System

Abstract

An object of the system according to the embodiment is to automatically generate a file name of data or a document and efficiently manage the file name.SOLUTION: A system includes an input unit, a generation unit, a storage unit, and a management unit. The input unit inputs data and documents. The generation unit analyzes the data or document input by the input unit, and generates a file name. The storage unit stores data or a document using the file name generated by the generation unit. The management unit manages a change history of the file name stored by the storage 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, when file names for data or documents are manually assigned, there is an issue of individual differences, which makes it time-consuming to search for files.

[0005] The system according to the embodiment aims to automatically generate file names for data and documents and to efficiently manage them. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, a generation unit, a storage unit, and a management unit. The input unit inputs data or documents. The generation unit analyzes the data or documents input by the input unit and generates file names. The storage unit stores the data or documents using the file names generated by the generation unit. The management unit manages the change history of file names stored by the storage unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate file names for data and documents and manage them efficiently. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The automated file naming system according to an embodiment of the present invention utilizes a generation AI to streamline data and document management. The system inputs data or documents, analyzes them, and generates appropriate file names using the generated file names. The automated file naming system then stores the data or documents under these file names. For example, the system inputs the received data or digitized documents into the generation AI. The generation AI analyzes the contents of the input data or documents and generates appropriate file names. For example, the generation AI automatically assigns file names based on the document's title and content. Since the generation AI generates file names according to pre-defined naming rules, file names are assigned consistently across individuals. The generated file names are then used to store the data or documents. This not only reduces the labor required for naming files but also shortens the time required to search for files. For example, users can quickly find the desired file by searching for file names containing specific keywords. Furthermore, the generation AI can also manage file name change history. This allows users to track past file names and, if necessary, revert the file name to its original state. For example, even if a file name is accidentally changed, users can refer to the history managed by the generation AI to restore the original file name. As a result, the automated file naming system streamlines data and document management, significantly reducing work time and the time spent searching for things. For example, when managing large volumes of documents within a company, using generative AI makes it possible to store and search documents quickly and accurately. In addition, consistent file names are assigned, eliminating individual differences, making it easier to share information within a team.

[0029] An automated file naming system according to an embodiment includes an input unit, a generation unit, a storage unit, and a management unit. The input unit inputs data and documents. Examples of the data and documents include, but are not limited to, text data, image data, and PDF documents. The input unit, for example, uses a scanner to digitize paper documents and input them as data. The input unit can also directly input data and documents provided in digital format. The generation unit uses a generation AI to analyze the data and documents input by the input unit and generate an appropriate file name. The generation unit generates a file name based on, for example, the title or content of the document. The generation AI analyzes the content of the document using a text generation AI (e.g., LLM) and generates an appropriate file name. The generation unit can also generate file names according to a predetermined naming rule. For example, the generation unit generates file names using a naming rule based on a date or category. The storage unit stores the data and documents using the file name generated by the generation unit. The storage unit saves the data and documents in a database, for example. The storage unit can also store the data and documents using cloud storage. Furthermore, the storage unit has a function for searching for file names containing specific keywords. For example, the storage unit can quickly search for file names containing specific keywords and find a target file. The management unit manages the change history of file names stored by the storage unit. For example, the management unit tracks the change history of file names and records what file names were given in the past. This allows the management unit to restore file names as needed. For example, the management unit can restore a file name that was changed by mistake to its original name. As a result, the file name automation system according to the embodiment improves the efficiency of data and document management, significantly reducing work time and the time spent searching for items.

[0030] The generation unit can generate a file name based on the title or content of the document. The generation unit, for example, analyzes the document title and generates an appropriate file name. For example, the generation unit extracts keywords from the document title and generates a file name based on them. The generation unit can also analyze the content of the document and generate an appropriate file name. For example, the generation unit extracts important phrases from the content of the document and generates a file name based on them. In this way, an appropriate file name is generated based on the content of the document. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the content of the document into the generation AI, which analyzes it and generates a file name.

[0031] The generation unit can generate file names based on a predetermined naming rule. The generation unit generates file names using, for example, a date-based naming rule. For example, the generation unit includes the creation date of the document in the file name. The generation unit can also generate file names using a category-based naming rule. For example, the generation unit includes the document category (e.g., technical document, business document, etc.) in the file name. This generates consistent file names. Some or all of the above-mentioned processing in the generation unit may be performed using or without the generation AI. For example, the generation unit can input a predetermined naming rule into the generation AI, and the generation AI can generate file names according to the rule.

[0032] The storage unit can store data and documents based on the generated file name. The storage unit, for example, stores the data and documents in a database. For example, the storage unit stores the data and documents in a database using the generated file name. The storage unit can also store the data and documents using cloud storage. For example, the storage unit stores the data and documents in cloud storage using the generated file name. This allows the data and documents to be efficiently stored using the generated file name. Some or all of the above-mentioned processing in the storage unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the storage unit can input the generated file name to the generation AI, and the generation AI can store the data and documents based on the file name.

[0033] The storage unit can search for file names that include a specific keyword. For example, the storage unit searches for file names that include a specific keyword. For example, the storage unit quickly searches for file names that include a keyword specified by the user to find the desired file. The storage unit can also search for file names based on frequently occurring keywords. For example, the storage unit preferentially searches for file names that include a frequently occurring keyword. This allows for a quick search for file names that include a specific keyword. Some or all of the above-mentioned processing in the storage unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the storage unit can input a specific keyword into the generation AI, and the generation AI can search for file names that include that keyword.

[0034] The management unit can track the change history of file names. The management unit, for example, tracks the change history of file names. For example, the management unit records the change history of file names and tracks what file names were given in the past. The management unit can also use a version control system to track the change history. For example, the management unit manages the change history of file names using a version control system. In this way, by managing the change history of file names, past file names can be tracked. Some or all of the above-mentioned processing in the management unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the management unit can input the change history of file names into the generation AI, and the generation AI can manage that history.

[0035] The management unit can record what file names have been given in the past. For example, the management unit records what file names have been given in the past. For example, the management unit saves the file name change history in a log file. The management unit can also use a database to record past file names. For example, the management unit saves the file name change history in a database. This makes it possible to track past file names and restore them to their original names as needed. Some or all of the above-mentioned processing in the management unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the management unit can input past file names into the generation AI, and the generation AI can manage the history.

[0036] The input unit can analyze the user's past input history and select an appropriate input method. For example, the input unit prioritizes and suggests input methods (such as voice and text) that the user has frequently used in the past. For example, the input unit analyzes the user's past input history and selects the optimal input method. The input unit can also predict and suggest the input method to be used in a specific time period. For example, the input unit predicts and suggests the input method to be used in a specific time period based on the user's past input history. The input unit can also select the optimal input method based on the type of data the user has previously input. For example, the input unit analyzes the type of data the user has previously input and selects the optimal input method based on that. This enables efficient input of data and documents by selecting the optimal input method based on the past input history. Some or all of the above-mentioned processing in the input unit may be performed using or without the generation AI. For example, the input unit can input the user's past input history into the generation AI, which can then analyze the history and select the optimal input method.

[0037] The input unit can filter data and documents based on the user's current project or areas of interest when inputting the data and documents. For example, the input unit inputs only data and documents related to the user's current project. For example, the input unit prioritizes input of data and documents related to the user's current project. The input unit can also prioritize input of related data and documents based on the user's areas of interest. For example, the input unit prioritizes input of related data and documents based on the user's areas of interest. The input unit can also filter data and documents based on areas in which the user has previously shown interest. For example, the input unit filters data and documents based on areas in which the user has previously shown interest. This enables efficient input by filtering data and documents based on the user's current project or areas of interest. Some or all of the above-described processing in the input unit may be performed using or without the generation AI. For example, the input unit can input the user's current project or areas of interest to the generation AI, and the generation AI can filter data and documents based on that information.

[0038] When inputting data or documents, the input unit can select an appropriate input means depending on the user's input method. For example, if the user desires voice input, the input unit inputs the data or documents using voice recognition technology. For example, the input unit converts voice into text using voice recognition software and inputs the data or documents. Furthermore, if the user desires text input, the input unit can also provide keyboard input preferentially. For example, the input unit provides keyboard input preferentially and inputs the data or documents. Furthermore, if the user desires image input, the input unit can also use image recognition technology to input the data or documents. For example, the input unit analyzes images using image recognition technology and inputs the data or documents. This enables efficient input of data and documents by selecting the optimal input means depending on the user's input method. Some or all of the above-described processing in the input unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the input unit can input the user's input method to a generation AI, which can select an appropriate input means depending on the method.

[0039] When inputting data or documents, the input unit can prioritize inputting highly relevant data taking into account the user's geographical location information. For example, when the user is in a specific area, the input unit prioritizes inputting data or documents related to that area. For example, when the user is in a specific area, the input unit prioritizes inputting data or documents related to that area. Furthermore, when the user is moving, the input unit can also input highly relevant data or documents based on the user's current location. For example, when the user is moving, the input unit inputs highly relevant data or documents based on the user's current location. Furthermore, when the user is in a specific location, the input unit can also prioritize inputting data or documents related to that location. For example, when the user is in a specific location, the input unit prioritizes inputting data or documents related to that location. This enables efficient input by prioritizing highly relevant data taking into account the user's geographical location information. Some or all of the above-described processing in the input unit may be performed using or without the generation AI. For example, the input unit can input the user's geographical location information to the generation AI, and the generation AI can prioritize inputting highly relevant data or documents based on that information.

[0040] The input unit can analyze a user's social media activity and input related data when inputting data or documents. The input unit can, for example, input related data or documents based on information shared by the user on social media. For example, the input unit can input related data or documents based on information shared by the user on social media. The input unit can also analyze the content of a user's social media posts and input related data or documents. For example, the input unit can analyze the content of a user's social media posts and input related data or documents. The input unit can also input related data or documents based on the activities of the user's friends on social media. For example, the input unit can input related data or documents based on the activities of the user's friends on social media. This enables efficient input by analyzing the user's social media activity and inputting related data. Some or all of the above-described processing in the input unit can be performed using or without a generation AI. For example, the input unit can input the user's social media activity to a generation AI, and the generation AI can input related data or documents based on that information.

[0041] The input unit can customize the input method based on the user's past feedback when inputting data or documents. For example, the input unit suggests the optimal input method based on feedback provided by the user in the past. For example, the input unit suggests the optimal input method based on feedback provided by the user in the past. The input unit can also preferentially provide a specific input method based on the user's past feedback. For example, the input unit preferentially provides a specific input method based on the user's past feedback. The input unit can also customize the input interface by reflecting the user's feedback. For example, the input unit customizes the input interface by reflecting the user's feedback. This enables efficient input by customizing the input method by reflecting the user's past feedback. Some or all of the above-mentioned processing in the input unit may be performed using a generation AI or may be performed without using a generation AI. For example, the input unit can input the user's past feedback to the generation AI, and the generation AI can customize the input method based on that information.

[0042] When generating a file name, the generation unit can adjust the level of detail of the file name according to the importance of the data or document. For example, the generation unit generates a detailed file name for data or documents of high importance. For example, the generation unit generates a detailed file name for data or documents of high importance. The generation unit can also generate a concise file name for data or documents of low importance. For example, the generation unit generates a concise file name for data or documents of low importance. The generation unit can also adjust the length and content of the file name according to the importance of the data or document. For example, the generation unit adjusts the length and content of the file name according to the importance of the data or document. In this way, an appropriate file name is generated by adjusting the level of detail of the file name based on the importance of the data or document. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the importance of the data or document to the generation AI, and the generation AI can adjust the level of detail of the file name based on that information.

[0043] When generating a file name, the generation unit can apply different generation algorithms based on the category of the data or document. For example, the generation unit generates a file name by applying different naming rules depending on the document category. For example, the generation unit generates a file name by applying different naming rules depending on the document category. The generation unit can also generate a file name using different algorithms depending on the data category. For example, the generation unit generates a file name using different algorithms depending on the data category. The generation unit can also select an optimal file name generation algorithm for each category. For example, the generation unit selects an optimal file name generation algorithm for each category. As a result, appropriate file names are generated by applying different generation algorithms depending on the category of the data or document. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the category of the data or document into the generation AI, and the generation AI can apply different generation algorithms based on that information.

[0044] When generating file names, the generation unit can improve the accuracy of generation based on the user's past generation results. For example, the generation unit generates similar file names by referring to file names generated by the user in the past. For example, the generation unit generates similar file names by referring to file names generated by the user in the past. The generation unit can also select an optimal file name generation algorithm based on the user's past generation results. For example, the generation unit selects an optimal file name generation algorithm based on the user's past generation results. The generation unit can also analyze the user's past generation results to improve the accuracy of generation. For example, the generation unit analyzes the user's past generation results to improve the accuracy of generation. In this way, the accuracy of generation is improved by referring to the user's past generation results. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past generation results into the generation AI, and the generation AI can improve the accuracy of generation based on that information.

[0045] When generating file names, the generation unit can determine the priority of file names according to the submission time of the data or document. For example, the generation unit prioritizes generating file names for data or documents that are soon to be submitted. For example, the generation unit prioritizes generating file names for data or documents that are soon to be submitted. The generation unit can also postpone generating file names for data or documents that are further away from the submission time. For example, the generation unit postpones generating file names for data or documents that are further away from the submission time. The generation unit can also adjust the order in which file names are generated based on the submission time. For example, the generation unit adjusts the order in which file names are generated based on the submission time. This enables efficient file name generation by determining the priority of file names based on the submission time of data or documents. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the submission time of data or documents into the generation AI, and the generation AI can determine the priority of file names based on that information.

[0046] When generating file names, the generation unit can adjust the order of file names based on the relevance of data or documents. For example, the generation unit prioritizes generating file names for highly relevant data or documents. For example, the generation unit prioritizes generating file names for highly relevant data or documents. The generation unit can also postpone generating file names for less relevant data or documents. For example, the generation unit postpones generating file names for less relevant data or documents. The generation unit can also adjust the order of file name generation based on the relevance of data or documents. For example, the generation unit adjusts the order of file name generation based on the relevance of data or documents. This enables efficient file name generation by adjusting the order of file names based on the relevance of data or documents. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the relevance of data or documents to the generation AI, and the generation AI can adjust the order of file names based on that information.

[0047] When generating a file name, the generation unit can adjust the use of technical terms in the file name based on the user's level of expertise. For example, if the user has technical expertise, the generation unit generates a file name including technical terms. For example, if the user has technical expertise, the generation unit generates a file name including technical terms. The generation unit can also generate a file name using general terms if the user does not have technical expertise. For example, if the user does not have technical expertise, the generation unit generates a file name using general terms. The generation unit can also adjust the terminology in the file name according to the user's level of expertise. For example, the generation unit adjusts the terminology in the file name according to the user's level of expertise. In this way, an appropriate file name is generated by adjusting the use of technical terms in the file name according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using a generation AI or may be performed without using a generation AI. For example, the generation unit can input the user's level of expertise into the generation AI, and the generation AI can adjust the use of technical terms in the file name based on that information.

[0048] The storage unit can determine storage priorities according to the importance of data and documents when storing them. For example, the storage unit prioritizes storing data and documents with high importance. For example, the storage unit prioritizes storing data and documents with high importance. The storage unit can also postpone storing data and documents with low importance. For example, the storage unit postpones storing data and documents with low importance. The storage unit can also adjust storage priorities based on the importance of data and documents. For example, the storage unit adjusts storage priorities based on the importance of data and documents. This enables efficient storage by determining storage priorities based on the importance of data and documents. Some or all of the above-described processing in the storage unit may be performed using or without the generation AI. For example, the storage unit can input the importance of data and documents into the generation AI, and the generation AI can determine storage priorities based on that information.

[0049] The storage unit can apply different storage methods based on the categories of data and documents during storage. For example, the storage unit applies different storage methods depending on the document category. For example, the storage unit applies different storage methods depending on the document category. The storage unit can also apply different storage methods depending on the data category. For example, the storage unit applies different storage methods depending on the data category. The storage unit can also select the optimal storage method for each category. For example, the storage unit selects the optimal storage method for each category. This enables efficient storage by applying different storage methods depending on the categories of data and documents. Some or all of the above-mentioned processing in the storage unit may be performed using or without the generation AI. For example, the storage unit can input the categories of data and documents into the generation AI, and the generation AI can apply different storage methods based on that information.

[0050] The storage unit can improve the accuracy of storage based on the user's past storage history during storage. For example, the storage unit stores similar data and documents by referring to data and documents stored by the user in the past. For example, the storage unit stores similar data and documents by referring to data and documents stored by the user in the past. The storage unit can also select an optimal storage method from the user's past storage history. For example, the storage unit selects an optimal storage method from the user's past storage history. The storage unit can also analyze the user's past storage history to improve the accuracy of storage. For example, the storage unit analyzes the user's past storage history to improve the accuracy of storage. In this way, the accuracy of storage is improved by referring to the user's past storage history. Some or all of the above-mentioned processing in the storage unit may be performed using or without the generation AI. For example, the storage unit can input the user's past storage history into the generation AI, and the generation AI can improve the accuracy of storage based on that information.

[0051] The storage unit can store data and documents taking into account the geographic distribution of the data and documents. For example, the storage unit selects the optimal storage location based on the geographic distribution of the data and documents. The storage unit can also store geographically close data and documents together. For example, the storage unit stores geographically close data and documents together. The storage unit can also adjust the storage method of the data and documents taking into account the geographic distribution. For example, the storage unit adjusts the storage method of the data and documents taking into account the geographic distribution. This enables efficient storage by storing data and documents taking into account the geographic distribution of the data and documents. Some or all of the above-mentioned processing in the storage unit may be performed using or without the generation AI. For example, the storage unit can input the geographic distribution of the data and documents into the generation AI, and the generation AI can perform storage based on that information.

[0052] The storage unit can improve the accuracy of storage by referring to literature related to the data or documents during storage. The storage unit, for example, refers to literature related to the data or documents to select the optimal storage method. For example, the storage unit refers to literature related to the data or documents to select the optimal storage method. The storage unit can also determine a storage location for the data or documents based on the related literature. For example, the storage unit determines a storage location for the data or documents based on the related literature. The storage unit can also improve the accuracy of storage by referring to the related literature. For example, the storage unit improves the accuracy of storage by referring to the related literature. In this way, the accuracy of storage is improved by referring to literature related to the data or documents. Some or all of the above-mentioned processing in the storage unit may be performed using or without the generation AI. For example, the storage unit can input literature related to the data or documents into the generation AI, and the generation AI can improve the accuracy of storage based on that information.

[0053] The storage unit can store data and documents based on their market value at the time of storage. For example, the storage unit prioritizes storage of data and documents with high market value. For example, the storage unit prioritizes storage of data and documents with high market value. The storage unit can also postpone storage of data and documents with low market value. For example, the storage unit postpones storage of data and documents with low market value. The storage unit can also determine storage priorities based on the market value of the data and documents. For example, the storage unit determines storage priorities based on the market value of the data and documents. This enables efficient storage by taking the market value of the data and documents into consideration. Some or all of the above-described processing in the storage unit may be performed using or without the generation AI. For example, the storage unit can input the market value of the data and documents into the generation AI, and the generation AI can perform storage based on that information.

[0054] When managing the change history, the management unit can optimize the current history by referring to past history data. The management unit, for example, optimizes the current history based on past history data. For example, the management unit optimizes the current history based on past history data. The management unit can also select an optimal change history management method by referring to past history data. For example, the management unit selects an optimal change history management method by referring to past history data. The management unit can also analyze past history data and optimize the current history. For example, the management unit analyzes past history data and optimizes the current history. In this way, the current history can be optimized by referring to the past history data. Some or all of the above-mentioned processing in the management unit may be performed using or without the generation AI. For example, the management unit can input past history data into the generation AI, and the generation AI can optimize the current history based on that information.

[0055] The management unit can apply different history management methods based on the categories of data and documents when managing change histories. The management unit applies different history management methods, for example, depending on the document category. For example, the management unit applies different history management methods depending on the document category. The management unit can also apply different history management methods depending on the data category. For example, the management unit applies different history management methods depending on the data category. The management unit can also select the optimal history management method for each category. For example, the management unit selects the optimal history management method for each category. This enables efficient history management by applying different history management methods to each category of data and documents. Some or all of the above-mentioned processing in the management unit may be performed using or without the generation AI. For example, the management unit can input the category of data or documents into the generation AI, and the generation AI can apply different history management methods based on that information.

[0056] When managing change history, the management unit can manage the history based on attribute information of the submitter of the data or document. The management unit, for example, selects an optimal history management method based on the attribute information of the submitter. For example, the management unit selects an optimal history management method based on the attribute information of the submitter. The management unit can also adjust the history display method taking into account the attribute information of the submitter. For example, the management unit adjusts the history display method taking into account the attribute information of the submitter. The management unit can also improve the accuracy of history management based on the attribute information of the submitter. For example, the management unit improves the accuracy of history management based on the attribute information of the submitter. This enables efficient history management by taking into account the attribute information of the submitter of the data or document. Some or all of the above-mentioned processing in the management unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the management unit can input the attribute information of the submitter into the generation AI, and the generation AI can manage the history based on that information.

[0057] When managing change histories, the management unit can analyze changes in history based on the submission date of data or documents. For example, the management unit prioritizes analyzing the history of data or documents that were submitted recently. For example, the management unit prioritizes analyzing the history of data or documents that were submitted recently. The management unit can also postpone analyzing the history of data or documents that were submitted more recently. For example, the management unit postpones analyzing the history of data or documents that were submitted more recently. The management unit can also analyze changes in history based on the submission date. For example, the management unit analyzes changes in history based on the submission date. This enables efficient history management by analyzing changes in history based on the submission date of data or documents. Some or all of the above-described processing in the management unit may be performed using or without the generation AI. For example, the management unit can input the submission date of data or documents into the generation AI, and the generation AI can analyze changes in history based on that information.

[0058] When managing change histories, the management unit can manage the histories based on market data related to the data or documents. The management unit, for example, selects an optimal history management method based on the related market data. For example, the management unit selects an optimal history management method based on the related market data. The management unit can also adjust the history display method by referring to the related market data. For example, the management unit adjusts the history display method by referring to the related market data. The management unit can also improve the accuracy of history management based on the related market data. For example, the management unit improves the accuracy of history management based on the related market data. This enables efficient history management by referring to the market data related to the data or documents. Some or all of the above-mentioned processing in the management unit may be performed using or without the generation AI. For example, the management unit can input market data related to the data or documents into the generation AI, and the generation AI can manage the history based on that information.

[0059] When managing change histories, the management unit can manage the histories based on the technical maturity of the data and documents. For example, the management unit prioritizes management of the histories of data and documents with high technical maturity. For example, the management unit prioritizes management of the histories of data and documents with high technical maturity. The management unit can also postpone management of the histories of data and documents with low technical maturity. For example, the management unit manages the histories of data and documents with low technical maturity. The management unit can also determine the priority of history management based on the technical maturity. For example, the management unit determines the priority of history management based on the technical maturity. This enables efficient history management by taking the technical maturity of the data and documents into consideration. Some or all of the above-mentioned processing in the management unit may be performed using or without the generation AI. For example, the management unit can input the technical maturity of the data and documents into the generation AI, and the generation AI can manage the history based on that information.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The input unit can analyze the user's past input history and suggest the optimal input method. For example, it can prioritize and suggest input methods (voice, text, etc.) that the user has used frequently in the past. It can also predict and suggest the input method to be used during a specific time period. For example, if the user has used voice input frequently during a specific time period in the past, it will suggest voice input during that time period. It can also select the optimal input method based on the type of data the user has input in the past. For example, if the user has input a lot of image data in the past, it will suggest image input with priority. This allows for efficient input of data and documents by selecting the optimal input method based on the user's past input history.

[0062] When storing data or documents, the storage unit can prioritize storing highly relevant data by taking into account the user's geographical location information. For example, if the user is in a specific area, data and documents related to that area can be stored with priority. Also, if the user is traveling, highly relevant data and documents can be stored based on the user's current location. Furthermore, if the user is in a specific location, data and documents related to that location can be stored with priority. This enables efficient storage by prioritizing highly relevant data by taking into account the user's geographical location information.

[0063] When generating a file name, the generator can adjust the level of detail in the file name according to the importance of the data or document. For example, a detailed file name can be generated for data or documents of high importance. A concise file name can also be generated for data or documents of low importance. Furthermore, the length and content of the file name can be adjusted according to the importance of the data or document. In this way, an appropriate file name can be generated by adjusting the level of detail in the file name based on the importance of the data or document.

[0064] The storage unit can store data and documents based on their market value. For example, data and documents with high market value can be stored preferentially. Data and documents with low market value can also be stored later. Furthermore, the storage priority can be determined based on the market value of the data and documents. This allows for efficient storage by storing data and documents taking their market value into consideration.

[0065] When managing change histories, the management department can manage the histories based on the technical maturity of the data and documents. For example, the management department can prioritize the management of the histories of data and documents with high technical maturity. It can also manage the histories of data and documents with low technical maturity later. Furthermore, it can determine the priority of history management based on the technical maturity. This allows for efficient history management by taking the technical maturity of data and documents into consideration.

[0066] The storage unit can improve the accuracy of storage by referring to literature related to the data or documents when storing them. For example, the storage unit can select the optimal storage method by referring to literature related to the data or documents. It can also determine the storage location of the data or documents based on the literature. It can also improve the accuracy of storage by referring to the literature related to the data or documents. In this way, the accuracy of storage is improved by referring to literature related to the data or documents.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The input unit inputs data or documents. Examples of data or documents include text data, image data, and PDF documents. The input unit digitizes paper documents using a scanner and inputs them as data. Data or documents provided in digital format can also be directly input. Step 2: The generation unit uses a generation AI to analyze the data and documents input by the input unit and generate an appropriate file name. The generation unit generates a file name based on the document title and content. The generation AI uses a text generation AI (e.g., LLM) to analyze the document content and generate an appropriate file name. The generation unit can also generate file names according to pre-set naming rules. For example, the generation unit generates file names using naming rules based on date or category. Step 3: The storage unit stores the data or documents using the file name generated by the generation unit. The storage unit saves the data or documents in a database. It can also store data or documents using cloud storage. Furthermore, the storage unit has a function to search for file names containing specific keywords. For example, it can quickly search for file names containing specific keywords to find the desired file. Step 4: The management unit manages the change history of file names stored by the storage unit. The management unit tracks the change history of file names and records what file names were given in the past. This allows the file name to be restored if necessary. For example, a file name that was changed by mistake can be restored to its original name.

[0069] (Example 2) The automated file naming system according to an embodiment of the present invention utilizes a generation AI to streamline data and document management. The system inputs data or documents, analyzes them, and generates appropriate file names using the generated file names. The automated file naming system then stores the data or documents under these file names. For example, the system inputs the received data or digitized documents into the generation AI. The generation AI analyzes the contents of the input data or documents and generates appropriate file names. For example, the generation AI automatically assigns file names based on the document's title and content. Since the generation AI generates file names according to pre-defined naming rules, file names are assigned consistently across individuals. The generated file names are then used to store the data or documents. This not only reduces the labor required for naming files but also shortens the time required to search for files. For example, users can quickly find the desired file by searching for file names containing specific keywords. Furthermore, the generation AI can also manage file name change history. This allows users to track past file names and, if necessary, revert the file name to its original state. For example, even if a file name is accidentally changed, users can refer to the history managed by the generation AI to restore the original file name. As a result, the automated file naming system streamlines data and document management, significantly reducing work time and the time spent searching for things. For example, when managing large volumes of documents within a company, using generative AI makes it possible to store and search documents quickly and accurately. In addition, consistent file names are assigned, eliminating individual differences, making it easier to share information within a team.

[0070] An automated file naming system according to an embodiment includes an input unit, a generation unit, a storage unit, and a management unit. The input unit inputs data and documents. Examples of the data and documents include, but are not limited to, text data, image data, and PDF documents. The input unit, for example, uses a scanner to digitize paper documents and input them as data. The input unit can also directly input data and documents provided in digital format. The generation unit uses a generation AI to analyze the data and documents input by the input unit and generate an appropriate file name. The generation unit generates a file name based on, for example, the title or content of the document. The generation AI analyzes the content of the document using a text generation AI (e.g., LLM) and generates an appropriate file name. The generation unit can also generate file names according to a predetermined naming rule. For example, the generation unit generates file names using a naming rule based on a date or category. The storage unit stores the data and documents using the file name generated by the generation unit. The storage unit saves the data and documents in a database, for example. The storage unit can also store the data and documents using cloud storage. Furthermore, the storage unit has a function for searching for file names containing specific keywords. For example, the storage unit can quickly search for file names containing specific keywords and find a target file. The management unit manages the change history of file names stored by the storage unit. For example, the management unit tracks the change history of file names and records what file names were given in the past. This allows the management unit to restore file names as needed. For example, the management unit can restore a file name that was changed by mistake to its original name. As a result, the file name automation system according to the embodiment improves the efficiency of data and document management, significantly reducing work time and the time spent searching for items.

[0071] The generation unit can generate a file name based on the title or content of the document. The generation unit, for example, analyzes the document title and generates an appropriate file name. For example, the generation unit extracts keywords from the document title and generates a file name based on them. The generation unit can also analyze the content of the document and generate an appropriate file name. For example, the generation unit extracts important phrases from the content of the document and generates a file name based on them. In this way, an appropriate file name is generated based on the content of the document. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the content of the document into the generation AI, which analyzes it and generates a file name.

[0072] The generation unit can generate file names based on a predetermined naming rule. The generation unit generates file names using, for example, a date-based naming rule. For example, the generation unit includes the creation date of the document in the file name. The generation unit can also generate file names using a category-based naming rule. For example, the generation unit includes the document category (e.g., technical document, business document, etc.) in the file name. This generates consistent file names. Some or all of the above-mentioned processing in the generation unit may be performed using or without the generation AI. For example, the generation unit can input a predetermined naming rule into the generation AI, and the generation AI can generate file names according to the rule.

[0073] The storage unit can store data and documents based on the generated file name. The storage unit, for example, stores the data and documents in a database. For example, the storage unit stores the data and documents in a database using the generated file name. The storage unit can also store the data and documents using cloud storage. For example, the storage unit stores the data and documents in cloud storage using the generated file name. This allows the data and documents to be efficiently stored using the generated file name. Some or all of the above-mentioned processing in the storage unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the storage unit can input the generated file name to the generation AI, and the generation AI can store the data and documents based on the file name.

[0074] The storage unit can search for file names that include a specific keyword. For example, the storage unit searches for file names that include a specific keyword. For example, the storage unit quickly searches for file names that include a keyword specified by the user to find the desired file. The storage unit can also search for file names based on frequently occurring keywords. For example, the storage unit preferentially searches for file names that include a frequently occurring keyword. This allows for a quick search for file names that include a specific keyword. Some or all of the above-mentioned processing in the storage unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the storage unit can input a specific keyword into the generation AI, and the generation AI can search for file names that include that keyword.

[0075] The management unit can track the change history of file names. The management unit, for example, tracks the change history of file names. For example, the management unit records the change history of file names and tracks what file names were given in the past. The management unit can also use a version control system to track the change history. For example, the management unit manages the change history of file names using a version control system. In this way, by managing the change history of file names, past file names can be tracked. Some or all of the above-mentioned processing in the management unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the management unit can input the change history of file names into the generation AI, and the generation AI can manage that history.

[0076] The management unit can record what file names have been given in the past. For example, the management unit records what file names have been given in the past. For example, the management unit saves the file name change history in a log file. The management unit can also use a database to record past file names. For example, the management unit saves the file name change history in a database. This makes it possible to track past file names and restore them to their original names as needed. Some or all of the above-mentioned processing in the management unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the management unit can input past file names into the generation AI, and the generation AI can manage the history.

[0077] The input unit can estimate a user's emotion and adjust the input timing of data or documents according to the estimated user emotion. For example, the input unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the input unit calculates an emotion score based on changes in facial expression and adjusts the input timing. The input unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the input unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the input timing. The input unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the input unit calculates an emotion score based on heart rate fluctuations and adjusts the input timing. This enables efficient data and document input by adjusting the input timing according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generative AI. Some or all of the above-described processing in the input unit may be performed using or without the generation AI. For example, the input unit may input user emotion data to the generation AI, which may then analyze the emotion data and adjust the input timing.

[0078] The input unit can analyze the user's past input history and select an appropriate input method. For example, the input unit prioritizes and suggests input methods (such as voice and text) that the user has frequently used in the past. For example, the input unit analyzes the user's past input history and selects the optimal input method. The input unit can also predict and suggest the input method to be used in a specific time period. For example, the input unit predicts and suggests the input method to be used in a specific time period based on the user's past input history. The input unit can also select the optimal input method based on the type of data the user has previously input. For example, the input unit analyzes the type of data the user has previously input and selects the optimal input method based on that. This enables efficient input of data and documents by selecting the optimal input method based on the past input history. Some or all of the above-mentioned processing in the input unit may be performed using or without the generation AI. For example, the input unit can input the user's past input history into the generation AI, which can then analyze the history and select the optimal input method.

[0079] The input unit can filter data and documents based on the user's current project or areas of interest when inputting the data and documents. For example, the input unit inputs only data and documents related to the user's current project. For example, the input unit prioritizes input of data and documents related to the user's current project. The input unit can also prioritize input of related data and documents based on the user's areas of interest. For example, the input unit prioritizes input of related data and documents based on the user's areas of interest. The input unit can also filter data and documents based on areas in which the user has previously shown interest. For example, the input unit filters data and documents based on areas in which the user has previously shown interest. This enables efficient input by filtering data and documents based on the user's current project or areas of interest. Some or all of the above-described processing in the input unit may be performed using or without the generation AI. For example, the input unit can input the user's current project or areas of interest to the generation AI, and the generation AI can filter data and documents based on that information.

[0080] When inputting data or documents, the input unit can select an appropriate input means depending on the user's input method. For example, if the user desires voice input, the input unit inputs the data or documents using voice recognition technology. For example, the input unit converts voice into text using voice recognition software and inputs the data or documents. Furthermore, if the user desires text input, the input unit can also provide keyboard input preferentially. For example, the input unit provides keyboard input preferentially and inputs the data or documents. Furthermore, if the user desires image input, the input unit can also use image recognition technology to input the data or documents. For example, the input unit analyzes images using image recognition technology and inputs the data or documents. This enables efficient input of data and documents by selecting the optimal input means depending on the user's input method. Some or all of the above-described processing in the input unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the input unit can input the user's input method to a generation AI, which can select an appropriate input means depending on the method.

[0081] The input unit can estimate the user's emotions and determine the priority of data and documents to be input according to the estimated user emotions. For example, when the user is stressed, the input unit prioritizes input of less important data and documents. For example, when the user is stressed, the input unit prioritizes input of less important data and documents. The input unit can also prioritize input of more important data and documents when the user is relaxed. For example, when the user is relaxed, the input unit prioritizes input of more important data and documents. The input unit can also prioritize input of more urgent data and documents when the user is in a hurry. For example, when the user is in a hurry, the input unit prioritizes input of more urgent data and documents. This enables efficient input by determining the priority of data and documents according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the input unit may be performed using or without the generation AI. For example, the input unit may input user emotion data to the generation AI, which may then analyze the emotion data and determine the priority of data or documents.

[0082] When inputting data or documents, the input unit can prioritize inputting highly relevant data taking into account the user's geographical location information. For example, when the user is in a specific area, the input unit prioritizes inputting data or documents related to that area. For example, when the user is in a specific area, the input unit prioritizes inputting data or documents related to that area. Furthermore, when the user is moving, the input unit can also input highly relevant data or documents based on the user's current location. For example, when the user is moving, the input unit inputs highly relevant data or documents based on the user's current location. Furthermore, when the user is in a specific location, the input unit can also prioritize inputting data or documents related to that location. For example, when the user is in a specific location, the input unit prioritizes inputting data or documents related to that location. This enables efficient input by prioritizing highly relevant data taking into account the user's geographical location information. Some or all of the above-described processing in the input unit may be performed using or without the generation AI. For example, the input unit can input the user's geographical location information to the generation AI, and the generation AI can prioritize inputting highly relevant data or documents based on that information.

[0083] The input unit can analyze a user's social media activity and input related data when inputting data or documents. The input unit can, for example, input related data or documents based on information shared by the user on social media. For example, the input unit can input related data or documents based on information shared by the user on social media. The input unit can also analyze the content of a user's social media posts and input related data or documents. For example, the input unit can analyze the content of a user's social media posts and input related data or documents. The input unit can also input related data or documents based on the activities of the user's friends on social media. For example, the input unit can input related data or documents based on the activities of the user's friends on social media. This enables efficient input by analyzing the user's social media activity and inputting related data. Some or all of the above-described processing in the input unit can be performed using or without a generation AI. For example, the input unit can input the user's social media activity to a generation AI, and the generation AI can input related data or documents based on that information.

[0084] The input unit can customize the input method based on the user's past feedback when inputting data or documents. For example, the input unit suggests the optimal input method based on feedback provided by the user in the past. For example, the input unit suggests the optimal input method based on feedback provided by the user in the past. The input unit can also preferentially provide a specific input method based on the user's past feedback. For example, the input unit preferentially provides a specific input method based on the user's past feedback. The input unit can also customize the input interface by reflecting the user's feedback. For example, the input unit customizes the input interface by reflecting the user's feedback. This enables efficient input by customizing the input method by reflecting the user's past feedback. Some or all of the above-mentioned processing in the input unit may be performed using a generation AI or may be performed without using a generation AI. For example, the input unit can input the user's past feedback to the generation AI, and the generation AI can customize the input method based on that information.

[0085] The generation unit can estimate the user's emotions and adjust the way the filename is expressed depending on the estimated user's emotions. For example, if the user is relaxed, the generation unit generates a filename with a friendly expression. For example, if the user is relaxed, the generation unit generates a filename with a friendly expression. The generation unit can also generate a concise and easy-to-understand filename if the user is in a hurry. For example, if the user is in a hurry, the generation unit generates a concise and easy-to-understand filename. The generation unit can also generate a visually appealing filename if the user is excited. For example, if the user is excited, the generation unit generates a visually appealing filename. In this way, an appropriate filename is generated by adjusting the way the filename is expressed depending on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the generation unit can input the user's emotional data into the generation AI, which can then analyze the emotional data and adjust the way the file name is expressed.

[0086] When generating a file name, the generation unit can adjust the level of detail of the file name according to the importance of the data or document. For example, the generation unit generates a detailed file name for data or documents of high importance. For example, the generation unit generates a detailed file name for data or documents of high importance. The generation unit can also generate a concise file name for data or documents of low importance. For example, the generation unit generates a concise file name for data or documents of low importance. The generation unit can also adjust the length and content of the file name according to the importance of the data or document. For example, the generation unit adjusts the length and content of the file name according to the importance of the data or document. In this way, an appropriate file name is generated by adjusting the level of detail of the file name based on the importance of the data or document. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the importance of the data or document to the generation AI, and the generation AI can adjust the level of detail of the file name based on that information.

[0087] When generating a file name, the generation unit can apply different generation algorithms based on the category of the data or document. For example, the generation unit generates a file name by applying different naming rules depending on the document category. For example, the generation unit generates a file name by applying different naming rules depending on the document category. The generation unit can also generate a file name using different algorithms depending on the data category. For example, the generation unit generates a file name using different algorithms depending on the data category. The generation unit can also select an optimal file name generation algorithm for each category. For example, the generation unit selects an optimal file name generation algorithm for each category. As a result, appropriate file names are generated by applying different generation algorithms depending on the category of the data or document. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the category of the data or document into the generation AI, and the generation AI can apply different generation algorithms based on that information.

[0088] When generating file names, the generation unit can improve the accuracy of generation based on the user's past generation results. For example, the generation unit generates similar file names by referring to file names generated by the user in the past. For example, the generation unit generates similar file names by referring to file names generated by the user in the past. The generation unit can also select an optimal file name generation algorithm based on the user's past generation results. For example, the generation unit selects an optimal file name generation algorithm based on the user's past generation results. The generation unit can also analyze the user's past generation results to improve the accuracy of generation. For example, the generation unit analyzes the user's past generation results to improve the accuracy of generation. In this way, the accuracy of generation is improved by referring to the user's past generation results. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past generation results into the generation AI, and the generation AI can improve the accuracy of generation based on that information.

[0089] The generation unit can estimate the user's emotions and adjust the length of the file name according to the estimated user's emotions. For example, if the user is relaxed, the generation unit generates a longer file name. For example, if the user is relaxed, the generation unit generates a longer file name. The generation unit can also generate a shorter file name if the user is in a hurry. For example, if the user is in a hurry, the generation unit generates a shorter file name. The generation unit can also generate a visually appealing file name if the user is excited. For example, if the user is excited, the generation unit generates a visually appealing file name. In this way, an appropriate file name is generated by adjusting the length of the file name according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the generation unit can input the user's emotion data into the generation AI, and the generation AI can analyze the emotion data and adjust the length of the file name.

[0090] When generating file names, the generation unit can determine the priority of file names according to the submission time of the data or document. For example, the generation unit prioritizes generating file names for data or documents that are soon to be submitted. For example, the generation unit prioritizes generating file names for data or documents that are soon to be submitted. The generation unit can also postpone generating file names for data or documents that are further away from the submission time. For example, the generation unit postpones generating file names for data or documents that are further away from the submission time. The generation unit can also adjust the order in which file names are generated based on the submission time. For example, the generation unit adjusts the order in which file names are generated based on the submission time. This enables efficient file name generation by determining the priority of file names based on the submission time of data or documents. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the submission time of data or documents into the generation AI, and the generation AI can determine the priority of file names based on that information.

[0091] When generating file names, the generation unit can adjust the order of file names based on the relevance of data or documents. For example, the generation unit prioritizes generating file names for highly relevant data or documents. For example, the generation unit prioritizes generating file names for highly relevant data or documents. The generation unit can also postpone generating file names for less relevant data or documents. For example, the generation unit postpones generating file names for less relevant data or documents. The generation unit can also adjust the order of file name generation based on the relevance of data or documents. For example, the generation unit adjusts the order of file name generation based on the relevance of data or documents. This enables efficient file name generation by adjusting the order of file names based on the relevance of data or documents. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the relevance of data or documents to the generation AI, and the generation AI can adjust the order of file names based on that information.

[0092] When generating a file name, the generation unit can adjust the use of technical terms in the file name based on the user's level of expertise. For example, if the user has technical expertise, the generation unit generates a file name including technical terms. For example, if the user has technical expertise, the generation unit generates a file name including technical terms. The generation unit can also generate a file name using general terms if the user does not have technical expertise. For example, if the user does not have technical expertise, the generation unit generates a file name using general terms. The generation unit can also adjust the terminology in the file name according to the user's level of expertise. For example, the generation unit adjusts the terminology in the file name according to the user's level of expertise. In this way, an appropriate file name is generated by adjusting the use of technical terms in the file name according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using a generation AI or may be performed without using a generation AI. For example, the generation unit can input the user's level of expertise into the generation AI, and the generation AI can adjust the use of technical terms in the file name based on that information.

[0093] The storage unit can estimate the user's emotions and adjust the storage method based on the estimated user's emotions. For example, when the user is relaxed, the storage unit provides a detailed storage method. For example, when the user is relaxed, the storage unit provides a detailed storage method. Furthermore, when the user is in a hurry, the storage unit can provide a concise storage method. For example, when the user is in a hurry, the storage unit provides a concise storage method. Furthermore, when the user is excited, the storage unit can provide a visually appealing storage method. For example, when the user is excited, the storage unit provides a visually appealing storage method. This enables efficient storage of data and documents by adjusting the storage method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the storage unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the storage unit can input the user's emotional data into the generation AI, which can then analyze the emotional data and adjust the storage method.

[0094] The storage unit can determine storage priorities according to the importance of data and documents when storing them. For example, the storage unit prioritizes storing data and documents with high importance. For example, the storage unit prioritizes storing data and documents with high importance. The storage unit can also postpone storing data and documents with low importance. For example, the storage unit postpones storing data and documents with low importance. The storage unit can also adjust storage priorities based on the importance of data and documents. For example, the storage unit adjusts storage priorities based on the importance of data and documents. This enables efficient storage by determining storage priorities based on the importance of data and documents. Some or all of the above-described processing in the storage unit may be performed using or without the generation AI. For example, the storage unit can input the importance of data and documents into the generation AI, and the generation AI can determine storage priorities based on that information.

[0095] The storage unit can apply different storage methods based on the categories of data and documents during storage. For example, the storage unit applies different storage methods depending on the document category. For example, the storage unit applies different storage methods depending on the document category. The storage unit can also apply different storage methods depending on the data category. For example, the storage unit applies different storage methods depending on the data category. The storage unit can also select the optimal storage method for each category. For example, the storage unit selects the optimal storage method for each category. This enables efficient storage by applying different storage methods depending on the categories of data and documents. Some or all of the above-mentioned processing in the storage unit may be performed using or without the generation AI. For example, the storage unit can input the categories of data and documents into the generation AI, and the generation AI can apply different storage methods based on that information.

[0096] The storage unit can improve the accuracy of storage based on the user's past storage history during storage. For example, the storage unit stores similar data and documents by referring to data and documents stored by the user in the past. For example, the storage unit stores similar data and documents by referring to data and documents stored by the user in the past. The storage unit can also select an optimal storage method from the user's past storage history. For example, the storage unit selects an optimal storage method from the user's past storage history. The storage unit can also analyze the user's past storage history to improve the accuracy of storage. For example, the storage unit analyzes the user's past storage history to improve the accuracy of storage. In this way, the accuracy of storage is improved by referring to the user's past storage history. Some or all of the above-mentioned processing in the storage unit may be performed using or without the generation AI. For example, the storage unit can input the user's past storage history into the generation AI, and the generation AI can improve the accuracy of storage based on that information.

[0097] The storage unit can estimate the user's emotions and determine the priority of data and documents to be stored based on the estimated user emotions. For example, when the user is relaxed, the storage unit prioritizes storing data and documents of high importance. For example, when the user is relaxed, the storage unit prioritizes storing data and documents of high importance. Furthermore, when the user is in a hurry, the storage unit can postpone storing data and documents of low importance. For example, when the user is in a hurry, the storage unit postpones storing data and documents of low importance. Furthermore, when the user is excited, the storage unit can prioritize storing data and documents that are visually appealing. For example, when the user is excited, the storage unit prioritizes storing data and documents that are visually appealing. This enables efficient storage by determining the priority of data and documents according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processes in the storage unit may be performed using or without the generation AI. For example, the storage unit may input user emotion data into the generation AI, which may then analyze the emotion data and determine the priority of the data and documents to be stored.

[0098] The storage unit can store data and documents taking into account the geographic distribution of the data and documents. For example, the storage unit selects the optimal storage location based on the geographic distribution of the data and documents. The storage unit can also store geographically close data and documents together. For example, the storage unit stores geographically close data and documents together. The storage unit can also adjust the storage method of the data and documents taking into account the geographic distribution. For example, the storage unit adjusts the storage method of the data and documents taking into account the geographic distribution. This enables efficient storage by storing data and documents taking into account the geographic distribution of the data and documents. Some or all of the above-mentioned processing in the storage unit may be performed using or without the generation AI. For example, the storage unit can input the geographic distribution of the data and documents into the generation AI, and the generation AI can perform storage based on that information.

[0099] The storage unit can improve the accuracy of storage by referring to literature related to the data or documents during storage. The storage unit, for example, refers to literature related to the data or documents to select the optimal storage method. For example, the storage unit refers to literature related to the data or documents to select the optimal storage method. The storage unit can also determine a storage location for the data or documents based on the related literature. For example, the storage unit determines a storage location for the data or documents based on the related literature. The storage unit can also improve the accuracy of storage by referring to the related literature. For example, the storage unit improves the accuracy of storage by referring to the related literature. In this way, the accuracy of storage is improved by referring to literature related to the data or documents. Some or all of the above-mentioned processing in the storage unit may be performed using or without the generation AI. For example, the storage unit can input literature related to the data or documents into the generation AI, and the generation AI can improve the accuracy of storage based on that information.

[0100] The storage unit can store data and documents based on their market value at the time of storage. For example, the storage unit prioritizes storage of data and documents with high market value. For example, the storage unit prioritizes storage of data and documents with high market value. The storage unit can also postpone storage of data and documents with low market value. For example, the storage unit postpones storage of data and documents with low market value. The storage unit can also determine storage priorities based on the market value of the data and documents. For example, the storage unit determines storage priorities based on the market value of the data and documents. This enables efficient storage by taking the market value of the data and documents into consideration. Some or all of the above-described processing in the storage unit may be performed using or without the generation AI. For example, the storage unit can input the market value of the data and documents into the generation AI, and the generation AI can perform storage based on that information.

[0101] The management unit can estimate the user's emotions and adjust the display method of the change history based on the estimated user's emotions. For example, when the user is relaxed, the management unit displays a detailed change history. For example, when the user is relaxed, the management unit displays a detailed change history. The management unit can also display a concise change history when the user is in a hurry. For example, when the user is in a hurry, the management unit displays a concise change history. The management unit can also display a visually appealing change history when the user is excited. For example, when the user is excited, the management unit displays a visually appealing change history. This enables efficient history management by adjusting the display method of the change history according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the management unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the management department can input the user's emotional data into the generation AI, which can then analyze the emotional data and adjust how the change history is displayed.

[0102] When managing the change history, the management unit can optimize the current history by referring to past history data. The management unit, for example, optimizes the current history based on past history data. For example, the management unit optimizes the current history based on past history data. The management unit can also select an optimal change history management method by referring to past history data. For example, the management unit selects an optimal change history management method by referring to past history data. The management unit can also analyze past history data and optimize the current history. For example, the management unit analyzes past history data and optimizes the current history. In this way, the current history can be optimized by referring to the past history data. Some or all of the above-mentioned processing in the management unit may be performed using or without the generation AI. For example, the management unit can input past history data into the generation AI, and the generation AI can optimize the current history based on that information.

[0103] The management unit can apply different history management methods based on the categories of data and documents when managing change histories. The management unit applies different history management methods, for example, depending on the document category. For example, the management unit applies different history management methods depending on the document category. The management unit can also apply different history management methods depending on the data category. For example, the management unit applies different history management methods depending on the data category. The management unit can also select the optimal history management method for each category. For example, the management unit selects the optimal history management method for each category. This enables efficient history management by applying different history management methods to each category of data and documents. Some or all of the above-mentioned processing in the management unit may be performed using or without the generation AI. For example, the management unit can input the category of data or documents into the generation AI, and the generation AI can apply different history management methods based on that information.

[0104] When managing change history, the management unit can manage the history based on attribute information of the submitter of the data or document. The management unit, for example, selects an optimal history management method based on the attribute information of the submitter. For example, the management unit selects an optimal history management method based on the attribute information of the submitter. The management unit can also adjust the history display method taking into account the attribute information of the submitter. For example, the management unit adjusts the history display method taking into account the attribute information of the submitter. The management unit can also improve the accuracy of history management based on the attribute information of the submitter. For example, the management unit improves the accuracy of history management based on the attribute information of the submitter. This enables efficient history management by taking into account the attribute information of the submitter of the data or document. Some or all of the above-mentioned processing in the management unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the management unit can input the attribute information of the submitter into the generation AI, and the generation AI can manage the history based on that information.

[0105] The management unit can estimate the user's emotions and adjust the importance of the change history according to the estimated user's emotions. For example, when the user is relaxed, the management unit prioritizes displaying change history with high importance. For example, when the user is relaxed, the management unit prioritizes displaying change history with high importance. Furthermore, when the user is in a hurry, the management unit can postpone displaying change history with low importance. For example, when the user is in a hurry, the management unit postpones displaying change history with low importance. Furthermore, when the user is excited, the management unit can prioritize displaying change history that is visually appealing. For example, when the user is excited, the management unit prioritizes displaying change history that is visually appealing. This enables efficient history management by adjusting the importance of the change history according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the management unit can be performed using the generation AI or without the generation AI. For example, the management department can input the user's emotional data into the generation AI, which can then analyze the emotional data and adjust the importance of the change history.

[0106] When managing change histories, the management unit can analyze changes in history based on the submission date of data or documents. For example, the management unit prioritizes analyzing the history of data or documents that were submitted recently. For example, the management unit prioritizes analyzing the history of data or documents that were submitted recently. The management unit can also postpone analyzing the history of data or documents that were submitted more recently. For example, the management unit postpones analyzing the history of data or documents that were submitted more recently. The management unit can also analyze changes in history based on the submission date. For example, the management unit analyzes changes in history based on the submission date. This enables efficient history management by analyzing changes in history based on the submission date of data or documents. Some or all of the above-described processing in the management unit may be performed using or without the generation AI. For example, the management unit can input the submission date of data or documents into the generation AI, and the generation AI can analyze changes in history based on that information.

[0107] When managing change histories, the management unit can manage the histories based on market data related to the data or documents. The management unit, for example, selects an optimal history management method based on the related market data. For example, the management unit selects an optimal history management method based on the related market data. The management unit can also adjust the history display method by referring to the related market data. For example, the management unit adjusts the history display method by referring to the related market data. The management unit can also improve the accuracy of history management based on the related market data. For example, the management unit improves the accuracy of history management based on the related market data. This enables efficient history management by referring to the market data related to the data or documents. Some or all of the above-mentioned processing in the management unit may be performed using or without the generation AI. For example, the management unit can input market data related to the data or documents into the generation AI, and the generation AI can manage the history based on that information.

[0108] When managing change histories, the management unit can manage the histories based on the technical maturity of the data and documents. For example, the management unit prioritizes management of the histories of data and documents with high technical maturity. For example, the management unit prioritizes management of the histories of data and documents with high technical maturity. The management unit can also postpone management of the histories of data and documents with low technical maturity. For example, the management unit manages the histories of data and documents with low technical maturity. The management unit can also determine the priority of history management based on the technical maturity. For example, the management unit determines the priority of history management based on the technical maturity. This enables efficient history management by taking the technical maturity of the data and documents into consideration. Some or all of the above-mentioned processing in the management unit may be performed using or without the generation AI. For example, the management unit can input the technical maturity of the data and documents into the generation AI, and the generation AI can manage the history based on that information. === Hard Collateral 1-1 === Each of the multiple elements including the input unit, generation unit, storage unit, and management unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit inputs data or documents using the camera 42 or microphone 38B of the smart device 14, and the generation unit generates an appropriate file name using a generation AI by the specific processing unit 290 of the data processing device 12. The storage unit stores the data or document in the database 24 using the generated file name, and the management unit manages the file name change history by the specific processing unit 290 of the data processing device 12. Furthermore, the input unit can capture a user's facial expression using the camera 42 of the smart device 14, estimate the emotion using an emotion estimation algorithm, and adjust the input timing. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned input unit, generation unit, storage unit, and management unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit inputs data or documents using the camera 42 or microphone 238 of the smart glasses 214, and the generation unit generates an appropriate file name using a generation AI by the specific processing unit 290 of the data processing device 12. The storage unit stores the data or document in the database 24 using the generated file name, and the management unit manages the file name change history by the specific processing unit 290 of the data processing device 12. Furthermore, the input unit can capture a user's facial expression using the camera 42 of the smart glasses 214, estimate the emotion using an emotion estimation algorithm, and adjust the input timing. === Hard Collateral 1-3 === Each of the multiple elements including the input unit, generation unit, storage unit, and management unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the input unit inputs data or documents using the camera 42 or microphone 238 of the headset-type terminal 314, and the generation unit generates an appropriate file name using a generation AI by the specific processing unit 290 of the data processing device 12. The storage unit stores the data or document in the database 24 using the generated file name, and the management unit manages the file name change history by the specific processing unit 290 of the data processing device 12. Furthermore, the input unit can capture a user's facial expression using the camera 42 of the headset-type terminal 314, estimate the emotion using an emotion estimation algorithm, and adjust the input timing. === Hard Collateral 1-4 === Each of the multiple elements including the input unit, generation unit, storage unit, and management unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit inputs data or documents using the camera 42 or microphone 238 of the robot 414, and the generation unit generates an appropriate file name using a generation AI by the specific processing unit 290 of the data processing device 12. The storage unit stores the data or document in the database 24 using the generated file name, and the management unit manages the file name change history by the specific processing unit 290 of the data processing device 12. Furthermore, the input unit can capture a user's facial expression using the camera 42 of the robot 414, estimate the emotion using an emotion estimation algorithm, and adjust the input timing.

[0109] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0110] The input unit can analyze the user's past input history and suggest the optimal input method. For example, it can prioritize and suggest input methods (voice, text, etc.) that the user has used frequently in the past. It can also predict and suggest the input method to be used during a specific time period. For example, if the user has used voice input frequently during a specific time period in the past, it will suggest voice input during that time period. It can also select the optimal input method based on the type of data the user has input in the past. For example, if the user has input a lot of image data in the past, it will suggest image input with priority. This allows for efficient input of data and documents by selecting the optimal input method based on the user's past input history.

[0111] The generation unit can estimate the user's emotions and adjust the way the file name is expressed depending on the estimated user's emotions. For example, if the user is relaxed, a file name with a friendly expression can be generated. If the user is in a hurry, a concise and easy-to-understand file name can be generated. Furthermore, if the user is excited, a visually appealing file name can be generated. In this way, an appropriate file name can be generated by adjusting the way the file name is expressed depending on the user's emotions. Emotion estimation is achieved using an emotion engine or generation AI.

[0112] When storing data or documents, the storage unit can prioritize storing highly relevant data by taking into account the user's geographical location information. For example, if the user is in a specific area, data and documents related to that area can be stored with priority. Also, if the user is traveling, highly relevant data and documents can be stored based on the user's current location. Furthermore, if the user is in a specific location, data and documents related to that location can be stored with priority. This enables efficient storage by prioritizing highly relevant data by taking into account the user's geographical location information.

[0113] The management unit can estimate the user's emotions and adjust the display method of the change history based on the estimated user emotions. For example, if the user is relaxed, a detailed change history can be displayed. If the user is in a hurry, a concise change history can be displayed. Furthermore, if the user is excited, a visually appealing change history can be displayed. This enables efficient history management by adjusting the display method of the change history according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI.

[0114] When generating a file name, the generator can adjust the level of detail in the file name according to the importance of the data or document. For example, a detailed file name can be generated for data or documents of high importance. A concise file name can also be generated for data or documents of low importance. Furthermore, the length and content of the file name can be adjusted according to the importance of the data or document. In this way, an appropriate file name can be generated by adjusting the level of detail in the file name based on the importance of the data or document.

[0115] The input unit can estimate the user's emotions and prioritize the data and documents to be input based on the estimated user emotions. For example, if the user is feeling stressed, it can prioritize input of less important data and documents. Also, if the user is relaxed, it can prioritize input of more important data and documents. Furthermore, if the user is in a hurry, it can prioritize input of more urgent data and documents. This allows for efficient input by prioritizing data and documents based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI.

[0116] The storage unit can store data and documents based on their market value. For example, data and documents with high market value can be stored preferentially. Data and documents with low market value can also be stored later. Furthermore, the storage priority can be determined based on the market value of the data and documents. This allows for efficient storage by storing data and documents taking their market value into consideration.

[0117] When managing change histories, the management department can manage the histories based on the technical maturity of the data and documents. For example, the management department can prioritize the management of the histories of data and documents with high technical maturity. It can also manage the histories of data and documents with low technical maturity later. Furthermore, it can determine the priority of history management based on the technical maturity. This allows for efficient history management by taking the technical maturity of data and documents into consideration.

[0118] The generation unit can estimate the user's emotions and adjust the length of the file name according to the estimated user emotions. For example, if the user is relaxed, a longer file name can be generated. If the user is in a hurry, a shorter file name can be generated. Furthermore, if the user is excited, a visually appealing file name can be generated. In this way, appropriate file names can be generated by adjusting the length of the file name according to the user's emotions. Emotion estimation is achieved using an emotion engine or generation AI.

[0119] The storage unit can improve the accuracy of storage by referring to literature related to the data or documents when storing them. For example, the storage unit can select the optimal storage method by referring to literature related to the data or documents. It can also determine the storage location of the data or documents based on the literature. It can also improve the accuracy of storage by referring to the literature related to the data or documents. In this way, the accuracy of storage is improved by referring to literature related to the data or documents.

[0120] The processing flow of the second embodiment will be briefly explained below.

[0121] Step 1: The input unit inputs data or documents. Examples of data or documents include text data, image data, and PDF documents. The input unit digitizes paper documents using a scanner and inputs them as data. Data or documents provided in digital format can also be directly input. Step 2: The generation unit uses a generation AI to analyze the data and documents input by the input unit and generate an appropriate file name. The generation unit generates a file name based on the document title and content. The generation AI uses a text generation AI (e.g., LLM) to analyze the document content and generate an appropriate file name. The generation unit can also generate file names according to pre-set naming rules. For example, the generation unit generates file names using naming rules based on date or category. Step 3: The storage unit stores the data or documents using the file name generated by the generation unit. The storage unit saves the data or documents in a database. It can also store data or documents using cloud storage. Furthermore, the storage unit has a function to search for file names containing specific keywords. For example, it can quickly search for file names containing specific keywords to find the desired file. Step 4: The management unit manages the change history of file names stored by the storage unit. The management unit tracks the change history of file names and records what file names were given in the past. This allows the file name to be restored if necessary. For example, a file name that was changed by mistake can be restored to its original name.

[0122] 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.

[0123] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0127] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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."

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] [Explanation of symbols]

[0194] 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. an input section for inputting data or documents; a generation unit that analyzes the data or document input by the input unit and generates a file name; a storage unit that stores data and documents using the file name generated by the generation unit; a management unit that manages the change history of the file names stored by the storage unit; A system characterized by:

2. The generation unit Generate filenames based on document title or content 2. The system of claim 1.

3. The generation unit Generate filenames based on predefined naming conventions 2. The system of claim 1.

4. The storage unit includes: Store data and documents based on generated file names 2. The system of claim 1.

5. The storage unit includes: Find filenames containing specific keywords 2. The system of claim 1.

6. The management unit Track file name changes 2. The system of claim 1.

7. The management unit Record what file names were given in the past 2. The system of claim 1.

8. The input unit Estimate the user's emotions and adjust the timing of data and document input according to the estimated user emotions.

2. The system of claim 1.

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