geographic distribution of the data improves the efficiency of data management. Some or all of the above-mentioned processing in the management unit may be performed using, or without, the generation AI. For example, the management unit can input the geographic distribution of the data into the generation AI and have the generation AI select a storage destination.

[0081] The management unit can group storage destinations based on data relevance. Relevance includes, for example, common tags and related metadata. For example, the management unit allows the generation AI to analyze data relevance and group related data into the same storage destination. The management unit can also allow the generation AI to group data based on data relevance to improve data search efficiency. The management unit can also allow the generation AI to evaluate data relevance and optimize a data backup strategy. As a result, data search efficiency is improved by grouping storage destinations based on data relevance. Some or all of the above-mentioned processing in the management unit can be performed using, or without, the generation AI. For example, the management unit can input data relevance to the generation AI and have the generation AI group the storage destinations.

[0082] The management unit can divide the storage destinations according to the security level of the data. Security levels include, for example, confidential data and public data. In the management unit, for example, the generation AI evaluates the security level of the data and places confidential data in a high-security storage destination. In addition, the management unit can also place general data in low-cost storage based on the security level of the data. In addition, the management unit can also have the generation AI analyze the security level of the data and apply an encryption policy to the data. In this way, data security is improved by dividing the storage destinations based on the security level of the data. Some or all of the above-mentioned processing in the management unit may be performed using, or without, the generation AI. For example, the management unit can input the security level of the data to the generation AI and have the generation AI divide the storage destinations.

[0083] The attachment unit can estimate the user's emotions and adjust the metadata attachment method based on the estimated user emotions. For example, when the user is feeling stressed, the attachment unit can have the generation AI provide a simple metadata attachment method, thereby reducing the user's burden. Furthermore, when the user is relaxed, the attachment unit can have the generation AI provide detailed metadata attachment options and suggest a customizable attachment method. Furthermore, when the user is in a hurry, the attachment unit can enable the generation AI to prioritize voice input and quickly attach metadata. This reduces the user's burden by adjusting the metadata attachment method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the attachment unit can be performed using, for example, the generation AI. For example, the attachment unit can input user emotion data to the generation AI and cause the generation AI to execute a process of adjusting the metadata attachment method based on the emotion.

[0084] The assigning unit can adjust the level of detail of the metadata based on the importance of the data. The level of detail includes, for example, a detailed description or a concise description. For example, the assigning unit allows the generation AI to analyze the importance of the data and assign detailed metadata to important data. The assigning unit can also assign simplified metadata to general data depending on the importance of the data. The assigning unit can also allow the generation AI to evaluate the importance of the data and adjust the frequency of metadata updates. This improves the efficiency of data management by adjusting the level of detail of the metadata based on the importance of the data. Some or all of the above-mentioned processing in the assigning unit may be performed using, or without, the generation AI. For example, the assigning unit can input the importance of the data to the generation AI and cause the generation AI to execute processing to adjust the level of detail of the metadata.

[0085] The assignment unit can apply different metadata items depending on the data category. Categories include, for example, business categories and data categories. In the assignment unit, for example, the generation AI identifies the data category and assigns appropriate metadata items for each category. The assignment unit can also set metadata items required for a specific business based on the data category. The assignment unit can also analyze the data category and apply different metadata assignment policies for each category. This improves the efficiency of data management by applying metadata items according to the data category. Some or all of the above-mentioned processing in the assignment unit can be performed using, or without, the generation AI. For example, the assignment unit can input the data category to the generation AI and have the generation AI apply the metadata items.

[0086] The assignment unit can improve accuracy by referring to the user's past metadata assignment history. The past metadata assignment history includes, for example, a method for storing and analyzing history data. For example, the assignment unit uses a generation AI to analyze the user's past metadata assignment history and propose optimal metadata items. The assignment unit can also provide an efficient metadata assignment method by having the generation AI learn the user's past metadata assignment patterns. The assignment unit can also maintain consistency of the metadata based on the user's past metadata assignment history. This maintains consistency of the metadata by referring to the user's past metadata assignment history. Some or all of the above-described processing in the assignment unit may be performed using, or without, the generation AI. For example, the assignment unit can input the user's past metadata assignment history into the generation AI and have the generation AI assign the metadata.

[0087] The attachment unit can estimate the user's emotions and determine the priority of metadata based on the estimated user emotions. For example, when the user is feeling stressed, the attachment unit allows the generation AI to prioritize important metadata, thereby reducing the user's burden. Furthermore, when the user is relaxed, the attachment unit can also allow the generation AI to adjust the priority of metadata to suit the user's preferences. Furthermore, when the user is in a hurry, the attachment unit can also prioritize metadata that the generation AI can quickly assign. This reduces the user's burden by determining the priority of metadata based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the attachment unit may be performed using, for example, the generation AI. For example, the attachment unit can input the user's emotion data into the generation AI and cause the generation AI to execute a process of determining the priority of metadata based on emotions.

[0088] The assignment unit can adjust metadata items based on the time of data submission. The submission time includes, for example, a submission deadline and a submission date. For example, the assignment unit analyzes the time of data submission by the generation AI and assigns detailed metadata to the latest data. The assignment unit can also assign simplified metadata to older data based on the time of data submission by the generation AI. The assignment unit can also adjust the frequency of metadata updates taking into account the time of data submission by the generation AI. This improves the efficiency of data management by adjusting metadata items based on the time of data submission. Some or all of the above-mentioned processing in the assignment unit may be performed using, or without, the generation AI. For example, the assignment unit can input the time of data submission to the generation AI and cause the generation AI to execute processing to adjust the metadata items.

[0089] The assignment unit can adjust the order of metadata based on the relevance of the data. The order includes, for example, order of importance or order of relevance. For example, the assignment unit allows the generation AI to analyze the relevance of the data and assign related metadata items preferentially. The assignment unit can also optimize the order of metadata based on the relevance of the data. The assignment unit can also allow the generation AI to evaluate the relevance of the data and adjust the metadata assignment policy. This improves the efficiency of data management by adjusting the order of metadata based on the relevance of the data. Some or all of the above-mentioned processing in the assignment unit may be performed using, or without, the generation AI. For example, the assignment unit can input the relevance of the data to the generation AI and cause the generation AI to execute processing to adjust the order of the metadata.

[0090] The assigning unit can adjust the use of technical terms in the metadata according to the user's level of expertise. Expertise levels include, for example, beginner, intermediate, and advanced. For example, the assigning unit can have the generation AI evaluate the user's level of expertise and assign metadata using simple terms to beginners. The assigning unit can also assign metadata using detailed technical terms to experts based on the user's level of expertise. The assigning unit can also adjust the metadata description by having the generation AI take the user's level of expertise into consideration. This improves the efficiency of data management by adjusting the use of technical terms in the metadata according to the user's level of expertise. Some or all of the above-described processing in the assigning unit can be performed using, or without, the generation AI. For example, the assigning unit can input the user's level of expertise to the generation AI and cause the generation AI to perform a process of adjusting the use of technical terms in the metadata.

[0091] The setting unit can estimate the user's emotions and adjust the metadata item setting method based on the estimated user emotions. For example, when the user is feeling stressed, the setting unit can cause the generation AI to provide a simple metadata item setting method, thereby reducing the user's burden. Furthermore, when the user is relaxed, the setting unit can cause the generation AI to provide detailed metadata item setting options and suggest a customizable setting method. Furthermore, when the user is in a hurry, the setting unit can cause the generation AI to prioritize voice input and quickly set metadata items. This reduces the user's burden by adjusting the metadata item setting method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the setting unit can be performed using, for example, the generation AI, or without the generation AI. For example, the setting unit can input user emotion data into the generation AI and cause the generation AI to execute a process of adjusting the metadata item setting method based on the emotion.

[0092] The setting unit can adjust the level of detail of an item based on the importance of a specific task. The importance includes, for example, business impact and access frequency. For example, the setting unit allows the generation AI to analyze the importance of a task and set detailed metadata items for important tasks. The setting unit can also allow the generation AI to set simplified metadata items for general tasks according to the importance of the task. The setting unit can also allow the generation AI to evaluate the importance of a task and adjust the update frequency of the metadata items. This improves the efficiency of data management by adjusting the level of detail of an item based on the importance of a specific task. Some or all of the above-described processing in the setting unit may be performed using, or without, the generation AI. For example, the setting unit can input the importance of a task to the generation AI and cause the generation AI to execute a process of adjusting the level of detail of an item.

[0093] The setting unit can apply different setting algorithms depending on the business category. Setting algorithms include, for example, rule-based and machine learning-based algorithms. For example, the setting unit allows the generation AI to identify the business category and set appropriate metadata items for each category. The setting unit can also allow the generation AI to set metadata items required for a specific business based on the business category. The setting unit can also allow the generation AI to analyze the business category and apply different metadata setting policies for each category. This improves the efficiency of data management by applying setting algorithms according to the business category. Some or all of the above-mentioned processing in the setting unit may be performed using, or without, the generation AI. For example, the setting unit can input the business category to the generation AI and cause the generation AI to apply the setting algorithm.

[0094] The setting unit can improve accuracy by referring to the user's past setting history. The past setting history includes, for example, a method for saving and analyzing history data. For example, the setting unit uses a generation AI to analyze the user's past setting history and suggest optimal metadata items. The setting unit can also have the generation AI learn the user's past setting patterns and provide an efficient metadata setting method. The setting unit can also have the generation AI maintain consistency of the metadata based on the user's past setting history. In this way, the consistency of the metadata is maintained by referring to the user's past setting history. Some or all of the above-mentioned processing in the setting unit may be performed using, or without, the generation AI. For example, the setting unit can input the user's past setting history into the generation AI and have the generation AI set the metadata.

[0095] The setting unit can estimate the user's emotions and prioritize metadata items based on the estimated user emotions. For example, when the user is feeling stressed, the setting unit causes the generation AI to prioritize important metadata items, thereby reducing the user's burden. Furthermore, when the user is relaxed, the setting unit can also cause the generation AI to adjust the priority of metadata items to suit the user's preferences. Furthermore, when the user is in a hurry, the setting unit can also prioritize metadata items that the generation AI can set quickly. This reduces the user's burden by prioritizing metadata items according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the setting unit may be performed using, for example, the generation AI. For example, the setting unit can input user emotion data to the generation AI and cause the generation AI to execute a process of prioritizing metadata items based on emotions.

[0096] The setting unit can adjust the priority of items based on the time of submission of the work. The submission time includes, for example, a submission deadline and a submission date. For example, the setting unit analyzes the time of submission of the work by the generation AI and sets detailed metadata items for the latest work. The setting unit can also set simplified metadata items for older work based on the time of submission of the work by the generation AI. The setting unit can also adjust the update frequency of the metadata items by the generation AI taking into account the time of submission of the work. This improves the efficiency of data management by adjusting the priority of items based on the time of submission of the work. Some or all of the above-mentioned processing in the setting unit may be performed using, or without, the generation AI. For example, the setting unit can input the time of submission of the work to the generation AI and cause the generation AI to execute a process of adjusting the priority of items.

[0097] The setting unit can adjust the order of the items based on the business relevance. The order includes, for example, order of importance or order of relevance. For example, the setting unit allows the generation AI to analyze the business relevance and prioritize setting related metadata items. The setting unit can also allow the generation AI to optimize the order of the metadata items based on the business relevance. The setting unit can also allow the generation AI to evaluate the business relevance and adjust the setting policy for the metadata items. This improves the efficiency of data management by adjusting the order of the items based on the business relevance. Some or all of the above-mentioned processing in the setting unit may be performed using, or without, the generation AI. For example, the setting unit can input the business relevance to the generation AI and cause the generation AI to execute processing to adjust the order of the items.

[0098] The setting unit can adjust the use of technical terms for an item according to the user's level of expertise. Expertise levels include, for example, beginner, intermediate, and advanced. For example, the setting unit allows the generation AI to evaluate the user's level of expertise and set metadata items using simple terms for beginners. The setting unit can also allow the generation AI to set metadata items using detailed technical terms for experts based on the user's level of expertise. The setting unit can also allow the generation AI to adjust the description of the metadata item taking the user's level of expertise into consideration. This improves the efficiency of data management by adjusting the use of technical terms for an item according to the user's level of expertise. Some or all of the above-described processing in the setting unit can be performed using, or without, the generation AI. For example, the setting unit can input the user's level of expertise into the generation AI and cause the generation AI to perform a process of adjusting the use of technical terms for an item.

[0099] The organizing unit can estimate the user's emotions and adjust the data organization method based on the estimated user emotions. For example, if the user is feeling stressed, the organizing unit can cause the generation AI to provide a simple data organization method, thereby reducing the user's burden. Furthermore, if the user is relaxed, the organizing unit can also cause the generation AI to provide detailed data organization options and suggest a customizable organization method. Furthermore, if the user is in a hurry, the organizing unit can also provide a method for the generation AI to quickly organize data. This reduces the user's burden by adjusting the data organization method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the organizing unit can be performed using, for example, the generation AI, or without the generation AI. For example, the organizing unit can input the user's emotion data into the generation AI and cause the generation AI to execute a process of adjusting the data organization method based on the emotion.

[0100] The organizing unit can adjust the level of detail of the organizing based on the importance of the data. The level of detail includes, for example, a detailed explanation or a concise explanation. For example, the generating AI analyzes the importance of the data and applies a detailed organizing method to important data. The organizing unit can also apply a simplified organizing method to general data depending on the importance of the data. The organizing unit can also evaluate the importance of the data and adjust the frequency of organizing updates. This improves the efficiency of data management by adjusting the level of detail of the organizing based on the importance of the data. Some or all of the above-mentioned processing in the organizing unit may be performed using, or without, the generating AI. For example, the organizing unit can input the importance of the data to the generating AI and cause the generating AI to execute processing to adjust the level of detail of the organizing.

[0101] The sorting unit can apply different sorting algorithms depending on the data category. Examples of sorting algorithms include rule-based and machine learning-based algorithms. For example, the sorting unit uses a generation AI to identify the data category and apply an appropriate sorting algorithm for each category. The sorting unit can also use a generation AI to apply a sorting method required for a specific task based on the data category. The sorting unit can also use a generation AI to analyze the data category and apply different sorting policies for each category. This improves the efficiency of data management by applying a sorting algorithm according to the data category. Some or all of the above-described processing in the sorting unit can be performed using, or without, the generation AI. For example, the sorting unit can input the data category to the generation AI and have the generation AI apply the sorting algorithm.

[0102] The organizing unit can improve accuracy by referring to the user's past organizing history. The past organizing history includes, for example, a method for saving and analyzing history data. In the organizing unit, for example, the generation AI analyzes the user's past organizing history and proposes an optimal organizing method. In addition, the organizing unit can also provide an efficient organizing method by having the generation AI learn the user's past organizing patterns. In addition, the organizing unit can also maintain data consistency based on the user's past organizing history. In this way, data consistency is maintained by referring to the user's past organizing history. Some or all of the above-mentioned processing in the organizing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the organizing unit can input the user's past organizing history into the generation AI and cause the generation AI to execute processing to improve the accuracy of the organization.

[0103] The organizing unit can estimate the user's emotions and determine the priority of data organization based on the estimated user emotions. For example, when the user is feeling stressed, the organizing unit allows the generation AI to prioritize important data, thereby reducing the user's burden. Furthermore, when the user is relaxed, the organizing unit can also adjust the priority of data organization to suit the user's preferences. Furthermore, when the user is in a hurry, the organizing unit can prioritize data that the generation AI can quickly organize. This reduces the user's burden by determining the priority of data organization based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the organizing unit may be performed using, for example, the generation AI. For example, the organizing unit can input the user's emotion data into the generation AI and cause the generation AI to execute a process of determining the priority of data organization based on emotions.

[0104] The organizing unit can adjust the priority of organizing based on the time of data submission. The submission time includes, for example, a submission deadline and a submission date. For example, the organizing unit analyzes the time of data submission by the generation AI and prioritizes organizing the latest data. The organizing unit can also postpone older data based on the time of data submission by the generation AI. The organizing unit can also adjust the frequency of organizing updates taking into account the time of data submission by the generation AI. This improves the efficiency of data management by adjusting the priority of organizing based on the time of data submission. Some or all of the above-mentioned processing in the organizing unit may be performed using, or without, the generation AI. For example, the organizing unit can input the time of data submission to the generation AI and cause the generation AI to execute processing to adjust the priority of organizing.

[0105] The organizing unit can adjust the order of organizing based on the relevance of the data. The order includes, for example, order of importance or order of relevance. For example, the organizing unit uses a generation AI to analyze the relevance of the data and prioritize organizing related data. The organizing unit can also optimize the order of organizing based on the relevance of the data. The organizing unit can also evaluate the relevance of the data and adjust the organizing policy. This improves the efficiency of data management by adjusting the order of organizing based on the relevance of the data. Some or all of the above-mentioned processing in the organizing unit may be performed using, or without, the generation AI. For example, the organizing unit can input the relevance of the data to the generation AI and cause the generation AI to execute processing to adjust the order of organizing.

[0106] The organizing unit can adjust the use of specialized terminology for organization according to the user's level of expertise. Expertise levels include, for example, beginner, intermediate, and advanced. For example, the generating AI can evaluate the user's level of expertise and organize data using simple terms for beginners. The organizing unit can also organize data using detailed specialized terminology for experts based on the user's level of expertise. The organizing unit can also adjust the data description by having the generating AI take the user's level of expertise into consideration. This improves the efficiency of data management by adjusting the use of specialized terminology for organization according to the user's level of expertise. Some or all of the above-described processing in the organizing unit can be performed, for example, using the generating AI, or can be performed without using the generating AI. For example, the organizing unit can input the user's level of expertise into the generating AI and cause the generating AI to perform a process of adjusting the use of specialized terminology for organization. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned management unit, assignment unit, setting unit, and organization unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the management unit is realized by the specific processing unit 290 of the data processing device 12 and manages the storage destination of data aggregated in a data lake or a data warehouse. The assignment unit is realized, for example, by the control unit 46A of the smart device 14 and assigns metadata to data. The setting unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and sets metadata items required for specific tasks. The organization unit is realized, for example, by the control unit 46A of the smart device 14 and organizes data based on metadata. === Hard Collateral 1-2 === Each of the multiple elements including the above-described management unit, assignment unit, setting unit, and organization unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the management unit is realized by the specific processing unit 290 of the data processing device 12 and manages the storage destination of data aggregated in a data lake or a data warehouse. The assignment unit is realized, for example, by the control unit 46A of the smart glasses 214 and assigns metadata to data. The setting unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and sets metadata items required for specific tasks. The organization unit is realized, for example, by the control unit 46A of the smart glasses 214 and organizes data based on the metadata. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned management unit, assignment unit, setting unit, and organization unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the management unit is realized by the specific processing unit 290 of the data processing device 12 and manages the storage destination of data aggregated in a data lake or a data warehouse. The assignment unit is realized, for example, by the control unit 46A of the headset type terminal 314 and assigns metadata to data. The setting unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and sets metadata items required for specific tasks. The organization unit is realized, for example, by the control unit 46A of the headset type terminal 314 and organizes data based on the metadata. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned management unit, assignment unit, setting unit, and organization unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the management unit is realized by the specific processing unit 290 of the data processing device 12 and manages the storage destination of data aggregated in a data lake or a data warehouse. The assignment unit is realized, for example, by the control unit 46A of the robot 414 and assigns metadata to data. The setting unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and sets metadata items required for specific tasks. The organization unit is realized, for example, by the control unit 46A of the robot 414 and organizes data based on the metadata.

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

[0108] The management unit can select a storage destination based on the geographic distribution of data. For example, it can select the optimal storage destination for each region, taking into account the location of the data center and the location of the user. It can also replicate data based on the geographic distribution of data to strengthen disaster prevention measures. It can also optimize data transfer speeds by taking the geographic distribution of data into account. This improves the efficiency of data management by selecting a storage destination based on the geographic distribution of data.

[0109] The assigning 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 feeling stressed, a simple metadata assignment method can be provided, reducing the burden on the user. Alternatively, if the user is relaxed, detailed metadata assignment options can be provided and a customizable assignment method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to enable quick metadata assignment. In this way, the burden on the user can be reduced by adjusting the metadata assignment method according to the user's emotions.

[0110] The setting unit can adjust the priority of items based on the time of submission of work. For example, detailed metadata items can be set for the latest work, taking into account the submission deadline and submission date. Also, simplified metadata items can be set for older work. Furthermore, the update frequency of metadata items can be adjusted taking into account the time of submission of work. In this way, by adjusting the priority of items based on the time of submission of work, the efficiency of data management is improved.

[0111] The organizing unit can estimate the user's emotions and adjust the data organization method based on the estimated user emotions. For example, if the user is feeling stressed, a simple data organization method is provided to reduce the user's burden. Alternatively, if the user is relaxed, detailed data organization options can be provided and a customizable organization method can be suggested. Furthermore, if the user is in a hurry, a method that allows the user to quickly organize data can be provided. In this way, the burden on the user is reduced by adjusting the data organization method according to the user's emotions.

[0112] The management unit can optimize storage destinations based on the importance of the data. For example, important data can be placed in highly secure storage destinations, taking into account business impact and access frequency. Frequently accessed data can also be placed in high-speed storage. Furthermore, the importance of data can be evaluated and the frequency of backups can be adjusted. This improves the efficiency of data management by optimizing storage destinations based on the importance of data.

[0113] The metadata assignment unit can apply different metadata items depending on the data category. For example, it can identify business categories and data categories and assign appropriate metadata items to each category. It can also set metadata items required for specific business operations. It can also apply different metadata assignment policies to each category. This improves the efficiency of data management by applying metadata items according to the data category.

[0114] The organizer can adjust the order of organization based on the relevance of data. For example, it prioritizes organizing related data by taking into account the order of importance or relevance. It can also optimize the order of organization based on the relevance of data. It can also evaluate the relevance of data and adjust the organization policy. This improves the efficiency of data management by adjusting the order of organization based on the relevance of data.

[0115] The management unit can estimate the user's emotions and dynamically change the data storage destination based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI automatically optimizes the data storage destination, reducing the user's burden. Also, if the user is relaxed, the generation AI can flexibly change the data storage destination to suit the user's preferences. Furthermore, if the user is in a hurry, the generation AI can quickly change the data storage destination, achieving efficient data management. This reduces the user's burden by dynamically changing the data storage destination according to the user's emotions.

[0116] The assignment unit can improve accuracy by referring to the user's past metadata assignment history. For example, the assignment unit can analyze the user's past metadata assignment history using a method for storing and analyzing history data and propose optimal metadata items. The unit can also learn the user's past metadata assignment patterns and provide an efficient metadata assignment method. Furthermore, the unit can maintain consistency of metadata based on the user's past metadata assignment history. As a result, consistency of metadata is maintained by referring to the user's past metadata assignment history.

[0117] The organizing unit can estimate the user's emotions and determine the priority of data organization based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI will prioritize organizing important data, reducing the user's burden. Also, if the user is relaxed, the generation AI can adjust the priority of data organization to suit the user's preferences. Furthermore, if the user is in a hurry, the generation AI can prioritize data that can be organized quickly. In this way, the burden on the user is reduced by determining the priority of data organization according to the user's emotions.

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

[0119] Step 1: The management unit manages the data storage destination. For example, the management unit manages the storage destination of data aggregated in a data lake or data warehouse. The management unit can also perform database management and access control. For example, the management unit manages the storage location and access rights of each data item. Step 2: The assigning unit assigns metadata to the data. The assigning unit sets metadata items such as creation date, administrator, data type, data description, and update frequency, and assigns tags to the data. For example, the assigning unit assigns tags to the data to make management easier. Step 3: The setting unit sets metadata items. For example, the setting unit sets metadata items required for a specific business. For example, the setting unit sets metadata items required for a specific business to make it easier to manage data. Step 4: The organizer organizes the data based on the metadata. For example, the organizer classifies the data based on specific conditions and organizes it to make it easier to search. For example, the organizer classifies the data based on specific conditions and organizes it to make it easier to search.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

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

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

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

[0157] 7, 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.

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

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

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

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

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

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

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

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

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

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

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

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

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0191] [Explanation of symbols]

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

Claims

1. a management unit that manages data storage destinations; an assigning unit that assigns metadata to the data managed by the management unit; a setting unit that sets the items of the metadata assigned by the assigning unit; An organizing unit that organizes data based on the metadata set by the setting unit. A system characterized by:

2. The management unit Manage where data is stored in a data lake or data warehouse 2. The system of claim 1.

3. The granting unit Set metadata items such as creation date, administrator, data type, data description, and update frequency, and assign tags to the data.

2. The system of claim 1.

4. The setting unit Set metadata items required for specific tasks 2. The system of claim 1.

5. The organizing unit Categorize data based on specific criteria and organize it for easy searching 2. The system of claim 1.

6. The organizing unit Leveraging metadata to maintain data integrity 2. The system of claim 1.

7. The management unit Estimate the user's emotions and dynamically change the data storage location based on the estimated user emotions.

2. The system of claim 1.

8. The management unit Optimize storage location based on data importance 2. The system of claim 1.

9. The management unit Adjust storage location based on data access frequency 2. The system of claim 1.

10. The management unit Apply different storage policies to different types of data 2. The system of claim 1.

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