System

Abstract

An object of a system according to an embodiment is to appropriately propose a name, a storage location, and a format of a material.SOLUTION: A system according to an embodiment includes a name suggestion unit, a storage place suggestion unit, and a format suggestion unit. The name suggestion unit suggests a name of the material. The storage place proposal unit proposes a storage place of the material on the basis of the name proposed by the name proposal unit. The format proposal unit proposes a format of the material based on the repository proposed by the repository proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies are unable to appropriately suggest document names, storage locations, and formats, which can reduce the efficiency of document creation.

[0005] The system according to the embodiment aims to appropriately suggest the name, storage location, and format of a document. [Means for solving the problem]

[0006] The system according to the embodiment includes a name suggestion unit, a storage location suggestion unit, and a format suggestion unit. The name suggestion unit suggests a name for the document. The storage location suggestion unit suggests a storage location for the document based on the name suggested by the name suggestion unit. The format suggestion unit suggests a format for the document based on the storage location suggested by the storage location suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can appropriately suggest the name, storage location, and format of the material. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A document creation support system according to an embodiment of the present invention is a system that automatically suggests document names, storage locations, and formats. The document creation support system assists users in creating and saving documents by suggesting names and storage locations that comply with company rules. Furthermore, the system learns from the content of past documents and suggests optimal formats, significantly reducing the time required for document creation. For example, when a user creates a document, the document creation support system has a name suggestion unit that suggests names that comply with company rules. For example, it can automatically generate names that include the project name, date, and person in charge. Next, the document creation support system has a storage location suggestion unit that suggests storage locations that comply with company rules. For example, it can automatically suggest appropriate storage locations, such as project folders or department folders. Furthermore, the document creation support system has a format suggestion unit that learns from the content of past documents and suggests optimal formats. For example, it can learn the layout, style, and content structure of past documents and apply them to new documents. This significantly reduces the time required for document creation. The document creation support system thus eliminates the need for manual input and selection by suggesting document names and storage locations, and efficiently creates consistent documents by suggesting formats. This will improve work efficiency and overall productivity.

[0029] A document creation support system according to an embodiment includes a name suggestion unit, a storage location suggestion unit, and a format suggestion unit. The name suggestion unit suggests a name for the document. For example, the name suggestion unit suggests a name that complies with company rules. The name suggestion unit can also automatically generate a name that includes a project name, a date, the name of a person in charge, etc. For example, the name suggestion unit uses an algorithm that generates a name that includes a project name, a date, and the name of a person in charge. The storage location suggestion unit suggests a storage location for the document based on the name suggested by the name suggestion unit. The storage location suggestion unit suggests a storage location that complies with company rules, for example. The storage location suggestion unit can also automatically suggest an appropriate storage location, such as a project folder or a department folder. For example, the storage location suggestion unit uses an algorithm that suggests a project folder or a department folder. The format suggestion unit suggests a format for the document based on the storage location suggested by the storage location suggestion unit. For example, the format suggestion unit learns the content of past documents and suggests an optimal format. The format suggestion unit can also learn the layout, style, content structure, etc. of past documents and apply them to new documents. For example, the format suggestion unit uses an algorithm that learns the layout, style, and content structure of past documents. As a result, the document creation support system according to the embodiment automatically suggests the name, storage location, and format of the document, thereby improving the efficiency of document creation.

[0030] The name suggestion unit can suggest names that comply with company rules. For example, the name suggestion unit suggests names that comply with company naming rules. For example, the name suggestion unit uses an algorithm to generate names that include a project name, a date, and a person in charge. The name suggestion unit can also suggest names that comply with a specific format or pattern based on the company rules. For example, the name suggestion unit suggests names that combine a project name, a date, and a person in charge. In this way, consistency in names is maintained by automatically suggesting names that comply with company rules.

[0031] The storage location suggestion unit can suggest storage locations that comply with company rules. The storage location suggestion unit suggests storage locations that comply with, for example, company storage location regulations. For example, the storage location suggestion unit uses an algorithm that suggests project folders or department folders. The storage location suggestion unit can also suggest storage locations that comply with specific folder structures or access permissions based on company rules. For example, the storage location suggestion unit suggests project folders or department folders. In this way, consistency in storage locations is maintained by automatically suggesting storage locations that comply with company rules.

[0032] The format suggestion unit can learn the content of past documents and propose a format. The format suggestion unit, for example, learns the content of past documents and proposes the optimal format. For example, the format suggestion unit uses an algorithm that learns the layout, style, and content structure of past documents. The format suggestion unit can also propose a format to apply to new documents based on the content of past documents. For example, the format suggestion unit learns the layout, style, and content structure of past documents and applies them to new documents. In this way, the optimal format can be proposed by learning the content of past documents.

[0033] The name suggestion unit can automatically generate a name that includes a project name, a date, a person in charge, etc. The name suggestion unit automatically generates a name that includes, for example, a project name, a date, a person in charge, etc. For example, the name suggestion unit uses an algorithm that generates a name that combines the project name, the date, and the person in charge. The name suggestion unit can also generate a name that complies with a company's naming rules. For example, the name suggestion unit generates a name that includes the project name, the date, and the person in charge. This automatically generates a name that includes the project name, the date, the person in charge, etc., thereby reducing the effort required to input a name.

[0034] The storage location suggestion unit can automatically suggest an appropriate storage location, such as a project folder or a department folder. The storage location suggestion unit automatically suggests an appropriate storage location, such as a project folder or a department folder. For example, the storage location suggestion unit uses an algorithm that suggests a project folder or a department folder. The storage location suggestion unit can also suggest a storage location in accordance with a company's storage location regulations. For example, the storage location suggestion unit suggests a project folder or a department folder. This automatically suggests an appropriate storage location, such as a project folder or a department folder, thereby reducing the effort required to select a storage location.

[0035] The format suggestion unit can learn the layout, style, content structure, etc. of past documents and apply them to new documents. The format suggestion unit, for example, learns the layout, style, content structure, etc. of past documents and applies them to new documents. For example, the format suggestion unit uses an algorithm that learns the layout, style, and content structure of past documents. The format suggestion unit can also propose a format to apply to new documents based on the content of past documents. For example, the format suggestion unit learns the layout, style, content structure, etc. of past documents and applies them to new documents. In this way, consistency can be achieved in new documents by learning the layout, style, content structure, etc. of past documents.

[0036] The name suggestion unit can analyze patterns of past document names and generate a name. The name suggestion unit, for example, analyzes patterns of past document names and generates an optimal name. For example, the name suggestion unit analyzes patterns of past document names and extracts frequently occurring keywords to generate a name. The name suggestion unit can also analyze the length and format of past document names and propose an optimal name format. The name suggestion unit can also take into account the frequency of use of past document names and preferentially propose the most appropriate name. For example, the name suggestion unit generates an optimal name using an algorithm that analyzes patterns of past document names. In this way, the optimal name can be generated by analyzing patterns of past document names.

[0037] The name suggestion unit can customize the name format based on the user's job title and department. The name suggestion unit customizes the name format based on, for example, the user's job title and department. For example, the name suggestion unit includes a job title in the name according to the user's job title. The name suggestion unit can also suggest a name including a department name according to the user's department. The name suggestion unit can also suggest an optimal name format based on a combination of the user's job title and department. For example, the name suggestion unit uses an algorithm that customizes the name format based on the user's job title and department. This allows more appropriate names to be suggested by customizing the name format based on the user's job title and department.

[0038] The name suggestion unit can dynamically change a part of the name depending on the progress of the project. The name suggestion unit dynamically changes a part of the name depending on, for example, the progress of the project. For example, the name suggestion unit may include a progress stage in the name depending on the progress of the project. The name suggestion unit may also include a deadline in the name depending on the progress of the project. The name suggestion unit may also include the progress of the person in charge in the name depending on the progress of the project. For example, the name suggestion unit uses an algorithm that dynamically changes a part of the name depending on the progress of the project. This makes it possible to suggest more appropriate names by dynamically changing a part of the name depending on the progress of the project.

[0039] The name suggestion unit can customize a portion of the name based on the user's geographic location information. The name suggestion unit customizes a portion of the name based on the user's geographic location information, for example. For example, the name suggestion unit includes a place name in the name based on the user's location. The name suggestion unit can also include region-specific keywords in the name based on the user's location. The name suggestion unit can also customize the format of the name to suit the region based on the user's location. For example, the name suggestion unit uses an algorithm to customize a portion of the name based on the user's geographic location information. This allows more appropriate names to be suggested by taking the user's geographic location information into consideration.

[0040] The name suggestion unit can analyze the user's social media activity and include related keywords in the name. The name suggestion unit, for example, analyzes the user's social media activity and includes related keywords in the name. For example, the name suggestion unit analyzes the content of the user's posts on social media and includes related keywords in the name. The name suggestion unit can also include appropriate keywords in the name based on the user's social media activity history. The name suggestion unit can also include related keywords in the name based on the activity of the user's friends on social media. For example, the name suggestion unit can include related keywords in the name using an algorithm that analyzes the user's social media activity. In this way, more appropriate names can be suggested by analyzing the user's social media activity.

[0041] The name suggestion unit can improve the name suggestion method by reflecting the user's past feedback. For example, the name suggestion unit improves the name suggestion method by reflecting the user's past feedback. For example, the name suggestion unit analyzes the user's past feedback and improves the name suggestion method. The name suggestion unit can also adjust the name format based on the user's past feedback. The name suggestion unit can also optimize the name suggestion algorithm by referring to the user's past feedback. For example, the name suggestion unit uses an algorithm that improves the name suggestion method by reflecting the user's past feedback. In this way, more appropriate names can be suggested by reflecting the user's past feedback.

[0042] The storage location suggestion unit can analyze patterns of past storage locations and suggest storage locations. The storage location suggestion unit, for example, analyzes patterns of past storage locations and suggests the optimal storage location. For example, the storage location suggestion unit analyzes patterns of past storage locations and suggests frequently used folders. The storage location suggestion unit can also suggest the optimal storage location taking into account the frequency of use of past storage locations. The storage location suggestion unit can also analyze the structure of past storage locations and suggest the most appropriate folder. For example, the storage location suggestion unit suggests the optimal storage location using an algorithm that analyzes patterns of past storage locations. In this way, the optimal storage location can be suggested by analyzing patterns of past storage locations.

[0043] The storage location suggestion unit can customize the storage location based on the user's job title or department. The storage location suggestion unit customizes the storage location based on, for example, the user's job title or department. For example, the storage location suggestion unit customizes the storage location according to the user's job title. The storage location suggestion unit can also suggest a department folder according to the user's department. The storage location suggestion unit can also suggest an optimal storage location based on a combination of the user's job title and department. For example, the storage location suggestion unit uses an algorithm that customizes the storage location based on the user's job title or department. In this way, by customizing the storage location based on the user's job title or department, a more appropriate storage location can be suggested.

[0044] The storage location suggestion unit can dynamically change the storage location according to the progress of the project. The storage location suggestion unit dynamically changes the storage location according to, for example, the progress of the project. For example, the storage location suggestion unit may include a progress stage in the storage location according to the progress of the project. The storage location suggestion unit may also include a deadline date in the storage location according to the progress of the project. The storage location suggestion unit may also include the progress of the person in charge in the storage location according to the progress of the project. For example, the storage location suggestion unit uses an algorithm that dynamically changes the storage location according to the progress of the project. This makes it possible to suggest a more appropriate storage location by dynamically changing the storage location according to the progress of the project.

[0045] The storage location suggestion unit can customize the storage location based on the user's geographical location information. The storage location suggestion unit customizes the storage location based on, for example, the user's geographical location information. For example, the storage location suggestion unit customizes the storage location based on the user's location. The storage location suggestion unit can also suggest a region-specific folder based on the user's location. The storage location suggestion unit can also customize the format of the storage location to suit the region based on the user's location. For example, the storage location suggestion unit uses an algorithm that customizes the storage location based on the user's geographical location information. This allows a more appropriate storage location to be suggested by taking the user's geographical location information into consideration.

[0046] The storage location suggestion unit can analyze the user's social media activity and suggest related folders. The storage location suggestion unit, for example, analyzes the user's social media activity and suggests related folders. For example, the storage location suggestion unit analyzes the content of the user's posts on social media and suggests related folders. The storage location suggestion unit can also suggest appropriate folders based on the user's social media activity history. The storage location suggestion unit can also suggest related folders with reference to the activities of the user's friends on social media. For example, the storage location suggestion unit suggests related folders using an algorithm that analyzes the user's social media activity. In this way, a more appropriate storage location can be suggested by analyzing the user's social media activity.

[0047] The storage location suggestion unit can improve the method for suggesting storage locations by reflecting the user's past feedback. The storage location suggestion unit, for example, improves the method for suggesting storage locations by reflecting the user's past feedback. For example, the storage location suggestion unit analyzes the user's past feedback and improves the method for suggesting storage locations. The storage location suggestion unit can also adjust the format of the storage location based on the user's past feedback. The storage location suggestion unit can also optimize the algorithm for suggesting storage locations by referring to the user's past feedback. For example, the storage location suggestion unit uses an algorithm that improves the method for suggesting storage locations by reflecting the user's past feedback. In this way, a more appropriate storage location can be suggested by reflecting the user's past feedback.

[0048] The format suggestion unit can analyze the layout and style of past documents and suggest a format. For example, the format suggestion unit analyzes the layout and style of past documents and suggests an optimal format. For example, the format suggestion unit analyzes the layout of past documents and suggests an optimal format. The format suggestion unit can also analyze the style of past documents and suggest a consistent format. The format suggestion unit can also learn the content structure of past documents and suggest an optimal format. For example, the format suggestion unit uses an algorithm that analyzes the layout and style of past documents to suggest an optimal format. In this way, the format suggestion unit can suggest an optimal format by analyzing the layout and style of past documents.

[0049] The format suggestion unit can customize the format based on the user's job title or department. The format suggestion unit customizes the format based on the user's job title or department, for example. For example, the format suggestion unit customizes the format according to the user's job title. The format suggestion unit can also propose a department-specific format according to the user's department. The format suggestion unit can also propose an optimal format based on a combination of the user's job title and department. For example, the format suggestion unit uses an algorithm that customizes the format based on the user's job title or department. This makes it possible to propose a more appropriate format by customizing the format based on the user's job title or department.

[0050] The format suggestion unit can dynamically change the format depending on the progress of the project. The format suggestion unit dynamically changes the format depending on, for example, the progress of the project. For example, the format suggestion unit may include a progress stage in the format depending on the progress of the project. The format suggestion unit may also include a deadline in the format depending on the progress of the project. The format suggestion unit may also include the progress of the person in charge in the format depending on the progress of the project. For example, the format suggestion unit uses an algorithm that dynamically changes the format depending on the progress of the project. This makes it possible to propose a more appropriate format by dynamically changing the format depending on the progress of the project.

[0051] The format suggestion unit can customize the format based on the user's geographical location information. The format suggestion unit customizes the format based on, for example, the user's geographical location information. For example, the format suggestion unit includes region-specific elements in the format based on the user's location. The format suggestion unit can also customize the layout of the format to suit the region based on the user's location. The format suggestion unit can also adjust the style of the format to suit the region based on the user's location. For example, the format suggestion unit uses an algorithm to customize the format based on the user's geographical location information. This makes it possible to suggest a more appropriate format by taking the user's geographical location information into consideration.

[0052] The format suggestion unit can analyze the user's social media activity and suggest a related style. The format suggestion unit, for example, analyzes the user's social media activity and suggests a related style. For example, the format suggestion unit analyzes the content of the user's posts on social media and suggests a related style. The format suggestion unit can also suggest an appropriate style based on the user's social media activity history. The format suggestion unit can also suggest a related style by referring to the activity of the user's friends on social media. For example, the format suggestion unit suggests a related style using an algorithm that analyzes the user's social media activity. In this way, a more appropriate format can be suggested by analyzing the user's social media activity.

[0053] The format suggestion unit can improve the format suggestion method by reflecting the user's past feedback. For example, the format suggestion unit improves the format suggestion method by reflecting the user's past feedback. For example, the format suggestion unit analyzes the user's past feedback and improves the format suggestion method. The format suggestion unit can also adjust the format layout based on the user's past feedback. The format suggestion unit can also optimize the format suggestion algorithm by referring to the user's past feedback. For example, the format suggestion unit uses an algorithm that improves the format suggestion method by reflecting the user's past feedback. In this way, a more appropriate format can be suggested by reflecting the user's past feedback.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The document creation support system can further include a voice input unit. The voice input unit allows the user to specify the name, storage location, and format of the document by voice. For example, if the user specifies "Create a document for Project X" by voice, the name suggestion unit automatically generates a name such as "Project X_2023_Name of person in charge." The storage location suggestion unit can also suggest an appropriate storage location based on the voice instruction. Furthermore, the format suggestion unit can suggest an optimal format based on the voice instruction. This further improves the efficiency of document creation by using voice input.

[0056] The document creation support system can further include a translation unit. The translation unit can automatically translate documents created by users into other languages. For example, if a user creates a document in Japanese, the translation unit automatically translates it into English, Chinese, or other languages. The translation unit can also match the format of the translated document to the original document. This allows for the efficient creation of consistent documents, even in international projects.

[0057] The document creation support system can further include a template management unit. The template management unit manages document templates frequently used by users and can suggest new ones as needed. For example, when a user creates meeting minutes, the template management unit can suggest templates for minutes that have been used in the past. The template management unit can also suggest the most suitable template depending on the user's work content. This further improves the efficiency of document creation.

[0058] The document creation support system can further include a design proposal unit. The design proposal unit can propose visual designs for documents. For example, when a user creates presentation documents, the design proposal unit can propose optimal slide designs and color schemes. The design proposal unit can also propose appropriate graph and chart designs depending on the content of the documents. This allows visually appealing documents to be created efficiently.

[0059] The document creation support system may further include a collaboration unit. The collaboration unit enables multiple users to edit documents simultaneously. For example, project team members can collaborate on editing documents in real time and add comments and feedback. The collaboration unit can also manage editing history and track changes. This allows the entire team to create documents efficiently.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The name suggestion module suggests a name for the document. The name suggestion module can suggest names that comply with company rules, as well as automatically generate names that include the project name, date, and person in charge. For example, the name suggestion module uses an algorithm to generate names that include the project name, date, and person in charge. Step 2: The storage location suggestion unit suggests a storage location for the document based on the name suggested by the name suggestion unit. The storage location suggestion unit can suggest storage locations that comply with company rules and can automatically suggest appropriate storage locations, such as project folders or department folders. For example, the storage location suggestion unit uses an algorithm that suggests project folders or department folders. Step 3: The format suggestion unit proposes a format for the document based on the storage location proposed by the storage location suggestion unit. The format suggestion unit learns the content of past documents and proposes the optimal format. The format suggestion unit can also learn the layout, style, and content structure of past documents and apply them to new documents. For example, the format suggestion unit uses an algorithm that learns the layout, style, and content structure of past documents.

[0062] (Example 2) A document creation support system according to an embodiment of the present invention is a system that automatically suggests document names, storage locations, and formats. The document creation support system assists users in creating and saving documents by suggesting names and storage locations that comply with company rules. Furthermore, the system learns from the content of past documents and suggests optimal formats, significantly reducing the time required for document creation. For example, when a user creates a document, the document creation support system has a name suggestion unit that suggests names that comply with company rules. For example, it can automatically generate names that include the project name, date, and person in charge. Next, the document creation support system has a storage location suggestion unit that suggests storage locations that comply with company rules. For example, it can automatically suggest appropriate storage locations, such as project folders or department folders. Furthermore, the document creation support system has a format suggestion unit that learns from the content of past documents and suggests optimal formats. For example, it can learn the layout, style, and content structure of past documents and apply them to new documents. This significantly reduces the time required for document creation. The document creation support system thus eliminates the need for manual input and selection by suggesting document names and storage locations, and efficiently creates consistent documents by suggesting formats. This will improve work efficiency and overall productivity.

[0063] A document creation support system according to an embodiment includes a name suggestion unit, a storage location suggestion unit, and a format suggestion unit. The name suggestion unit suggests a name for the document. For example, the name suggestion unit suggests a name that complies with company rules. The name suggestion unit can also automatically generate a name that includes a project name, a date, the name of a person in charge, etc. For example, the name suggestion unit uses an algorithm that generates a name that includes a project name, a date, and the name of a person in charge. The storage location suggestion unit suggests a storage location for the document based on the name suggested by the name suggestion unit. The storage location suggestion unit suggests a storage location that complies with company rules, for example. The storage location suggestion unit can also automatically suggest an appropriate storage location, such as a project folder or a department folder. For example, the storage location suggestion unit uses an algorithm that suggests a project folder or a department folder. The format suggestion unit suggests a format for the document based on the storage location suggested by the storage location suggestion unit. For example, the format suggestion unit learns the content of past documents and suggests an optimal format. The format suggestion unit can also learn the layout, style, content structure, etc. of past documents and apply them to new documents. For example, the format suggestion unit uses an algorithm that learns the layout, style, and content structure of past documents. As a result, the document creation support system according to the embodiment automatically suggests the name, storage location, and format of the document, thereby improving the efficiency of document creation.

[0064] The name suggestion unit can suggest names that comply with company rules. For example, the name suggestion unit suggests names that comply with company naming rules. For example, the name suggestion unit uses an algorithm to generate names that include a project name, a date, and a person in charge. The name suggestion unit can also suggest names that comply with a specific format or pattern based on the company rules. For example, the name suggestion unit suggests names that combine a project name, a date, and a person in charge. In this way, consistency in names is maintained by automatically suggesting names that comply with company rules.

[0065] The storage location suggestion unit can suggest storage locations that comply with company rules. The storage location suggestion unit suggests storage locations that comply with, for example, company storage location regulations. For example, the storage location suggestion unit uses an algorithm that suggests project folders or department folders. The storage location suggestion unit can also suggest storage locations that comply with specific folder structures or access permissions based on company rules. For example, the storage location suggestion unit suggests project folders or department folders. In this way, consistency in storage locations is maintained by automatically suggesting storage locations that comply with company rules.

[0066] The format suggestion unit can learn the content of past documents and propose a format. The format suggestion unit, for example, learns the content of past documents and proposes the optimal format. For example, the format suggestion unit uses an algorithm that learns the layout, style, and content structure of past documents. The format suggestion unit can also propose a format to apply to new documents based on the content of past documents. For example, the format suggestion unit learns the layout, style, and content structure of past documents and applies them to new documents. In this way, the optimal format can be proposed by learning the content of past documents.

[0067] The name suggestion unit can automatically generate a name that includes a project name, a date, a person in charge, etc. The name suggestion unit automatically generates a name that includes, for example, a project name, a date, a person in charge, etc. For example, the name suggestion unit uses an algorithm that generates a name that combines the project name, the date, and the person in charge. The name suggestion unit can also generate a name that complies with a company's naming rules. For example, the name suggestion unit generates a name that includes the project name, the date, and the person in charge. This automatically generates a name that includes the project name, the date, the person in charge, etc., thereby reducing the effort required to input a name.

[0068] The storage location suggestion unit can automatically suggest an appropriate storage location, such as a project folder or a department folder. The storage location suggestion unit automatically suggests an appropriate storage location, such as a project folder or a department folder. For example, the storage location suggestion unit uses an algorithm that suggests a project folder or a department folder. The storage location suggestion unit can also suggest a storage location in accordance with a company's storage location regulations. For example, the storage location suggestion unit suggests a project folder or a department folder. This automatically suggests an appropriate storage location, such as a project folder or a department folder, thereby reducing the effort required to select a storage location.

[0069] The format suggestion unit can learn the layout, style, content structure, etc. of past documents and apply them to new documents. The format suggestion unit, for example, learns the layout, style, content structure, etc. of past documents and applies them to new documents. For example, the format suggestion unit uses an algorithm that learns the layout, style, and content structure of past documents. The format suggestion unit can also propose a format to apply to new documents based on the content of past documents. For example, the format suggestion unit learns the layout, style, content structure, etc. of past documents and applies them to new documents. In this way, consistency can be achieved in new documents by learning the layout, style, content structure, etc. of past documents.

[0070] The name suggestion unit can estimate the user's emotions and adjust the name suggestion method based on the estimated user's emotions. For example, the name suggestion unit can estimate the user's emotions and adjust the name suggestion method based on the estimated user's emotions. For example, if the user is stressed, the name suggestion unit can suggest a simple and short name. Also, if the user is relaxed, the name suggestion unit can suggest a name with detailed information. Also, if the user is in a hurry, the name suggestion unit can enhance the auto-complete function to allow for quick input. For example, the name suggestion unit can adjust the name suggestion method based on the user's emotions using an algorithm that estimates the user's emotions. In this way, by adjusting the name suggestion method according to the user's emotions, more appropriate names can be suggested. 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 generation AI.

[0071] The name suggestion unit can analyze patterns of past document names and generate a name. The name suggestion unit, for example, analyzes patterns of past document names and generates an optimal name. For example, the name suggestion unit analyzes patterns of past document names and extracts frequently occurring keywords to generate a name. The name suggestion unit can also analyze the length and format of past document names and propose an optimal name format. The name suggestion unit can also take into account the frequency of use of past document names and preferentially propose the most appropriate name. For example, the name suggestion unit generates an optimal name using an algorithm that analyzes patterns of past document names. In this way, the optimal name can be generated by analyzing patterns of past document names.

[0072] The name suggestion unit can customize the name format based on the user's job title and department. The name suggestion unit customizes the name format based on, for example, the user's job title and department. For example, the name suggestion unit includes a job title in the name according to the user's job title. The name suggestion unit can also suggest a name including a department name according to the user's department. The name suggestion unit can also suggest an optimal name format based on a combination of the user's job title and department. For example, the name suggestion unit uses an algorithm that customizes the name format based on the user's job title and department. This allows more appropriate names to be suggested by customizing the name format based on the user's job title and department.

[0073] The name suggestion unit can dynamically change a part of the name depending on the progress of the project. The name suggestion unit dynamically changes a part of the name depending on, for example, the progress of the project. For example, the name suggestion unit may include a progress stage in the name depending on the progress of the project. The name suggestion unit may also include a deadline in the name depending on the progress of the project. The name suggestion unit may also include the progress of the person in charge in the name depending on the progress of the project. For example, the name suggestion unit uses an algorithm that dynamically changes a part of the name depending on the progress of the project. This makes it possible to suggest more appropriate names by dynamically changing a part of the name depending on the progress of the project.

[0074] The name suggestion unit can estimate the user's emotions and adjust the length of the name based on the estimated user's emotions. For example, the name suggestion unit can estimate the user's emotions and adjust the length of the name based on the estimated user's emotions. For example, the name suggestion unit can suggest a short name if the user is stressed. The name suggestion unit can also suggest a long name with detailed information if the user is relaxed. The name suggestion unit can also suggest a short and concise name if the user is in a hurry. For example, the name suggestion unit can adjust the length of the name based on the user's emotions using an algorithm that estimates the user's emotions. In this way, by adjusting the length of the name according to the user's emotions, more appropriate names can be suggested. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0075] The name suggestion unit can customize a portion of the name based on the user's geographic location information. The name suggestion unit customizes a portion of the name based on the user's geographic location information, for example. For example, the name suggestion unit includes a place name in the name based on the user's location. The name suggestion unit can also include region-specific keywords in the name based on the user's location. The name suggestion unit can also customize the format of the name to suit the region based on the user's location. For example, the name suggestion unit uses an algorithm to customize a portion of the name based on the user's geographic location information. This allows more appropriate names to be suggested by taking the user's geographic location information into consideration.

[0076] The name suggestion unit can analyze the user's social media activity and include related keywords in the name. The name suggestion unit, for example, analyzes the user's social media activity and includes related keywords in the name. For example, the name suggestion unit analyzes the content of the user's posts on social media and includes related keywords in the name. The name suggestion unit can also include appropriate keywords in the name based on the user's social media activity history. The name suggestion unit can also include related keywords in the name based on the activity of the user's friends on social media. For example, the name suggestion unit can include related keywords in the name using an algorithm that analyzes the user's social media activity. In this way, more appropriate names can be suggested by analyzing the user's social media activity.

[0077] The name suggestion unit can improve the name suggestion method by reflecting the user's past feedback. For example, the name suggestion unit improves the name suggestion method by reflecting the user's past feedback. For example, the name suggestion unit analyzes the user's past feedback and improves the name suggestion method. The name suggestion unit can also adjust the name format based on the user's past feedback. The name suggestion unit can also optimize the name suggestion algorithm by referring to the user's past feedback. For example, the name suggestion unit uses an algorithm that improves the name suggestion method by reflecting the user's past feedback. In this way, more appropriate names can be suggested by reflecting the user's past feedback.

[0078] The storage location suggestion unit can estimate a user's emotions and adjust the method of suggesting storage locations based on the estimated user emotions. For example, the storage location suggestion unit can estimate a user's emotions and adjust the method of suggesting storage locations based on the estimated user emotions. For example, if the user is stressed, the storage location suggestion unit can suggest a simple and intuitive storage location. Furthermore, if the user is relaxed, the storage location suggestion unit can suggest a detailed folder structure. Furthermore, if the user is in a hurry, the storage location suggestion unit can suggest a quickly accessible storage location. For example, the storage location suggestion unit can adjust the method of suggesting storage locations based on the user's emotions using an algorithm that estimates the user's emotions. This allows for the suggestion of more appropriate storage locations by adjusting the method of suggesting storage locations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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.

[0079] The storage location suggestion unit can analyze patterns of past storage locations and suggest storage locations. The storage location suggestion unit, for example, analyzes patterns of past storage locations and suggests the optimal storage location. For example, the storage location suggestion unit analyzes patterns of past storage locations and suggests frequently used folders. The storage location suggestion unit can also suggest the optimal storage location taking into account the frequency of use of past storage locations. The storage location suggestion unit can also analyze the structure of past storage locations and suggest the most appropriate folder. For example, the storage location suggestion unit suggests the optimal storage location using an algorithm that analyzes patterns of past storage locations. In this way, the optimal storage location can be suggested by analyzing patterns of past storage locations.

[0080] The storage location suggestion unit can customize the storage location based on the user's job title or department. The storage location suggestion unit customizes the storage location based on, for example, the user's job title or department. For example, the storage location suggestion unit customizes the storage location according to the user's job title. The storage location suggestion unit can also suggest a department folder according to the user's department. The storage location suggestion unit can also suggest an optimal storage location based on a combination of the user's job title and department. For example, the storage location suggestion unit uses an algorithm that customizes the storage location based on the user's job title or department. In this way, by customizing the storage location based on the user's job title or department, a more appropriate storage location can be suggested.

[0081] The storage location suggestion unit can dynamically change the storage location according to the progress of the project. The storage location suggestion unit dynamically changes the storage location according to, for example, the progress of the project. For example, the storage location suggestion unit may include a progress stage in the storage location according to the progress of the project. The storage location suggestion unit may also include a deadline date in the storage location according to the progress of the project. The storage location suggestion unit may also include the progress of the person in charge in the storage location according to the progress of the project. For example, the storage location suggestion unit uses an algorithm that dynamically changes the storage location according to the progress of the project. This makes it possible to suggest a more appropriate storage location by dynamically changing the storage location according to the progress of the project.

[0082] The storage location suggestion unit can estimate the user's emotions and prioritize storage locations based on the estimated user emotions. The storage location suggestion unit, for example, estimates the user's emotions and prioritizes storage locations based on the estimated user emotions. For example, if the user is stressed, the storage location suggestion unit prioritizes the most easily accessible storage location. Furthermore, if the user is relaxed, the storage location suggestion unit can prioritize a detailed folder structure. Furthermore, if the user is in a hurry, the storage location suggestion unit can prioritize quickly accessible storage locations. For example, the storage location suggestion unit uses an algorithm to estimate the user's emotions to prioritize storage locations based on the user's emotions. This allows more appropriate storage locations to be suggested by prioritizing storage locations according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using 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.

[0083] The storage location suggestion unit can customize the storage location based on the user's geographical location information. The storage location suggestion unit customizes the storage location based on, for example, the user's geographical location information. For example, the storage location suggestion unit customizes the storage location based on the user's location. The storage location suggestion unit can also suggest a region-specific folder based on the user's location. The storage location suggestion unit can also customize the format of the storage location to suit the region based on the user's location. For example, the storage location suggestion unit uses an algorithm that customizes the storage location based on the user's geographical location information. This allows a more appropriate storage location to be suggested by taking the user's geographical location information into consideration.

[0084] The storage location suggestion unit can analyze the user's social media activity and suggest related folders. The storage location suggestion unit, for example, analyzes the user's social media activity and suggests related folders. For example, the storage location suggestion unit analyzes the content of the user's posts on social media and suggests related folders. The storage location suggestion unit can also suggest appropriate folders based on the user's social media activity history. The storage location suggestion unit can also suggest related folders with reference to the activities of the user's friends on social media. For example, the storage location suggestion unit suggests related folders using an algorithm that analyzes the user's social media activity. In this way, a more appropriate storage location can be suggested by analyzing the user's social media activity.

[0085] The storage location suggestion unit can improve the method for suggesting storage locations by reflecting the user's past feedback. The storage location suggestion unit, for example, improves the method for suggesting storage locations by reflecting the user's past feedback. For example, the storage location suggestion unit analyzes the user's past feedback and improves the method for suggesting storage locations. The storage location suggestion unit can also adjust the format of the storage location based on the user's past feedback. The storage location suggestion unit can also optimize the algorithm for suggesting storage locations by referring to the user's past feedback. For example, the storage location suggestion unit uses an algorithm that improves the method for suggesting storage locations by reflecting the user's past feedback. In this way, a more appropriate storage location can be suggested by reflecting the user's past feedback.

[0086] The format suggestion unit can estimate a user's emotions and adjust the format suggestion method based on the estimated user's emotions. For example, the format suggestion unit can estimate a user's emotions and adjust the format suggestion method based on the estimated user's emotions. For example, if the user is stressed, the format suggestion unit can suggest a simple and intuitive format. Furthermore, if the user is relaxed, the format suggestion unit can suggest a format that includes detailed information. Furthermore, if the user is in a hurry, the format suggestion unit can suggest a format that can be created quickly. For example, the format suggestion unit can adjust the format suggestion method based on the user's emotions using an algorithm that estimates the user's emotions. This allows the format suggestion method to be adjusted according to the user's emotions, thereby suggesting a more appropriate format. Emotion estimation is achieved using an emotion estimation function, for example, using 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.

[0087] The format suggestion unit can analyze the layout and style of past documents and suggest a format. For example, the format suggestion unit analyzes the layout and style of past documents and suggests an optimal format. For example, the format suggestion unit analyzes the layout of past documents and suggests an optimal format. The format suggestion unit can also analyze the style of past documents and suggest a consistent format. The format suggestion unit can also learn the content structure of past documents and suggest an optimal format. For example, the format suggestion unit uses an algorithm that analyzes the layout and style of past documents to suggest an optimal format. In this way, the format suggestion unit can suggest an optimal format by analyzing the layout and style of past documents.

[0088] The format suggestion unit can customize the format based on the user's job title or department. The format suggestion unit customizes the format based on the user's job title or department, for example. For example, the format suggestion unit customizes the format according to the user's job title. The format suggestion unit can also propose a department-specific format according to the user's department. The format suggestion unit can also propose an optimal format based on a combination of the user's job title and department. For example, the format suggestion unit uses an algorithm that customizes the format based on the user's job title or department. This makes it possible to propose a more appropriate format by customizing the format based on the user's job title or department.

[0089] The format suggestion unit can dynamically change the format depending on the progress of the project. The format suggestion unit dynamically changes the format depending on, for example, the progress of the project. For example, the format suggestion unit may include a progress stage in the format depending on the progress of the project. The format suggestion unit may also include a deadline in the format depending on the progress of the project. The format suggestion unit may also include the progress of the person in charge in the format depending on the progress of the project. For example, the format suggestion unit uses an algorithm that dynamically changes the format depending on the progress of the project. This makes it possible to propose a more appropriate format by dynamically changing the format depending on the progress of the project.

[0090] The format suggestion unit can estimate the user's emotions and adjust the level of detail of the format based on the estimated user's emotions. For example, the format suggestion unit can estimate the user's emotions and adjust the level of detail of the format based on the estimated user's emotions. For example, if the user is stressed, the format suggestion unit can suggest a simple, less detailed format. Also, if the user is relaxed, the format suggestion unit can suggest a format that includes detailed information. Also, if the user is in a hurry, the format suggestion unit can suggest a format that can be created quickly. For example, the format suggestion unit can adjust the level of detail of the format based on the user's emotions using an algorithm that estimates the user's emotions. This allows for the suggestion of a more appropriate format by adjusting the level of detail of the format according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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.

[0091] The format suggestion unit can customize the format based on the user's geographical location information. The format suggestion unit customizes the format based on, for example, the user's geographical location information. For example, the format suggestion unit includes region-specific elements in the format based on the user's location. The format suggestion unit can also customize the layout of the format to suit the region based on the user's location. The format suggestion unit can also adjust the style of the format to suit the region based on the user's location. For example, the format suggestion unit uses an algorithm to customize the format based on the user's geographical location information. This makes it possible to suggest a more appropriate format by taking the user's geographical location information into consideration.

[0092] The format suggestion unit can analyze the user's social media activity and suggest a related style. The format suggestion unit, for example, analyzes the user's social media activity and suggests a related style. For example, the format suggestion unit analyzes the content of the user's posts on social media and suggests a related style. The format suggestion unit can also suggest an appropriate style based on the user's social media activity history. The format suggestion unit can also suggest a related style by referring to the activity of the user's friends on social media. For example, the format suggestion unit suggests a related style using an algorithm that analyzes the user's social media activity. In this way, a more appropriate format can be suggested by analyzing the user's social media activity.

[0093] The format suggestion unit can improve the format suggestion method by reflecting the user's past feedback. For example, the format suggestion unit improves the format suggestion method by reflecting the user's past feedback. For example, the format suggestion unit analyzes the user's past feedback and improves the format suggestion method. The format suggestion unit can also adjust the format layout based on the user's past feedback. The format suggestion unit can also optimize the format suggestion algorithm by referring to the user's past feedback. For example, the format suggestion unit uses an algorithm that improves the format suggestion method by reflecting the user's past feedback. In this way, a more appropriate format can be suggested by reflecting the user's past feedback. === Hard Collateral 1-1 === For example, each of the multiple elements including the name suggestion unit, the storage location suggestion unit, and the format suggestion unit is realized by at least one of the smart device 14 and the data processing device 12. For example, the name suggestion unit is realized by the control unit 46A of the smart device 14 and automatically generates a name including a project name, date, and person in charge. The storage location suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests a storage location in accordance with company rules. The format suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and learns the contents of past documents and suggests the optimal format. === Hard Collateral 1-2 === For example, each of the multiple elements including the name suggestion unit, the storage location suggestion unit, and the format suggestion unit is realized by at least one of the smart glasses 214 and the data processing device 12. For example, the name suggestion unit is realized by the control unit 46A of the smart glasses 214 and automatically generates a name including a project name, date, and person in charge. The storage location suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests a storage location in accordance with company rules. The format suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and learns the contents of past documents and suggests an optimal format. === Hard Collateral 1-3 === For example, each of the multiple elements including the name suggestion unit, storage location suggestion unit, and format suggestion unit is realized by at least one of the headset type terminal 314 and the data processing device 12. For example, the name suggestion unit is realized by the control unit 46A of the headset type terminal 314, and automatically generates a name including a project name, date, and person in charge. The storage location suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and suggests a storage location in accordance with company rules. The format suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and learns the contents of past documents and suggests the optimal format. === Hard Collateral 1-4 === For example, each of the multiple elements including the name suggestion unit, storage location suggestion unit, and format suggestion unit is realized by at least one of the robot 414 and the data processing device 12. For example, the name suggestion unit is realized by the control unit 46A of the robot 414, and automatically generates a name including a project name, date, and person in charge. The storage location suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and suggests a storage location in accordance with company rules. The format suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and learns the contents of past documents and suggests the optimal format.

[0094] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0095] The document creation support system can further include a voice input unit. The voice input unit allows the user to specify the name, storage location, and format of the document by voice. For example, if the user specifies "Create a document for Project X" by voice, the name suggestion unit automatically generates a name such as "Project X_2023_Name of person in charge." The storage location suggestion unit can also suggest an appropriate storage location based on the voice instruction. Furthermore, the format suggestion unit can suggest an optimal format based on the voice instruction. This further improves the efficiency of document creation by using voice input.

[0096] The document creation support system can further include a translation unit. The translation unit can automatically translate documents created by users into other languages. For example, if a user creates a document in Japanese, the translation unit automatically translates it into English, Chinese, or other languages. The translation unit can also match the format of the translated document to the original document. This allows for the efficient creation of consistent documents, even in international projects.

[0097] The document creation support system can further include a template management unit. The template management unit manages document templates frequently used by users and can suggest new ones as needed. For example, when a user creates meeting minutes, the template management unit can suggest templates for minutes that have been used in the past. The template management unit can also suggest the most suitable template depending on the user's work content. This further improves the efficiency of document creation.

[0098] The document creation support system can further include a design proposal unit. The design proposal unit can propose visual designs for documents. For example, when a user creates presentation documents, the design proposal unit can propose optimal slide designs and color schemes. The design proposal unit can also propose appropriate graph and chart designs depending on the content of the documents. This allows visually appealing documents to be created efficiently.

[0099] The document creation support system may further include a collaboration unit. The collaboration unit enables multiple users to edit documents simultaneously. For example, project team members can collaborate on editing documents in real time and add comments and feedback. The collaboration unit can also manage editing history and track changes. This allows the entire team to create documents efficiently.

[0100] The document creation support system further includes an emotion estimation unit, which can adjust the tone and style of the document based on the user's emotion. For example, if the user is feeling stressed, the emotion estimation unit can suggest a simple and intuitive tone. Alternatively, if the user is relaxed, the emotion estimation unit can suggest a tone that includes detailed information. Furthermore, if the user is in a hurry, the emotion estimation unit can suggest a style that allows for quick creation. This allows for the creation of more appropriate documents by adjusting the tone and style of the document according to the user's emotion.

[0101] The material creation support system further includes an emotion estimation unit, and can adjust the content of the material based on the user's emotion. For example, if the user is feeling stressed, the emotion estimation unit can suggest content that is concise and to the point. Also, if the user is relaxed, the emotion estimation unit can suggest content that includes detailed explanations. Furthermore, if the user is in a hurry, the emotion estimation unit can suggest content that can be quickly understood. In this way, more appropriate materials can be created by adjusting the content of the material according to the user's emotion.

[0102] The document creation support system further includes an emotion estimation unit, which can adjust the layout of the document based on the user's emotion. For example, if the user is feeling stressed, the emotion estimation unit can suggest a simple and easy-to-read layout. Also, if the user is relaxed, the emotion estimation unit can suggest a layout that includes detailed information. Furthermore, if the user is in a hurry, the emotion estimation unit can suggest a layout that allows the user to quickly grasp information. In this way, by adjusting the layout of the document according to the user's emotion, more appropriate documents can be created.

[0103] The document creation support system further includes an emotion estimation unit, which can suggest a storage location for documents based on the user's emotion. For example, if the user is feeling stressed, the emotion estimation unit can suggest a simple and intuitive storage location. Also, if the user is relaxed, the emotion estimation unit can suggest a detailed folder structure. Furthermore, if the user is in a hurry, the emotion estimation unit can suggest a storage location that can be quickly accessed. In this way, by suggesting a storage location according to the user's emotion, a more appropriate storage location can be selected.

[0104] The document creation support system further includes an emotion estimation unit, which can suggest names for documents based on the user's emotions. For example, if the user is feeling stressed, the emotion estimation unit can suggest a simple and short name. Alternatively, if the user is relaxed, the emotion estimation unit can suggest a name that includes detailed information. Furthermore, if the user is in a hurry, the emotion estimation unit can enhance the auto-completion function to allow for quick input. This allows the user to select a more appropriate name by suggesting names according to the user's emotions.

[0105] The processing flow of the second embodiment will be briefly explained below.

[0106] Step 1: The name suggestion module suggests a name for the document. The name suggestion module can suggest names that comply with company rules, as well as automatically generate names that include the project name, date, and person in charge. For example, the name suggestion module uses an algorithm to generate names that include the project name, date, and person in charge. Step 2: The storage location suggestion unit suggests a storage location for the document based on the name suggested by the name suggestion unit. The storage location suggestion unit can suggest storage locations that comply with company rules and can automatically suggest appropriate storage locations, such as project folders or department folders. For example, the storage location suggestion unit uses an algorithm that suggests project folders or department folders. Step 3: The format suggestion unit proposes a format for the document based on the storage location proposed by the storage location suggestion unit. The format suggestion unit learns the content of past documents and proposes the optimal format. The format suggestion unit can also learn the layout, style, and content structure of past documents and apply them to new documents. For example, the format suggestion unit uses an algorithm that learns the layout, style, and content structure of past documents.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0111] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0112] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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 AI 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.

[0125] 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.

[0126] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0127] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0128] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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 AI 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.

[0141] 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.

[0142] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0143] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0144] 7, a 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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 AI 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.

[0158] 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.

[0159] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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, in order to avoid confusion and to 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.

[0177] 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.

[0178] [Explanation of symbols]

[0179] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a name suggestion section that suggests names for materials; a storage location suggestion unit that suggests a storage location for the material based on the name suggested by the name suggestion unit; a format suggestion unit that suggests a format of the material based on the storage location suggested by the storage location suggestion unit; Equipped with A system characterized by:

2. The name suggestion unit Suggest a name that follows company rules 2. The system of claim 1.

3. The storage location suggestion unit Suggest storage locations that comply with company rules 2. The system of claim 1.

4. The format suggestion unit Study the content of past materials and suggest formats 2. The system of claim 1.

5. The name suggestion unit Automatically generate names that include project names, dates, and contact names 2. The system of claim 1.

6. The storage location suggestion unit Automatically suggest appropriate save locations, such as project or department folders 2. The system of claim 1.

7. The format suggestion unit Study the layout, style, and content structure of previous materials and apply them to new materials 2. The system of claim 1.

8. The name suggestion unit Estimate user sentiment and adjust name suggestion methods based on the estimated user sentiment 2. The system of claim 1.

Images