system

Abstract

The system according to the embodiment aims to check the format of the text and improve wording to enable smooth approval applications. [Solution] A system according to an embodiment includes a confirmation unit, a presentation unit, and a circulation unit. The confirmation unit confirms the format and wording of the text. The presentation unit presents suggestions for improving the text confirmed by the confirmation unit. The circulation unit circulates the text to an authorizer based on the suggestions for improvement presented by the presentation unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, checking the format of documents and improving wording in approval applications is time-consuming, and there is a risk of documents being returned due to clerical errors.

[0005] The system according to the embodiment aims to check the format of the text and improve wording to enable smooth approval applications. [Means for solving the problem]

[0006] The system according to the embodiment includes a confirmation unit, a presentation unit, and a circulation unit. The confirmation unit confirms the format and wording of the text. The presentation unit presents improvement proposals for the text confirmed by the confirmation unit. The circulation unit circulates the text to an authorizer based on the improvement proposals presented by the presentation unit. [Effects of the Invention]

[0007] The system according to the embodiment checks the format of the text and improves wording, enabling smooth approval applications. [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 pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

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

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

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

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

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

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

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

[0028] (Example 1) The approval application support system according to an embodiment of the present invention checks the format of documents and suggests improvements to wording, allowing the approval process to be clearer. This system learns the unique phrases and practices of each local government or company, enabling approvals to be clearer. This enables smooth approval processes, eliminates returns due to minor clerical errors, and shortens the time it takes to approve documents. For example, when an applicant prepares an approval application document, the system provides a function to check the format and wording of the document. This function is performed by an AI that has learned the unique phrases and practices of each local government or company. For example, if specific wording or formatting is required, the applicant can modify the document according to the instructions. Next, the AI ​​checks the format and wording of the document and suggests improvements. For example, it points out grammatical errors or inappropriate expressions and suggests appropriate revisions. Furthermore, if specific wording or formatting is required, the applicant can modify the document according to the instructions. Furthermore, the system provides a function to check the format and wording of the document and allow approvals to be clearer. This allows the approver to quickly understand the content of the text and make the decision smoothly. This system eliminates rejections due to minor clerical errors and shortens the time it takes to make a decision. For example, rejections due to grammatical errors or inappropriate expressions will no longer occur, and approvals will be made quickly. Also, if specific wording or formatting is required, the text can be revised according to the instructions, providing the approver with text that is easy to understand. This allows the approval application support system to check the format of the text, suggest improvements to the wording, and circulate the document to the approver in an easy-to-understand manner, thereby achieving a smooth approval application process.

[0029] The approval application support system according to the embodiment includes a confirmation unit, a presentation unit, and a circulation unit. The confirmation unit confirms the format and wording of the text. For example, the confirmation unit confirms the format of the text, such as the font size, line spacing, and paragraph arrangement of the text. The confirmation unit can also check wording, such as the use of honorific language in the text, grammatical accuracy, and consistency of expression. For example, the confirmation unit confirms whether the font size of the text is appropriate. The confirmation unit can also confirm whether the line spacing of the text is appropriate. The confirmation unit can also confirm whether the paragraph arrangement of the text is appropriate. The presentation unit presents suggestions for improving the text confirmed by the confirmation unit. For example, the presentation unit points out grammatical errors and inappropriate expressions and suggests correcting them to appropriate expressions. Furthermore, if specific wording or formatting is required, the presentation unit can also suggest correcting the text in accordance with the instructions. For example, the presentation unit points out grammatical errors and suggests correcting them to appropriate expressions. The presentation unit can also point out inappropriate expressions and suggest revising them to appropriate expressions. Furthermore, if specific wording or formatting is required, the presentation unit can also suggest revising the text in accordance with the instructions. The circulation unit circulates the text to the authorizer based on the improvement proposal presented by the presentation unit. The circulation unit, for example, provides a format for circulating the text to the authorizer in an easy-to-understand manner. The circulation unit can also provide a means for quickly circulating the text to the authorizer. For example, the circulation unit provides a format for circulating the text to the authorizer in an easy-to-understand manner. The circulation unit can also provide a means for quickly circulating the text to the authorizer. As a result, the approval application support system according to the embodiment can confirm the format of the text, present suggestions for improving wording, and circulate the text to the authorizer in an easy-to-understand manner, thereby realizing a smooth approval application process.

[0030] The approval application support system includes a grammar unit that automatically corrects grammatical errors. The grammar unit automatically corrects grammatical errors. For example, the grammar unit checks and corrects subject-verb agreement. The grammar unit can also check and correct tense agreement. The grammar unit can also check and correct punctuation use. For example, the grammar unit checks and corrects subject-verb agreement. The grammar unit can also check and correct tense agreement. The grammar unit can also check and correct punctuation use. This automatically correcting grammatical errors can improve the quality of the text. Some or all of the above-described processing in the grammar unit may be performed using, for example, AI, or may be performed without using AI. For example, the grammar unit can input the text to a generation AI and have the generation AI correct grammatical errors.

[0031] The approval application support system includes a learning unit that learns past approval application documents. The learning unit learns past approval application documents. For example, the learning unit learns documents from a specific period. The learning unit can also learn documents related to a specific project. The learning unit can also learn documents related to a specific theme. For example, the learning unit learns documents from a specific period. The learning unit can also learn documents related to a specific project. The learning unit can also learn documents related to a specific theme. By learning past approval application documents, the accuracy of checking the format of text and suggesting improvements to wording can be improved. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past approval application documents into a generation AI and cause the generation AI to perform learning to improve the accuracy of checking the format of text and suggesting improvements to wording.

[0032] The approval application support system includes a setting unit that can be customized by the user. The setting unit allows the user to customize the system settings. The setting unit, for example, changes the user interface. The setting unit can also add or remove functions. The setting unit can also provide specific setting options. For example, the setting unit changes the user interface. The setting unit can also add or remove functions. The setting unit can also provide specific setting options. This allows the user to customize the system settings, thereby providing a system that is easier to use. Some or all of the above-described processing in the setting unit may be performed, for example, using AI or may be performed without using AI. For example, the setting unit inputs the user's setting request to a generation AI and causes the generation AI to customize the settings.

[0033] The confirmation unit can analyze the confirmation history of past approval application documents and select a confirmation method. The confirmation unit, for example, proposes an optimal confirmation procedure based on confirmation methods that have been successful in the past. Furthermore, if the confirmation unit determines from the past confirmation history that a particular confirmation method is effective, it can also preferentially propose that method. The confirmation unit can also analyze the past confirmation history and propose a method that minimizes the time required for confirmation. For example, the confirmation unit proposes an optimal confirmation procedure based on confirmation methods that have been successful in the past. Furthermore, if the confirmation unit determines from the past confirmation history that a particular confirmation method is effective, it can also preferentially propose that method. Furthermore, the confirmation unit can analyze the past confirmation history and propose a method that minimizes the time required for confirmation. In this way, by analyzing the past confirmation history, the optimal confirmation method can be selected and confirmation efficiency can be improved. Some or all of the above-described processing in the confirmation unit may be performed, for example, using AI, or may be performed without using AI. For example, the confirmation unit can input past confirmation history data into a generation AI and have the generation AI select the optimal confirmation method.

[0034] When checking text, the verification unit can filter it based on a specific project or theme. For example, the verification unit can prioritize checking only text related to a specific project. The verification unit can also filter text based on a specific theme and prioritize checking highly relevant text. The verification unit can also automatically classify text requiring verification based on a project or theme. For example, the verification unit can prioritize checking only text related to a specific project. The verification unit can also filter text based on a specific theme and prioritize checking highly relevant text. The verification unit can also automatically classify text requiring verification based on a project or theme. In this way, by filtering text based on a specific project or theme, highly relevant text can be prioritized for review. Some or all of the above-described processing in the verification unit can be performed using, for example, AI, or without AI. For example, the verification unit can input data related to a project or theme into the generation AI and cause the generation AI to filter the text.

[0035] When checking text, the confirmation unit can prioritize checking highly relevant text by taking into account the user's geographical location information. For example, if the user is in a specific area, the confirmation unit prioritizes checking text related to that area. The confirmation unit can also automatically filter highly relevant text based on the user's current location. The confirmation unit can also automatically classify text that requires confirmation based on the user's geographical location information. For example, if the user is in a specific area, the confirmation unit prioritizes checking text that is related to that area. The confirmation unit can also automatically filter highly relevant text based on the user's current location. The confirmation unit can also automatically classify text that requires confirmation based on the user's geographical location information. In this way, highly relevant text can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the confirmation unit may be performed using AI, for example, or may be performed without using AI. For example, the confirmation unit can input the user's geographical location information data to the generation AI and cause the generation AI to filter highly relevant text.

[0036] When checking text, the confirmation unit can analyze the user's social media activity and check related text. For example, the confirmation unit can prioritize checking related text from the user's social media activity. The confirmation unit can also automatically filter highly relevant text based on the content of the user's social media posts. The confirmation unit can also analyze the user's social media activity and automatically classify text that requires confirmation. For example, the confirmation unit can prioritize checking related text from the user's social media activity. The confirmation unit can also automatically filter highly relevant text based on the content of the user's social media posts. The confirmation unit can also analyze the user's social media activity and automatically classify text that requires confirmation. In this way, by analyzing the user's social media activity, highly relevant text can be prioritized for checking. Some or all of the above-mentioned processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit can input the user's social media activity data into the generation AI and cause the generation AI to check related text.

[0037] When presenting an improvement proposal, the presentation unit can adjust the level of detail of the improvement proposal based on the importance of the text. For example, the presentation unit presents a detailed improvement proposal for text with a high level of importance. The presentation unit can also present a concise improvement proposal for text with a low level of importance. The presentation unit can also automatically adjust the level of detail of the improvement proposal according to the importance of the text. For example, the presentation unit presents a detailed improvement proposal for text with a high level of importance. The presentation unit can also present a concise improvement proposal for text with a low level of importance. The presentation unit can also automatically adjust the level of detail of the improvement proposal according to the importance of the text. In this way, by adjusting the level of detail of the improvement proposal according to the importance of the text, it is possible to provide a more appropriate improvement proposal. Some or all of the above-mentioned processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input text importance data to the generation AI and cause the generation AI to adjust the level of detail of the improvement proposal.

[0038] When presenting an improvement suggestion, the presentation unit can apply different improvement algorithms depending on the category of the text. For example, the presentation unit can apply an improvement algorithm suited to legal language to a legal document. The presentation unit can also apply an improvement algorithm suited to technical language to a technical document. The presentation unit can also apply an improvement algorithm suited to business language to a general business document. For example, the presentation unit can apply an improvement algorithm suited to legal language to a legal document. The presentation unit can also apply an improvement algorithm suited to technical language to a technical document. The presentation unit can also apply an improvement algorithm suited to business language to a general business document. In this way, by applying an improvement algorithm depending on the category of the text, more appropriate improvement suggestions can be provided. Some or all of the above-mentioned processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input text category data to a generation AI and cause the generation AI to apply an improvement algorithm.

[0039] When presenting improvement proposals, the presentation unit can determine the priority of the improvement proposals based on the submission date of the text. For example, the presentation unit prioritizes presenting improvement proposals for text with an upcoming submission deadline. The presentation unit can also postpone presenting improvement proposals for text with a distant submission deadline. The presentation unit can also automatically adjust the priority of the improvement proposals based on the submission date of the text. For example, the presentation unit prioritizes presenting improvement proposals for text with an upcoming submission deadline. The presentation unit can also postpone presenting improvement proposals for text with a distant submission deadline. The presentation unit can also automatically adjust the priority of the improvement proposals based on the submission date of the text. In this way, more appropriate improvement proposals can be provided by determining the priority of the improvement proposals based on the submission date of the text. Some or all of the above-described processing by the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input text submission date data to the generation AI and cause the generation AI to determine the priority of the improvement proposals.

[0040] When presenting improvement suggestions, the presentation unit can adjust the order of the improvement suggestions based on the relevance of the text. For example, the presentation unit prioritizes presenting improvement suggestions for highly relevant text. The presentation unit can also postpone presenting improvement suggestions for less relevant text. The presentation unit can also automatically adjust the order of the improvement suggestions based on the relevance of the text. For example, the presentation unit prioritizes presenting improvement suggestions for highly relevant text. The presentation unit can also postpone presenting improvement suggestions for less relevant text. The presentation unit can also automatically adjust the order of the improvement suggestions based on the relevance of the text. In this way, by adjusting the order of the improvement suggestions based on the relevance of the text, more appropriate improvement suggestions can be provided. Some or all of the above-described processing by the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input text relevance data to a generation AI and cause the generation AI to adjust the order of the improvement suggestions.

[0041] When circulating a document, the circulation department can select a circulation method by referring to past circulation history. For example, the circulation department can propose an optimal circulation procedure based on a circulation method that has been successful in the past. Furthermore, if a particular circulation method has been determined to be effective based on the past circulation history, the circulation department can preferentially propose that method. Furthermore, the circulation department can analyze the past circulation history and propose a method that minimizes the time required for circulation. For example, the circulation department can propose an optimal circulation procedure based on a circulation method that has been successful in the past. Furthermore, if a particular circulation method has been determined to be effective based on the past circulation history, the circulation department can preferentially propose that method. Furthermore, the circulation department can analyze the past circulation history and propose a method that minimizes the time required for circulation. Thus, by referring to the past circulation history, the optimal circulation method can be selected and circulation efficiency can be improved. Some or all of the above-described processing in the circulation department can be performed, for example, using AI, or can be performed without AI. For example, the circulation department can input past circulation history data into a generation AI and have the generation AI select an optimal circulation method.

[0042] The circulation department can apply different circulation methods to different text categories during circulation. For example, the circulation department can apply a circulation method that emphasizes legal procedures to legal documents. The circulation department can also apply a circulation method that emphasizes technical content to technical documents. The circulation department can also apply a circulation method that emphasizes business content to general business documents. For example, the circulation department can apply a circulation method that emphasizes legal procedures to legal documents. The circulation department can also apply a circulation method that emphasizes technical content to technical documents. The circulation department can also apply a circulation method that emphasizes business content to general business documents. This allows for a more appropriate circulation method to be provided by applying a circulation method according to the text category. Some or all of the above-described processing in the circulation department can be performed using, for example, AI, or without AI. For example, the circulation department can input text category data into a generation AI and have the generation AI apply the circulation method.

[0043] The circulation unit can adjust the order of circulation based on the submission date of the text when circulating it. For example, the circulation unit prioritizes circulating text with an upcoming submission deadline. The circulation unit can also postpone circulating text with a more distant submission deadline. The circulation unit can also automatically adjust the order of circulation based on the submission date of the text. For example, the circulation unit prioritizes circulating text with an upcoming submission deadline. The circulation unit can also postpone circulating text with a more distant submission deadline. The circulation unit can also automatically adjust the order of circulation based on the submission date of the text. In this way, by adjusting the order of circulation based on the submission date of the text, the text can be circulated in a more appropriate order. Some or all of the above-mentioned processing in the circulation unit may be performed using, for example, AI, or may be performed without AI. For example, the circulation unit can input text submission date data into a generation AI and have the generation AI adjust the order of circulation.

[0044] When circulating a document, the circulation unit can circulate it by referring to market data related to the document. For example, the circulation unit prioritizes circulating important documents based on the relevant market data. The circulation unit can also automatically filter highly relevant documents by referring to the relevant market data. The circulation unit can also analyze the relevant market data and automatically classify documents that require circulation. For example, the circulation unit prioritizes circulating important documents based on the relevant market data. The circulation unit can also automatically filter highly relevant documents by referring to the relevant market data. The circulation unit can also analyze the relevant market data and automatically classify documents that require circulation. In this way, by referring to the relevant market data, highly relevant documents can be circulated preferentially. Some or all of the above-described processing in the circulation unit may be performed, for example, using AI or without AI. For example, the circulation unit can input the relevant market data into a generation AI and have the generation AI execute the circulation.

[0045] When correcting grammar, the grammar unit can select the optimal correction method by referring to past grammar correction history. For example, the grammar unit can suggest the optimal correction procedure based on grammar correction methods that have been successful in the past. Furthermore, if a specific correction method has been found to be effective based on the past grammar correction history, the grammar unit can preferentially suggest that method. Furthermore, the grammar unit can analyze the past grammar correction history and suggest a method that minimizes the time required for correction. For example, the grammar unit can suggest the optimal correction procedure based on grammar correction methods that have been successful in the past. Furthermore, if a specific correction method has been found to be effective based on the past grammar correction history, the grammar unit can preferentially suggest that method. Furthermore, the grammar unit can analyze the past grammar correction history and suggest a method that minimizes the time required for correction. Thus, by referring to the past grammar correction history, the optimal correction method can be selected and correction efficiency can be improved. Some or all of the above-described processing in the grammar unit may be performed using, for example, AI, or without AI. For example, the grammar unit can input past grammar correction history data into a generation AI and cause the generation AI to select the optimal correction method.

[0046] During grammar correction, the grammar unit can adjust the order of corrections based on the submission date of the text. For example, the grammar unit prioritizes grammar correction for text with an upcoming submission deadline. The grammar unit can also postpone grammar correction for text with a more distant submission deadline. The grammar unit can also automatically adjust the order of grammar corrections based on the submission date of the text. For example, the grammar unit prioritizes grammar correction for text with an upcoming submission deadline. The grammar unit can also postpone grammar correction for text with a more distant submission deadline. The grammar unit can also automatically adjust the order of grammar corrections based on the submission date of the text. In this way, adjusting the order of corrections based on the submission date of the text enables grammar corrections to be performed in a more appropriate order. Some or all of the above-described processing in the grammar unit may be performed using, for example, AI, or may be performed without AI. For example, the grammar unit can input text submission date data to the generation AI and cause the generation AI to adjust the order of corrections.

[0047] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. For example, the learning unit selects an optimal learning algorithm based on learning data that was effective in the past. Furthermore, if a specific algorithm was found to be effective based on the past learning data, the learning unit can preferentially apply that algorithm. Furthermore, the learning unit can analyze past learning data and select an algorithm that maximizes learning efficiency. For example, the learning unit selects an optimal learning algorithm based on learning data that was effective in the past. Furthermore, if a specific algorithm was found to be effective based on the past learning data, the learning unit can preferentially apply that algorithm. Furthermore, the learning unit can analyze past learning data and select an algorithm that maximizes learning efficiency. Thus, by referring to the past learning data, the optimal learning algorithm can be selected and learning efficiency can be improved. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past learning data to a generation AI and cause the generation AI to optimize the learning algorithm.

[0048] During learning, the learning unit can weight the learning data based on the time of submission of the text. For example, the learning unit prioritizes learning by increasing the weight for text with an upcoming submission deadline. The learning unit can also lower the weight for text with a more distant submission deadline, thereby postponing the learning. The learning unit can also automatically adjust the weighting of the learning data according to the time of submission of the text. For example, the learning unit prioritizes learning by increasing the weight for text with an upcoming submission deadline. The learning unit can also lower the weight for text with a more distant submission deadline, thereby postponing the learning. The learning unit can also automatically adjust the weighting of the learning data according to the time of submission of the text. In this way, weighting the learning data according to the time of submission of the text enables learning to be performed in a more appropriate order. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without AI. For example, the learning unit can input text submission time data to the generation AI and cause the generation AI to weight the learning data.

[0049] During configuration, the configuration unit can select the optimal configuration method by referring to past configuration history. The configuration unit, for example, proposes an optimal configuration procedure based on configuration methods that have been effective in the past. Furthermore, if a specific configuration method has been found to be effective based on the past configuration history, the configuration unit can preferentially propose that method. The configuration unit can also analyze the past configuration history and propose a method that minimizes the time required for configuration. For example, the configuration unit proposes an optimal configuration procedure based on configuration methods that have been effective in the past. Furthermore, if a specific configuration method has been found to be effective based on the past configuration history, the configuration unit can preferentially propose that method. Furthermore, the configuration unit can analyze the past configuration history and propose a method that minimizes the time required for configuration. Thus, by referring to the past configuration history, the optimal configuration method can be selected and configuration efficiency can be improved. Some or all of the above-described processing in the configuration unit may be performed, for example, using AI, or may be performed without using AI. For example, the configuration unit can input past configuration history data to a generation AI and cause the generation AI to select the optimal configuration method.

[0050] During setup, the setting unit can select a setting method by taking into account the user's device information. For example, if the user is using a smartphone, the setting unit can provide setting options tailored to the screen size. Furthermore, if the user is using a tablet, the setting unit can provide setting options optimized for a large screen. Furthermore, if the user is using a smartwatch, the setting unit can provide concise and highly visible setting options. For example, if the user is using a smartphone, the setting unit can provide setting options tailored to the screen size. Furthermore, if the user is using a tablet, the setting unit can provide setting options optimized for a large screen. Furthermore, if the user is using a smartwatch, the setting unit can provide concise and highly visible setting options. This allows for providing a more appropriate setting method by taking into account the user's device information. Some or all of the above-described processing in the setting unit may be performed using, for example, AI, or may be performed without using AI. For example, the setting unit can input the user's device information into a generation AI and cause the generation AI to select a setting method.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The verification unit can analyze the verification history of past approval application documents and select a verification method. For example, it can propose the optimal verification procedure based on verification methods that have been successful in the past. Also, if a specific verification method has been found to be effective based on the past verification history, it can preferentially propose that method. Furthermore, it can analyze the past verification history and propose a method to minimize the time required for verification. In this way, by analyzing the past verification history, it is possible to select the optimal verification method and improve verification efficiency.

[0053] When checking text, the checking unit can filter based on a specific project or theme. For example, it can prioritize checking only text related to a specific project. It can also filter text based on a specific theme to prioritize checking highly relevant text. It can also automatically classify texts that require checking based on a project or theme. As a result, by filtering texts based on a specific project or theme, it is possible to prioritize checking highly relevant texts.

[0054] When checking text, the checking unit can prioritize checking highly relevant text by taking into account the user's geographical location information. For example, if the user is in a specific area, text related to that area is prioritized for checking. It can also automatically filter highly relevant text based on the user's current location. Furthermore, it can automatically classify text that requires checking based on the user's geographical location information. This allows highly relevant text to be prioritized for checking by taking into account the user's geographical location information.

[0055] The verification unit can analyze the user's social media activity and verify relevant text. For example, it can prioritize verification of relevant text from the user's social media activity. It can also automatically filter highly relevant text based on the content of the user's social media posts. It can also analyze the user's social media activity and automatically classify text that requires verification. This allows highly relevant text to be prioritized for verification by analyzing the user's social media activity.

[0056] When presenting an improvement proposal, the presenting unit can adjust the level of detail of the improvement proposal based on the importance of the text. For example, a detailed improvement proposal can be presented for a text with a high level of importance. Also, a concise improvement proposal can be presented for a text with a low level of importance. Furthermore, the level of detail of the improvement proposal can be automatically adjusted according to the importance of the text. In this way, by adjusting the level of detail of the improvement proposal according to the importance of the text, it is possible to provide a more appropriate improvement proposal.

[0057] When presenting an improvement proposal, the presentation unit can apply different improvement algorithms depending on the category of the text. For example, an improvement algorithm suited to legal expressions can be applied to a legal document. Also, an improvement algorithm suited to technical expressions can be applied to a technical document. Furthermore, an improvement algorithm suited to business expressions can be applied to a general business document. In this way, by applying an improvement algorithm depending on the category of the text, more appropriate improvement proposals can be provided.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The verification department checks the format and wording of the text. Specifically, it checks the format, such as font size, line spacing, and paragraph arrangement, and checks wording, such as the use of honorifics, grammatical accuracy, and consistency of expression. Step 2: The suggestion section presents suggestions for improving the text reviewed by the review section. Specifically, it points out grammatical errors and inappropriate expressions and suggests correcting them to be more appropriate. If specific wording or formatting is required, the suggestion section suggests correcting the text in accordance with those instructions. Step 3: The Circulation Department circulates the document to the decision maker based on the improvement proposals presented by the Proposal Department. Specifically, it provides a format for circulating the document to the decision maker in an easy-to-understand manner, and provides a means for circulating it quickly.

[0060] (Example 2) The approval application support system according to an embodiment of the present invention checks the format of documents and suggests improvements to wording, allowing the approval process to be clearer. This system learns the unique phrases and practices of each local government or company, enabling approvals to be clearer. This enables smooth approval processes, eliminates returns due to minor clerical errors, and shortens the time it takes to approve documents. For example, when an applicant prepares an approval application document, the system provides a function to check the format and wording of the document. This function is performed by an AI that has learned the unique phrases and practices of each local government or company. For example, if specific wording or formatting is required, the applicant can modify the document according to the instructions. Next, the AI ​​checks the format and wording of the document and suggests improvements. For example, it points out grammatical errors or inappropriate expressions and suggests appropriate revisions. Furthermore, if specific wording or formatting is required, the applicant can modify the document according to the instructions. Furthermore, the system provides a function to check the format and wording of the document and allow approvals to be clearer. This allows the approver to quickly understand the content of the text and make the decision smoothly. This system eliminates rejections due to minor clerical errors and shortens the time it takes to make a decision. For example, rejections due to grammatical errors or inappropriate expressions will no longer occur, and approvals will be made quickly. Also, if specific wording or formatting is required, the text can be revised according to the instructions, providing the approver with text that is easy to understand. This allows the approval application support system to check the format of the text, suggest improvements to the wording, and circulate the document to the approver in an easy-to-understand manner, thereby achieving a smooth approval application process.

[0061] The approval application support system according to the embodiment includes a confirmation unit, a presentation unit, and a circulation unit. The confirmation unit confirms the format and wording of the text. For example, the confirmation unit confirms the format of the text, such as the font size, line spacing, and paragraph arrangement of the text. The confirmation unit can also check wording, such as the use of honorific language in the text, grammatical accuracy, and consistency of expression. For example, the confirmation unit confirms whether the font size of the text is appropriate. The confirmation unit can also confirm whether the line spacing of the text is appropriate. The confirmation unit can also confirm whether the paragraph arrangement of the text is appropriate. The presentation unit presents suggestions for improving the text confirmed by the confirmation unit. For example, the presentation unit points out grammatical errors and inappropriate expressions and suggests correcting them to appropriate expressions. Furthermore, if specific wording or formatting is required, the presentation unit can also suggest correcting the text in accordance with the instructions. For example, the presentation unit points out grammatical errors and suggests correcting them to appropriate expressions. The presentation unit can also point out inappropriate expressions and suggest revising them to appropriate expressions. Furthermore, if specific wording or formatting is required, the presentation unit can also suggest revising the text in accordance with the instructions. The circulation unit circulates the text to the authorizer based on the improvement proposal presented by the presentation unit. The circulation unit, for example, provides a format for circulating the text to the authorizer in an easy-to-understand manner. The circulation unit can also provide a means for quickly circulating the text to the authorizer. For example, the circulation unit provides a format for circulating the text to the authorizer in an easy-to-understand manner. The circulation unit can also provide a means for quickly circulating the text to the authorizer. As a result, the approval application support system according to the embodiment can confirm the format of the text, present suggestions for improving wording, and circulate the text to the authorizer in an easy-to-understand manner, thereby realizing a smooth approval application process.

[0062] The approval application support system includes a grammar unit that automatically corrects grammatical errors. The grammar unit automatically corrects grammatical errors. For example, the grammar unit checks and corrects subject-verb agreement. The grammar unit can also check and correct tense agreement. The grammar unit can also check and correct punctuation use. For example, the grammar unit checks and corrects subject-verb agreement. The grammar unit can also check and correct tense agreement. The grammar unit can also check and correct punctuation use. This automatically correcting grammatical errors can improve the quality of the text. Some or all of the above-described processing in the grammar unit may be performed using, for example, AI, or may be performed without using AI. For example, the grammar unit can input the text to a generation AI and have the generation AI correct grammatical errors.

[0063] The approval application support system includes a learning unit that learns past approval application documents. The learning unit learns past approval application documents. For example, the learning unit learns documents from a specific period. The learning unit can also learn documents related to a specific project. The learning unit can also learn documents related to a specific theme. For example, the learning unit learns documents from a specific period. The learning unit can also learn documents related to a specific project. The learning unit can also learn documents related to a specific theme. By learning past approval application documents, the accuracy of checking the format of text and suggesting improvements to wording can be improved. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past approval application documents into a generation AI and cause the generation AI to perform learning to improve the accuracy of checking the format of text and suggesting improvements to wording.

[0064] The approval application support system includes a setting unit that can be customized by the user. The setting unit allows the user to customize the system settings. The setting unit, for example, changes the user interface. The setting unit can also add or remove functions. The setting unit can also provide specific setting options. For example, the setting unit changes the user interface. The setting unit can also add or remove functions. The setting unit can also provide specific setting options. This allows the user to customize the system settings, thereby providing a system that is easier to use. Some or all of the above-described processing in the setting unit may be performed, for example, using AI or may be performed without using AI. For example, the setting unit inputs the user's setting request to a generation AI and causes the generation AI to customize the settings.

[0065] The confirmation unit can estimate the user's emotions and adjust the timing of checking the text based on the estimated user emotions. For example, if the user is feeling stressed, the confirmation unit can delay the timing of checking the text so that the user can check it in a relaxed state. Furthermore, if the user is relaxed, the confirmation unit can immediately check the text and provide prompt feedback. Furthermore, if the user is in a hurry, the confirmation unit can advance the timing of checking the text so that the user can check it quickly. For example, if the user is feeling stressed, the confirmation unit can delay the timing of checking the text so that the user can check it in a relaxed state. Furthermore, if the user is relaxed, the confirmation unit can immediately check the text and provide prompt feedback. Furthermore, if the user is in a hurry, the confirmation unit can advance the timing of checking the text so that the user can check it quickly. In this way, by adjusting the timing of checking the text according to the user's emotions, the text can be checked at a more appropriate time. 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. Some or all of the above-described processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit may input user emotion data to the generation AI and cause the generation AI to adjust the timing of confirmation of the text.

[0066] The confirmation unit can analyze the confirmation history of past approval application documents and select a confirmation method. The confirmation unit, for example, proposes an optimal confirmation procedure based on confirmation methods that have been successful in the past. Furthermore, if the confirmation unit determines from the past confirmation history that a particular confirmation method is effective, it can also preferentially propose that method. The confirmation unit can also analyze the past confirmation history and propose a method that minimizes the time required for confirmation. For example, the confirmation unit proposes an optimal confirmation procedure based on confirmation methods that have been successful in the past. Furthermore, if the confirmation unit determines from the past confirmation history that a particular confirmation method is effective, it can also preferentially propose that method. Furthermore, the confirmation unit can analyze the past confirmation history and propose a method that minimizes the time required for confirmation. In this way, by analyzing the past confirmation history, the optimal confirmation method can be selected and confirmation efficiency can be improved. Some or all of the above-described processing in the confirmation unit may be performed, for example, using AI, or may be performed without using AI. For example, the confirmation unit can input past confirmation history data into a generation AI and have the generation AI select the optimal confirmation method.

[0067] When checking text, the verification unit can filter it based on a specific project or theme. For example, the verification unit can prioritize checking only text related to a specific project. The verification unit can also filter text based on a specific theme and prioritize checking highly relevant text. The verification unit can also automatically classify text requiring verification based on a project or theme. For example, the verification unit can prioritize checking only text related to a specific project. The verification unit can also filter text based on a specific theme and prioritize checking highly relevant text. The verification unit can also automatically classify text requiring verification based on a project or theme. In this way, by filtering text based on a specific project or theme, highly relevant text can be prioritized for review. Some or all of the above-described processing in the verification unit can be performed using, for example, AI, or without AI. For example, the verification unit can input data related to a project or theme into the generation AI and cause the generation AI to filter the text.

[0068] The confirmation unit can estimate the user's emotions and determine the priority of text to be confirmed based on the estimated user emotions. For example, when the user is stressed, the confirmation unit postpones checking text of low importance. Furthermore, when the user is relaxed, the confirmation unit can prioritize checking text of high importance. Furthermore, when the user is in a hurry, the confirmation unit can prioritize checking text of high urgency. For example, when the user is stressed, the confirmation unit postpones checking text of low importance. Furthermore, when the user is relaxed, the confirmation unit can prioritize checking text of high importance. Furthermore, when the user is in a hurry, the confirmation unit can prioritize checking text of high urgency. In this way, by determining the priority of text according to the user's emotions, more appropriate text can be prioritized for confirmation. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit may input user emotion data into the generation AI and have the generation AI determine the priority of the text.

[0069] When checking text, the confirmation unit can prioritize checking highly relevant text by taking into account the user's geographical location information. For example, if the user is in a specific area, the confirmation unit prioritizes checking text related to that area. The confirmation unit can also automatically filter highly relevant text based on the user's current location. The confirmation unit can also automatically classify text that requires confirmation based on the user's geographical location information. For example, if the user is in a specific area, the confirmation unit prioritizes checking text that is related to that area. The confirmation unit can also automatically filter highly relevant text based on the user's current location. The confirmation unit can also automatically classify text that requires confirmation based on the user's geographical location information. In this way, highly relevant text can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the confirmation unit may be performed using AI, for example, or may be performed without using AI. For example, the confirmation unit can input the user's geographical location information data to the generation AI and cause the generation AI to filter highly relevant text.

[0070] When checking text, the confirmation unit can analyze the user's social media activity and check related text. For example, the confirmation unit can prioritize checking related text from the user's social media activity. The confirmation unit can also automatically filter highly relevant text based on the content of the user's social media posts. The confirmation unit can also analyze the user's social media activity and automatically classify text that requires confirmation. For example, the confirmation unit can prioritize checking related text from the user's social media activity. The confirmation unit can also automatically filter highly relevant text based on the content of the user's social media posts. The confirmation unit can also analyze the user's social media activity and automatically classify text that requires confirmation. In this way, by analyzing the user's social media activity, highly relevant text can be prioritized for checking. Some or all of the above-mentioned processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit can input the user's social media activity data into the generation AI and cause the generation AI to check related text.

[0071] The presentation unit can estimate the user's emotions and adjust the way in which the improvement proposal is presented based on the estimated user's emotions. For example, if the user is feeling stressed, the presentation unit presents a simple and easy-to-understand improvement proposal. Furthermore, if the user is relaxed, the presentation unit can present a detailed improvement proposal. Furthermore, if the user is in a hurry, the presentation unit can present an improvement proposal that can be implemented quickly. For example, if the user is feeling stressed, the presentation unit presents a simple and easy-to-understand improvement proposal. Furthermore, if the user is relaxed, the presentation unit can present a detailed improvement proposal. Furthermore, if the user is in a hurry, the presentation unit can present an improvement proposal that can be implemented quickly. This allows for adjusting the way in which the improvement proposal is presented based on the user's emotions, thereby providing more appropriate improvement proposals. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the presentation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the presentation unit can input the user's emotional data into the generation AI and have the generation AI adjust the way the improvement proposal is expressed.

[0072] When presenting an improvement proposal, the presentation unit can adjust the level of detail of the improvement proposal based on the importance of the text. For example, the presentation unit presents a detailed improvement proposal for text with a high level of importance. The presentation unit can also present a concise improvement proposal for text with a low level of importance. The presentation unit can also automatically adjust the level of detail of the improvement proposal according to the importance of the text. For example, the presentation unit presents a detailed improvement proposal for text with a high level of importance. The presentation unit can also present a concise improvement proposal for text with a low level of importance. The presentation unit can also automatically adjust the level of detail of the improvement proposal according to the importance of the text. In this way, by adjusting the level of detail of the improvement proposal according to the importance of the text, it is possible to provide a more appropriate improvement proposal. Some or all of the above-mentioned processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input text importance data to the generation AI and cause the generation AI to adjust the level of detail of the improvement proposal.

[0073] When presenting an improvement suggestion, the presentation unit can apply different improvement algorithms depending on the category of the text. For example, the presentation unit can apply an improvement algorithm suited to legal language to a legal document. The presentation unit can also apply an improvement algorithm suited to technical language to a technical document. The presentation unit can also apply an improvement algorithm suited to business language to a general business document. For example, the presentation unit can apply an improvement algorithm suited to legal language to a legal document. The presentation unit can also apply an improvement algorithm suited to technical language to a technical document. The presentation unit can also apply an improvement algorithm suited to business language to a general business document. In this way, by applying an improvement algorithm depending on the category of the text, more appropriate improvement suggestions can be provided. Some or all of the above-mentioned processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input text category data to a generation AI and cause the generation AI to apply an improvement algorithm.

[0074] The presentation unit can estimate the user's emotions and adjust the length of the improvement proposal based on the estimated user's emotions. For example, if the user is feeling stressed, the presentation unit can present short, concise improvement proposals. Furthermore, if the user is relaxed, the presentation unit can present longer improvement proposals with detailed explanations. Furthermore, if the user is in a hurry, the presentation unit can present short, quickly implemented improvement proposals. For example, if the user is feeling stressed, the presentation unit can present short, concise improvement proposals. Furthermore, if the user is relaxed, the presentation unit can present longer improvement proposals with detailed explanations. Furthermore, if the user is in a hurry, the presentation unit can present short, quickly implemented improvement proposals. By adjusting the length of the improvement proposals according to the user's emotions, more appropriate improvement proposals can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the presentation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the presentation unit can input the user's emotional data into the generation AI and cause the generation AI to adjust the length of the improvement proposal.

[0075] When presenting improvement proposals, the presentation unit can determine the priority of the improvement proposals based on the submission date of the text. For example, the presentation unit prioritizes presenting improvement proposals for text with an upcoming submission deadline. The presentation unit can also postpone presenting improvement proposals for text with a distant submission deadline. The presentation unit can also automatically adjust the priority of the improvement proposals based on the submission date of the text. For example, the presentation unit prioritizes presenting improvement proposals for text with an upcoming submission deadline. The presentation unit can also postpone presenting improvement proposals for text with a distant submission deadline. The presentation unit can also automatically adjust the priority of the improvement proposals based on the submission date of the text. In this way, more appropriate improvement proposals can be provided by determining the priority of the improvement proposals based on the submission date of the text. Some or all of the above-described processing by the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input text submission date data to the generation AI and cause the generation AI to determine the priority of the improvement proposals.

[0076] When presenting improvement suggestions, the presentation unit can adjust the order of the improvement suggestions based on the relevance of the text. For example, the presentation unit prioritizes presenting improvement suggestions for highly relevant text. The presentation unit can also postpone presenting improvement suggestions for less relevant text. The presentation unit can also automatically adjust the order of the improvement suggestions based on the relevance of the text. For example, the presentation unit prioritizes presenting improvement suggestions for highly relevant text. The presentation unit can also postpone presenting improvement suggestions for less relevant text. The presentation unit can also automatically adjust the order of the improvement suggestions based on the relevance of the text. In this way, by adjusting the order of the improvement suggestions based on the relevance of the text, more appropriate improvement suggestions can be provided. Some or all of the above-described processing by the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input text relevance data to a generation AI and cause the generation AI to adjust the order of the improvement suggestions.

[0077] The circulation unit can estimate the user's emotions and adjust the display method of the circular message based on the estimated user's emotions. For example, if the user is feeling stressed, the circulation unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the circulation unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the circulation unit can provide a display method that focuses on the main points. For example, if the user is feeling stressed, the circulation unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the circulation unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the circulation unit can provide a display method that focuses on the main points. This allows the display method of the circular message to be adjusted according to the user's emotions, thereby providing a more appropriate display method. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the circulation unit may be performed using, for example, AI, or without AI. For example, the circulation unit can input the user's emotional data into the generation AI and have the generation AI adjust the way the circulation is displayed.

[0078] When circulating a document, the circulation department can select a circulation method by referring to past circulation history. For example, the circulation department can propose an optimal circulation procedure based on a circulation method that has been successful in the past. Furthermore, if a particular circulation method has been determined to be effective based on the past circulation history, the circulation department can preferentially propose that method. Furthermore, the circulation department can analyze the past circulation history and propose a method that minimizes the time required for circulation. For example, the circulation department can propose an optimal circulation procedure based on a circulation method that has been successful in the past. Furthermore, if a particular circulation method has been determined to be effective based on the past circulation history, the circulation department can preferentially propose that method. Furthermore, the circulation department can analyze the past circulation history and propose a method that minimizes the time required for circulation. Thus, by referring to the past circulation history, the optimal circulation method can be selected and circulation efficiency can be improved. Some or all of the above-described processing in the circulation department can be performed, for example, using AI, or can be performed without AI. For example, the circulation department can input past circulation history data into a generation AI and have the generation AI select an optimal circulation method.

[0079] The circulation department can apply different circulation methods to different text categories during circulation. For example, the circulation department can apply a circulation method that emphasizes legal procedures to legal documents. The circulation department can also apply a circulation method that emphasizes technical content to technical documents. The circulation department can also apply a circulation method that emphasizes business content to general business documents. For example, the circulation department can apply a circulation method that emphasizes legal procedures to legal documents. The circulation department can also apply a circulation method that emphasizes technical content to technical documents. The circulation department can also apply a circulation method that emphasizes business content to general business documents. This allows for a more appropriate circulation method to be provided by applying a circulation method according to the text category. Some or all of the above-described processing in the circulation department can be performed using, for example, AI, or without AI. For example, the circulation department can input text category data into a generation AI and have the generation AI apply the circulation method.

[0080] The circulation unit can estimate the user's emotions and determine the priority of circulation based on the estimated user emotions. For example, if the user is feeling stressed, the circulation unit can postpone less important messages. Furthermore, if the user is relaxed, the circulation unit can prioritize circulating more important messages. Furthermore, if the user is in a hurry, the circulation unit can prioritize circulating more urgent messages. For example, if the user is feeling stressed, the circulation unit can postpone less important messages. Furthermore, if the user is relaxed, the circulation unit can prioritize circulating more important messages. Furthermore, if the user is in a hurry, the circulation unit can prioritize circulating more urgent messages. In this way, by determining the priority of circulation according to the user's emotions, more appropriate messages can be prioritized for circulation. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the circulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the circulation unit may input user emotion data into the generation AI and have the generation AI determine the priority of the circulation.

[0081] The circulation unit can adjust the order of circulation based on the submission date of the text when circulating it. For example, the circulation unit prioritizes circulating text with an upcoming submission deadline. The circulation unit can also postpone circulating text with a more distant submission deadline. The circulation unit can also automatically adjust the order of circulation based on the submission date of the text. For example, the circulation unit prioritizes circulating text with an upcoming submission deadline. The circulation unit can also postpone circulating text with a more distant submission deadline. The circulation unit can also automatically adjust the order of circulation based on the submission date of the text. In this way, by adjusting the order of circulation based on the submission date of the text, the text can be circulated in a more appropriate order. Some or all of the above-mentioned processing in the circulation unit may be performed using, for example, AI, or may be performed without AI. For example, the circulation unit can input text submission date data into a generation AI and have the generation AI adjust the order of circulation.

[0082] When circulating a document, the circulation unit can circulate it by referring to market data related to the document. For example, the circulation unit prioritizes circulating important documents based on the relevant market data. The circulation unit can also automatically filter highly relevant documents by referring to the relevant market data. The circulation unit can also analyze the relevant market data and automatically classify documents that require circulation. For example, the circulation unit prioritizes circulating important documents based on the relevant market data. The circulation unit can also automatically filter highly relevant documents by referring to the relevant market data. The circulation unit can also analyze the relevant market data and automatically classify documents that require circulation. In this way, by referring to the relevant market data, highly relevant documents can be circulated preferentially. Some or all of the above-described processing in the circulation unit may be performed, for example, using AI or without AI. For example, the circulation unit can input the relevant market data into a generation AI and have the generation AI execute the circulation.

[0083] The grammar unit can estimate the user's emotions and adjust the grammar correction method based on the estimated user's emotions. For example, if the user is stressed, the grammar unit can perform simple and easy-to-understand grammar correction. Furthermore, if the user is relaxed, the grammar unit can perform detailed grammar correction. Furthermore, if the user is in a hurry, the grammar unit can perform grammar correction that can be quickly executed. For example, if the user is stressed, the grammar unit can perform simple and easy-to-understand grammar correction. Furthermore, if the user is relaxed, the grammar unit can perform detailed grammar correction. Furthermore, if the user is in a hurry, the grammar unit can perform grammar correction that can be quickly executed. This allows for adjusting the grammar correction method according to the user's emotions to provide more appropriate grammar correction. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the grammar unit may be performed using, for example, an AI, or without an AI. For example, the grammar section can input the user's emotional data into the generation AI and have the generation AI adjust the grammar correction method.

[0084] When correcting grammar, the grammar unit can select the optimal correction method by referring to past grammar correction history. For example, the grammar unit can suggest the optimal correction procedure based on grammar correction methods that have been successful in the past. Furthermore, if a specific correction method has been found to be effective based on the past grammar correction history, the grammar unit can preferentially suggest that method. Furthermore, the grammar unit can analyze the past grammar correction history and suggest a method that minimizes the time required for correction. For example, the grammar unit can suggest the optimal correction procedure based on grammar correction methods that have been successful in the past. Furthermore, if a specific correction method has been found to be effective based on the past grammar correction history, the grammar unit can preferentially suggest that method. Furthermore, the grammar unit can analyze the past grammar correction history and suggest a method that minimizes the time required for correction. Thus, by referring to the past grammar correction history, the optimal correction method can be selected and correction efficiency can be improved. Some or all of the above-described processing in the grammar unit may be performed using, for example, AI, or without AI. For example, the grammar unit can input past grammar correction history data into a generation AI and cause the generation AI to select the optimal correction method.

[0085] The grammar unit can estimate the user's emotions and determine the priority of grammar corrections based on the estimated user emotions. For example, if the user is stressed, the grammar unit postpones grammar corrections of lower importance. Furthermore, if the user is relaxed, the grammar unit can prioritize grammar corrections of higher importance. Furthermore, if the user is in a hurry, the grammar unit can prioritize grammar corrections of higher urgency. For example, if the user is stressed, the grammar unit postpones grammar corrections of lower importance. Furthermore, if the user is relaxed, the grammar unit can prioritize grammar corrections of higher importance. Furthermore, if the user is in a hurry, the grammar unit can prioritize grammar corrections of higher urgency. Thus, by determining the priority of grammar corrections according to the user's emotions, more appropriate grammar corrections can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the grammar unit may be performed using, for example, AI, or may be performed without using AI. For example, the grammar unit may input user emotion data to the generation AI and have the generation AI determine the priority of grammar corrections.

[0086] During grammar correction, the grammar unit can adjust the order of corrections based on the submission date of the text. For example, the grammar unit prioritizes grammar correction for text with an upcoming submission deadline. The grammar unit can also postpone grammar correction for text with a more distant submission deadline. The grammar unit can also automatically adjust the order of grammar corrections based on the submission date of the text. For example, the grammar unit prioritizes grammar correction for text with an upcoming submission deadline. The grammar unit can also postpone grammar correction for text with a more distant submission deadline. The grammar unit can also automatically adjust the order of grammar corrections based on the submission date of the text. In this way, adjusting the order of corrections based on the submission date of the text enables grammar corrections to be performed in a more appropriate order. Some or all of the above-described processing in the grammar unit may be performed using, for example, AI, or may be performed without AI. For example, the grammar unit can input text submission date data to the generation AI and cause the generation AI to adjust the order of corrections.

[0087] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. For example, if the user is stressed, the learning unit selects simple and easy-to-understand training data. Furthermore, if the user is relaxed, the learning unit can select detailed training data. Furthermore, if the user is in a hurry, the learning unit can select data that can be quickly learned. For example, if the user is stressed, the learning unit selects simple and easy-to-understand training data. Furthermore, if the user is relaxed, the learning unit can select detailed training data. Furthermore, if the user is in a hurry, the learning unit can select data that can be quickly learned. By selecting training data according to the user's emotions, more appropriate training data can be provided. 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the learning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the learning unit can input user emotion data into the generation AI and have the generation AI select learning data.

[0088] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. For example, the learning unit selects an optimal learning algorithm based on learning data that was effective in the past. Furthermore, if a specific algorithm was found to be effective based on the past learning data, the learning unit can preferentially apply that algorithm. Furthermore, the learning unit can analyze past learning data and select an algorithm that maximizes learning efficiency. For example, the learning unit selects an optimal learning algorithm based on learning data that was effective in the past. Furthermore, if a specific algorithm was found to be effective based on the past learning data, the learning unit can preferentially apply that algorithm. Furthermore, the learning unit can analyze past learning data and select an algorithm that maximizes learning efficiency. Thus, by referring to the past learning data, the optimal learning algorithm can be selected and learning efficiency can be improved. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past learning data to a generation AI and cause the generation AI to optimize the learning algorithm.

[0089] The learning unit can estimate the user's emotions and adjust the frequency of learning based on the estimated user emotions. For example, if the user is feeling stressed, the learning unit reduces the learning frequency to reduce the burden. Furthermore, if the user is relaxed, the learning unit can increase the learning frequency to efficiently progress the learning. Furthermore, if the user is in a hurry, the learning unit can adjust the learning frequency to quickly progress the learning. For example, if the user is feeling stressed, the learning unit reduces the learning frequency to reduce the burden. Furthermore, if the user is relaxed, the learning unit can increase the learning frequency to efficiently progress the learning. Furthermore, if the user is in a hurry, the learning unit can adjust the learning frequency to quickly progress the learning. In this way, by adjusting the learning frequency according to the user's emotions, learning can be performed at a more appropriate frequency. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, an AI, or without using an AI. For example, the learning unit can input user emotion data into the generation AI and cause the generation AI to adjust the frequency of learning.

[0090] During learning, the learning unit can weight the learning data based on the time of submission of the text. For example, the learning unit prioritizes learning by increasing the weight for text with an upcoming submission deadline. The learning unit can also lower the weight for text with a more distant submission deadline, thereby postponing the learning. The learning unit can also automatically adjust the weighting of the learning data according to the time of submission of the text. For example, the learning unit prioritizes learning by increasing the weight for text with an upcoming submission deadline. The learning unit can also lower the weight for text with a more distant submission deadline, thereby postponing the learning. The learning unit can also automatically adjust the weighting of the learning data according to the time of submission of the text. In this way, weighting the learning data according to the time of submission of the text enables learning to be performed in a more appropriate order. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without AI. For example, the learning unit can input text submission time data to the generation AI and cause the generation AI to weight the learning data.

[0091] The setting unit can estimate the user's emotions and adjust the customization method for the settings based on the estimated user's emotions. For example, the setting unit can provide simple and easy-to-understand setting options when the user is stressed. The setting unit can also provide detailed setting options when the user is relaxed. The setting unit can also provide quick setting options when the user is in a hurry. For example, the setting unit can provide simple and easy-to-understand setting options when the user is stressed. The setting unit can also provide detailed setting options when the user is relaxed. The setting unit can also provide quick setting options when the user is in a hurry. This allows the customization method for the settings to be adjusted according to the user's emotions, thereby providing more appropriate settings. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the setting unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the setting unit can input the user's emotional data into the generation AI and cause the generation AI to adjust the customization method of the settings.

[0092] During configuration, the configuration unit can select the optimal configuration method by referring to past configuration history. The configuration unit, for example, proposes an optimal configuration procedure based on configuration methods that have been effective in the past. Furthermore, if a specific configuration method has been found to be effective based on the past configuration history, the configuration unit can preferentially propose that method. The configuration unit can also analyze the past configuration history and propose a method that minimizes the time required for configuration. For example, the configuration unit proposes an optimal configuration procedure based on configuration methods that have been effective in the past. Furthermore, if a specific configuration method has been found to be effective based on the past configuration history, the configuration unit can preferentially propose that method. Furthermore, the configuration unit can analyze the past configuration history and propose a method that minimizes the time required for configuration. Thus, by referring to the past configuration history, the optimal configuration method can be selected and configuration efficiency can be improved. Some or all of the above-described processing in the configuration unit may be performed, for example, using AI, or may be performed without using AI. For example, the configuration unit can input past configuration history data to a generation AI and cause the generation AI to select the optimal configuration method.

[0093] The setting unit can estimate the user's emotions and determine the priority of settings based on the estimated user's emotions. For example, when the user is stressed, the setting unit postpones settings with lower importance. Furthermore, when the user is relaxed, the setting unit can prioritize settings with higher importance. Furthermore, when the user is in a hurry, the setting unit can prioritize settings with higher urgency. For example, when the user is stressed, the setting unit postpones settings with lower importance. Furthermore, when the user is relaxed, the setting unit can prioritize settings with higher importance. Furthermore, when the user is in a hurry, the setting unit can prioritize settings with higher urgency. In this way, by determining the priority of settings according to the user's emotions, more appropriate settings can be prioritized. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the setting unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the setting unit can input the user's emotional data into the generation AI and have the generation AI determine the priority of the settings.

[0094] During setup, the setting unit can select a setting method by taking into account the user's device information. For example, if the user is using a smartphone, the setting unit can provide setting options tailored to the screen size. Furthermore, if the user is using a tablet, the setting unit can provide setting options optimized for a large screen. Furthermore, if the user is using a smartwatch, the setting unit can provide concise and highly visible setting options. For example, if the user is using a smartphone, the setting unit can provide setting options tailored to the screen size. Furthermore, if the user is using a tablet, the setting unit can provide setting options optimized for a large screen. Furthermore, if the user is using a smartwatch, the setting unit can provide concise and highly visible setting options. This allows for providing a more appropriate setting method by taking into account the user's device information. Some or all of the above-described processing in the setting unit may be performed using, for example, AI, or may be performed without using AI. For example, the setting unit can input the user's device information into a generation AI and cause the generation AI to select a setting method. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned confirmation unit, presentation unit, circulation unit, grammar unit, learning unit, and setting unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the confirmation unit can check the format and wording of the text using the control unit 46A of the smart device 14. The presentation unit can present suggestions for improving the text using the specific processing unit 290 of the data processing device 12. The circulation unit can circulate the text to the authorizer using the control unit 46A of the smart device 14. The grammar unit can automatically correct grammatical errors using the specific processing unit 290 of the data processing device 12. The learning unit can learn from past approval application documents using the specific processing unit 290 of the data processing device 12. The setting unit allows the user to customize system settings using the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned confirmation unit, presentation unit, circulation unit, grammar unit, learning unit, and setting unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the confirmation unit can check the format and wording of the text via the control unit 46A of the smart glasses 214. The presentation unit can present suggestions for improving the text via the specific processing unit 290 of the data processing device 12. The circulation unit can circulate the text to the authorizer via the control unit 46A of the smart glasses 214. The grammar unit can automatically correct grammatical errors via the specific processing unit 290 of the data processing device 12. The learning unit can learn from past approval application documents via the specific processing unit 290 of the data processing device 12. The setting unit allows the user to customize system settings via the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned confirmation unit, presentation unit, circulation unit, grammar unit, learning unit, and setting unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the confirmation unit can check the format and wording of the text using the control unit 46A of the headset terminal 314. The presentation unit can present suggestions for improving the text using the specific processing unit 290 of the data processing device 12. The circulation unit can circulate the text to the authorizer using the control unit 46A of the headset terminal 314. The grammar unit can automatically correct grammatical errors using the specific processing unit 290 of the data processing device 12. The learning unit can learn from past approval application documents using the specific processing unit 290 of the data processing device 12. The setting unit allows the user to customize the system settings using the control unit 46A of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned confirmation unit, presentation unit, circulation unit, grammar unit, learning unit, and setting unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the confirmation unit can check the format and wording of the text using the control unit 46A of the robot 414. The presentation unit can present suggestions for improving the text using the specific processing unit 290 of the data processing device 12. The circulation unit can circulate the text to the authorizer using the control unit 46A of the robot 414. The grammar unit can automatically correct grammatical errors using the specific processing unit 290 of the data processing device 12. The learning unit can learn from past approval application documents using the specific processing unit 290 of the data processing device 12. The setting unit allows the user to customize the system settings using the control unit 46A of the robot 414.

[0095] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0096] The confirmation unit can estimate the user's emotions and adjust the timing of checking the text based on the estimated user emotions. For example, if the user is feeling stressed, the confirmation timing can be delayed so that the user can check the text in a relaxed state. Also, if the user is relaxed, the confirmation can be performed immediately to provide quick feedback. Furthermore, if the user is in a hurry, the confirmation timing can be advanced so that the user can check the text quickly. In this way, by adjusting the timing of checking the text according to the user's emotions, the text can be checked at a more appropriate time.

[0097] The verification unit can analyze the verification history of past approval application documents and select a verification method. For example, it can propose the optimal verification procedure based on verification methods that have been successful in the past. Also, if a specific verification method has been found to be effective based on the past verification history, it can preferentially propose that method. Furthermore, it can analyze the past verification history and propose a method to minimize the time required for verification. In this way, by analyzing the past verification history, it is possible to select the optimal verification method and improve verification efficiency.

[0098] When checking text, the checking unit can filter based on a specific project or theme. For example, it can prioritize checking only text related to a specific project. It can also filter text based on a specific theme to prioritize checking highly relevant text. It can also automatically classify texts that require checking based on a project or theme. As a result, by filtering texts based on a specific project or theme, it is possible to prioritize checking highly relevant texts.

[0099] The confirmation unit can estimate the user's emotions and determine the priority of messages to be confirmed based on the estimated user's emotions. For example, if the user is feeling stressed, messages of low importance can be postponed. Also, if the user is relaxed, messages of high importance can be prioritized for confirmation. Furthermore, if the user is in a hurry, messages of high urgency can be prioritized for confirmation. In this way, by determining the priority of messages according to the user's emotions, more appropriate messages can be prioritized for confirmation.

[0100] When checking text, the checking unit can prioritize checking highly relevant text by taking into account the user's geographical location information. For example, if the user is in a specific area, text related to that area is prioritized for checking. It can also automatically filter highly relevant text based on the user's current location. Furthermore, it can automatically classify text that requires checking based on the user's geographical location information. This allows highly relevant text to be prioritized for checking by taking into account the user's geographical location information.

[0101] The verification unit can analyze the user's social media activity and verify relevant text. For example, it can prioritize verification of relevant text from the user's social media activity. It can also automatically filter highly relevant text based on the content of the user's social media posts. It can also analyze the user's social media activity and automatically classify text that requires verification. This allows highly relevant text to be prioritized for verification by analyzing the user's social media activity.

[0102] The presentation unit can estimate the user's emotions and adjust the way in which improvement suggestions are presented based on the estimated user emotions. For example, if the user is feeling stressed, a simple and easy-to-understand improvement suggestion can be presented. If the user is relaxed, a detailed improvement suggestion can be presented. Furthermore, if the user is in a hurry, a quick and implementable improvement suggestion can be presented. In this way, by adjusting the way in which improvement suggestions are presented according to the user's emotions, more appropriate improvement suggestions can be provided.

[0103] When presenting an improvement proposal, the presenting unit can adjust the level of detail of the improvement proposal based on the importance of the text. For example, a detailed improvement proposal can be presented for a text with a high level of importance. Also, a concise improvement proposal can be presented for a text with a low level of importance. Furthermore, the level of detail of the improvement proposal can be automatically adjusted according to the importance of the text. In this way, by adjusting the level of detail of the improvement proposal according to the importance of the text, it is possible to provide a more appropriate improvement proposal.

[0104] When presenting an improvement proposal, the presentation unit can apply different improvement algorithms depending on the category of the text. For example, an improvement algorithm suited to legal expressions can be applied to a legal document. Also, an improvement algorithm suited to technical expressions can be applied to a technical document. Furthermore, an improvement algorithm suited to business expressions can be applied to a general business document. In this way, by applying an improvement algorithm depending on the category of the text, more appropriate improvement proposals can be provided.

[0105] The presentation unit can estimate the user's emotions and adjust the length of the improvement suggestions based on the estimated user emotions. For example, if the user is feeling stressed, a short, to-the-point improvement suggestion can be presented. Alternatively, if the user is relaxed, a longer improvement suggestion with detailed explanations can be presented. Furthermore, if the user is in a hurry, a short improvement suggestion that can be implemented quickly can be presented. In this way, by adjusting the length of the improvement suggestion according to the user's emotions, more appropriate improvement suggestions can be provided.

[0106] The processing flow of the second embodiment will be briefly explained below.

[0107] Step 1: The verification department checks the format and wording of the text. Specifically, it checks the format, such as font size, line spacing, and paragraph arrangement, and checks wording, such as the use of honorifics, grammatical accuracy, and consistency of expression. Step 2: The suggestion section presents suggestions for improving the text reviewed by the review section. Specifically, it points out grammatical errors and inappropriate expressions and suggests correcting them to be more appropriate. If specific wording or formatting is required, the suggestion section suggests correcting the text in accordance with those instructions. Step 3: The Circulation Department circulates the document to the decision maker based on the improvement proposals presented by the Proposal Department. Specifically, it provides a format for circulating the document to the decision maker in an easy-to-understand manner, and provides a means for circulating it quickly.

[0108] 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.

[0109] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0110] 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.

[0111] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0112] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0126] 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.

[0127] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0128] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0142] 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.

[0143] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0144] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0159] 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.

[0160] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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.

[0166] 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."

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] [Explanation of symbols]

[0180] 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 confirmation section that checks the format and wording of the text; a suggestion unit that suggests improvements to the text confirmed by the confirmation unit; a circulation unit that circulates a document to a decision maker based on the improvement plan presented by the presentation unit; Equipped with A system characterized by:

2. It has a grammar section that automatically corrects grammatical errors. The system of claim 1 .

3. Equipped with a learning department that studies past approval application documents The system of claim 1 .

4. Equipped with a setting section that can be customized by the user The system of claim 1 .

5. The confirmation unit Estimate the user's emotions and adjust the timing of confirmation of the text based on the estimated user emotions. The system of claim 1 .

6. The confirmation unit Analyze the confirmation history of past approval application documents and select a confirmation method The system of claim 1 .

7. The confirmation unit When reviewing text, filter it based on a specific project or theme The system of claim 1 .

8. The confirmation unit Estimate the user's feelings and prioritize confirmation messages based on the estimated user feelings. The system of claim 1 .

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