System

Abstract

An object of a system according to an embodiment is to efficiently organize and centrally manage data.SOLUTION: A system according to an embodiment includes a data acquisition unit, a metadata generation unit, and a data arrangement unit. The data acquisition unit acquires data. The metadata generation unit adds metadata to the data acquired by the data acquisition unit. The data organization unit organizes the data based on the metadata assigned by the metadata generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, centralized management and organization of data is often done manually, making efficient data management difficult.

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

[0006] A system according to an embodiment includes a data acquisition unit, a metadata generation unit, and a data organization unit. The data acquisition unit acquires data. The metadata generation unit assigns metadata to the data acquired by the data acquisition unit. The data organization unit organizes the data based on the metadata assigned by the metadata generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently organize and centrally manage 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 management system according to an embodiment of the present invention is a system in which a generative AI centrally manages data and automates data wrangling, thereby improving the efficiency of data organization and management.

[0029] A data management system according to an embodiment includes a data acquisition unit, a metadata generation unit, and a data organization unit. The data acquisition unit acquires data. For example, the data acquisition unit aggregates data collected from different departments into a single data lake. The data acquisition unit can also aggregate data into a data warehouse. The data acquisition unit can also acquire data in real time. For example, the data acquisition unit acquires data through an API. The metadata generation unit assigns metadata to the data acquired by the data acquisition unit. For example, the metadata generation unit assigns information such as the data creation date, administrator, data type, data description, and update frequency as metadata. The metadata generation unit can also analyze the content of the data using a generation AI and automatically generate appropriate metadata. The metadata generation unit can also customize tags based on conditions set by a user. For example, data related to a specific project can be tagged with the project name. The data organization unit organizes the data based on the metadata assigned by the metadata generation unit. For example, the data organization unit can search and filter data based on specific conditions. The data organization unit can also monitor the update status of the data using a generation AI and update the metadata as necessary. The data organization unit can also analyze the importance of data and prioritize data storage destinations according to the importance. This allows the data management system according to the embodiment to centrally manage and efficiently organize data. For example, the data management system centrally manages data collected from different departments within a company, and the generation AI automatically assigns metadata, making it easier to organize and manage the data. Furthermore, data can be quickly searched and filtered based on specific conditions, improving work efficiency. Furthermore, tags can be customized to meet user needs, enabling flexible data management.

[0030] The data acquisition unit can analyze the importance of the data and prioritize data storage destinations according to its importance. In the data acquisition unit, for example, the generation AI analyzes the contents of the data and scores its importance. For example, data that is directly related to business or legally important is given a high score and stored preferentially in storage that can be accessed quickly. In addition, the generation AI in the data acquisition unit analyzes the frequency of data use and prioritizes storing frequently accessed data in cache. For example, data that is frequently used in daily operations is placed in a location that can be accessed quickly. In addition, the generation AI in the data acquisition unit analyzes the frequency of data updates and prioritizes storing frequently updated data in dynamic storage. For example, data that is updated in real time is placed in storage that can immediately reflect the data. This enables efficient storage according to the data's importance.

[0031] The metadata generation unit can analyze the interrelationships between data and automatically link highly related data. For example, the generation AI in the metadata generation unit analyzes the content of the data and links highly related data. For example, it automatically links documents and data sets related to the same project. The generation AI in the metadata generation unit also analyzes the metadata of the data and links data with common tags. For example, it displays data with the same tag in a single view. The generation AI in the metadata generation unit also analyzes the data usage history and links data frequently accessed by the same user. For example, it groups data frequently used by the same user into a single folder. This allows highly related data to be linked efficiently.

[0032] The data acquisition unit can optimize the physical storage location of data and improve access speed. In the data acquisition unit, for example, the generation AI analyzes the access patterns of data and determines the optimal physical storage location. For example, frequently accessed data is placed in high-speed storage. In addition, the generation AI analyzes the size and format of data and determines the optimal physical storage location. For example, data is placed in storage that can efficiently store large amounts of data. In addition, the generation AI analyzes the frequency of data use and determines the optimal physical storage location. For example, data that is used less frequently is placed in low-cost storage. This can improve data access speed.

[0033] The metadata generation unit centrally manages different data formats, and the generation AI can automatically convert formats. In the metadata generation unit, for example, the generation AI analyzes different data formats and performs format conversion as needed. For example, it converts audio data into text and saves it. The metadata generation unit also centrally manages different data formats and performs format conversion as needed. For example, it converts image data into text to make it searchable. The metadata generation unit also centrally manages different data formats and performs format conversion as needed. For example, it converts video data into still images and saves it. This makes it possible to efficiently manage different data formats and perform format conversion as needed.

[0034] The metadata generation unit can analyze the content of the data and automatically generate advanced metadata based on the context. In the metadata generation unit, for example, a generation AI analyzes the content of the data and automatically generates advanced metadata based on the context. For example, related keywords and topics are assigned as metadata depending on the content of the data. In addition, the metadata generation unit can analyze the content of the data and automatically generate advanced metadata based on the context. For example, related people and places are assigned as metadata depending on the content of the data. In addition, the metadata generation unit can analyze the content of the data and automatically generate advanced metadata based on the context. For example, related events and occurrences are assigned as metadata depending on the content of the data. This makes it possible to automatically generate advanced metadata based on the context.

[0035] The metadata generation unit can analyze data usage history and assign metadata based on usage frequency and user behavior. For example, the generation AI in the metadata generation unit analyzes data usage history and assigns metadata based on usage frequency. For example, it assigns a "high frequency use" tag to frequently used data. The metadata generation unit also analyzes data usage history and assigns metadata based on user behavior. For example, it assigns an "important" tag to data that is frequently accessed by a specific user. The metadata generation unit also analyzes data usage history and assigns metadata based on usage frequency and user behavior. For example, it assigns a "peak time use" tag to data that is frequently used during a specific time period. This makes it possible to assign metadata based on usage history.

[0036] The metadata generation unit can automatically generate metadata in different languages, enabling multilingual data management. In the metadata generation unit, for example, a generation AI analyzes the contents of the data and automatically generates metadata in different languages. For example, metadata is assigned in multiple languages, such as English, Japanese, and Chinese. In addition, the metadata generation unit can analyze the contents of the data and automatically generate metadata in different languages. For example, data descriptions and tags are assigned in multiple languages. In addition, the metadata generation unit can analyze the contents of the data and automatically generate metadata in different languages. For example, the data creation date and administrator information are assigned in multiple languages. This enables multilingual data management.

[0037] The metadata generation unit can analyze the visual features of the data and assign metadata to image data as well. For example, the generation AI in the metadata generation unit analyzes the visual features of the image data and assigns metadata. For example, it analyzes objects and scenes in the image and assigns related tags. The metadata generation unit can also analyze the visual features of the image data and assign metadata. For example, it can analyze the color and composition of the image and assign related tags. The metadata generation unit can also analyze the visual features of the image data and assign metadata. For example, it can analyze people and places in the image and assign related tags. This allows metadata to be assigned to image data as well.

[0038] The data organization unit can analyze data usage patterns and propose the optimal data organization method. For example, the generation AI in the data organization unit analyzes data usage patterns and proposes the optimal data organization method. For example, it prioritizes organizing frequently used data. The generation AI in the data organization unit also analyzes data usage patterns and proposes the optimal data organization method. For example, it archives data that is used less frequently. The data organization unit also analyzes data usage patterns and proposes the optimal data organization method. For example, it prioritizes organizing data that is used during specific time periods. This makes it possible to propose the optimal data organization method based on usage patterns.

[0039] The data reduction unit can analyze data redundancy and automatically delete duplicate data. For example, the generation AI analyzes the contents of the data, identifies duplicate data, and automatically deletes it. For example, if there are multiple files with the same content, the most recent one is kept and the others are deleted. The data reduction unit also analyzes the metadata of the data, identifies duplicate data, and automatically deletes it. For example, duplicates are deleted by comparing data with the same creation date or the same administrator. The data reduction unit also analyzes the usage history of the data, identifies duplicate data, and automatically deletes it. For example, if the same data is stored in multiple locations, it is left in the most frequently used location and the others are deleted. This allows duplicate data to be automatically deleted.

[0040] The data organization unit can provide a data visualization tool, allowing the data organization status to be intuitively grasped. In the data organization unit, for example, the generation AI provides a data visualization tool and displays the data organization status in graphs or charts. For example, the frequency of data use and update status are visually displayed. In addition, the generation AI provides a data visualization tool and displays the data organization status in a heat map. For example, the importance and frequency of use of data are indicated by color. In addition, the generation AI provides a data visualization tool and displays the data organization status in a dashboard. For example, the data organization status and the status of metadata assignment can be grasped at a glance. This allows the data organization status to be intuitively grasped.

[0041] The data reduction unit automates data backup and restoration, ensuring data safety. For example, the generation AI automates data backup and periodically backs up data. For example, automatic backups are performed every night. The data reduction unit also automates data restoration and quickly restores data as needed. For example, automatic restoration is performed when data is corrupted or deleted. The data reduction unit also automates data backup and restoration, ensuring data safety. For example, backups are stored in different locations to protect data even in the event of a disaster. This ensures data safety.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The data acquisition unit can evaluate the reliability of data according to the data source and prioritize the acquisition of highly reliable data. For example, the data acquisition unit can evaluate data from official data sources as highly reliable and prioritize the acquisition of such data. The data acquisition unit can also prioritize the acquisition of data from data sources that the user has evaluated as highly reliable. Furthermore, the data acquisition unit can evaluate reliability based on past data usage history and prioritize the acquisition of highly reliable data. This ensures the reliability of data and enables high-quality data management.

[0044] The metadata generator can analyze the security level of data and assign appropriate security tags. For example, the metadata generator can assign a "confidential" tag to highly confidential data to strengthen access restrictions. The metadata generator can also assign a "public" tag to data that can be made public to facilitate access. Furthermore, the metadata generator can evaluate the security risk of data and assign a "warning" tag to high-risk data. This strengthens data security management.

[0045] The data acquisition unit can analyze the frequency of data acquisition and propose an optimal acquisition schedule. For example, it can acquire frequently updated data in real time. It can also acquire periodically updated data based on a schedule. It can also acquire less frequently used data as needed. This improves data acquisition efficiency and optimizes resources.

[0046] The data organization unit can analyze the user's behavioral patterns and propose the optimal data organization method. For example, it can prioritize the organization of frequently used data. It can also prioritize the organization of data used during specific time periods. It can also customize the data organization method based on the user's behavioral patterns. This enables efficient data organization according to the user's behavior.

[0047] The data acquisition unit can evaluate the reliability of the data source and filter out unreliable data. For example, data from unreliable data sources can be excluded. It can also filter out data from data sources that the user has evaluated as unreliable. Furthermore, it can evaluate reliability based on past data usage history and filter out unreliable data. This can improve the quality of data.

[0048] The data reduction unit can analyze the visual characteristics of data and prioritize the rearrangement of visually important data. For example, it prioritizes the rearrangement of visually prominent data. It can also rearrange data that are highly visually related together. It can also highlight visually important data. This allows for efficient rearrangement of visually important data.

[0049] The data acquisition unit analyzes the geographical information of the data acquisition source and can prioritize acquisition of data with high geographical relevance. For example, data from the same region can be prioritized. Data from geographically close data sources can also be prioritized. Furthermore, geographically important data can be prioritized. This allows for efficient acquisition of data with high geographical relevance.

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

[0051] Step 1: The data acquisition department acquires data. For example, the data acquisition department aggregates data collected from different departments into a single data lake. The data acquisition department can also aggregate data into a data warehouse. Furthermore, the data acquisition department can acquire data in real time, acquiring data through APIs. Step 2: The metadata generation unit assigns metadata to the data acquired by the data acquisition unit. For example, the metadata generation unit assigns information such as the data creation date, administrator, data type, data description, and update frequency as metadata. It can also use generation AI to analyze the content of the data and automatically generate appropriate metadata. Furthermore, tags can be customized based on conditions set by the user, and data related to a specific project can be tagged with the project name. Step 3: The data organizer organizes the data based on the metadata provided by the metadata generator. For example, the data organizer can search and filter data based on specific conditions. It can also use the generation AI to monitor data updates and update the metadata as needed. Furthermore, the data organizer can analyze the importance of the data and prioritize where to store the data based on its importance.

[0052] (Example 2) A data management system according to an embodiment of the present invention is a system in which a generative AI centrally manages data and automates data wrangling, thereby improving the efficiency of data organization and management.

[0053] A data management system according to an embodiment includes a data acquisition unit, a metadata generation unit, and a data organization unit. The data acquisition unit acquires data. For example, the data acquisition unit aggregates data collected from different departments into a single data lake. The data acquisition unit can also aggregate data into a data warehouse. The data acquisition unit can also acquire data in real time. For example, the data acquisition unit acquires data through an API. The metadata generation unit assigns metadata to the data acquired by the data acquisition unit. For example, the metadata generation unit assigns information such as the data creation date, administrator, data type, data description, and update frequency as metadata. The metadata generation unit can also analyze the content of the data using a generation AI and automatically generate appropriate metadata. The metadata generation unit can also customize tags based on conditions set by a user. For example, data related to a specific project can be tagged with the project name. The data organization unit organizes the data based on the metadata assigned by the metadata generation unit. For example, the data organization unit can search and filter data based on specific conditions. The data organization unit can also monitor the update status of the data using a generation AI and update the metadata as necessary. The data organization unit can also analyze the importance of data and prioritize data storage destinations according to the importance. This allows the data management system according to the embodiment to centrally manage and efficiently organize data. For example, the data management system centrally manages data collected from different departments within a company, and the generation AI automatically assigns metadata, making it easier to organize and manage the data. Furthermore, data can be quickly searched and filtered based on specific conditions, improving work efficiency. Furthermore, tags can be customized to meet user needs, enabling flexible data management.

[0054] The data acquisition unit can analyze the importance of the data and prioritize data storage destinations according to its importance. In the data acquisition unit, for example, the generation AI analyzes the contents of the data and scores its importance. For example, data that is directly related to business or legally important is given a high score and stored preferentially in storage that can be accessed quickly. In addition, the generation AI in the data acquisition unit analyzes the frequency of data use and prioritizes storing frequently accessed data in cache. For example, data that is frequently used in daily operations is placed in a location that can be accessed quickly. In addition, the generation AI in the data acquisition unit analyzes the frequency of data updates and prioritizes storing frequently updated data in dynamic storage. For example, data that is updated in real time is placed in storage that can immediately reflect the data. This enables efficient storage according to the data's importance.

[0055] The metadata generation unit can analyze the interrelationships between data and automatically link highly related data. For example, the generation AI in the metadata generation unit analyzes the content of the data and links highly related data. For example, it automatically links documents and data sets related to the same project. The generation AI in the metadata generation unit also analyzes the metadata of the data and links data with common tags. For example, it displays data with the same tag in a single view. The generation AI in the metadata generation unit also analyzes the data usage history and links data frequently accessed by the same user. For example, it groups data frequently used by the same user into a single folder. This allows highly related data to be linked efficiently.

[0056] The data reduction unit can use the emotion estimation function to analyze the emotions of the data creator or user, and prioritize managing emotionally important data. For example, the data reduction unit uses the generation AI to analyze the emotions of the data creator, and prioritize managing emotionally important data. For example, data created by a creator with strong emotions is preferentially saved. The data reduction unit also uses the generation AI to analyze the emotions of the data user, and prioritize managing emotionally important data. For example, data for which the user has a high emotion score is preferentially saved. The data reduction unit also uses the generation AI to analyze the emotions at the time the data was created, and prioritize managing emotionally important data. For example, data with strong positive emotions at the time of creation is preferentially saved. This allows emotionally important data to be managed preferentially.

[0057] The data acquisition unit can optimize the physical storage location of data and improve access speed. In the data acquisition unit, for example, the generation AI analyzes the access patterns of data and determines the optimal physical storage location. For example, frequently accessed data is placed in high-speed storage. In addition, the generation AI analyzes the size and format of data and determines the optimal physical storage location. For example, data is placed in storage that can efficiently store large amounts of data. In addition, the generation AI analyzes the frequency of data use and determines the optimal physical storage location. For example, data that is used less frequently is placed in low-cost storage. This can improve data access speed.

[0058] The metadata generation unit centrally manages different data formats, and the generation AI can automatically convert formats. In the metadata generation unit, for example, the generation AI analyzes different data formats and performs format conversion as needed. For example, it converts audio data into text and saves it. The metadata generation unit also centrally manages different data formats and performs format conversion as needed. For example, it converts image data into text to make it searchable. The metadata generation unit also centrally manages different data formats and performs format conversion as needed. For example, it converts video data into still images and saves it. This makes it possible to efficiently manage different data formats and perform format conversion as needed.

[0059] The data reduction unit can dynamically adjust access permissions to data based on the emotions of the data user using the emotion estimation function. In the data reduction unit, for example, the generation AI analyzes the emotions of the data user and dynamically adjusts the access permissions based on the emotion score. For example, it extends access permissions to users with positive emotions. In addition, the data reduction unit can dynamically adjust access permissions based on the emotion score by analyzing the emotions of the data user using the generation AI. For example, it restricts access permissions to users with negative emotions. In addition, the data reduction unit can dynamically adjust access permissions based on the emotion score by analyzing the emotions of the data user using the generation AI. For example, it preferentially grants access permissions to users with high emotion scores. This makes it possible to dynamically adjust access permissions based on the user's emotions.

[0060] The metadata generation unit can analyze the content of the data and automatically generate advanced metadata based on the context. In the metadata generation unit, for example, a generation AI analyzes the content of the data and automatically generates advanced metadata based on the context. For example, related keywords and topics are assigned as metadata depending on the content of the data. In addition, the metadata generation unit can analyze the content of the data and automatically generate advanced metadata based on the context. For example, related people and places are assigned as metadata depending on the content of the data. In addition, the metadata generation unit can analyze the content of the data and automatically generate advanced metadata based on the context. For example, related events and occurrences are assigned as metadata depending on the content of the data. This makes it possible to automatically generate advanced metadata based on the context.

[0061] The metadata generation unit can analyze data usage history and assign metadata based on usage frequency and user behavior. For example, the generation AI in the metadata generation unit analyzes data usage history and assigns metadata based on usage frequency. For example, it assigns a "high frequency use" tag to frequently used data. The metadata generation unit also analyzes data usage history and assigns metadata based on user behavior. For example, it assigns an "important" tag to data that is frequently accessed by a specific user. The metadata generation unit also analyzes data usage history and assigns metadata based on usage frequency and user behavior. For example, it assigns a "peak time use" tag to data that is frequently used during a specific time period. This makes it possible to assign metadata based on usage history.

[0062] The metadata generation unit can use the emotion estimation function to analyze the emotions of the data user and assign metadata with emotional value. For example, the generation AI in the metadata generation unit analyzes the emotions of the data user and assigns metadata with emotional value. For example, it assigns a "high sensitivity" tag to data with positive emotions. The metadata generation unit also analyzes the emotions of the data user and assigns metadata with emotional value. For example, it assigns a "caution" tag to data with negative emotions. The metadata generation unit also analyzes the emotions of the data user and assigns metadata with emotional value. For example, it assigns an "emotional value" tag to data with a high emotion score. This makes it possible to assign metadata with emotional value.

[0063] The metadata generation unit can automatically generate metadata in different languages, enabling multilingual data management. In the metadata generation unit, for example, a generation AI analyzes the contents of the data and automatically generates metadata in different languages. For example, metadata is assigned in multiple languages, such as English, Japanese, and Chinese. In addition, the metadata generation unit can analyze the contents of the data and automatically generate metadata in different languages. For example, data descriptions and tags are assigned in multiple languages. In addition, the metadata generation unit can analyze the contents of the data and automatically generate metadata in different languages. For example, the data creation date and administrator information are assigned in multiple languages. This enables multilingual data management.

[0064] The metadata generation unit can analyze the visual features of the data and assign metadata to image data as well. For example, the generation AI in the metadata generation unit analyzes the visual features of the image data and assigns metadata. For example, it analyzes objects and scenes in the image and assigns related tags. The metadata generation unit can also analyze the visual features of the image data and assign metadata. For example, it can analyze the color and composition of the image and assign related tags. The metadata generation unit can also analyze the visual features of the image data and assign metadata. For example, it can analyze people and places in the image and assign related tags. This allows metadata to be assigned to image data as well.

[0065] The metadata generation unit can dynamically change the display order of metadata based on the emotions of the data user using the emotion estimation function. In the metadata generation unit, for example, the generation AI analyzes the emotions of the data user and dynamically changes the display order of metadata based on the emotion score. For example, metadata with positive emotions is preferentially displayed. In addition, the metadata generation unit can dynamically change the display order of metadata based on the emotion score using the generation AI. For example, metadata with negative emotions is displayed later. In addition, the metadata generation unit can dynamically change the display order of metadata based on the emotion score using the generation AI. For example, metadata with a high emotion score is preferentially displayed. This makes it possible to dynamically change the display order of metadata based on the user's emotions.

[0066] The data organization unit can analyze data usage patterns and propose the optimal data organization method. For example, the generation AI in the data organization unit analyzes data usage patterns and proposes the optimal data organization method. For example, it prioritizes organizing frequently used data. The generation AI in the data organization unit also analyzes data usage patterns and proposes the optimal data organization method. For example, it archives data that is used less frequently. The data organization unit also analyzes data usage patterns and proposes the optimal data organization method. For example, it prioritizes organizing data that is used during specific time periods. This makes it possible to propose the optimal data organization method based on usage patterns.

[0067] The data reduction unit can analyze data redundancy and automatically delete duplicate data. For example, the generation AI analyzes the contents of the data, identifies duplicate data, and automatically deletes it. For example, if there are multiple files with the same content, the most recent one is kept and the others are deleted. The data reduction unit also analyzes the metadata of the data, identifies duplicate data, and automatically deletes it. For example, duplicates are deleted by comparing data with the same creation date or the same administrator. The data reduction unit also analyzes the usage history of the data, identifies duplicate data, and automatically deletes it. For example, if the same data is stored in multiple locations, it is left in the most frequently used location and the others are deleted. This allows duplicate data to be automatically deleted.

[0068] The data sorting unit can use the emotion estimation function to analyze the emotions of the data user and prioritize organizing emotionally important data. In the data sorting unit, for example, the generation AI analyzes the emotions of the data user and prioritizes organizing emotionally important data. For example, data with positive emotions is prioritized. In addition, the data sorting unit can use the generation AI to analyze the emotions of the data user and prioritize organizing emotionally important data. For example, data with negative emotions is postponed. In addition, the data sorting unit can use the generation AI to analyze the emotions of the data user and prioritize organizing emotionally important data. For example, data with a high emotion score is prioritized. This allows emotionally important data to be prioritized.

[0069] The data organization unit can provide a data visualization tool, allowing the data organization status to be intuitively grasped. In the data organization unit, for example, the generation AI provides a data visualization tool and displays the data organization status in graphs or charts. For example, the frequency of data use and update status are visually displayed. In addition, the generation AI provides a data visualization tool and displays the data organization status in a heat map. For example, the importance and frequency of use of data are indicated by color. In addition, the generation AI provides a data visualization tool and displays the data organization status in a dashboard. For example, the data organization status and the status of metadata assignment can be grasped at a glance. This allows the data organization status to be intuitively grasped.

[0070] The data reduction unit automates data backup and restoration, ensuring data safety. For example, the generation AI automates data backup and periodically backs up data. For example, automatic backups are performed every night. The data reduction unit also automates data restoration and quickly restores data as needed. For example, automatic restoration is performed when data is corrupted or deleted. The data reduction unit also automates data backup and restoration, ensuring data safety. For example, backups are stored in different locations to protect data even in the event of a disaster. This ensures data safety.

[0071] The data organizing unit can customize the data organizing method based on the emotions of the data user using the emotion estimation function. For example, the generation AI in the data organizing unit analyzes the emotions of the data user and customizes the data organizing method based on the emotion score. For example, data with positive emotions is organized preferentially. The data organizing unit also analyzes the emotions of the data user and customizes the data organizing method based on the emotion score. For example, data with negative emotions is organized later. The data organizing unit also analyzes the emotions of the data user and customizes the data organizing method based on the emotion score. For example, data with a high emotion score is organized preferentially. This makes it possible to customize the data organizing method based on the user's emotions.

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

[0073] The data acquisition unit can evaluate the reliability of data according to the data source and prioritize the acquisition of highly reliable data. For example, the data acquisition unit can evaluate data from official data sources as highly reliable and prioritize the acquisition of such data. The data acquisition unit can also prioritize the acquisition of data from data sources that the user has evaluated as highly reliable. Furthermore, the data acquisition unit can evaluate reliability based on past data usage history and prioritize the acquisition of highly reliable data. This ensures the reliability of data and enables high-quality data management.

[0074] The metadata generator can analyze the security level of data and assign appropriate security tags. For example, the metadata generator can assign a "confidential" tag to highly confidential data to strengthen access restrictions. The metadata generator can also assign a "public" tag to data that can be made public to facilitate access. Furthermore, the metadata generator can evaluate the security risk of data and assign a "warning" tag to high-risk data. This strengthens data security management.

[0075] The data reduction unit can use the emotion estimation function to customize the display format of data based on the emotion of the user of the data. For example, for a user with a positive emotion, the data can be displayed in bright colors. For a user with a negative emotion, the data can be displayed in subdued colors. Furthermore, for a user with a high emotion score, the data can be highlighted. This makes it possible to display data according to the user's emotion.

[0076] The data acquisition unit can analyze the frequency of data acquisition and propose an optimal acquisition schedule. For example, it can acquire frequently updated data in real time. It can also acquire periodically updated data based on a schedule. It can also acquire less frequently used data as needed. This improves data acquisition efficiency and optimizes resources.

[0077] The metadata generation unit can use the emotion estimation function to evaluate the importance of metadata based on the emotion of the user of the data, and prioritize displaying important metadata. For example, metadata with positive emotions can be displayed with priority. It can also postpone metadata with negative emotions. It can also highlight metadata with high emotion scores. This makes it possible to display metadata based on the user's emotions.

[0078] The data organization unit can analyze the user's behavioral patterns and propose the optimal data organization method. For example, it can prioritize the organization of frequently used data. It can also prioritize the organization of data used during specific time periods. It can also customize the data organization method based on the user's behavioral patterns. This enables efficient data organization according to the user's behavior.

[0079] The data acquisition unit can evaluate the reliability of the data source and filter out unreliable data. For example, data from unreliable data sources can be excluded. It can also filter out data from data sources that the user has evaluated as unreliable. Furthermore, it can evaluate reliability based on past data usage history and filter out unreliable data. This can improve the quality of data.

[0080] The metadata generation unit can customize the metadata generation method based on the emotions of the user of the data using the emotion estimation function. For example, detailed metadata can be generated for users with positive emotions. Simplified metadata can also be generated for users with negative emotions. Furthermore, special metadata can be generated for users with high emotion scores. This makes it possible to generate metadata according to the emotions of the user.

[0081] The data reduction unit can analyze the visual characteristics of data and prioritize the rearrangement of visually important data. For example, it prioritizes the rearrangement of visually prominent data. It can also rearrange data that are highly visually related together. It can also highlight visually important data. This allows for efficient rearrangement of visually important data.

[0082] The data acquisition unit analyzes the geographical information of the data acquisition source and can prioritize acquisition of data with high geographical relevance. For example, data from the same region can be prioritized. Data from geographically close data sources can also be prioritized. Furthermore, geographically important data can be prioritized. This allows for efficient acquisition of data with high geographical relevance.

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

[0084] Step 1: The data acquisition department acquires data. For example, the data acquisition department aggregates data collected from different departments into a single data lake. The data acquisition department can also aggregate data into a data warehouse. Furthermore, the data acquisition department can acquire data in real time, acquiring data through APIs. Step 2: The metadata generation unit assigns metadata to the data acquired by the data acquisition unit. For example, the metadata generation unit assigns information such as the data creation date, administrator, data type, data description, and update frequency as metadata. It can also use generation AI to analyze the content of the data and automatically generate appropriate metadata. Furthermore, tags can be customized based on conditions set by the user, and data related to a specific project can be tagged with the project name. Step 3: The data organizer organizes the data based on the metadata provided by the metadata generator. For example, the data organizer can search and filter data based on specific conditions. It can also use the generation AI to monitor data updates and update the metadata as needed. Furthermore, the data organizer can analyze the importance of the data and prioritize where to store the data based on its importance.

[0085] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0087] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

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

[0091] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

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

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

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

[0095] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0098] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. 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.

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

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0102] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0104] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

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

[0106] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

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

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

[0110] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0113] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0119] 7, 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.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0126] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0129] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 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 processing similar to that of the specific processing unit 290 using these models.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0135] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0136] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0137] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

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

[0139] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0140] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0141] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0142] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0143] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0144] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0145] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0146] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0147] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0148] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0149] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0150] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

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

[0152] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a data acquisition unit for acquiring data; a metadata generation unit that assigns metadata to the data acquired by the data acquisition unit; a data organizing unit that organizes data based on the metadata assigned by the metadata generating unit. A system characterized by:

2. The data acquisition unit Analyzing the importance of the data and prioritizing storage locations for the data according to the importance.

2. The system of claim 1.

3. The metadata generation unit Analyzing the interrelationships of the data and automatically linking the highly related data 2. The system of claim 1.

4. The data reduction unit Analyzing the emotions of the creators and users of the data, and managing the emotionally important data with priority.

2. The system of claim 1.

5. The data acquisition unit Optimize the physical storage location of the data and improve access speed 2. The system of claim 1.

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