system

Abstract

The system according to this embodiment aims to enable applicants and approvers to proceed with the approval process smoothly and without stress. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, a verification unit, an approval unit, and a check unit. The collection unit accesses the company intranet to collect information. The analysis unit analyzes the information collected by the collection unit. The generation unit creates an approval document based on the information analyzed by the analysis unit. The verification unit allows the applicant to review and revise the approval document generated by the generation unit. The approval unit allows the approver to approve the approval document reviewed and revised by the verification unit. The check unit checks for any deficiencies in the approval document generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the process of creating, checking, modifying, and approving a petition is complicated and stressful for both the applicant and the approver.

[0005] The system according to the embodiment aims to enable the applicant and the approver to smoothly proceed with the petition without stress.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a verification unit, an approval unit, and a checking unit. The collection unit accesses the company intranet to collect information. The analysis unit analyzes the information collected by the collection unit. The generation unit creates an approval document based on the information analyzed by the analysis unit. The verification unit allows the applicant to review and revise the approval document generated by the generation unit. The approval unit allows the approver to approve the approval document reviewed and revised by the verification unit. The checking unit checks for any deficiencies in the approval document generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can enable applicants and approvers to proceed with the approval process smoothly and without stress. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The approval document creation system according to an embodiment of the present invention is a system that uses an AI agent to enable both the applicant and the approver to proceed with the approval process smoothly and without stress. This approval document creation system accesses the company intranet to collect information, analyzes the collected information, and automatically creates the approval document. For example, the approval document creation system automatically collects the "approval manual" and "materials for investment approval, etc." posted on the company intranet. Next, based on the collected information, the approval document creation system automatically generates documents and figures according to the template and writing rules of the approval manual. This eliminates the hassle and stress of having to return or resubmit documents due to errors in the writing, and realizes an approval process that does not cause stress to the applicant or approver. For example, the approval document creation system allows the applicant to simply check the approval document that has been automatically created and make corrections as needed. In addition, the approval document creation system allows the approver to check the approval document without errors and make approval quickly. In this way, using the approval document creation system allows both the applicant and the approver to proceed with the approval process smoothly and without stress.

[0029] The approval document creation system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a verification unit, an approval unit, and a check unit. The collection unit accesses the company intranet to collect information. The collection unit collects, for example, templates and writing rules for the "approval manual" posted on the company intranet, and materials for investment approval, etc. The collection unit accesses the company intranet to collect necessary information. The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the collected information and identifies necessary documents and numerical values. The analysis unit analyzes the collected information using, for example, text analysis technology. The generation unit creates the approval document based on the information analyzed by the analysis unit. The generation unit automatically creates the approval document based on the analysis results. The generation unit automatically generates documents and numerical values ​​according to, for example, the templates and writing rules for the approval manual. The verification unit allows the applicant to verify and correct the approval document generated by the generation unit. The verification unit, for example, allows the applicant to review the proposal document and make corrections as needed. The verification unit allows the applicant to review the proposal document and correct any items that can be corrected. The approval unit allows the approver to approve the proposal document that has been reviewed and corrected by the verification unit. The approval unit allows the approver to review the proposal document and make approval. The approval unit allows the approver to review the proposal document and make approval according to the approval conditions. The checking unit checks for any deficiencies in the proposal document generated by the generation unit. The checking unit checks for any deficiencies in the generated proposal document and prompts corrections as needed. The checking unit checks for any missing required items or typographical errors and prompts corrections. As a result, the proposal document creation system according to this embodiment allows both the applicant and the approver to proceed with the proposal smoothly and without stress.

[0030] The data collection department accesses the company intranet to gather information. For example, the department collects templates and rules for the "approval manual" posted on the company intranet, as well as materials used for investment approval. Specifically, the department accesses various databases and file servers on the company intranet to comprehensively collect the information necessary for creating approval documents. For example, it collects samples of past approval documents, the latest investment plans, budget proposals, and risk assessment materials provided by each department. The department also collects feedback and comments from various departments within the company and can incorporate them into the creation of approval documents. This allows the department to quickly and accurately collect the information necessary for creating approval documents and smoothly provide the data to the analysis department. Furthermore, the department centrally manages the collected information and can link with other systems and departments as needed. For example, the collected information is stored on a cloud server and made accessible to the analysis and generation departments. The department can also adjust the frequency and accuracy of information collection to enable flexible responses to specific situations and conditions. This allows the data collection unit to efficiently and effectively collect information, thereby improving the overall performance of the system.

[0031] The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the collected information to identify necessary documents and numerical data. Specifically, the analysis unit uses text analysis technology to analyze the collected information. For example, it uses natural language processing technology to extract important keywords and phrases from collected documents and identify the information necessary for approval documents. For numerical data, it uses statistical analysis and data mining technology to extract important figures and trends. Furthermore, the analysis unit can search for similar approval documents based on past approval document data and provide relevant information. This allows the analysis unit to quickly and accurately analyze the collected information and provide the information necessary for creating approval documents. In addition, the analysis unit uses AI to process data in real time and understand the surrounding circumstances. For example, the AI ​​automatically classifies the contents of approval documents based on the collected information and evaluates their importance and priority. The AI ​​can also utilize past data and statistical information to perform long-term risk assessments and trend analyses. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The generation unit creates approval documents based on information analyzed by the analysis unit. For example, the generation unit automatically creates approval documents based on the analysis results. Specifically, the generation unit automatically generates documents and numerical data according to the approval manual template and writing rules. For instance, it automatically fills in each section of the approval document based on keywords and numerical data provided by the analysis unit. Furthermore, the generation unit uses document generation technology to produce natural-sounding sentences and organize the content of the approval document. In addition, the generation unit can automatically adjust the format and layout of the approval document to create a clear and easy-to-understand document. This allows the generation unit to create approval documents efficiently and accurately, reducing the burden on applicants. Moreover, the generation unit can optimize the content of the approval document using AI. For example, the AI ​​can suggest the optimal document structure and expression based on past approval document data, improving the quality of the approval document. Finally, the generation unit saves the generated approval documents to a cloud server, making them accessible to the verification and approval units. This allows the generation unit to create approval documents efficiently and effectively, improving the overall system performance.

[0033] The verification unit allows the applicant to review and revise the approval document generated by the generation unit. Specifically, the verification unit presents the generated approval document to the applicant, prompting them to review and revise its contents. The applicant can review each section of the approval document and make necessary revisions. For example, the applicant can review the document's content and numerical data, correcting any errors or omissions. The verification unit also provides an interface to facilitate revisions. For instance, the applicant can edit the approval document on a web browser, reflecting revisions in real time. Furthermore, the verification unit manages the revision history, tracking who revised which part. This allows the verification unit to support applicants in efficiently and accurately reviewing and revising approval documents, thereby improving their quality. Additionally, the verification unit can use AI to analyze the applicant's revisions, identifying revision trends and patterns. This allows the verification unit to improve future approval document creation processes.

[0034] The approval department approves the proposal documents that have been reviewed and revised by the verification department. Specifically, the approval department presents the reviewed and revised proposal documents to the approvers and prompts them to review and approve the contents. The approvers review each section of the proposal document and approve it according to the approval criteria. For example, the approvers review the content and numerical data of the document and determine whether it meets the approval criteria. The approval department also provides an interface to make it easier for approvers to approve. For example, approvers can review the proposal document on a web browser and approve it by clicking an approve button. Furthermore, the approval department can manage the approval history and track who approved which proposal document. This allows the approval department to streamline the approval process and approve proposal documents quickly. In addition, the approval department can optimize the approval process using AI. For example, the AI ​​can analyze approval trends and patterns based on past approval data and suggest improvements to the approval process. This allows the approval department to process approval requests efficiently and effectively, thereby improving the overall performance of the system.

[0035] The checking unit checks for deficiencies in the approval documents generated by the generation unit. For example, the checking unit checks for deficiencies in the generated approval documents and prompts for corrections as needed. Specifically, the checking unit checks each section of the generated approval document, checking for missing required items and typographical errors. For example, it checks whether required items such as the title, date, applicant name, and approver name of the approval document are correctly filled in. The checking unit also checks the consistency of the document content and numerical data, and prompts for corrections if there are errors or inconsistencies. Furthermore, the checking unit can automatically detect deficiencies in the approval documents using AI. For example, the AI ​​can analyze the document content using natural language processing technology to detect typographical errors and grammatical errors. The AI ​​can also check the consistency of numerical data and detect outliers and inconsistencies. As a result, the checking unit can efficiently and accurately check for deficiencies in the approval documents and improve the quality of the approval documents. Furthermore, the checking unit can manage the checking history and track who checked which parts. This allows the checking department to thoroughly manage the quality of approval documents and improve the overall reliability of the system.

[0036] The data collection unit can access the company intranet to collect templates and rules for approval manuals, as well as materials for investment approvals, etc. For example, the data collection unit can access the company intranet to collect templates and rules for approval manuals. The data collection unit can also collect materials for investment approvals, etc. For example, the data collection unit can access the company intranet to collect necessary information. This improves the efficiency of creating approval documents by automatically collecting the necessary information. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can access the company intranet, input the collected information into a generating AI, and have the generating AI perform the information collection.

[0037] The analysis unit can analyze the collected information and identify the necessary documents and numerical values. For example, the analysis unit can analyze the collected information and identify the necessary documents and numerical values. For example, the analysis unit can analyze the collected information using text analysis technology. The analysis unit can also analyze the collected information using data mining technology. For example, the analysis unit can analyze the collected information using statistical analysis technology. By analyzing the collected information, the documents and numerical values ​​necessary for creating approval documents can be identified. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected information into a generating AI and have the generating AI perform the analysis of the information.

[0038] The generation unit can automatically create approval documents based on the analysis results. For example, the generation unit can automatically create approval documents based on the analysis results. For example, the generation unit can automatically generate documents and numerical data according to the template and rules of the approval manual. For example, the generation unit can also create approval documents using templates. For example, the generation unit can automatically input data and create approval documents. This improves the efficiency of creating approval documents by automatically creating them based on the analysis results. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the analysis results into a generation AI and have the generation AI create the approval documents.

[0039] The verification unit allows the applicant to review the approval document and make corrections as necessary. For example, the verification unit allows the applicant to review the approval document and make corrections as necessary. For example, the verification unit allows the applicant to review the approval document and correct items that can be corrected. The verification unit can also review the approval document using a checklist, for example. The verification unit can also review the approval document following a review procedure, for example. This improves the accuracy of the approval document by allowing the applicant to review it and make corrections as necessary. Some or all of the above processes in the verification unit may be performed using AI, for example, or not using AI. For example, the verification unit can input the generated approval document into a generating AI and have the generating AI perform the review of the approval document.

[0040] The approval department allows approvers to review and approve proposals. The approval department can, for example, have approvers review proposals and approve them. The approval department can, for example, have approvers review proposals and approve them according to the approval conditions. The approval department can also, for example, approve proposals according to the approval flow. The approval department can also, for example, approve proposals according to the approval procedure. This allows the approval process to proceed smoothly by having approvers review and approve proposals. Some or all of the above processes in the approval department may be performed using AI, for example, or not using AI. For example, the approval department can input the generated proposal into a generation AI and have the generation AI perform the approval of the proposal.

[0041] The checking unit can check for deficiencies in the generated approval document and prompt corrections as necessary. For example, the checking unit can check for deficiencies in the generated approval document and prompt corrections as necessary. For example, the checking unit can check for missing required items or typographical errors and prompt corrections. For example, the checking unit can use a checklist to check for deficiencies in the approval document. For example, the checking unit can check the approval document according to criteria for deficiencies. This improves the quality of the approval document by checking for deficiencies in the generated approval document and prompting corrections as necessary. Some or all of the above processes in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can input the generated approval document into a generation AI and have the generation AI perform the check for deficiencies.

[0042] The data collection unit can analyze the access history of the company's intranet and select the optimal information collection method. For example, the data collection unit can identify frequently accessed pages from the access history of the company's intranet and prioritize the collection of information from those pages. For example, the data collection unit can analyze the access history and prioritize the collection of information from pages that experience concentrated access during specific time periods. For example, the data collection unit can identify pages frequently used by specific users based on the access history and collect information from those pages. In this way, the optimal information collection method can be selected by analyzing the access history of the company's intranet. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the access history data of the company's intranet into a generating AI and have the generating AI select the optimal information collection method.

[0043] The data collection unit can filter information based on the user's current projects and areas of interest during data collection. For example, the data collection unit can prioritize collecting information related to the user's current projects. For example, the data collection unit can filter and collect highly relevant information based on the user's areas of interest. For example, the data collection unit can appropriately filter and collect necessary information according to the user's project progress. This allows for the collection of highly relevant information by filtering information based on the user's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's project information and areas of interest data into a generating AI and have the generating AI perform the information filtering.

[0044] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of region-related information based on the user's current location. For example, the data collection unit can collect information related to nearby projects based on the user's geographical location information. For example, the data collection unit can prioritize the collection of region-specific information by considering the user's location information. This allows for the priority collection of highly relevant information by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information data into a generating AI and have the generating AI perform the data collection.

[0045] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can identify topics of interest from the user's social media activity and collect relevant information. For example, the data collection unit can analyze the content of posts from accounts the user follows and collect relevant information. For example, the data collection unit can collect information of interest based on the content of the user's social media posts. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI perform the data collection.

[0046] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit performs a detailed analysis on information of high importance. For example, the analysis unit performs a concise analysis on information of low importance. For example, the analysis unit adjusts the level of detail of the analysis in stages according to the importance of the information. In this way, by adjusting the level of detail of the analysis based on the importance of the information, important information can be analyzed in detail. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0047] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a specific financial analysis algorithm to financial information. For example, the analysis unit can apply an analysis algorithm specialized for project management to project information. For example, the analysis unit can apply an algorithm suitable for human resources data analysis to human resources information. By applying different analysis algorithms depending on the category of information, appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information category data into a generating AI and have the generating AI execute the application of analysis algorithms.

[0048] The analysis unit can determine the priority of analysis based on the timing of information collection during the analysis process. For example, the analysis unit prioritizes the analysis of the most recent information. For example, the analysis unit analyzes older information as needed. For example, the analysis unit adjusts the priority of analysis in stages according to the timing of information collection. This allows for the prioritization of analysis of the most recent information by determining the priority of analysis based on the timing of information collection. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information collection timing data into a generating AI and have the generating AI determine the priority of analysis.

[0049] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit prioritizes the analysis of highly relevant information. For example, the analysis unit postpones the analysis of less relevant information. For example, the analysis unit adjusts the order of analysis step by step according to the relevance of the information. In this way, by adjusting the order of analysis based on the relevance of the information, highly relevant information can be analyzed preferentially. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0050] The generation unit can adjust the level of detail in the approval document based on the importance of the information when generating the approval document. For example, the generation unit generates a detailed approval document for information of high importance. For example, the generation unit generates a concise approval document for information of low importance. For example, the generation unit adjusts the level of detail in the approval document in stages according to the importance of the information. In this way, by adjusting the level of detail in the approval document based on the importance of the information, it is possible to generate an approval document that describes important information in detail. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input information importance data into a generation AI and have the generation AI perform the adjustment of the level of detail in the approval document.

[0051] The generation unit can apply different generation algorithms depending on the category of information when generating approval documents. For example, the generation unit applies a specific financial generation algorithm to financial information. For example, the generation unit applies a generation algorithm specialized for project management to project information. For example, the generation unit applies an algorithm suitable for generating personnel data to personnel information. By applying different generation algorithms depending on the category of information, an appropriate approval document can be generated. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input information category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0052] The generation unit can determine the priority of approval documents based on the timing of information collection when generating them. For example, the generation unit prioritizes reflecting the latest information in the approval documents. For example, the generation unit reflects older information in the approval documents as needed. For example, the generation unit adjusts the priority of the approval documents in stages according to the timing of information collection. This allows the latest information to be reflected in the approval documents preferentially by determining the priority of the approval documents based on the timing of information collection. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input information collection timing data into a generation AI and have the generation AI perform the determination of the priority of the approval documents.

[0053] The generation unit can adjust the order of approval documents based on the relevance of the information when generating them. For example, the generation unit prioritizes reflecting highly relevant information in the approval documents. For example, the generation unit postpones reflecting less relevant information in the approval documents. For example, the generation unit adjusts the order of approval documents in stages according to the relevance of the information. By adjusting the order of approval documents based on the relevance of the information, highly relevant information can be prioritized and reflected in the approval documents. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input information relevance data into a generation AI and have the generation AI perform the adjustment of the order of approval documents.

[0054] The verification unit can select the optimal verification method by referring to past verification history during the verification process. For example, the verification unit can prioritize displaying items that are frequently checked from past verification history. For example, the verification unit can prioritize displaying items that a particular user frequently checks based on past verification history. For example, the verification unit can analyze past verification history and propose the optimal verification method. This allows the optimal verification method to be selected by referring to past verification history. Some or all of the above processes in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input past verification history data into a generating AI and have the generating AI select the optimal verification method.

[0055] The verification unit can customize the verification procedure based on the user's current project status during verification. For example, the verification unit prioritizes displaying necessary verification items according to the user's current project status. For example, the verification unit customizes the verification procedure based on the user's project progress. For example, the verification unit proposes the optimal verification procedure considering the user's project status. In this way, by customizing the verification procedure based on the user's current project status, an appropriate verification procedure can be provided. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the user's project status data into a generating AI and have the generating AI perform the customization of the verification procedure.

[0056] The verification unit can select the optimal verification method during verification, taking into account the user's geographical location information. For example, the verification unit may prioritize displaying verification items related to the user's region based on the user's current location. For example, the verification unit may display verification items related to nearby projects based on the user's geographical location information. For example, the verification unit may prioritize displaying region-specific verification items, taking into account the user's location information. This allows for the priority display of highly relevant verification items by considering the user's geographical location information. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit may input the user's geographical location information data into a generating AI and have the generating AI perform the selection of the verification method.

[0057] The verification unit can analyze the user's social media activity and propose verification steps during the verification process. For example, the verification unit can identify topics of interest from the user's social media activity and display relevant verification items. For example, the verification unit can analyze the content of posts from accounts the user follows and display relevant verification items. For example, the verification unit can display verification items of interest based on the content of the user's social media posts. In this way, relevant verification items can be displayed by analyzing the user's social media activity. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the user's social media data into a generating AI and have the generating AI execute the proposal of verification steps.

[0058] The approval unit can select the optimal approval method by referring to past approval history during the approval process. For example, the approval unit may prioritize displaying items that are frequently approved based on past approval history. For example, the approval unit may prioritize displaying items that a particular user frequently approves based on past approval history. For example, the approval unit may analyze past approval history and propose the optimal approval method. This allows the optimal approval method to be selected by referring to past approval history. Some or all of the above processes in the approval unit may be performed using AI, for example, or without AI. For example, the approval unit may input past approval history data into a generating AI and have the generating AI select the optimal approval method.

[0059] The approval unit can customize the approval procedure based on the user's current project status during the approval process. For example, the approval unit can prioritize displaying necessary approval items according to the user's current project status. For example, the approval unit can customize the approval procedure based on the user's project progress. For example, the approval unit can propose the optimal approval procedure considering the user's project status. This allows for the provision of an appropriate approval procedure by customizing the approval procedure based on the user's current project status. Some or all of the above processes in the approval unit may be performed using AI, for example, or without AI. For example, the approval unit can input the user's project status data into a generating AI and have the generating AI perform the customization of the approval procedure.

[0060] The approval unit can select the optimal approval method when approving, taking into account the user's geographical location information. For example, the approval unit may prioritize displaying approval items relevant to a region based on the user's current location. For example, the approval unit may display approval items related to nearby projects based on the user's geographical location information. For example, the approval unit may prioritize displaying region-specific approval items, taking into account the user's location information. This allows for the priority display of highly relevant approval items by considering the user's geographical location information. Some or all of the above processing in the approval unit may be performed using AI, for example, or without AI. For example, the approval unit may input the user's geographical location data into a generating AI and have the generating AI select the approval method.

[0061] The approval unit can analyze the user's social media activity and propose approval procedures during the approval process. For example, the approval unit can identify topics of interest from the user's social media activity and display relevant approval items. For example, the approval unit can analyze the content of posts from accounts the user follows and display relevant approval items. For example, the approval unit can display approval items of interest based on the content of the user's social media posts. In this way, relevant approval items can be displayed by analyzing the user's social media activity. Some or all of the above processing in the approval unit may be performed using AI, for example, or without AI. For example, the approval unit can input the user's social media data into a generating AI and have the generating AI execute the proposal of approval procedures.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] The approval document creation system can also include a notification unit. This notification unit can provide real-time updates to applicants and approvers regarding the progress of the approval process. For example, it can send a notification to the applicant when the approval document is generated, prompting them to review it. It can also send a notification to the applicant when the approver has approved the document. Furthermore, if the approval process is delayed, it can send a reminder notification to the approver, encouraging a prompt response. This ensures a smooth approval process and reduces the burden on both applicants and approvers.

[0064] The approval document creation system can also include a feedback unit. This feedback unit can collect feedback from applicants and approvers during the approval document creation and approval process, and use this feedback to improve the system. For example, the feedback unit can collect opinions and suggestions for improvement from applicants after the approval document has been created. It can also collect feedback on the approval process after approvers have approved the document. Furthermore, the feedback unit can analyze the collected feedback, identify areas for system improvement, and reflect these improvements in the next approval document creation and approval process. This improves the quality of the approval document creation system and increases satisfaction for both applicants and approvers.

[0065] The approval document creation system can also be equipped with a learning unit. This learning unit can learn from past approval document creation and approval processes to optimize future processes. For example, it can analyze past approval document content and approval process data to generate optimal templates and writing rules for approval documents. Furthermore, it can learn approver preferences and tendencies based on past approval process data, thereby streamlining the approval process. Additionally, it can learn from past feedback data, identify areas for system improvement, and incorporate these improvements into future approval document creation and approval processes. This improves the accuracy of the approval document creation system and reduces the burden on both applicants and approvers.

[0066] The approval document creation system can also include an alert function. This alert function can send alerts to applicants and approvers when important events or deadlines are approaching during the approval document creation and approval process. For example, the alert function can send an alert to the applicant when the deadline for submitting the approval document is approaching, prompting submission. It can also send an alert to the approver when the deadline for approval is approaching, encouraging prompt action. Furthermore, if there are significant deficiencies in the content of the approval document, the alert function can send an alert to the applicant, prompting corrections. This ensures that the approval document creation and approval process proceeds smoothly and that important events and deadlines are not overlooked.

[0067] The approval document creation system can also include a customization section. This customization section allows for the customization of approval document templates and writing rules according to the individual needs and preferences of applicants and approvers. For example, the customization section can provide an optimal approval document template based on the applicant's work content and project characteristics. It can also adjust the writing rules based on the approver's preferences and past approval history. Furthermore, the customization section can continuously improve the approval document templates and writing rules based on feedback from applicants and approvers. This allows the approval document creation system to better meet the individual needs of applicants and approvers, making it more user-friendly.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The collection department accesses the company intranet to gather information. For example, they collect templates and rules for the "approval manual," as well as documents related to investment approval, which are posted on the company intranet. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, it uses text analysis technology to analyze the collected information and identify necessary documents and numerical data. Step 3: The generation unit creates the approval document based on the information analyzed by the analysis unit. For example, it automatically creates the approval document based on the analysis results and automatically generates documents and figures according to the approval manual template and writing rules. Step 4: The verification unit allows the applicant to review and revise the approval document generated by the generation unit. For example, the applicant reviews the approval document and makes revisions as needed. Step 5: The approval department approves the proposal document, which has been reviewed and revised by the verification department. For example, the approver reviews the proposal document and approves it according to the approval conditions. Step 6: The checking unit checks for any deficiencies in the approval document generated by the generation unit. For example, it checks for missing required items or typographical errors in the generated approval document and prompts for corrections as necessary.

[0070] (Example of form 2) The approval document creation system according to an embodiment of the present invention is a system that uses an AI agent to enable both the applicant and the approver to proceed with the approval process smoothly and without stress. This approval document creation system accesses the company intranet to collect information, analyzes the collected information, and automatically creates the approval document. For example, the approval document creation system automatically collects the "approval manual" and "materials for investment approval, etc." posted on the company intranet. Next, based on the collected information, the approval document creation system automatically generates documents and figures according to the template and writing rules of the approval manual. This eliminates the hassle and stress of having to return or resubmit documents due to errors in the writing, and realizes an approval process that does not cause stress to the applicant or approver. For example, the approval document creation system allows the applicant to simply check the approval document that has been automatically created and make corrections as needed. In addition, the approval document creation system allows the approver to check the approval document without errors and make approval quickly. In this way, using the approval document creation system allows both the applicant and the approver to proceed with the approval process smoothly and without stress.

[0071] The approval document creation system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a verification unit, an approval unit, and a check unit. The collection unit accesses the company intranet to collect information. The collection unit collects, for example, templates and writing rules for the "approval manual" posted on the company intranet, and materials for investment approval, etc. The collection unit accesses the company intranet to collect necessary information. The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the collected information and identifies necessary documents and numerical values. The analysis unit analyzes the collected information using, for example, text analysis technology. The generation unit creates the approval document based on the information analyzed by the analysis unit. The generation unit automatically creates the approval document based on the analysis results. The generation unit automatically generates documents and numerical values ​​according to, for example, the templates and writing rules for the approval manual. The verification unit allows the applicant to verify and correct the approval document generated by the generation unit. The verification unit, for example, allows the applicant to review the proposal document and make corrections as needed. The verification unit allows the applicant to review the proposal document and correct any items that can be corrected. The approval unit allows the approver to approve the proposal document that has been reviewed and corrected by the verification unit. The approval unit allows the approver to review the proposal document and make approval. The approval unit allows the approver to review the proposal document and make approval according to the approval conditions. The checking unit checks for any deficiencies in the proposal document generated by the generation unit. The checking unit checks for any deficiencies in the generated proposal document and prompts corrections as needed. The checking unit checks for any missing required items or typographical errors and prompts corrections. As a result, the proposal document creation system according to this embodiment allows both the applicant and the approver to proceed with the proposal smoothly and without stress.

[0072] The data collection department accesses the company intranet to gather information. For example, the department collects templates and rules for the "approval manual" posted on the company intranet, as well as materials used for investment approval. Specifically, the department accesses various databases and file servers on the company intranet to comprehensively collect the information necessary for creating approval documents. For example, it collects samples of past approval documents, the latest investment plans, budget proposals, and risk assessment materials provided by each department. The department also collects feedback and comments from various departments within the company and can incorporate them into the creation of approval documents. This allows the department to quickly and accurately collect the information necessary for creating approval documents and smoothly provide the data to the analysis department. Furthermore, the department centrally manages the collected information and can link with other systems and departments as needed. For example, the collected information is stored on a cloud server and made accessible to the analysis and generation departments. The department can also adjust the frequency and accuracy of information collection to enable flexible responses to specific situations and conditions. This allows the data collection unit to efficiently and effectively collect information, thereby improving the overall performance of the system.

[0073] The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the collected information to identify necessary documents and numerical data. Specifically, the analysis unit uses text analysis technology to analyze the collected information. For example, it uses natural language processing technology to extract important keywords and phrases from collected documents and identify the information necessary for approval documents. For numerical data, it uses statistical analysis and data mining technology to extract important figures and trends. Furthermore, the analysis unit can search for similar approval documents based on past approval document data and provide relevant information. This allows the analysis unit to quickly and accurately analyze the collected information and provide the information necessary for creating approval documents. In addition, the analysis unit uses AI to process data in real time and understand the surrounding circumstances. For example, the AI ​​automatically classifies the contents of approval documents based on the collected information and evaluates their importance and priority. The AI ​​can also utilize past data and statistical information to perform long-term risk assessments and trend analyses. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0074] The generation unit creates approval documents based on information analyzed by the analysis unit. For example, the generation unit automatically creates approval documents based on the analysis results. Specifically, the generation unit automatically generates documents and numerical data according to the approval manual template and writing rules. For instance, it automatically fills in each section of the approval document based on keywords and numerical data provided by the analysis unit. Furthermore, the generation unit uses document generation technology to produce natural-sounding sentences and organize the content of the approval document. In addition, the generation unit can automatically adjust the format and layout of the approval document to create a clear and easy-to-understand document. This allows the generation unit to create approval documents efficiently and accurately, reducing the burden on applicants. Moreover, the generation unit can optimize the content of the approval document using AI. For example, the AI ​​can suggest the optimal document structure and expression based on past approval document data, improving the quality of the approval document. Finally, the generation unit saves the generated approval documents to a cloud server, making them accessible to the verification and approval units. This allows the generation unit to create approval documents efficiently and effectively, improving the overall system performance.

[0075] The verification unit allows the applicant to review and revise the approval document generated by the generation unit. Specifically, the verification unit presents the generated approval document to the applicant, prompting them to review and revise its contents. The applicant can review each section of the approval document and make necessary revisions. For example, the applicant can review the document's content and numerical data, correcting any errors or omissions. The verification unit also provides an interface to facilitate revisions. For instance, the applicant can edit the approval document on a web browser, reflecting revisions in real time. Furthermore, the verification unit manages the revision history, tracking who revised which part. This allows the verification unit to support applicants in efficiently and accurately reviewing and revising approval documents, thereby improving their quality. Additionally, the verification unit can use AI to analyze the applicant's revisions, identifying revision trends and patterns. This allows the verification unit to improve future approval document creation processes.

[0076] The approval department approves the proposal documents that have been reviewed and revised by the verification department. Specifically, the approval department presents the reviewed and revised proposal documents to the approvers and prompts them to review and approve the contents. The approvers review each section of the proposal document and approve it according to the approval criteria. For example, the approvers review the content and numerical data of the document and determine whether it meets the approval criteria. The approval department also provides an interface to make it easier for approvers to approve. For example, approvers can review the proposal document on a web browser and approve it by clicking an approve button. Furthermore, the approval department can manage the approval history and track who approved which proposal document. This allows the approval department to streamline the approval process and approve proposal documents quickly. In addition, the approval department can optimize the approval process using AI. For example, the AI ​​can analyze approval trends and patterns based on past approval data and suggest improvements to the approval process. This allows the approval department to process approval requests efficiently and effectively, thereby improving the overall performance of the system.

[0077] The checking unit checks for deficiencies in the approval documents generated by the generation unit. For example, the checking unit checks for deficiencies in the generated approval documents and prompts for corrections as needed. Specifically, the checking unit checks each section of the generated approval document, checking for missing required items and typographical errors. For example, it checks whether required items such as the title, date, applicant name, and approver name of the approval document are correctly filled in. The checking unit also checks the consistency of the document content and numerical data, and prompts for corrections if there are errors or inconsistencies. Furthermore, the checking unit can automatically detect deficiencies in the approval documents using AI. For example, the AI ​​can analyze the document content using natural language processing technology to detect typographical errors and grammatical errors. The AI ​​can also check the consistency of numerical data and detect outliers and inconsistencies. As a result, the checking unit can efficiently and accurately check for deficiencies in the approval documents and improve the quality of the approval documents. Furthermore, the checking unit can manage the checking history and track who checked which parts. This allows the checking department to thoroughly manage the quality of approval documents and improve the overall reliability of the system.

[0078] The data collection unit can access the company intranet to collect templates and rules for approval manuals, as well as materials for investment approvals, etc. For example, the data collection unit can access the company intranet to collect templates and rules for approval manuals. The data collection unit can also collect materials for investment approvals, etc. For example, the data collection unit can access the company intranet to collect necessary information. This improves the efficiency of creating approval documents by automatically collecting the necessary information. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can access the company intranet, input the collected information into a generating AI, and have the generating AI perform the information collection.

[0079] The analysis unit can analyze the collected information and identify the necessary documents and numerical values. For example, the analysis unit can analyze the collected information and identify the necessary documents and numerical values. For example, the analysis unit can analyze the collected information using text analysis technology. The analysis unit can also analyze the collected information using data mining technology. For example, the analysis unit can analyze the collected information using statistical analysis technology. By analyzing the collected information, the documents and numerical values ​​necessary for creating approval documents can be identified. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected information into a generating AI and have the generating AI perform the analysis of the information.

[0080] The generation unit can automatically create approval documents based on the analysis results. For example, the generation unit can automatically create approval documents based on the analysis results. For example, the generation unit can automatically generate documents and numerical data according to the template and rules of the approval manual. For example, the generation unit can also create approval documents using templates. For example, the generation unit can automatically input data and create approval documents. This improves the efficiency of creating approval documents by automatically creating them based on the analysis results. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the analysis results into a generation AI and have the generation AI create the approval documents.

[0081] The verification unit allows the applicant to review the approval document and make corrections as necessary. For example, the verification unit allows the applicant to review the approval document and make corrections as necessary. For example, the verification unit allows the applicant to review the approval document and correct items that can be corrected. The verification unit can also review the approval document using a checklist, for example. The verification unit can also review the approval document following a review procedure, for example. This improves the accuracy of the approval document by allowing the applicant to review it and make corrections as necessary. Some or all of the above processes in the verification unit may be performed using AI, for example, or not using AI. For example, the verification unit can input the generated approval document into a generating AI and have the generating AI perform the review of the approval document.

[0082] The approval department allows approvers to review and approve proposals. The approval department can, for example, have approvers review proposals and approve them. The approval department can, for example, have approvers review proposals and approve them according to the approval conditions. The approval department can also, for example, approve proposals according to the approval flow. The approval department can also, for example, approve proposals according to the approval procedure. This allows the approval process to proceed smoothly by having approvers review and approve proposals. Some or all of the above processes in the approval department may be performed using AI, for example, or not using AI. For example, the approval department can input the generated proposal into a generation AI and have the generation AI perform the approval of the proposal.

[0083] The checking unit can check for deficiencies in the generated approval document and prompt corrections as necessary. For example, the checking unit can check for deficiencies in the generated approval document and prompt corrections as necessary. For example, the checking unit can check for missing required items or typographical errors and prompt corrections. For example, the checking unit can use a checklist to check for deficiencies in the approval document. For example, the checking unit can check the approval document according to criteria for deficiencies. This improves the quality of the approval document by checking for deficiencies in the generated approval document and prompting corrections as necessary. Some or all of the above processes in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can input the generated approval document into a generation AI and have the generation AI perform the check for deficiencies.

[0084] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit reduces the frequency of information collection to alleviate the user's burden. For example, if the user is relaxed, the data collection unit increases the frequency of information collection to collect information efficiently. For example, if the user is in a hurry, the data collection unit speeds up the timing of information collection to quickly gather necessary information. In this way, the user's burden is reduced by adjusting the timing of information collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0085] The data collection unit can analyze the access history of the company's intranet and select the optimal information collection method. For example, the data collection unit can identify frequently accessed pages from the access history of the company's intranet and prioritize the collection of information from those pages. For example, the data collection unit can analyze the access history and prioritize the collection of information from pages that experience concentrated access during specific time periods. For example, the data collection unit can identify pages frequently used by specific users based on the access history and collect information from those pages. In this way, the optimal information collection method can be selected by analyzing the access history of the company's intranet. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the access history data of the company's intranet into a generating AI and have the generating AI select the optimal information collection method.

[0086] The data collection unit can filter information based on the user's current projects and areas of interest during data collection. For example, the data collection unit can prioritize collecting information related to the user's current projects. For example, the data collection unit can filter and collect highly relevant information based on the user's areas of interest. For example, the data collection unit can appropriately filter and collect necessary information according to the user's project progress. This allows for the collection of highly relevant information by filtering information based on the user's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's project information and areas of interest data into a generating AI and have the generating AI perform the information filtering.

[0087] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting information of high importance. For example, if the user is relaxed, the data collection unit will prioritize collecting detailed information. For example, if the user is in a hurry, the data collection unit will prioritize collecting information that can be collected quickly. In this way, important information can be prioritized by determining the priority of information to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform the determination of information priorities.

[0088] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of region-related information based on the user's current location. For example, the data collection unit can collect information related to nearby projects based on the user's geographical location information. For example, the data collection unit can prioritize the collection of region-specific information by considering the user's location information. This allows for the priority collection of highly relevant information by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information data into a generating AI and have the generating AI perform the data collection.

[0089] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can identify topics of interest from the user's social media activity and collect relevant information. For example, the data collection unit can analyze the content of posts from accounts the user follows and collect relevant information. For example, the data collection unit can collect information of interest based on the content of the user's social media posts. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI perform the data collection.

[0090] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit provides simple and easy-to-understand analysis results. For example, if the user is relaxed, the analysis unit provides detailed analysis results. For example, if the user is in a hurry, the analysis unit provides concise analysis results that get straight to the point. In this way, by adjusting the presentation of the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is 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 processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0091] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit performs a detailed analysis on information of high importance. For example, the analysis unit performs a concise analysis on information of low importance. For example, the analysis unit adjusts the level of detail of the analysis in stages according to the importance of the information. In this way, by adjusting the level of detail of the analysis based on the importance of the information, important information can be analyzed in detail. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0092] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a specific financial analysis algorithm to financial information. For example, the analysis unit can apply an analysis algorithm specialized for project management to project information. For example, the analysis unit can apply an algorithm suitable for human resources data analysis to human resources information. By applying different analysis algorithms depending on the category of information, appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information category data into a generating AI and have the generating AI execute the application of analysis algorithms.

[0093] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit provides a short, concise analysis result. For example, if the user is relaxed, the analysis unit provides a detailed analysis result. For example, if the user is stressed, the analysis unit provides a simple and easy-to-understand analysis result. By adjusting the length of the analysis according to the user's emotions, the analysis unit can provide the user with an analysis result of an appropriate length. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0094] The analysis unit can determine the priority of analysis based on the timing of information collection during the analysis process. For example, the analysis unit prioritizes the analysis of the most recent information. For example, the analysis unit analyzes older information as needed. For example, the analysis unit adjusts the priority of analysis in stages according to the timing of information collection. This allows for the prioritization of analysis of the most recent information by determining the priority of analysis based on the timing of information collection. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information collection timing data into a generating AI and have the generating AI determine the priority of analysis.

[0095] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit prioritizes the analysis of highly relevant information. For example, the analysis unit postpones the analysis of less relevant information. For example, the analysis unit adjusts the order of analysis step by step according to the relevance of the information. In this way, by adjusting the order of analysis based on the relevance of the information, highly relevant information can be analyzed preferentially. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0096] The generation unit can estimate the user's emotions and adjust the presentation of the approval document based on the estimated emotions. For example, if the user is stressed, the generation unit will generate a simple and highly visual approval document. For example, if the user is relaxed, the generation unit will generate an approval document containing detailed information. For example, if the user is in a hurry, the generation unit will generate a concise approval document that gets straight to the point. In this way, by adjusting the presentation of the approval document according to the user's emotions, it is possible to generate an approval document that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is 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 processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the presentation of the approval document.

[0097] The generation unit can adjust the level of detail in the approval document based on the importance of the information when generating the approval document. For example, the generation unit generates a detailed approval document for information of high importance. For example, the generation unit generates a concise approval document for information of low importance. For example, the generation unit adjusts the level of detail in the approval document in stages according to the importance of the information. In this way, by adjusting the level of detail in the approval document based on the importance of the information, it is possible to generate an approval document that describes important information in detail. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input information importance data into a generation AI and have the generation AI perform the adjustment of the level of detail in the approval document.

[0098] The generation unit can apply different generation algorithms depending on the category of information when generating approval documents. For example, the generation unit applies a specific financial generation algorithm to financial information. For example, the generation unit applies a generation algorithm specialized for project management to project information. For example, the generation unit applies an algorithm suitable for generating personnel data to personnel information. By applying different generation algorithms depending on the category of information, an appropriate approval document can be generated. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input information category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0099] The generation unit can estimate the user's emotions and adjust the length of the proposal document based on the estimated emotions. For example, if the user is in a hurry, the generation unit will generate a short, concise proposal document. If the user is relaxed, the generation unit will generate a longer proposal document with detailed explanations. If the user is stressed, the generation unit will generate a simple, easy-to-read proposal document. By adjusting the length of the proposal document according to the user's emotions, it is possible to generate a proposal document of an appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is 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 processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the length of the proposal document.

[0100] The generation unit can determine the priority of approval documents based on the timing of information collection when generating them. For example, the generation unit prioritizes reflecting the latest information in the approval documents. For example, the generation unit reflects older information in the approval documents as needed. For example, the generation unit adjusts the priority of the approval documents in stages according to the timing of information collection. This allows the latest information to be reflected in the approval documents preferentially by determining the priority of the approval documents based on the timing of information collection. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input information collection timing data into a generation AI and have the generation AI perform the determination of the priority of the approval documents.

[0101] The generation unit can adjust the order of approval documents based on the relevance of the information when generating them. For example, the generation unit prioritizes reflecting highly relevant information in the approval documents. For example, the generation unit postpones reflecting less relevant information in the approval documents. For example, the generation unit adjusts the order of approval documents in stages according to the relevance of the information. By adjusting the order of approval documents based on the relevance of the information, highly relevant information can be prioritized and reflected in the approval documents. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input information relevance data into a generation AI and have the generation AI perform the adjustment of the order of approval documents.

[0102] The verification unit can estimate the user's emotions and adjust the verification method based on the estimated emotions. For example, if the user is stressed, the verification unit provides a simple and highly visible verification method. For example, if the user is relaxed, the verification unit provides a verification method that includes detailed information. For example, if the user is in a hurry, the verification unit provides a concise verification method that gets straight to the point. By adjusting the verification method according to the user's emotions, the verification method can be made easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is 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 processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the user's emotion data into the generative AI and have the generative AI perform the adjustment of the verification method.

[0103] The verification unit can select the optimal verification method by referring to past verification history during the verification process. For example, the verification unit can prioritize displaying items that are frequently checked from past verification history. For example, the verification unit can prioritize displaying items that a particular user frequently checks based on past verification history. For example, the verification unit can analyze past verification history and propose the optimal verification method. This allows the optimal verification method to be selected by referring to past verification history. Some or all of the above processes in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input past verification history data into a generating AI and have the generating AI select the optimal verification method.

[0104] The verification unit can customize the verification procedure based on the user's current project status during verification. For example, the verification unit prioritizes displaying necessary verification items according to the user's current project status. For example, the verification unit customizes the verification procedure based on the user's project progress. For example, the verification unit proposes the optimal verification procedure considering the user's project status. In this way, by customizing the verification procedure based on the user's current project status, an appropriate verification procedure can be provided. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the user's project status data into a generating AI and have the generating AI perform the customization of the verification procedure.

[0105] The verification unit can estimate the user's emotions and determine the priority of verifications based on the estimated emotions. For example, if the user is stressed, the verification unit will prioritize displaying high-importance verification items. For example, if the user is relaxed, the verification unit will prioritize displaying detailed verification items. For example, if the user is in a hurry, the verification unit will prioritize displaying items that can be checked quickly. In this way, by determining the priority of verifications according to the user's emotions, important verification items can be displayed preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the verification unit may be performed using AI, for example, or not using AI. For example, the verification unit can input user emotion data into the generative AI and have the generative AI perform the determination of verification priorities.

[0106] The verification unit can select the optimal verification method during verification, taking into account the user's geographical location information. For example, the verification unit may prioritize displaying verification items related to the user's region based on the user's current location. For example, the verification unit may display verification items related to nearby projects based on the user's geographical location information. For example, the verification unit may prioritize displaying region-specific verification items, taking into account the user's location information. This allows for the priority display of highly relevant verification items by considering the user's geographical location information. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit may input the user's geographical location information data into a generating AI and have the generating AI perform the selection of the verification method.

[0107] The verification unit can analyze the user's social media activity and propose verification steps during the verification process. For example, the verification unit can identify topics of interest from the user's social media activity and display relevant verification items. For example, the verification unit can analyze the content of posts from accounts the user follows and display relevant verification items. For example, the verification unit can display verification items of interest based on the content of the user's social media posts. In this way, relevant verification items can be displayed by analyzing the user's social media activity. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the user's social media data into a generating AI and have the generating AI execute the proposal of verification steps.

[0108] The approval unit can estimate the user's emotions and adjust the approval method based on the estimated emotions. For example, if the user is stressed, the approval unit provides a simple and easily understandable approval method. For example, if the user is relaxed, the approval unit provides an approval method that includes detailed information. For example, if the user is in a hurry, the approval unit provides a concise and to-the-point approval method. By adjusting the approval method according to the user's emotions, an approval method that is easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the approval unit may be performed using AI, for example, or not using AI. For example, the approval unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the approval method.

[0109] The approval unit can select the optimal approval method by referring to past approval history during the approval process. For example, the approval unit may prioritize displaying items that are frequently approved based on past approval history. For example, the approval unit may prioritize displaying items that a particular user frequently approves based on past approval history. For example, the approval unit may analyze past approval history and propose the optimal approval method. This allows the optimal approval method to be selected by referring to past approval history. Some or all of the above processes in the approval unit may be performed using AI, for example, or without AI. For example, the approval unit may input past approval history data into a generating AI and have the generating AI select the optimal approval method.

[0110] The approval unit can customize the approval procedure based on the user's current project status during the approval process. For example, the approval unit can prioritize displaying necessary approval items according to the user's current project status. For example, the approval unit can customize the approval procedure based on the user's project progress. For example, the approval unit can propose the optimal approval procedure considering the user's project status. This allows for the provision of an appropriate approval procedure by customizing the approval procedure based on the user's current project status. Some or all of the above processes in the approval unit may be performed using AI, for example, or without AI. For example, the approval unit can input the user's project status data into a generating AI and have the generating AI perform the customization of the approval procedure.

[0111] The approval unit can estimate the user's emotions and determine approval priorities based on the estimated emotions. For example, if the user is stressed, the approval unit will prioritize displaying high-priority approval items. For example, if the user is relaxed, the approval unit will prioritize displaying detailed approval items. For example, if the user is in a hurry, the approval unit will prioritize displaying items that can be approved quickly. In this way, important approval items can be prioritized by determining approval priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the approval unit may be performed using AI or not using AI. For example, the approval unit can input user emotion data into a generative AI and have the generative AI determine the approval priorities.

[0112] The approval unit can select the optimal approval method when approving, taking into account the user's geographical location information. For example, the approval unit may prioritize displaying approval items relevant to a region based on the user's current location. For example, the approval unit may display approval items related to nearby projects based on the user's geographical location information. For example, the approval unit may prioritize displaying region-specific approval items, taking into account the user's location information. This allows for the priority display of highly relevant approval items by considering the user's geographical location information. Some or all of the above processing in the approval unit may be performed using AI, for example, or without AI. For example, the approval unit may input the user's geographical location data into a generating AI and have the generating AI select the approval method.

[0113] The approval unit can analyze the user's social media activity and propose approval procedures during the approval process. For example, the approval unit can identify topics of interest from the user's social media activity and display relevant approval items. For example, the approval unit can analyze the content of posts from accounts the user follows and display relevant approval items. For example, the approval unit can display approval items of interest based on the content of the user's social media posts. In this way, relevant approval items can be displayed by analyzing the user's social media activity. Some or all of the above processing in the approval unit may be performed using AI, for example, or without AI. For example, the approval unit can input the user's social media data into a generating AI and have the generating AI execute the proposal of approval procedures.

[0114] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0115] The approval document creation system can also include a notification unit. This notification unit can provide real-time updates to applicants and approvers regarding the progress of the approval process. For example, it can send a notification to the applicant when the approval document is generated, prompting them to review it. It can also send a notification to the applicant when the approver has approved the document. Furthermore, if the approval process is delayed, it can send a reminder notification to the approver, encouraging a prompt response. This ensures a smooth approval process and reduces the burden on both applicants and approvers.

[0116] The approval document creation system can also include a feedback unit. This feedback unit can collect feedback from applicants and approvers during the approval document creation and approval process, and use this feedback to improve the system. For example, the feedback unit can collect opinions and suggestions for improvement from applicants after the approval document has been created. It can also collect feedback on the approval process after approvers have approved the document. Furthermore, the feedback unit can analyze the collected feedback, identify areas for system improvement, and reflect these improvements in the next approval document creation and approval process. This improves the quality of the approval document creation system and increases satisfaction for both applicants and approvers.

[0117] The approval document creation system can also be equipped with a learning unit. This learning unit can learn from past approval document creation and approval processes to optimize future processes. For example, it can analyze past approval document content and approval process data to generate optimal templates and writing rules for approval documents. Furthermore, it can learn approver preferences and tendencies based on past approval process data, thereby streamlining the approval process. Additionally, it can learn from past feedback data, identify areas for system improvement, and incorporate these improvements into future approval document creation and approval processes. This improves the accuracy of the approval document creation system and reduces the burden on both applicants and approvers.

[0118] The approval document creation system can also include an alert function. This alert function can send alerts to applicants and approvers when important events or deadlines are approaching during the approval document creation and approval process. For example, the alert function can send an alert to the applicant when the deadline for submitting the approval document is approaching, prompting submission. It can also send an alert to the approver when the deadline for approval is approaching, encouraging prompt action. Furthermore, if there are significant deficiencies in the content of the approval document, the alert function can send an alert to the applicant, prompting corrections. This ensures that the approval document creation and approval process proceeds smoothly and that important events and deadlines are not overlooked.

[0119] The approval document creation system can also include a customization section. This customization section allows for the customization of approval document templates and writing rules according to the individual needs and preferences of applicants and approvers. For example, the customization section can provide an optimal approval document template based on the applicant's work content and project characteristics. It can also adjust the writing rules based on the approver's preferences and past approval history. Furthermore, the customization section can continuously improve the approval document templates and writing rules based on feedback from applicants and approvers. This allows the approval document creation system to better meet the individual needs of applicants and approvers, making it more user-friendly.

[0120] The approval document creation system can further utilize an emotion estimation function to adjust the approval document creation and approval process based on the emotions of the applicant and approver. For example, using the emotion estimation function, if the applicant is feeling stressed, the approval document creation process can be simplified to reduce their burden. If the approver is relaxed, a detailed approval document can be provided to facilitate the approval process. Furthermore, if the applicant and approver are in a hurry, a concise approval document that gets straight to the point can be provided to encourage a quick response. In this way, the approval document creation and approval process can be adjusted according to the emotions of the applicant and approver, reducing stress and achieving efficient approval.

[0121] The approval document creation system can further utilize a sentiment estimation function to adjust the content and timing of notifications based on the emotions of the applicant and approver. For example, if the applicant is feeling stressed, the frequency of notifications can be reduced to alleviate their burden. Conversely, if the approver is relaxed, detailed notifications can be sent to facilitate the approval process. Furthermore, if the applicant and approver are in a hurry, important notifications can be sent quickly to encourage prompt action. In this way, the content and timing of notifications can be adjusted according to the emotions of the applicant and approver, reducing stress and achieving efficient approval processes.

[0122] The approval document creation system can further utilize an emotion estimation function to adjust the content and timing of feedback based on the emotions of the applicant and approver. For example, if the applicant is feeling stressed, the frequency of feedback can be reduced to alleviate their burden. Conversely, if the approver is relaxed, detailed feedback can be provided to help improve the system. Furthermore, if the applicant and approver are in a hurry, important feedback can be collected quickly to encourage prompt action. In this way, the content and timing of feedback can be adjusted according to the emotions of the applicant and approver, reducing stress and achieving efficient approval processes.

[0123] The approval document creation system can further utilize an emotion estimation function to adjust the content and timing of alerts based on the emotions of the applicant and approver. For example, using the emotion estimation function, if the applicant is feeling stressed, the frequency of alerts can be reduced to alleviate their burden. Conversely, if the approver is relaxed, detailed alerts can be sent to facilitate the approval process. Furthermore, if the applicant and approver are in a hurry, important alerts can be sent quickly to encourage prompt action. In this way, the content and timing of alerts can be adjusted according to the emotions of the applicant and approver, reducing stress and achieving efficient approval processes.

[0124] The approval document creation system can further utilize an emotion estimation function to adjust the content and timing of customizations based on the emotions of the applicant and approver. For example, if the applicant is feeling stressed, the frequency of customizations can be reduced to alleviate their burden. If the approver is relaxed, detailed customizations can be provided to improve the system's usability. Furthermore, if the applicant and approver are in a hurry, important customizations can be provided quickly to encourage a prompt response. In this way, the content and timing of customizations can be adjusted according to the emotions of the applicant and approver, reducing stress and achieving efficient approval processes.

[0125] The following briefly describes the processing flow for example form 2.

[0126] Step 1: The collection department accesses the company intranet to gather information. For example, they collect templates and rules for the "approval manual," as well as documents related to investment approval, which are posted on the company intranet. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, it uses text analysis technology to analyze the collected information and identify necessary documents and numerical data. Step 3: The generation unit creates the approval document based on the information analyzed by the analysis unit. For example, it automatically creates the approval document based on the analysis results and automatically generates documents and figures according to the approval manual template and writing rules. Step 4: The verification unit allows the applicant to review and revise the approval document generated by the generation unit. For example, the applicant reviews the approval document and makes revisions as needed. Step 5: The approval department approves the proposal document, which has been reviewed and revised by the verification department. For example, the approver reviews the proposal document and approves it according to the approval conditions. Step 6: The checking unit checks for any deficiencies in the approval document generated by the generation unit. For example, it checks for missing required items or typographical errors in the generated approval document and prompts for corrections as necessary.

[0127] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0128] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0129] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0130] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, verification unit, approval unit, and checking unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit accesses the company intranet via the communication I / F 44 of the smart device 14 and collects the necessary information. The analysis unit analyzes the collected information by the specific processing unit 290 of the data processing unit 12. The generation unit automatically creates an approval document based on the analysis results by the specific processing unit 290 of the data processing unit 12. The verification unit allows the applicant to review the approval document using the control unit 46A of the smart device 14 and make corrections as necessary. The approval unit allows the approver to review the approval document using the specific processing unit 290 of the data processing unit 12 and make approvals. The checking unit checks for deficiencies in the approval document generated by the specific processing unit 290 of the data processing unit 12 and prompts for corrections as necessary. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0131] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0132] As shown in Figure 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.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, verification unit, approval unit, and checking unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit accesses the company intranet via the communication I / F 44 of the smart glasses 214 and collects the necessary information. The analysis unit analyzes the collected information, for example, by the specific processing unit 290 of the data processing unit 12. The generation unit automatically creates an approval document based on the analysis results, for example, by the specific processing unit 290 of the data processing unit 12. The verification unit allows the applicant to review the approval document via the control unit 46A of the smart glasses 214 and make corrections as necessary. The approval unit allows the approver to review the approval document via the specific processing unit 290 of the data processing unit 12 and make approval, for example. The checking unit checks for any deficiencies in the approval document generated by the specific processing unit 290 of the data processing unit 12 and prompts for corrections as necessary. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0147] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0148] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0155] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, verification unit, approval unit, and checking unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit accesses the company intranet via the communication I / F 44 of the headset terminal 314 and collects the necessary information. The analysis unit analyzes the collected information by, for example, the specific processing unit 290 of the data processing unit 12. The generation unit automatically creates an approval document based on the analysis results by, for example, the specific processing unit 290 of the data processing unit 12. The verification unit allows, for example, the applicant to review the approval document via the control unit 46A of the headset terminal 314 and make corrections as necessary. The approval unit allows, for example, the approver to review the approval document via the specific processing unit 290 of the data processing unit 12 and make approval. The checking unit checks for deficiencies in the approval document generated by the specific processing unit 290 of the data processing unit 12 and prompts corrections as necessary. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0163] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0164] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0165] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0166] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0167] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0168] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0169] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0170] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0171] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0172] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0173] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0174] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0175] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0176] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0177] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0178] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0179] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, verification unit, approval unit, and checking unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit accesses the company intranet via the communication I / F 44 of the robot 414 and collects the necessary information. The analysis unit analyzes the information collected by, for example, the specific processing unit 290 of the data processing unit 12. The generation unit automatically creates an approval document based on the analysis results by, for example, the specific processing unit 290 of the data processing unit 12. The verification unit allows, for example, the control unit 46A of the robot 414 to allow the applicant to review the approval document and make corrections as necessary. The approval unit allows, for example, the specific processing unit 290 of the data processing unit 12 to allow the approver to review the approval document and make approval. The checking unit checks for deficiencies in the approval document generated by the specific processing unit 290 of the data processing unit 12 and prompts for corrections as necessary. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0180] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0181] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0182] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0183] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0184] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0185] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0186] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0187] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0188] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0189] 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.

[0190] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0191] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0192] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0193] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0194] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0195] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0196] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0197] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0198] (Note 1) The collection department accesses the company intranet to collect information, An analysis unit analyzes the information collected by the aforementioned collection unit, A generation unit that creates an approval document based on the information analyzed by the aforementioned analysis unit, A confirmation unit is used by the applicant to review and correct the approval document generated by the generation unit. The approval unit approves the approval document that has been confirmed and corrected by the aforementioned confirmation unit, The system includes a checking unit that checks for deficiencies in the approval document generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Access the company intranet to collect templates and rules for approval manuals, as well as documents for investment approvals, etc. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze the collected information and identify the necessary documents and figures. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Automatically generate approval documents based on analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned verification unit is The applicant reviews the approval document and makes any necessary revisions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned approval unit, The approver reviews the proposal and gives their approval. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned checking unit is Check the generated approval documents for any errors and prompt for corrections as necessary. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze the access history of the company intranet to select the most suitable method for collecting information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When gathering information, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is The system estimates the user's emotions and adjusts the wording of the approval document based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating a proposal document, adjust the level of detail in the document based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating approval documents, different generation algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is Estimate the user's emotions and adjust the length of the proposal document based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating approval documents, prioritize the documents based on when the information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is When generating approval documents, adjust the order of the documents based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned verification unit is We estimate the user's emotions and adjust the confirmation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned verification unit is During verification, the system will refer to past verification history to select the most suitable verification method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned verification unit is During verification, customize the verification process based on the user's current project status. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned verification unit is The system estimates the user's emotions and determines the priority of confirmations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned verification unit is During verification, the optimal verification method will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned verification unit is During the verification process, we analyze the user's social media activity and suggest verification steps. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned approval unit, It estimates the user's emotions and adjusts the approval method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned approval unit, During the approval process, the system will refer to past approval history to select the most suitable approval method. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned approval unit, During the approval process, customize the approval procedure based on the user's current project status. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned approval unit, It estimates the user's emotions and determines approval priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned approval unit, During the approval process, the system will select the most appropriate approval method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned approval unit, During the approval process, we analyze the user's social media activity and suggest approval procedures. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0199] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection department accesses the company intranet to collect information, An analysis unit analyzes the information collected by the aforementioned collection unit, A generation unit that creates an approval document based on the information analyzed by the aforementioned analysis unit, A confirmation unit is used by the applicant to review and correct the approval document generated by the generation unit, The approval unit approves the approval document that has been confirmed and corrected by the aforementioned confirmation unit, The system includes a checking unit that checks for deficiencies in the approval document generated by the generation unit. A system characterized by the following features.

2. The aforementioned collection unit is Access the company intranet to collect templates and rules for approval manuals, as well as documents for investment approvals, etc. The system according to feature 1.

3. The aforementioned analysis unit, Analyze the collected information and identify the necessary documents and figures. The system according to feature 1.

4. The generating unit is Automatically generate approval documents based on analysis results. The system according to feature 1.

5. The aforementioned verification unit is The applicant reviews the approval document and makes any necessary revisions. The system according to feature 1.

6. The aforementioned approval unit, The approver reviews the proposal and gives their approval. The system according to feature 1.

7. The aforementioned checking unit is Check the generated approval documents for any errors and prompt for corrections as necessary. The system according to feature 1.

8. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system according to feature 1.

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