system

Abstract

The system according to this embodiment aims to provide a comprehensive service ranging from observing business processes to automatically creating manuals, proposing improvements to business efficiency, providing personalized coaching, generating training plans, visualizing business flows, and providing a knowledge-sharing platform. [Solution] The system according to the embodiment comprises an observation unit, a creation unit, a proposal unit, a coaching unit, a generation unit, a visualization unit, and an integration unit. The observation unit observes the user's business processes. The creation unit automatically creates the necessary manuals based on the business processes observed by the observation unit. The proposal unit makes suggestions for improving work efficiency and accuracy based on the manuals created by the creation unit. The coaching unit provides personalized coaching based on each employee's skill level and work performance. The generation unit generates individual training plans and manuals based on the coaching provided by the coaching unit. The visualization unit provides a business flow visualization tool to make business manuals visually easy to understand. The integration unit provides a platform for employees to share knowledge with each other, and an AI agent analyzes the posted content to pick up useful information and integrate it into the manual.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is 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 the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds 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, observation of business processes, creation of manuals, proposal of business efficiency improvement, coaching for employee skill improvement, etc. are carried out manually, resulting in problems of low efficiency, lack of accuracy and consistency.

[0005] The system according to the embodiment aims to consistently perform from observation of business processes to automatic creation of manuals, proposal of business efficiency improvement, provision of personalized coaching, generation of training plans, visualization of business flows, and provision of a knowledge sharing platform.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an observation unit, a creation unit, a proposal unit, a coaching unit, a generation unit, a visualization unit, and an integration unit. The observation unit observes the user's business processes. The creation unit automatically creates the necessary manuals based on the business processes observed by the observation unit. The proposal unit makes suggestions for improving work efficiency and accuracy based on the manuals created by the creation unit. The coaching unit provides personalized coaching based on each employee's skill level and work performance. The generation unit generates individual training plans and manuals based on the coaching provided by the coaching unit. The visualization unit provides a business flow visualization tool to make business manuals visually easy to understand. The integration unit provides a platform for employees to share knowledge with each other, and an AI agent analyzes the posted content to pick out useful information and integrate it into the manual. [Effects of the Invention]

[0007] The system according to this embodiment can consistently perform tasks ranging from observing business processes to automatically creating manuals, proposing improvements to business efficiency, providing personalized coaching, generating training plans, visualizing business flows, and providing a knowledge sharing platform. [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 tagged storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memories (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the tagged communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, a specific processing unit 290 (see FIG. 2) acquires 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 business manual generation system according to an embodiment of the present invention is a system that uses an AI agent to learn the user's work situation, analyzes it in real time, and automatically generates business manuals. This business manual generation system observes the user's work process and automatically creates the necessary manuals. Next, it makes suggestions for improving work efficiency and accuracy. Furthermore, it provides personalized coaching based on each employee's skill level and work performance, and generates individual training plans and manuals. It also provides a business flow visualization tool to make business manuals visually easy to understand. In addition, it provides a platform for employees to share knowledge with each other, and the AI ​​agent analyzes the posted content, picks out useful information, and integrates it into the manual. For example, the business manual generation system observes the user's work process. At this time, the business manual generation system analyzes the user's work flow in detail and identifies which parts need to be manualized. For example, tasks that involve a lot of routine work, such as data entry and the creation of standardized reports, often need to be manualized. This allows the business manual generation system to automatically create the necessary manuals. Next, the business manual generation system makes suggestions for improving work efficiency and accuracy. Specifically, it analyzes each step of the work flow in detail, eliminates redundant processes, and proposes the optimal work flow. For example, streamlining data entry procedures can improve operational efficiency. We also offer specific suggestions to improve accuracy. For instance, implementing error checking during data entry can prevent input errors. Furthermore, the operational manual generation system provides personalized coaching based on each employee's skill level and performance. Specifically, it analyzes each employee's performance in detail and generates individual training plans and manuals. For example, employees with low data entry skills will receive a data entry training plan. We also provide a workflow visualization tool to make operational manuals visually easy to understand, allowing employees to intuitively grasp work procedures.Furthermore, the business manual generation system provides a platform for employees to share knowledge among themselves. Specifically, employees post information related to their work, and the business manual generation system analyzes the posts, picks out useful information, and integrates it into the manual. For example, if an employee posts an idea for improving work efficiency, the business manual generation system analyzes that idea and shares it with other employees, thereby improving overall work efficiency. This mechanism automates the creation of business manuals, leading to improved efficiency and accuracy. In addition, personalized coaching is provided based on each employee's skill level and work performance, promoting employee growth. Moreover, by utilizing business flow visualization tools and the knowledge sharing platform, information sharing among employees is promoted, improving overall work efficiency. As a result, the business manual generation system observes users' work processes and automatically creates the necessary manuals, thereby improving efficiency and accuracy.

[0029] The business manual generation system according to this embodiment comprises an observation unit, a creation unit, a proposal unit, a coaching unit, a generation unit, a visualization unit, and an integration unit. The observation unit observes the user's business processes. The observation unit, for example, analyzes the user's business flow in detail and identifies which parts need to be manualized. The observation unit can identify tasks that involve a lot of routine work, such as data entry and the creation of standardized reports. The observation unit can, for example, observe each step of the business flow in detail and identify the parts that need to be manualized. The observation unit can, for example, make suggestions based on the observation results to improve the efficiency and accuracy of operations. The creation unit automatically creates the necessary manuals based on the business processes observed by the observation unit. The creation unit can, for example, automatically generate business manuals based on the data provided by the observation unit. The creation unit can, for example, create manuals corresponding to each step of the business flow. The creation unit can, for example, optimize the content of the manuals to improve the efficiency and accuracy of operations. The proposal unit makes suggestions for improving the efficiency and accuracy of operations based on the manuals created by the creation unit. The proposal department can, for example, analyze each step of a business workflow in detail, eliminate redundant processes, and propose an optimal workflow. The proposal department can, for example, improve the efficiency of operations by simplifying data entry procedures. The proposal department can, for example, make specific suggestions to improve the accuracy of operations. The proposal department can, for example, prevent input errors by introducing error checking during data entry. The coaching department provides personalized coaching based on each employee's skill level and work performance. The coaching department can, for example, analyze each employee's work performance in detail and generate individual training plans and manuals. The coaching department can, for example, provide data entry training plans to employees with low data entry skills. The coaching department can, for example, provide individual coaching to improve work efficiency and accuracy. The generation department generates individual training plans and manuals based on the coaching provided by the coaching department.The generation unit can, for example, automatically generate individual training plans and manuals based on data provided by the coaching unit. The generation unit can, for example, provide training plans tailored to each employee's skill level and work performance. The generation unit can, for example, optimize the content of training plans and manuals to improve work efficiency and accuracy. The visualization unit provides a business flow visualization tool to make work manuals visually easy to understand. The visualization unit can, for example, provide a tool to visually display business flows. The visualization unit can, for example, provide a visual display so that work procedures can be intuitively understood. The visualization unit can, for example, optimize the visual display to improve work efficiency and accuracy. The integration unit provides a platform for employees to share knowledge with each other, and an AI agent analyzes the posted content to pick out useful information and integrate it into the manual. The integration unit can, for example, have employees post information related to their work, and an AI agent analyzes the posted content to pick out useful information. The integration unit can, for example, update the manual based on the posted content to improve work efficiency and accuracy. The integration unit can, for example, promote information sharing among employees and improve overall operational efficiency. As a result, the operational manual generation system according to the embodiment can observe the user's work processes and automatically create the necessary manuals, thereby improving operational efficiency and accuracy.

[0030] The observation unit observes the user's business processes. For example, the observation unit analyzes the user's workflow in detail and identifies which parts need to be manualized. The observation unit can identify tasks that involve a lot of routine work, such as data entry and the creation of standardized reports. For example, the observation unit can observe each step of the workflow in detail and identify parts that need to be manualized. For example, the observation unit can make suggestions based on the observation results to improve the efficiency and accuracy of operations. The observation unit monitors the user's business processes in real time and collects detailed data for each step. For example, it records in detail how the user enters data, what tools they use, and when errors occur. This allows the observation unit to identify bottlenecks and inefficiencies in the business process and provide specific data for improvement. The observation unit can use AI to analyze patterns in business processes and propose the optimal workflow. For example, the AI ​​can identify the most efficient work procedure based on past business data and recommend that procedure to the user. Furthermore, the observation unit can continuously monitor users' business processes and update the manual content in response to changes in the work environment. This allows the observation unit to always provide manuals that are up-to-date with the latest business processes, supporting improved efficiency and accuracy. In addition, the observation unit can collect user feedback and provide data to improve the manual content. For example, it can collect information such as which parts of the manual users found difficult to understand and which parts were helpful, and optimize the manual content. This allows the observation unit to provide manuals that meet user needs, achieving improved efficiency and accuracy.

[0031] The creation unit automatically generates necessary manuals based on business processes observed by the observation unit. For example, the creation unit can automatically generate business manuals based on data provided by the observation unit. For example, the creation unit can create manuals corresponding to each step of a business workflow. For example, the creation unit can optimize the content of the manuals to improve efficiency and accuracy. The creation unit utilizes AI to analyze data provided by the observation unit and generate optimal manuals. For example, the AI ​​analyzes each step of the business process in detail and automatically extracts necessary procedures and points to note. Furthermore, the creation unit can customize the content of the manuals according to the user's skill level and work environment. For example, it provides detailed procedures for beginners and concise procedures for experienced users. The creation unit can continuously improve the content of the manuals based on user feedback. For example, it collects information such as which parts of the manual users found difficult to understand and which parts were helpful, and optimizes the content of the manuals. This allows the creation unit to provide manuals that meet user needs, achieving improved efficiency and accuracy. In addition, the creation unit can provide tools to make the manual content visually easier to understand. For example, business workflows can be displayed using diagrams and charts to allow users to understand them intuitively. This allows the creation team to support users in effectively utilizing the manual.

[0032] The Proposal Department makes suggestions for improving operational efficiency and accuracy based on manuals created by the Creation Department. For example, the Proposal Department can analyze each step of a business workflow in detail, eliminate redundant processes, and propose an optimal workflow. For example, the Proposal Department can improve operational efficiency by simplifying data entry procedures. For example, the Proposal Department can make specific suggestions to improve operational accuracy. For example, the Proposal Department can prevent input errors by introducing error checking during data entry. The Proposal Department utilizes AI to support the optimization of business processes. For example, AI identifies the most efficient business procedure based on past business data and recommends that procedure to the user. In addition, the Proposal Department can identify areas for improvement in business processes based on user feedback and propose specific improvement measures. For example, it collects information such as which parts of the work are taking the user a lot of time and where errors are occurring, and identifies areas for improvement in the business process. This allows the Proposal Department to provide specific improvement measures that meet user needs, thereby achieving improved operational efficiency and accuracy. Furthermore, the Proposal Department can provide specific procedures for implementing the improvements to business processes. For example, it provides specific methods for simplifying data entry procedures and specific procedures for implementing error checking. This allows the proposal department to support users in effectively implementing improvements to their business processes.

[0033] The coaching department provides personalized coaching based on each employee's skill level and work performance. For example, the coaching department can analyze each employee's work performance in detail and generate individual training plans and manuals. For example, the coaching department can provide a data entry training plan to an employee with low data entry skills. For example, the coaching department can provide individual coaching to improve work efficiency and accuracy. The coaching department uses AI to monitor each employee's work performance in real time and provide training plans tailored to their skill level. For example, the AI ​​analyzes each employee's work data, identifies areas where skills are lacking, and proposes specific training plans. Furthermore, the coaching department can continuously improve the content of training plans based on employee feedback. For example, it collects information such as which parts of the training plan employees found difficult to understand and which parts were helpful, and optimizes the content of the training plan. This allows the coaching department to provide training plans that meet employee needs and achieve improved work efficiency and accuracy. In addition, the coaching department can monitor the implementation status of training plans and support employee skill improvement. For example, the coaching department can monitor the progress of training plans in real time and provide additional support as needed. This allows the coaching department to help employees effectively improve their skills.

[0034] The generation unit generates individual training plans and manuals based on coaching provided by the coaching unit. For example, the generation unit can automatically generate individual training plans and manuals based on data provided by the coaching unit. For example, the generation unit can provide training plans tailored to each employee's skill level and work performance. For example, the generation unit can optimize the content of training plans and manuals to improve work efficiency and accuracy. The generation unit utilizes AI to analyze data provided by the coaching unit and generate optimal training plans and manuals. For example, the AI ​​identifies areas of skill deficiency based on each employee's work data and proposes specific training plans. Furthermore, the generation unit can continuously improve the content of training plans and manuals based on user feedback. For example, it collects information such as which parts of the training plan users found difficult to understand and which parts were helpful, optimizing the content of the training plans and manuals. This allows the generation unit to provide training plans and manuals that meet user needs, achieving improved work efficiency and accuracy. In addition, the generation unit can provide tools to make the content of training plans and manuals visually easier to understand. For example, business workflows can be displayed using diagrams and charts to allow users to understand them intuitively. This enables the generation unit to support users in effectively utilizing training plans and manuals.

[0035] The Visualization Department provides business flow visualization tools to make business manuals visually easy to understand. For example, the Visualization Department can provide tools to visually display business flows. For example, the Visualization Department can provide visual displays to make business procedures intuitively understandable. For example, the Visualization Department can optimize visual displays to improve business efficiency and accuracy. The Visualization Department utilizes AI to suggest the optimal method for visually displaying business flows. For example, AI analyzes each step of a business process and identifies the most effective visual display method. Furthermore, the Visualization Department can continuously improve the content of visual displays based on user feedback. For example, it collects information such as which parts of the visual display users find difficult to understand and which parts were helpful, and optimizes the content of the visual display. This allows the Visualization Department to provide visual displays that meet user needs, thereby improving business efficiency and accuracy. In addition, the Visualization Department can update the content of visual displays in real time to accommodate the latest business processes. For example, if there are changes in the business flow, the Visualization Department immediately incorporates the new data and updates the visual display. This allows the visualization unit to always provide a visual representation that corresponds to the latest business processes, helping users to perform their tasks effectively.

[0036] The Integration Department provides a platform for employees to share knowledge, with an AI agent analyzing posts to pick out useful information and integrating it into manuals. For example, employees can post information related to their work, and the AI ​​agent can analyze the posts to pick out useful information. The Integration Department can update manuals based on the posted content to improve work efficiency and accuracy. For example, the Integration Department can promote information sharing among employees and improve overall work efficiency. The Integration Department uses AI to analyze information posted by employees in real time and automatically extract useful information. For example, the AI ​​analyzes the posted content using natural language processing technology to identify important information and know-how related to the work. Furthermore, when integrating the extracted information into manuals, the Integration Department can perform checks to maintain the integrity and consistency of the information. This allows the Integration Department to always provide manuals that reflect the latest information and support improved work efficiency and accuracy. In addition, the Integration Department can provide an interface to promote information sharing among employees. For example, it can provide a user-friendly interface that allows employees to easily post information and a function to comment on and provide feedback on posted content. This allows the integration department to revitalize communication among employees and promote knowledge sharing. Furthermore, the integration department can provide guidelines and templates to improve the quality of posted content. For example, it can establish formatting and content standards for posts, encouraging employees to adhere to them. This allows the integration department to maintain consistent quality in posted content and ensure consistency in manuals.

[0037] The observation unit can analyze the user's workflow in detail and identify which parts need to be documented. For example, the observation unit can closely observe the user's workflow and identify the parts that need to be documented. The observation unit can identify tasks that involve a lot of routine work, such as data entry or the creation of standardized reports. For example, the observation unit can closely observe each step of the workflow and identify the parts that need to be documented. The observation unit can make suggestions based on the observation results to improve the efficiency and accuracy of operations. In this way, by analyzing the workflow in detail, the parts that need to be documented can be identified. Some or all of the above processes in the observation unit may be performed using AI, for example, or not using AI. For example, the observation unit can input the user's workflow into AI, and the AI ​​can analyze the workflow and identify the parts that need to be documented.

[0038] The proposal department can analyze each step of the business flow in detail, eliminate redundant processes, and propose an optimal business flow. For example, the proposal department can observe each step of the business flow in detail and identify redundant processes. For example, the proposal department can improve the efficiency of operations by simplifying data entry procedures. For example, the proposal department can make specific suggestions to improve the accuracy of operations. For example, the proposal department can prevent input errors by introducing error checking during data entry. For example, the proposal department can analyze each step of the business flow in detail and propose an optimal business flow. This improves operational efficiency by eliminating redundant processes and proposing an optimal business flow. Some or all of the above processes performed by the proposal department may be performed using AI, or not. For example, the proposal department can input each step of the business flow into AI, which can identify redundant processes and propose an optimal business flow.

[0039] The proposal department can make specific suggestions to improve the accuracy of operations. For example, the proposal department can observe each step of the workflow in detail and identify areas for improvement to enhance accuracy. For example, the proposal department can prevent input errors by introducing error checking during data entry. The proposal department can make specific suggestions to improve the accuracy of operations. For example, the proposal department can analyze each step of the workflow in detail and propose the optimal method to improve accuracy. By making specific suggestions to improve the accuracy of operations, the accuracy of operations is improved. Some or all of the above processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input each step of the workflow into AI, which can identify areas for improvement to enhance accuracy and propose the optimal method.

[0040] The coaching department can analyze each employee's work performance in detail and generate individual training plans and manuals. For example, the coaching department can closely observe each employee's work performance and identify skills that require training. For example, the coaching department can provide a data entry training plan to an employee with low data entry skills. For example, the coaching department can generate individual training plans and manuals to improve work efficiency and accuracy. For example, the coaching department can analyze each employee's work performance in detail and generate individual training plans and manuals. This allows for the generation of individual training plans and manuals by analyzing each employee's work performance in detail. Some or all of the above processes in the coaching department may be performed using AI, or not. For example, the coaching department can input each employee's work performance into AI, which can identify skills that require training and generate individual training plans and manuals.

[0041] The Visualization Department can provide a business flow visualization tool to make business manuals visually easy to understand. For example, the Visualization Department can provide a tool for visually displaying business flows. For example, the Visualization Department can provide a visual representation to make business procedures intuitively understandable. For example, the Visualization Department can optimize visual representations to improve the efficiency and accuracy of operations. For example, the Visualization Department can provide a tool for visually displaying business flows. This makes business manuals visually easy to understand, allowing employees to intuitively understand business procedures. Some or all of the above-described processes in the Visualization Department may be performed using AI, or not. For example, the Visualization Department can provide a tool where business flows are input into AI, and the AI ​​visually displays them.

[0042] The Integration Department provides a platform for employees to share knowledge, and an AI agent can analyze posted content to pick out useful information and integrate it into manuals. For example, employees can post information related to their work, and the AI ​​agent can analyze the posted content to pick out useful information. The Integration Department can update manuals based on the posted content to improve the efficiency and accuracy of work. For example, the Integration Department can promote information sharing among employees and improve overall work efficiency. For example, employees can post information related to their work, and the AI ​​agent can analyze the posted content to pick out useful information. This improves work efficiency by allowing employees to share knowledge among themselves. Some or all of the above processes in the Integration Department may be performed using AI, or not using AI. For example, the Integration Department can input information posted by employees into the AI, and the AI ​​can pick out useful information and integrate it into manuals.

[0043] The observation unit can analyze the user's past work history and select the optimal observation method. For example, the observation unit can analyze patterns in the user's past work and select an efficient observation method. For example, the observation unit can determine the priority of observation for a specific task based on the user's past work history. For example, the observation unit can adjust the timing and frequency of observation based on the user's past work history. For example, the observation unit can analyze the user's past work history and select the optimal observation method. This allows the optimal observation method to be selected by analyzing the user's past work history. Some or all of the above processes in the observation unit may be performed using AI, for example, or without AI. For example, the observation unit can input the user's past work history into AI, which can then analyze the work history and select the optimal observation method.

[0044] The observation unit can filter data based on the user's current projects and areas of interest during observation. For example, the observation unit can observe only tasks related to the project the user is currently working on. The observation unit can narrow down the observation target based on the user's areas of interest. The observation unit can adjust the priority of observations according to the progress of the user's current projects. The observation unit can filter data based on the user's current projects and areas of interest during observation. This allows for efficient observation by narrowing down the observation target based on the user's current projects and areas of interest. Some or all of the above processing in the observation unit may be performed using AI, for example, or without AI. For example, the observation unit can input data on the user's current projects and areas of interest into the AI, which can then perform the filtering.

[0045] The observation unit can prioritize observing tasks that are highly relevant, taking into account the user's geographical location information during observation. For example, if the user is in the office, the observation unit will prioritize observing tasks performed within the office. For example, if the user is on a business trip, the observation unit can prioritize observing tasks performed at the business trip destination. For example, if the user is working remotely, the observation unit can prioritize observing tasks performed at home. The observation unit can prioritize observing tasks that are highly relevant, taking into account the user's geographical location information during observation. This allows for the priority observation of tasks that are highly relevant by considering the user's geographical location information. Some or all of the above processing in the observation unit may be performed using AI, for example, or without AI. For example, the observation unit can input the user's geographical location information into AI, which can identify highly relevant tasks and prioritize their observation.

[0046] The observation unit can analyze the user's social media activity and observe related tasks during observation. For example, the observation unit can observe tasks related to the work the user has shared on social media. For example, the observation unit can identify and observe tasks of interest from the user's social media activity. For example, the observation unit can observe tasks related to projects the user has mentioned on social media. For example, the observation unit can analyze the user's social media activity and observe related tasks during observation. This allows the observation of related tasks by analyzing the user's social media activity. Some or all of the above processing in the observation unit may be performed using AI, for example, or without AI. For example, the observation unit can input the user's social media activity into AI, which can then identify and observe related tasks.

[0047] The creation unit can adjust the level of detail in a manual based on the importance of the task when creating it. For example, the creation unit can create a manual with detailed procedures for important tasks. For example, the creation unit can create a manual with concise procedures for less important tasks. The creation unit can adjust the level of detail in a manual in stages according to the importance of the task. For example, the creation unit can adjust the level of detail in a manual based on the importance of the task when creating it. This allows the creation unit to provide a manual with an appropriate level of detail by adjusting the level of detail based on the importance of the task. Some or all of the above processes in the creation unit may be performed using AI, for example, or not using AI. For example, the creation unit can input task importance data into AI, and the AI ​​can adjust the level of detail in the manual.

[0048] The creation unit can apply different creation algorithms depending on the category of work when creating manuals. For example, for data entry tasks, the creation unit can apply an algorithm that creates a manual including efficient input procedures. For example, for report creation tasks, the creation unit can apply an algorithm that creates a manual including accurate writing methods. For example, for customer service tasks, the creation unit can apply an algorithm that creates a manual including appropriate communication methods. The creation unit can apply different creation algorithms depending on the category of work when creating manuals. This allows for the provision of appropriate manuals by applying different creation algorithms depending on the category of work. Some or all of the above processing in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input business category data into AI, and the AI ​​can apply an appropriate creation algorithm.

[0049] The creation unit can determine the priority of manuals based on the timing of the work when creating them. For example, the creation unit can prioritize creating manuals for tasks scheduled to be performed in the near future. For example, the creation unit can postpone creating manuals for tasks scheduled to be performed in the long term. The creation unit can adjust the order in which manuals are created according to the timing of the work. For example, the creation unit can determine the priority of manuals based on the timing of the work when creating them. This allows manuals to be provided at the appropriate time by determining the priority of manuals based on the timing of the work. Some or all of the above processes in the creation unit may be performed using AI, for example, or not using AI. For example, the creation unit can input work timing data into AI, and the AI ​​can determine the priority of manuals.

[0050] The creation unit can adjust the order of manuals based on the relevance of tasks when creating manuals. For example, the creation unit can create manuals consecutively for highly related tasks. For example, the creation unit can create separate manuals for less related tasks. The creation unit can adjust the order of manual creation according to the relevance of tasks. For example, the creation unit can adjust the order of manuals based on the relevance of tasks when creating manuals. This allows for the learning of highly related tasks consecutively by adjusting the order of manuals based on the relevance of tasks. Some or all of the above processing in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input task relevance data into AI, and the AI ​​can adjust the order of manuals.

[0051] The proposal department can adjust the level of detail in a proposal based on the importance of the task. For example, the proposal department can provide a proposal with detailed procedures for important tasks, and a proposal with concise procedures for less important tasks. The proposal department can adjust the level of detail in a stepwise manner according to the importance of the task. The proposal department can adjust the level of detail in a proposal based on the importance of the task at the time of proposal. This allows the proposal department to provide a proposal with the appropriate level of detail by adjusting the level of detail based on the importance of the task. Some or all of the above processing in the proposal department may be performed using AI, or not. For example, the proposal department can input task importance data into the AI, and the AI ​​can adjust the level of detail in the proposal.

[0052] The proposal department can apply different proposal algorithms depending on the category of work when making a proposal. For example, for data entry work, the proposal department can apply an algorithm that provides proposals including efficient input procedures. For example, for report creation work, the proposal department can apply an algorithm that provides proposals including accurate description methods. For example, for customer service work, the proposal department can apply an algorithm that provides proposals including appropriate communication methods. The proposal department can apply different proposal algorithms depending on the category of work when making a proposal. This allows for the provision of appropriate proposals by applying different proposal algorithms depending on the category of work. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input work category data into AI, and the AI ​​can apply an appropriate proposal algorithm.

[0053] The proposal department can determine the priority of proposals based on the timing of the work's implementation. For example, the proposal department will prioritize proposals for work scheduled to be implemented in the near future. For example, the proposal department can postpone proposals for work scheduled to be implemented in the long term. The proposal department can adjust the priority of proposals according to the timing of the work's implementation. For example, the proposal department can determine the priority of proposals based on the timing of the work's implementation at the time of proposal submission. This allows proposals to be provided at the appropriate time by determining the priority of proposals based on the timing of the work's implementation. Some or all of the above processing in the proposal department may be performed using AI, or not. For example, the proposal department can input work implementation timing data into AI, and the AI ​​can determine the priority of proposals.

[0054] The proposal unit can adjust the order of proposals based on the relevance of the tasks when making a proposal. For example, the proposal unit can make proposals consecutively for highly relevant tasks. For example, the proposal unit can make separate proposals for less relevant tasks. The proposal unit can adjust the order of proposals according to the relevance of the tasks. For example, the proposal unit can adjust the order of proposals based on the relevance of the tasks when making a proposal. This allows the system to learn highly relevant tasks consecutively by adjusting the order of proposals based on the relevance of the tasks. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input task relevance data into the AI, and the AI ​​can adjust the order of proposals.

[0055] The coaching department can analyze the user's past work performance during coaching to select the optimal coaching method. For example, the coaching department can select an effective coaching method based on the user's past work performance. For example, the coaching department can determine the priority of coaching for specific skills based on the user's past work performance. For example, the coaching department can analyze the user's past work performance and adjust the timing and frequency of coaching. For example, the coaching department can analyze the user's past work performance during coaching to select the optimal coaching method. This allows the optimal coaching method to be selected by analyzing the user's past work performance. Some or all of the above processes in the coaching department may be performed using AI, or not. For example, the coaching department can input the user's past work performance data into AI, and the AI ​​can select the optimal coaching method.

[0056] The coaching unit can customize the coaching methods based on the user's current skill level during coaching. For example, the coaching unit can select appropriate coaching methods according to the user's current skill level. For example, the coaching unit can adjust the content of the coaching based on the user's skill level. For example, the coaching unit can adjust the pace of the coaching according to the user's skill level. For example, the coaching unit can customize the coaching methods based on the user's current skill level during coaching. By customizing the coaching methods based on the user's current skill level, more appropriate coaching can be provided. Some or all of the above processes in the coaching unit may be performed using AI, for example, or without AI. For example, the coaching unit can input the user's skill level data into the AI, which can then select appropriate coaching methods.

[0057] The coaching department can select the optimal coaching method during coaching by considering the user's geographical location. For example, if the user is in the office, the coaching department can select a coaching method that can be conducted within the office. For example, if the user is on a business trip, the coaching department can select a coaching method that can be conducted at the business trip location. For example, if the user is working remotely, the coaching department can select a coaching method that can be conducted at home. The coaching department can select the optimal coaching method during coaching by considering the user's geographical location. This allows the coaching department to select the optimal coaching method by considering the user's geographical location. Some or all of the above processing in the coaching department may be performed using AI, for example, or without AI. For example, the coaching department can input the user's geographical location information into the AI, and the AI ​​can select the optimal coaching method.

[0058] The coaching department can analyze a user's social media activity during coaching sessions and propose coaching methods. For example, the coaching department can propose coaching related to skills the user has shared on social media. For example, the coaching department can identify skills of interest from a user's social media activity and propose coaching. For example, the coaching department can propose coaching related to skills the user has mentioned on social media. For example, the coaching department can analyze a user's social media activity during coaching sessions and propose coaching methods. This allows the coaching department to propose appropriate coaching methods by analyzing the user's social media activity. Some or all of the above processes in the coaching department may be performed using AI, for example, or not. For example, the coaching department can input the user's social media activity data into an AI, which can then propose appropriate coaching methods.

[0059] The generation unit can analyze the user's past training history to select the optimal training plan when generating a training plan. For example, the generation unit can select an effective training plan based on the user's past training history. For example, the generation unit can determine the priority of training for a specific skill based on the user's past training history. For example, the generation unit can analyze the user's past training history and adjust the timing and frequency of training. For example, the generation unit can analyze the user's past training history to select the optimal training plan when generating a training plan. This allows the optimal training plan to be selected by analyzing the user's past training history. 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 user's past training history data into AI, and the AI ​​can select the optimal training plan.

[0060] The generation unit can customize the training plan based on the user's current work situation when generating the training plan. For example, the generation unit can select appropriate training methods according to the user's current work situation. For example, the generation unit can adjust the training content based on the user's work situation. For example, the generation unit can adjust the training progress speed according to the user's work situation. For example, the generation unit can customize the training plan based on the user's current work situation when generating the training plan. This allows for the provision of a more appropriate training plan by customizing the training methods based on the user's current work situation. 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 work situation data into AI, and the AI ​​can select appropriate training methods.

[0061] The generation unit can select the optimal training plan by considering the user's geographical location information when generating a training plan. For example, if the user is in the office, the generation unit can select a training plan that can be done in the office. For example, if the user is on a business trip, the generation unit can select a training plan that can be done at the business trip destination. For example, if the user is working remotely, the generation unit can select a training plan that can be done at home. The generation unit can select the optimal training plan by considering the user's geographical location information when generating a training plan. This allows for the selection of the optimal training plan by considering the user's geographical location information. 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 the user's geographical location information into the AI, and the AI ​​can select the optimal training plan.

[0062] The generation unit can analyze the user's social media activity and propose training plan methods when generating a training plan. For example, the generation unit can propose training plans related to skills shared by the user on social media. For example, the generation unit can identify skills of interest from the user's social media activity and propose training plans. For example, the generation unit can propose training plans related to skills mentioned by the user on social media. For example, the generation unit can analyze the user's social media activity and propose training plan methods when generating a training plan. This allows for the proposal of appropriate training plan methods by analyzing the user's social media activity. 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 the user's social media activity data into AI, which can then propose appropriate training plan methods.

[0063] The visualization unit can analyze the user's past business workflow and select the optimal visualization method during visualization. For example, the visualization unit can select an effective visualization method based on the user's past business workflow. For example, the visualization unit can determine the visualization priority for a specific task from the user's past business workflow. For example, the visualization unit can analyze the user's past business workflow and adjust the timing and frequency of visualization. For example, the visualization unit can analyze the user's past business workflow and select the optimal visualization method during visualization. This allows the optimal visualization method to be selected by analyzing the user's past business workflow. Some or all of the above processes in the visualization unit may be performed using AI, or not. For example, the visualization unit can input the user's past business workflow data into AI, and the AI ​​can select the optimal visualization method.

[0064] The visualization unit can customize the visualization methods based on the user's current work situation during visualization. For example, the visualization unit can select an appropriate visualization method according to the user's current work situation. For example, the visualization unit can adjust the content of the visualization based on the user's work situation. For example, the visualization unit can adjust the speed of the visualization according to the user's work situation. For example, the visualization unit can customize the visualization methods based on the user's current work situation during visualization. By customizing the visualization methods based on the user's current work situation, a more appropriate visualization can be provided. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input user work situation data into AI, and the AI ​​can select an appropriate visualization method.

[0065] The visualization unit can select the optimal visualization method by considering the user's geographical location information during visualization. For example, if the user is in the office, the visualization unit can select a visualization method that can be performed within the office. For example, if the user is on a business trip, the visualization unit can select a visualization method that can be performed at the business trip destination. For example, if the user is working remotely, the visualization unit can select a visualization method that can be performed at home. The visualization unit can select the optimal visualization method by considering the user's geographical location information during visualization. This allows for the selection of the optimal visualization method by considering the user's geographical location information. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the user's geographical location information into the AI, and the AI ​​can select the optimal visualization method.

[0066] The visualization unit can analyze the user's social media activity and propose visualization methods during the visualization process. For example, the visualization unit can propose visualizations related to information shared by the user on social media. For example, the visualization unit can identify information of interest from the user's social media activity and propose visualizations. For example, the visualization unit can propose visualizations related to information mentioned by the user on social media. For example, the visualization unit can analyze the user's social media activity and propose visualization methods during the visualization process. This allows the visualization unit to propose appropriate visualization methods by analyzing the user's social media activity. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the user's social media activity data into AI, which can then propose appropriate visualization methods.

[0067] The integration unit can select the most relevant information by analyzing the user's past posting history during the integration process. For example, the integration unit can select effective information based on the user's past posting history. For example, the integration unit can determine the priority of integration for specific information based on the user's past posting history. For example, the integration unit can analyze the user's past posting history and adjust the timing and frequency of integration. For example, the integration unit can select the most relevant information by analyzing the user's past posting history during the integration process. This allows for the selection of optimal information by analyzing the user's past posting history. Some or all of the above-described processes in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the user's past posting history data into AI, which can then select the most relevant information.

[0068] The integration unit can customize the means of integration based on the user's current work situation during integration. For example, the integration unit can select an appropriate integration method according to the user's current work situation. For example, the integration unit can adjust the content of the integration based on the user's work situation. For example, the integration unit can adjust the speed of the integration process according to the user's work situation. For example, the integration unit can customize the means of integration based on the user's current work situation during integration. This allows for the provision of more appropriate information by customizing the means of integration based on the user's current work situation. Some or all of the above-described processes in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input user work situation data into AI, and the AI ​​can select an appropriate integration method.

[0069] The integration unit can select the most suitable information during integration, taking into account the user's geographical location. For example, if the user is in the office, the integration unit will prioritize integrating information that can be done within the office. For example, if the user is on a business trip, the integration unit can prioritize integrating information that can be done at the business trip destination. For example, if the user is working remotely, the integration unit can prioritize integrating information that can be done at home. The integration unit can select the most suitable information during integration, taking into account the user's geographical location. This allows for the selection of optimal information by considering the user's geographical location. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the user's geographical location into AI, which can then select the most suitable information.

[0070] The integration unit can analyze the user's social media activity and propose integration methods during integration. For example, the integration unit can propose integrations related to information shared by the user on social media. For example, the integration unit can identify information of interest from the user's social media activity and propose integrations. For example, the integration unit can propose integrations related to information mentioned by the user on social media. For example, the integration unit can analyze the user's social media activity and propose integration methods during integration. This allows the integration unit to propose appropriate integration methods by analyzing the user's social media activity. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the user's social media activity data into AI, which can then propose appropriate integration methods.

[0071] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0072] The business manual generation system can analyze a user's past work history and select the optimal observation method when observing their business processes. For example, it can analyze patterns in tasks the user has performed in the past and select an efficient observation method. It can also determine the priority of observation for specific tasks based on the user's past work history. Furthermore, it can adjust the timing and frequency of observations based on the user's past work history. In this way, by analyzing the user's past work history, the optimal observation method can be selected.

[0073] The business manual generation system allows the observation unit to filter data based on the user's current projects and areas of interest. For example, it can observe only tasks related to the project the user is currently working on. It can also narrow down the observation target based on the user's areas of interest. Furthermore, it can adjust the priority of observations according to the progress of the user's current project. This allows for efficient observation by narrowing down the observation target based on the user's current projects and areas of interest.

[0074] The business manual generation system's observation unit can prioritize observing highly relevant tasks by considering the user's geographical location. For example, if the user is in the office, tasks performed within the office can be prioritized. If the user is on a business trip, tasks performed at the destination can be prioritized. Furthermore, if the user is working remotely, tasks performed at home can be prioritized. In this way, by considering the user's geographical location, the system can prioritize observing highly relevant tasks.

[0075] The business manual generation system's observation unit can analyze users' social media activity and observe related tasks. For example, it can observe tasks related to work shared by users on social media. It can also identify and observe tasks of interest from users' social media activity. Furthermore, it can observe tasks related to projects mentioned by users on social media. In this way, by analyzing users' social media activity, it is possible to observe related tasks.

[0076] The business manual generation system allows the creation department to adjust the level of detail in the manual based on the importance of the task. For example, it can create manuals with detailed procedures for important tasks, and manuals with concise procedures for less important tasks. Furthermore, it can adjust the level of detail in the manual in stages according to the importance of the task. This allows the system to provide manuals with the appropriate level of detail by adjusting the level of detail based on the importance of the task.

[0077] The business manual generation system allows the creation department to apply different creation algorithms depending on the category of work when creating manuals. For example, for data entry tasks, an algorithm can be applied to create manuals that include efficient input procedures. For report creation tasks, an algorithm can be applied to create manuals that include accurate writing methods. Furthermore, for customer service tasks, an algorithm can be applied to create manuals that include appropriate communication methods. In this way, by applying different creation algorithms depending on the category of work, the system can provide appropriate manuals.

[0078] The following briefly describes the processing flow for example form 1.

[0079] Step 1: The observation team observes the user's business processes. For example, the observation team analyzes the user's workflow in detail and identifies which parts need to be documented. For example, the observation team can identify tasks that involve a lot of routine work, such as data entry or the creation of standardized reports. For example, the observation team can observe each step of the workflow in detail and identify the parts that need to be documented. Step 2: The creation unit automatically generates the necessary manuals based on the business processes observed by the observation unit. For example, the creation unit can automatically generate business manuals based on data provided by the observation unit. For example, the creation unit can create manuals corresponding to each step of a business flow. For example, the creation unit can optimize the content of the manuals to improve the efficiency and accuracy of operations. Step 3: The proposal department makes suggestions for improving operational efficiency and accuracy based on the manual created by the creation department. For example, the proposal department can analyze each step of the workflow in detail, eliminate redundant processes, and propose an optimal workflow. For example, the proposal department can improve operational efficiency by simplifying data entry procedures. For example, the proposal department can make specific suggestions to improve operational accuracy. For example, the proposal department can prevent input errors by introducing error checking during data entry. Step 4: The coaching department provides personalized coaching based on each employee's skill level and work performance. For example, the coaching department can analyze each employee's work performance in detail and generate individual training plans and manuals. For example, the coaching department can provide a data entry training plan to an employee with low data entry skills. For example, the coaching department can provide individual coaching to improve work efficiency and accuracy. Step 5: The generation unit generates individual training plans and manuals based on the coaching provided by the coaching unit. For example, the generation unit can automatically generate individual training plans and manuals based on data provided by the coaching unit. For example, the generation unit can provide training plans tailored to each employee's skill level and work performance. For example, the generation unit can optimize the content of training plans and manuals to improve work efficiency and accuracy. Step 6: The Visualization Unit provides a business flow visualization tool to make business manuals visually easy to understand. For example, the Visualization Unit can provide a tool to visually display business flows. For example, the Visualization Unit can provide a visual display to make business procedures intuitively understandable. For example, the Visualization Unit can optimize the visual display to improve the efficiency and accuracy of business operations. Step 7: The Integration Department provides a platform for employees to share knowledge, and an AI agent analyzes the posted content to pick out useful information and integrate it into manuals. For example, employees can post information related to their work, and the AI ​​agent can analyze the posted content to pick out useful information. For example, the Integration Department can update manuals based on the posted content to improve the efficiency and accuracy of operations. For example, the Integration Department can promote information sharing among employees and improve overall work efficiency.

[0080] (Example of form 2) The business manual generation system according to an embodiment of the present invention is a system that uses an AI agent to learn the user's work situation, analyzes it in real time, and automatically generates business manuals. This business manual generation system observes the user's work process and automatically creates the necessary manuals. Next, it makes suggestions for improving work efficiency and accuracy. Furthermore, it provides personalized coaching based on each employee's skill level and work performance, and generates individual training plans and manuals. It also provides a business flow visualization tool to make business manuals visually easy to understand. In addition, it provides a platform for employees to share knowledge with each other, and the AI ​​agent analyzes the posted content, picks out useful information, and integrates it into the manual. For example, the business manual generation system observes the user's work process. At this time, the business manual generation system analyzes the user's work flow in detail and identifies which parts need to be manualized. For example, tasks that involve a lot of routine work, such as data entry and the creation of standardized reports, often need to be manualized. This allows the business manual generation system to automatically create the necessary manuals. Next, the business manual generation system makes suggestions for improving work efficiency and accuracy. Specifically, it analyzes each step of the work flow in detail, eliminates redundant processes, and proposes the optimal work flow. For example, streamlining data entry procedures can improve operational efficiency. We also offer specific suggestions to improve accuracy. For instance, implementing error checking during data entry can prevent input errors. Furthermore, the operational manual generation system provides personalized coaching based on each employee's skill level and performance. Specifically, it analyzes each employee's performance in detail and generates individual training plans and manuals. For example, employees with low data entry skills will receive a data entry training plan. We also provide a workflow visualization tool to make operational manuals visually easy to understand, allowing employees to intuitively grasp work procedures.Furthermore, the business manual generation system provides a platform for employees to share knowledge among themselves. Specifically, employees post information related to their work, and the business manual generation system analyzes the posts, picks out useful information, and integrates it into the manual. For example, if an employee posts an idea for improving work efficiency, the business manual generation system analyzes that idea and shares it with other employees, thereby improving overall work efficiency. This mechanism automates the creation of business manuals, leading to improved efficiency and accuracy. In addition, personalized coaching is provided based on each employee's skill level and work performance, promoting employee growth. Moreover, by utilizing business flow visualization tools and the knowledge sharing platform, information sharing among employees is promoted, improving overall work efficiency. As a result, the business manual generation system observes users' work processes and automatically creates the necessary manuals, thereby improving efficiency and accuracy.

[0081] The business manual generation system according to this embodiment comprises an observation unit, a creation unit, a proposal unit, a coaching unit, a generation unit, a visualization unit, and an integration unit. The observation unit observes the user's business processes. The observation unit, for example, analyzes the user's business flow in detail and identifies which parts need to be manualized. The observation unit can identify tasks that involve a lot of routine work, such as data entry and the creation of standardized reports. The observation unit can, for example, observe each step of the business flow in detail and identify the parts that need to be manualized. The observation unit can, for example, make suggestions based on the observation results to improve the efficiency and accuracy of operations. The creation unit automatically creates the necessary manuals based on the business processes observed by the observation unit. The creation unit can, for example, automatically generate business manuals based on the data provided by the observation unit. The creation unit can, for example, create manuals corresponding to each step of the business flow. The creation unit can, for example, optimize the content of the manuals to improve the efficiency and accuracy of operations. The proposal unit makes suggestions for improving the efficiency and accuracy of operations based on the manuals created by the creation unit. The proposal department can, for example, analyze each step of a business workflow in detail, eliminate redundant processes, and propose an optimal workflow. The proposal department can, for example, improve the efficiency of operations by simplifying data entry procedures. The proposal department can, for example, make specific suggestions to improve the accuracy of operations. The proposal department can, for example, prevent input errors by introducing error checking during data entry. The coaching department provides personalized coaching based on each employee's skill level and work performance. The coaching department can, for example, analyze each employee's work performance in detail and generate individual training plans and manuals. The coaching department can, for example, provide data entry training plans to employees with low data entry skills. The coaching department can, for example, provide individual coaching to improve work efficiency and accuracy. The generation department generates individual training plans and manuals based on the coaching provided by the coaching department.The generation unit can, for example, automatically generate individual training plans and manuals based on data provided by the coaching unit. The generation unit can, for example, provide training plans tailored to each employee's skill level and work performance. The generation unit can, for example, optimize the content of training plans and manuals to improve work efficiency and accuracy. The visualization unit provides a business flow visualization tool to make work manuals visually easy to understand. The visualization unit can, for example, provide a tool to visually display business flows. The visualization unit can, for example, provide a visual display so that work procedures can be intuitively understood. The visualization unit can, for example, optimize the visual display to improve work efficiency and accuracy. The integration unit provides a platform for employees to share knowledge with each other, and an AI agent analyzes the posted content to pick out useful information and integrate it into the manual. The integration unit can, for example, have employees post information related to their work, and an AI agent analyzes the posted content to pick out useful information. The integration unit can, for example, update the manual based on the posted content to improve work efficiency and accuracy. The integration unit can, for example, promote information sharing among employees and improve overall operational efficiency. As a result, the operational manual generation system according to the embodiment can observe the user's work processes and automatically create the necessary manuals, thereby improving operational efficiency and accuracy.

[0082] The observation unit observes the user's business processes. For example, the observation unit analyzes the user's workflow in detail and identifies which parts need to be manualized. The observation unit can identify tasks that involve a lot of routine work, such as data entry and the creation of standardized reports. For example, the observation unit can observe each step of the workflow in detail and identify parts that need to be manualized. For example, the observation unit can make suggestions based on the observation results to improve the efficiency and accuracy of operations. The observation unit monitors the user's business processes in real time and collects detailed data for each step. For example, it records in detail how the user enters data, what tools they use, and when errors occur. This allows the observation unit to identify bottlenecks and inefficiencies in the business process and provide specific data for improvement. The observation unit can use AI to analyze patterns in business processes and propose the optimal workflow. For example, the AI ​​can identify the most efficient work procedure based on past business data and recommend that procedure to the user. Furthermore, the observation unit can continuously monitor users' business processes and update the manual content in response to changes in the work environment. This allows the observation unit to always provide manuals that are up-to-date with the latest business processes, supporting improved efficiency and accuracy. In addition, the observation unit can collect user feedback and provide data to improve the manual content. For example, it can collect information such as which parts of the manual users found difficult to understand and which parts were helpful, and optimize the manual content. This allows the observation unit to provide manuals that meet user needs, achieving improved efficiency and accuracy.

[0083] The creation unit automatically generates necessary manuals based on business processes observed by the observation unit. For example, the creation unit can automatically generate business manuals based on data provided by the observation unit. For example, the creation unit can create manuals corresponding to each step of a business workflow. For example, the creation unit can optimize the content of the manuals to improve efficiency and accuracy. The creation unit utilizes AI to analyze data provided by the observation unit and generate optimal manuals. For example, the AI ​​analyzes each step of the business process in detail and automatically extracts necessary procedures and points to note. Furthermore, the creation unit can customize the content of the manuals according to the user's skill level and work environment. For example, it provides detailed procedures for beginners and concise procedures for experienced users. The creation unit can continuously improve the content of the manuals based on user feedback. For example, it collects information such as which parts of the manual users found difficult to understand and which parts were helpful, and optimizes the content of the manuals. This allows the creation unit to provide manuals that meet user needs, achieving improved efficiency and accuracy. In addition, the creation unit can provide tools to make the manual content visually easier to understand. For example, business workflows can be displayed using diagrams and charts to allow users to understand them intuitively. This allows the creation team to support users in effectively utilizing the manual.

[0084] The Proposal Department makes suggestions for improving operational efficiency and accuracy based on manuals created by the Creation Department. For example, the Proposal Department can analyze each step of a business workflow in detail, eliminate redundant processes, and propose an optimal workflow. For example, the Proposal Department can improve operational efficiency by simplifying data entry procedures. For example, the Proposal Department can make specific suggestions to improve operational accuracy. For example, the Proposal Department can prevent input errors by introducing error checking during data entry. The Proposal Department utilizes AI to support the optimization of business processes. For example, AI identifies the most efficient business procedure based on past business data and recommends that procedure to the user. In addition, the Proposal Department can identify areas for improvement in business processes based on user feedback and propose specific improvement measures. For example, it collects information such as which parts of the work are taking the user a lot of time and where errors are occurring, and identifies areas for improvement in the business process. This allows the Proposal Department to provide specific improvement measures that meet user needs, thereby achieving improved operational efficiency and accuracy. Furthermore, the Proposal Department can provide specific procedures for implementing the improvements to business processes. For example, it provides specific methods for simplifying data entry procedures and specific procedures for implementing error checking. This allows the proposal department to support users in effectively implementing improvements to their business processes.

[0085] The coaching department provides personalized coaching based on each employee's skill level and work performance. For example, the coaching department can analyze each employee's work performance in detail and generate individual training plans and manuals. For example, the coaching department can provide a data entry training plan to an employee with low data entry skills. For example, the coaching department can provide individual coaching to improve work efficiency and accuracy. The coaching department uses AI to monitor each employee's work performance in real time and provide training plans tailored to their skill level. For example, the AI ​​analyzes each employee's work data, identifies areas where skills are lacking, and proposes specific training plans. Furthermore, the coaching department can continuously improve the content of training plans based on employee feedback. For example, it collects information such as which parts of the training plan employees found difficult to understand and which parts were helpful, and optimizes the content of the training plan. This allows the coaching department to provide training plans that meet employee needs and achieve improved work efficiency and accuracy. In addition, the coaching department can monitor the implementation status of training plans and support employee skill improvement. For example, the coaching department can monitor the progress of training plans in real time and provide additional support as needed. This allows the coaching department to help employees effectively improve their skills.

[0086] The generation unit generates individual training plans and manuals based on coaching provided by the coaching unit. For example, the generation unit can automatically generate individual training plans and manuals based on data provided by the coaching unit. For example, the generation unit can provide training plans tailored to each employee's skill level and work performance. For example, the generation unit can optimize the content of training plans and manuals to improve work efficiency and accuracy. The generation unit utilizes AI to analyze data provided by the coaching unit and generate optimal training plans and manuals. For example, the AI ​​identifies areas of skill deficiency based on each employee's work data and proposes specific training plans. Furthermore, the generation unit can continuously improve the content of training plans and manuals based on user feedback. For example, it collects information such as which parts of the training plan users found difficult to understand and which parts were helpful, optimizing the content of the training plans and manuals. This allows the generation unit to provide training plans and manuals that meet user needs, achieving improved work efficiency and accuracy. In addition, the generation unit can provide tools to make the content of training plans and manuals visually easier to understand. For example, business workflows can be displayed using diagrams and charts to allow users to understand them intuitively. This enables the generation unit to support users in effectively utilizing training plans and manuals.

[0087] The Visualization Department provides business flow visualization tools to make business manuals visually easy to understand. For example, the Visualization Department can provide tools to visually display business flows. For example, the Visualization Department can provide visual displays to make business procedures intuitively understandable. For example, the Visualization Department can optimize visual displays to improve business efficiency and accuracy. The Visualization Department utilizes AI to suggest the optimal method for visually displaying business flows. For example, AI analyzes each step of a business process and identifies the most effective visual display method. Furthermore, the Visualization Department can continuously improve the content of visual displays based on user feedback. For example, it collects information such as which parts of the visual display users find difficult to understand and which parts were helpful, and optimizes the content of the visual display. This allows the Visualization Department to provide visual displays that meet user needs, thereby improving business efficiency and accuracy. In addition, the Visualization Department can update the content of visual displays in real time to accommodate the latest business processes. For example, if there are changes in the business flow, the Visualization Department immediately incorporates the new data and updates the visual display. This allows the visualization unit to always provide a visual representation that corresponds to the latest business processes, helping users to perform their tasks effectively.

[0088] The Integration Department provides a platform for employees to share knowledge, with an AI agent analyzing posts to pick out useful information and integrating it into manuals. For example, employees can post information related to their work, and the AI ​​agent can analyze the posts to pick out useful information. The Integration Department can update manuals based on the posted content to improve work efficiency and accuracy. For example, the Integration Department can promote information sharing among employees and improve overall work efficiency. The Integration Department uses AI to analyze information posted by employees in real time and automatically extract useful information. For example, the AI ​​analyzes the posted content using natural language processing technology to identify important information and know-how related to the work. Furthermore, when integrating the extracted information into manuals, the Integration Department can perform checks to maintain the integrity and consistency of the information. This allows the Integration Department to always provide manuals that reflect the latest information and support improved work efficiency and accuracy. In addition, the Integration Department can provide an interface to promote information sharing among employees. For example, it can provide a user-friendly interface that allows employees to easily post information and a function to comment on and provide feedback on posted content. This allows the integration department to revitalize communication among employees and promote knowledge sharing. Furthermore, the integration department can provide guidelines and templates to improve the quality of posted content. For example, it can establish formatting and content standards for posts, encouraging employees to adhere to them. This allows the integration department to maintain consistent quality in posted content and ensure consistency in manuals.

[0089] The observation unit can analyze the user's workflow in detail and identify which parts need to be documented. For example, the observation unit can closely observe the user's workflow and identify the parts that need to be documented. The observation unit can identify tasks that involve a lot of routine work, such as data entry or the creation of standardized reports. For example, the observation unit can closely observe each step of the workflow and identify the parts that need to be documented. The observation unit can make suggestions based on the observation results to improve the efficiency and accuracy of operations. In this way, by analyzing the workflow in detail, the parts that need to be documented can be identified. Some or all of the above processes in the observation unit may be performed using AI, for example, or not using AI. For example, the observation unit can input the user's workflow into AI, and the AI ​​can analyze the workflow and identify the parts that need to be documented.

[0090] The proposal department can analyze each step of the business flow in detail, eliminate redundant processes, and propose an optimal business flow. For example, the proposal department can observe each step of the business flow in detail and identify redundant processes. For example, the proposal department can improve the efficiency of operations by simplifying data entry procedures. For example, the proposal department can make specific suggestions to improve the accuracy of operations. For example, the proposal department can prevent input errors by introducing error checking during data entry. For example, the proposal department can analyze each step of the business flow in detail and propose an optimal business flow. This improves operational efficiency by eliminating redundant processes and proposing an optimal business flow. Some or all of the above processes performed by the proposal department may be performed using AI, or not. For example, the proposal department can input each step of the business flow into AI, which can identify redundant processes and propose an optimal business flow.

[0091] The proposal department can make specific suggestions to improve the accuracy of operations. For example, the proposal department can observe each step of the workflow in detail and identify areas for improvement to enhance accuracy. For example, the proposal department can prevent input errors by introducing error checking during data entry. The proposal department can make specific suggestions to improve the accuracy of operations. For example, the proposal department can analyze each step of the workflow in detail and propose the optimal method to improve accuracy. By making specific suggestions to improve the accuracy of operations, the accuracy of operations is improved. Some or all of the above processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input each step of the workflow into AI, which can identify areas for improvement to enhance accuracy and propose the optimal method.

[0092] The coaching department can analyze each employee's work performance in detail and generate individual training plans and manuals. For example, the coaching department can closely observe each employee's work performance and identify skills that require training. For example, the coaching department can provide a data entry training plan to an employee with low data entry skills. For example, the coaching department can generate individual training plans and manuals to improve work efficiency and accuracy. For example, the coaching department can analyze each employee's work performance in detail and generate individual training plans and manuals. This allows for the generation of individual training plans and manuals by analyzing each employee's work performance in detail. Some or all of the above processes in the coaching department may be performed using AI, or not. For example, the coaching department can input each employee's work performance into AI, which can identify skills that require training and generate individual training plans and manuals.

[0093] The Visualization Department can provide a business flow visualization tool to make business manuals visually easy to understand. For example, the Visualization Department can provide a tool for visually displaying business flows. For example, the Visualization Department can provide a visual representation to make business procedures intuitively understandable. For example, the Visualization Department can optimize visual representations to improve the efficiency and accuracy of operations. For example, the Visualization Department can provide a tool for visually displaying business flows. This makes business manuals visually easy to understand, allowing employees to intuitively understand business procedures. Some or all of the above-described processes in the Visualization Department may be performed using AI, or not. For example, the Visualization Department can provide a tool where business flows are input into AI, and the AI ​​visually displays them.

[0094] The Integration Department provides a platform for employees to share knowledge, and an AI agent can analyze posted content to pick out useful information and integrate it into manuals. For example, employees can post information related to their work, and the AI ​​agent can analyze the posted content to pick out useful information. The Integration Department can update manuals based on the posted content to improve the efficiency and accuracy of work. For example, the Integration Department can promote information sharing among employees and improve overall work efficiency. For example, employees can post information related to their work, and the AI ​​agent can analyze the posted content to pick out useful information. This improves work efficiency by allowing employees to share knowledge among themselves. Some or all of the above processes in the Integration Department may be performed using AI, or not using AI. For example, the Integration Department can input information posted by employees into the AI, and the AI ​​can pick out useful information and integrate it into manuals.

[0095] The observation unit can estimate the user's emotions and adjust the timing of observations based on the estimated emotions. For example, if the user is stressed, the observation unit can reduce the frequency of observations and conduct observations when the user is relaxed. For example, if the user is concentrating, the observation unit can interrupt observations and resume them when the user's concentration is broken. For example, if the user is tired, the observation unit can pause observations and resume them after a break. The observation unit can estimate the user's emotions and adjust the timing of observations based on the estimated emotions. By adjusting the timing of observations based on the user's emotions, observations can be conducted at more appropriate times. 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 observation unit may be performed using AI, for example, or without AI. For example, the observation unit can input user emotion data into an AI, which can estimate the emotions and adjust the timing of observations.

[0096] The observation unit can analyze the user's past work history and select the optimal observation method. For example, the observation unit can analyze patterns in the user's past work and select an efficient observation method. For example, the observation unit can determine the priority of observation for a specific task based on the user's past work history. For example, the observation unit can adjust the timing and frequency of observation based on the user's past work history. For example, the observation unit can analyze the user's past work history and select the optimal observation method. This allows the optimal observation method to be selected by analyzing the user's past work history. Some or all of the above processes in the observation unit may be performed using AI, for example, or without AI. For example, the observation unit can input the user's past work history into AI, which can then analyze the work history and select the optimal observation method.

[0097] The observation unit can filter data based on the user's current projects and areas of interest during observation. For example, the observation unit can observe only tasks related to the project the user is currently working on. The observation unit can narrow down the observation target based on the user's areas of interest. The observation unit can adjust the priority of observations according to the progress of the user's current projects. The observation unit can filter data based on the user's current projects and areas of interest during observation. This allows for efficient observation by narrowing down the observation target based on the user's current projects and areas of interest. Some or all of the above processing in the observation unit may be performed using AI, for example, or without AI. For example, the observation unit can input data on the user's current projects and areas of interest into the AI, which can then perform the filtering.

[0098] The observation unit can estimate the user's emotions and determine the priority of tasks to observe based on the estimated emotions. For example, if the user is stressed, the observation unit will prioritize observing less stressful tasks. For example, if the user is relaxed, the observation unit can prioritize observing important tasks. For example, if the user is tired, the observation unit can prioritize observing simple tasks. The observation unit can estimate the user's emotions and determine the priority of tasks to observe based on the estimated emotions. This allows for prioritizing the observation of more appropriate tasks based on 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 observation unit may be performed using AI, or not using AI. For example, the observation unit can input user emotion data into an AI, which can estimate the emotions and determine the priority of tasks to observe.

[0099] The observation unit can prioritize observing tasks that are highly relevant, taking into account the user's geographical location information during observation. For example, if the user is in the office, the observation unit will prioritize observing tasks performed within the office. For example, if the user is on a business trip, the observation unit can prioritize observing tasks performed at the business trip destination. For example, if the user is working remotely, the observation unit can prioritize observing tasks performed at home. The observation unit can prioritize observing tasks that are highly relevant, taking into account the user's geographical location information during observation. This allows for the priority observation of tasks that are highly relevant by considering the user's geographical location information. Some or all of the above processing in the observation unit may be performed using AI, for example, or without AI. For example, the observation unit can input the user's geographical location information into AI, which can identify highly relevant tasks and prioritize their observation.

[0100] The observation unit can analyze the user's social media activity and observe related tasks during observation. For example, the observation unit can observe tasks related to the work the user has shared on social media. For example, the observation unit can identify and observe tasks of interest from the user's social media activity. For example, the observation unit can observe tasks related to projects the user has mentioned on social media. For example, the observation unit can analyze the user's social media activity and observe related tasks during observation. This allows the observation of related tasks by analyzing the user's social media activity. Some or all of the above processing in the observation unit may be performed using AI, for example, or without AI. For example, the observation unit can input the user's social media activity into AI, which can then identify and observe related tasks.

[0101] The creation unit can estimate the user's emotions and adjust the way the manual is written based on those estimated emotions. For example, if the user is stressed, the creation unit may adopt a simple and easy-to-understand style of expression. For example, if the user is relaxed, the creation unit may adopt a style of expression that includes detailed explanations. For example, if the user is in a hurry, the creation unit may adopt a concise style of expression that gets straight to the point. The creation unit can estimate the user's emotions and adjust the way the manual is written based on those estimated emotions. This allows for the provision of a more appropriate manual by adjusting the way the manual is written based on 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 creation unit may be performed using AI, for example, or not using AI. For example, the creation unit can input user emotion data into AI, which can estimate the emotions and adjust the way the manual is written.

[0102] The creation unit can adjust the level of detail in a manual based on the importance of the task when creating it. For example, the creation unit can create a manual with detailed procedures for important tasks. For example, the creation unit can create a manual with concise procedures for less important tasks. The creation unit can adjust the level of detail in a manual in stages according to the importance of the task. For example, the creation unit can adjust the level of detail in a manual based on the importance of the task when creating it. This allows the creation unit to provide a manual with an appropriate level of detail by adjusting the level of detail based on the importance of the task. Some or all of the above processes in the creation unit may be performed using AI, for example, or not using AI. For example, the creation unit can input task importance data into AI, and the AI ​​can adjust the level of detail in the manual.

[0103] The creation unit can apply different creation algorithms depending on the category of work when creating manuals. For example, for data entry tasks, the creation unit can apply an algorithm that creates a manual including efficient input procedures. For example, for report creation tasks, the creation unit can apply an algorithm that creates a manual including accurate writing methods. For example, for customer service tasks, the creation unit can apply an algorithm that creates a manual including appropriate communication methods. The creation unit can apply different creation algorithms depending on the category of work when creating manuals. This allows for the provision of appropriate manuals by applying different creation algorithms depending on the category of work. Some or all of the above processing in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input business category data into AI, and the AI ​​can apply an appropriate creation algorithm.

[0104] The creation unit can estimate the user's emotions and adjust the length of the manual based on the estimated emotions. For example, if the user is in a hurry, the creation unit can create a short, concise manual. For example, if the user is relaxed, the creation unit can create a longer manual with detailed explanations. For example, if the user is excited, the creation unit can create a manual with visually stimulating effects. The creation unit can estimate the user's emotions and adjust the length of the manual based on the estimated emotions. This allows for the provision of more appropriate manuals by adjusting the length based on 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 creation unit may be performed using AI or not using AI. For example, the creation unit can input user emotion data into AI, which can estimate the emotions and adjust the length of the manual.

[0105] The creation unit can determine the priority of manuals based on the timing of the work when creating them. For example, the creation unit can prioritize creating manuals for tasks scheduled to be performed in the near future. For example, the creation unit can postpone creating manuals for tasks scheduled to be performed in the long term. The creation unit can adjust the order in which manuals are created according to the timing of the work. For example, the creation unit can determine the priority of manuals based on the timing of the work when creating them. This allows manuals to be provided at the appropriate time by determining the priority of manuals based on the timing of the work. Some or all of the above processes in the creation unit may be performed using AI, for example, or not using AI. For example, the creation unit can input work timing data into AI, and the AI ​​can determine the priority of manuals.

[0106] The creation unit can adjust the order of manuals based on the relevance of tasks when creating manuals. For example, the creation unit can create manuals consecutively for highly related tasks. For example, the creation unit can create separate manuals for less related tasks. The creation unit can adjust the order of manual creation according to the relevance of tasks. For example, the creation unit can adjust the order of manuals based on the relevance of tasks when creating manuals. This allows for the learning of highly related tasks consecutively by adjusting the order of manuals based on the relevance of tasks. Some or all of the above processing in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input task relevance data into AI, and the AI ​​can adjust the order of manuals.

[0107] The suggestion unit can estimate the user's emotions and adjust the way the suggestion is presented based on those emotions. For example, if the user is stressed, the suggestion unit may adopt a simple and easy-to-understand presentation. For example, if the user is relaxed, the suggestion unit may adopt a presentation that includes detailed explanations. For example, if the user is in a hurry, the suggestion unit may adopt a concise presentation that gets straight to the point. The suggestion unit can estimate the user's emotions and adjust the way the suggestion is presented based on those emotions. This allows for the provision of more appropriate suggestions by adjusting the presentation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into an AI, which can estimate the emotions and adjust the presentation of the suggestion.

[0108] The proposal department can adjust the level of detail in a proposal based on the importance of the task. For example, the proposal department can provide a proposal with detailed procedures for important tasks, and a proposal with concise procedures for less important tasks. The proposal department can adjust the level of detail in a stepwise manner according to the importance of the task. The proposal department can adjust the level of detail in a proposal based on the importance of the task at the time of proposal. This allows the proposal department to provide a proposal with the appropriate level of detail by adjusting the level of detail based on the importance of the task. Some or all of the above processing in the proposal department may be performed using AI, or not. For example, the proposal department can input task importance data into the AI, and the AI ​​can adjust the level of detail in the proposal.

[0109] The proposal department can apply different proposal algorithms depending on the category of work when making a proposal. For example, for data entry work, the proposal department can apply an algorithm that provides proposals including efficient input procedures. For example, for report creation work, the proposal department can apply an algorithm that provides proposals including accurate description methods. For example, for customer service work, the proposal department can apply an algorithm that provides proposals including appropriate communication methods. The proposal department can apply different proposal algorithms depending on the category of work when making a proposal. This allows for the provision of appropriate proposals by applying different proposal algorithms depending on the category of work. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input work category data into AI, and the AI ​​can apply an appropriate proposal algorithm.

[0110] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide a short, to-the-point suggestion. If the user is relaxed, the suggestion unit can provide a longer suggestion with detailed explanations. If the user is excited, the suggestion unit can provide a suggestion with visually stimulating effects. The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. This allows for the provision of more appropriate suggestions by adjusting the length of the suggestion based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into an AI, which can estimate the emotions and adjust the length of the suggestion.

[0111] The proposal department can determine the priority of proposals based on the timing of the work's implementation. For example, the proposal department will prioritize proposals for work scheduled to be implemented in the near future. For example, the proposal department can postpone proposals for work scheduled to be implemented in the long term. The proposal department can adjust the priority of proposals according to the timing of the work's implementation. For example, the proposal department can determine the priority of proposals based on the timing of the work's implementation at the time of proposal submission. This allows proposals to be provided at the appropriate time by determining the priority of proposals based on the timing of the work's implementation. Some or all of the above processing in the proposal department may be performed using AI, or not. For example, the proposal department can input work implementation timing data into AI, and the AI ​​can determine the priority of proposals.

[0112] The proposal unit can adjust the order of proposals based on the relevance of the tasks when making a proposal. For example, the proposal unit can make proposals consecutively for highly relevant tasks. For example, the proposal unit can make separate proposals for less relevant tasks. The proposal unit can adjust the order of proposals according to the relevance of the tasks. For example, the proposal unit can adjust the order of proposals based on the relevance of the tasks when making a proposal. This allows the system to learn highly relevant tasks consecutively by adjusting the order of proposals based on the relevance of the tasks. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input task relevance data into the AI, and the AI ​​can adjust the order of proposals.

[0113] The coaching unit can estimate the user's emotions and adjust the coaching method based on the estimated emotions. For example, if the user is stressed, the coaching unit may adopt a relaxing coaching method. For example, if the user is relaxed, the coaching unit may adopt a coaching method that includes detailed explanations. For example, if the user is in a hurry, the coaching unit may adopt a concise coaching method that gets straight to the point. The coaching unit can estimate the user's emotions and adjust the coaching method based on the estimated emotions. This allows for more appropriate coaching by adjusting the coaching method based on 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 coaching unit may be performed using AI, for example, or without AI. For example, the coaching department can input user emotional data into an AI, which can then estimate the emotions and adjust the coaching method accordingly.

[0114] The coaching department can analyze the user's past work performance during coaching to select the optimal coaching method. For example, the coaching department can select an effective coaching method based on the user's past work performance. For example, the coaching department can determine the priority of coaching for specific skills based on the user's past work performance. For example, the coaching department can analyze the user's past work performance and adjust the timing and frequency of coaching. For example, the coaching department can analyze the user's past work performance during coaching to select the optimal coaching method. This allows the optimal coaching method to be selected by analyzing the user's past work performance. Some or all of the above processes in the coaching department may be performed using AI, or not. For example, the coaching department can input the user's past work performance data into AI, and the AI ​​can select the optimal coaching method.

[0115] The coaching unit can customize the coaching methods based on the user's current skill level during coaching. For example, the coaching unit can select appropriate coaching methods according to the user's current skill level. For example, the coaching unit can adjust the content of the coaching based on the user's skill level. For example, the coaching unit can adjust the pace of the coaching according to the user's skill level. For example, the coaching unit can customize the coaching methods based on the user's current skill level during coaching. By customizing the coaching methods based on the user's current skill level, more appropriate coaching can be provided. Some or all of the above processes in the coaching unit may be performed using AI, for example, or without AI. For example, the coaching unit can input the user's skill level data into the AI, which can then select appropriate coaching methods.

[0116] The coaching unit can estimate the user's emotions and determine coaching priorities based on those estimated emotions. For example, if the user is stressed, the coaching unit will prioritize less stressful coaching. For example, if the user is relaxed, the coaching unit will prioritize coaching on important skills. For example, if the user is tired, the coaching unit will prioritize coaching on simple skills. The coaching unit can estimate the user's emotions and determine coaching priorities based on those estimated emotions. This allows for more appropriate coaching by prioritizing coaching based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 coaching unit may be performed using AI or not using AI. For example, the coaching unit can input user emotion data into AI, which can estimate emotions and determine coaching priorities.

[0117] The coaching department can select the optimal coaching method during coaching by considering the user's geographical location. For example, if the user is in the office, the coaching department can select a coaching method that can be conducted within the office. For example, if the user is on a business trip, the coaching department can select a coaching method that can be conducted at the business trip location. For example, if the user is working remotely, the coaching department can select a coaching method that can be conducted at home. The coaching department can select the optimal coaching method during coaching by considering the user's geographical location. This allows the coaching department to select the optimal coaching method by considering the user's geographical location. Some or all of the above processing in the coaching department may be performed using AI, for example, or without AI. For example, the coaching department can input the user's geographical location information into the AI, and the AI ​​can select the optimal coaching method.

[0118] The coaching department can analyze a user's social media activity during coaching sessions and propose coaching methods. For example, the coaching department can propose coaching related to skills the user has shared on social media. For example, the coaching department can identify skills of interest from a user's social media activity and propose coaching. For example, the coaching department can propose coaching related to skills the user has mentioned on social media. For example, the coaching department can analyze a user's social media activity during coaching sessions and propose coaching methods. This allows the coaching department to propose appropriate coaching methods by analyzing the user's social media activity. Some or all of the above processes in the coaching department may be performed using AI, for example, or not. For example, the coaching department can input the user's social media activity data into an AI, which can then propose appropriate coaching methods.

[0119] The generation unit can estimate the user's emotions and adjust the content of the training plan based on the estimated emotions. For example, if the user is stressed, the generation unit can provide a relaxing training plan. For example, if the user is relaxed, the generation unit can provide a detailed training plan. For example, if the user is in a hurry, the generation unit can provide a concise training plan that gets straight to the point. The generation unit can estimate the user's emotions and adjust the content of the training plan based on the estimated emotions. This allows for the provision of a more appropriate training plan by adjusting the content of the training plan based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI 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 an AI, which can estimate the emotions and adjust the content of the training plan.

[0120] The generation unit can analyze the user's past training history to select the optimal training plan when generating a training plan. For example, the generation unit can select an effective training plan based on the user's past training history. For example, the generation unit can determine the priority of training for a specific skill based on the user's past training history. For example, the generation unit can analyze the user's past training history and adjust the timing and frequency of training. For example, the generation unit can analyze the user's past training history to select the optimal training plan when generating a training plan. This allows the optimal training plan to be selected by analyzing the user's past training history. 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 user's past training history data into AI, and the AI ​​can select the optimal training plan.

[0121] The generation unit can customize the training plan based on the user's current work situation when generating the training plan. For example, the generation unit can select appropriate training methods according to the user's current work situation. For example, the generation unit can adjust the training content based on the user's work situation. For example, the generation unit can adjust the training progress speed according to the user's work situation. For example, the generation unit can customize the training plan based on the user's current work situation when generating the training plan. This allows for the provision of a more appropriate training plan by customizing the training methods based on the user's current work situation. 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 work situation data into AI, and the AI ​​can select appropriate training methods.

[0122] The generation unit can estimate the user's emotions and prioritize training plans based on those emotions. For example, if the user is stressed, the generation unit will prioritize less stressful training. For example, if the user is relaxed, the generation unit will prioritize training on important skills. For example, if the user is tired, the generation unit will prioritize training on easy skills. The generation unit can estimate the user's emotions and prioritize training plans based on those emotions. This allows for the provision of more appropriate training plans by prioritizing training plans based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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 generation unit may be performed using AI or not using AI. For example, the generation unit can input user emotion data into an AI, which can estimate the emotions and determine the priority of the training plan.

[0123] The generation unit can select the optimal training plan by considering the user's geographical location information when generating a training plan. For example, if the user is in the office, the generation unit can select a training plan that can be done in the office. For example, if the user is on a business trip, the generation unit can select a training plan that can be done at the business trip destination. For example, if the user is working remotely, the generation unit can select a training plan that can be done at home. The generation unit can select the optimal training plan by considering the user's geographical location information when generating a training plan. This allows for the selection of the optimal training plan by considering the user's geographical location information. 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 the user's geographical location information into the AI, and the AI ​​can select the optimal training plan.

[0124] The generation unit can analyze the user's social media activity and propose training plan methods when generating a training plan. For example, the generation unit can propose training plans related to skills shared by the user on social media. For example, the generation unit can identify skills of interest from the user's social media activity and propose training plans. For example, the generation unit can propose training plans related to skills mentioned by the user on social media. For example, the generation unit can analyze the user's social media activity and propose training plan methods when generating a training plan. This allows for the proposal of appropriate training plan methods by analyzing the user's social media activity. 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 the user's social media activity data into AI, which can then propose appropriate training plan methods.

[0125] The visualization unit can estimate the user's emotions and adjust the visualization method based on the estimated emotions. For example, if the user is stressed, the visualization unit can provide a simple and highly visible visualization method. For example, if the user is relaxed, the visualization unit can provide a visualization method that includes detailed information. For example, if the user is in a hurry, the visualization unit can provide a visualization method that gets straight to the point. The visualization unit can estimate the user's emotions and adjust the visualization method based on the estimated emotions. This allows for more appropriate visualizations to be provided by adjusting the visualization method based on 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 visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input user emotion data into AI, which can estimate the emotions and adjust the visualization method.

[0126] The visualization unit can analyze the user's past business workflow and select the optimal visualization method during visualization. For example, the visualization unit can select an effective visualization method based on the user's past business workflow. For example, the visualization unit can determine the visualization priority for a specific task from the user's past business workflow. For example, the visualization unit can analyze the user's past business workflow and adjust the timing and frequency of visualization. For example, the visualization unit can analyze the user's past business workflow and select the optimal visualization method during visualization. This allows the optimal visualization method to be selected by analyzing the user's past business workflow. Some or all of the above processes in the visualization unit may be performed using AI, or not. For example, the visualization unit can input the user's past business workflow data into AI, and the AI ​​can select the optimal visualization method.

[0127] The visualization unit can customize the visualization methods based on the user's current work situation during visualization. For example, the visualization unit can select an appropriate visualization method according to the user's current work situation. For example, the visualization unit can adjust the content of the visualization based on the user's work situation. For example, the visualization unit can adjust the speed of the visualization according to the user's work situation. For example, the visualization unit can customize the visualization methods based on the user's current work situation during visualization. By customizing the visualization methods based on the user's current work situation, a more appropriate visualization can be provided. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input user work situation data into AI, and the AI ​​can select an appropriate visualization method.

[0128] The visualization unit can estimate the user's emotions and determine visualization priorities based on the estimated emotions. For example, if the user is stressed, the visualization unit will prioritize visualizations that reduce stress. For example, if the user is relaxed, the visualization unit can prioritize visualizations of important information. For example, if the user is tired, the visualization unit can prioritize visualizations of simple information. The visualization unit can estimate the user's emotions and determine visualization priorities based on the estimated emotions. This allows for more appropriate visualizations to be provided by prioritizing visualizations based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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 visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit may input user emotion data into an AI, which may estimate the emotion and determine the visualization priority, but this is not limited to such examples. Some or all of the above-described processes in the visualization unit may be performed using an AI or not using an AI. For example, the visualization unit may input user emotion data into an AI, which may estimate the emotion and determine the visualization priority.

[0129] The visualization unit can select the optimal visualization method by considering the user's geographical location information during visualization. For example, if the user is in the office, the visualization unit can select a visualization method that can be performed within the office. For example, if the user is on a business trip, the visualization unit can select a visualization method that can be performed at the business trip destination. For example, if the user is working remotely, the visualization unit can select a visualization method that can be performed at home. The visualization unit can select the optimal visualization method by considering the user's geographical location information during visualization. This allows for the selection of the optimal visualization method by considering the user's geographical location information. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the user's geographical location information into the AI, and the AI ​​can select the optimal visualization method.

[0130] The visualization unit can analyze the user's social media activity and propose visualization methods during the visualization process. For example, the visualization unit can propose visualizations related to information shared by the user on social media. For example, the visualization unit can identify information of interest from the user's social media activity and propose visualizations. For example, the visualization unit can propose visualizations related to information mentioned by the user on social media. For example, the visualization unit can analyze the user's social media activity and propose visualization methods during the visualization process. This allows the visualization unit to propose appropriate visualization methods by analyzing the user's social media activity. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the user's social media activity data into AI, which can then propose appropriate visualization methods.

[0131] The integration unit can estimate the user's emotions and determine the priority of information to integrate based on the estimated emotions. For example, if the user is stressed, the integration unit will prioritize integrating less stressful information. For example, if the user is relaxed, the integration unit can prioritize integrating important information. For example, if the user is tired, the integration unit can prioritize integrating simple information. The integration unit can estimate the user's emotions and determine the priority of information to integrate based on the estimated emotions. This allows for the provision of more appropriate information by prioritizing the information to integrate based on 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 integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input user emotion data into an AI, which can estimate the emotions and determine the priority of information to integrate.

[0132] The integration unit can select the most relevant information by analyzing the user's past posting history during the integration process. For example, the integration unit can select effective information based on the user's past posting history. For example, the integration unit can determine the priority of integration for specific information based on the user's past posting history. For example, the integration unit can analyze the user's past posting history and adjust the timing and frequency of integration. For example, the integration unit can select the most relevant information by analyzing the user's past posting history during the integration process. This allows for the selection of optimal information by analyzing the user's past posting history. Some or all of the above-described processes in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the user's past posting history data into AI, which can then select the most relevant information.

[0133] The integration unit can customize the means of integration based on the user's current work situation during integration. For example, the integration unit can select an appropriate integration method according to the user's current work situation. For example, the integration unit can adjust the content of the integration based on the user's work situation. For example, the integration unit can adjust the speed of the integration process according to the user's work situation. For example, the integration unit can customize the means of integration based on the user's current work situation during integration. This allows for the provision of more appropriate information by customizing the means of integration based on the user's current work situation. Some or all of the above-described processes in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input user work situation data into AI, and the AI ​​can select an appropriate integration method.

[0134] The integration unit can estimate the user's emotions and adjust the display method of the integrated information based on the estimated user emotions. For example, if the user is stressed, the integration unit can provide a simple and highly visible display method. For example, if the user is relaxed, the integration unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the integration unit can provide a concise display method. The integration unit can estimate the user's emotions and adjust the display method of the integrated information based on the estimated emotions. This allows for the provision of more appropriate information by adjusting the display method of the integrated information based on 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 integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input user emotion data into AI, which can estimate emotions and adjust the display method of the integrated information.

[0135] The integration unit can select the most suitable information during integration, taking into account the user's geographical location. For example, if the user is in the office, the integration unit will prioritize integrating information that can be done within the office. For example, if the user is on a business trip, the integration unit can prioritize integrating information that can be done at the business trip destination. For example, if the user is working remotely, the integration unit can prioritize integrating information that can be done at home. The integration unit can select the most suitable information during integration, taking into account the user's geographical location. This allows for the selection of optimal information by considering the user's geographical location. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the user's geographical location into AI, which can then select the most suitable information.

[0136] The integration unit can analyze the user's social media activity and propose integration methods during integration. For example, the integration unit can propose integrations related to information shared by the user on social media. For example, the integration unit can identify information of interest from the user's social media activity and propose integrations. For example, the integration unit can propose integrations related to information mentioned by the user on social media. For example, the integration unit can analyze the user's social media activity and propose integration methods during integration. This allows the integration unit to propose appropriate integration methods by analyzing the user's social media activity. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the user's social media activity data into AI, which can then propose appropriate integration methods.

[0137] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0138] The business manual generation system not only observes the user's business processes and automatically creates the necessary manuals, but can also estimate the user's emotions and adjust the manual content based on those emotions. For example, if the user is stressed, it can provide a simple and easy-to-understand manual. If the user is relaxed, it can provide a manual with detailed explanations. Furthermore, if the user is in a hurry, it can provide a concise manual that gets straight to the point. In this way, by adjusting the manual content based on the user's emotions, a more appropriate manual can be provided.

[0139] The business manual generation system can analyze a user's past work history and select the optimal observation method when observing their business processes. For example, it can analyze patterns in tasks the user has performed in the past and select an efficient observation method. It can also determine the priority of observation for specific tasks based on the user's past work history. Furthermore, it can adjust the timing and frequency of observations based on the user's past work history. In this way, by analyzing the user's past work history, the optimal observation method can be selected.

[0140] The business manual generation system can estimate the user's emotions and adjust the timing of observations based on those estimates. For example, if a user is stressed, the frequency of observations can be reduced, and observations can be conducted when the user is relaxed. Also, if a user is concentrating, observations can be interrupted and resumed when their concentration breaks. Furthermore, if a user is tired, observations can be paused and resumed after a break. In this way, by adjusting the timing of observations based on the user's emotions, observations can be conducted at more appropriate times.

[0141] The business manual generation system allows the observation unit to filter data based on the user's current projects and areas of interest. For example, it can observe only tasks related to the project the user is currently working on. It can also narrow down the observation target based on the user's areas of interest. Furthermore, it can adjust the priority of observations according to the progress of the user's current project. This allows for efficient observation by narrowing down the observation target based on the user's current projects and areas of interest.

[0142] The business manual generation system can estimate the user's emotions and determine the priority of tasks to observe based on those emotions. For example, if the user is stressed, it can prioritize observing less stressful tasks. If the user is relaxed, it can prioritize observing important tasks. Furthermore, if the user is tired, it can prioritize observing simple tasks. By determining the priority of tasks to observe based on the user's emotions, it is possible to prioritize observing more appropriate tasks.

[0143] The business manual generation system's observation unit can prioritize observing highly relevant tasks by considering the user's geographical location. For example, if the user is in the office, tasks performed within the office can be prioritized. If the user is on a business trip, tasks performed at the destination can be prioritized. Furthermore, if the user is working remotely, tasks performed at home can be prioritized. In this way, by considering the user's geographical location, the system can prioritize observing highly relevant tasks.

[0144] The business manual generation system's observation unit can analyze users' social media activity and observe related tasks. For example, it can observe tasks related to work shared by users on social media. It can also identify and observe tasks of interest from users' social media activity. Furthermore, it can observe tasks related to projects mentioned by users on social media. In this way, by analyzing users' social media activity, it is possible to observe related tasks.

[0145] The business manual generation system can estimate the user's emotions and adjust the manual's expression based on those emotions. For example, if the user is stressed, a simple and easy-to-understand expression can be used. If the user is relaxed, a more detailed explanation can be included. Furthermore, if the user is in a hurry, a concise expression that gets straight to the point can be used. By adjusting the manual's expression based on the user's emotions, a more appropriate manual can be provided.

[0146] The business manual generation system allows the creation department to adjust the level of detail in the manual based on the importance of the task. For example, it can create manuals with detailed procedures for important tasks, and manuals with concise procedures for less important tasks. Furthermore, it can adjust the level of detail in the manual in stages according to the importance of the task. This allows the system to provide manuals with the appropriate level of detail by adjusting the level of detail based on the importance of the task.

[0147] The business manual generation system allows the creation department to apply different creation algorithms depending on the category of work when creating manuals. For example, for data entry tasks, an algorithm can be applied to create manuals that include efficient input procedures. For report creation tasks, an algorithm can be applied to create manuals that include accurate writing methods. Furthermore, for customer service tasks, an algorithm can be applied to create manuals that include appropriate communication methods. In this way, by applying different creation algorithms depending on the category of work, the system can provide appropriate manuals.

[0148] The following briefly describes the processing flow for example form 2.

[0149] Step 1: The observation team observes the user's business processes. For example, the observation team analyzes the user's workflow in detail and identifies which parts need to be documented. For example, the observation team can identify tasks that involve a lot of routine work, such as data entry or the creation of standardized reports. For example, the observation team can observe each step of the workflow in detail and identify the parts that need to be documented. Step 2: The creation unit automatically generates the necessary manuals based on the business processes observed by the observation unit. For example, the creation unit can automatically generate business manuals based on data provided by the observation unit. For example, the creation unit can create manuals corresponding to each step of a business flow. For example, the creation unit can optimize the content of the manuals to improve the efficiency and accuracy of operations. Step 3: The proposal department makes suggestions for improving operational efficiency and accuracy based on the manual created by the creation department. For example, the proposal department can analyze each step of the workflow in detail, eliminate redundant processes, and propose an optimal workflow. For example, the proposal department can improve operational efficiency by simplifying data entry procedures. For example, the proposal department can make specific suggestions to improve operational accuracy. For example, the proposal department can prevent input errors by introducing error checking during data entry. Step 4: The coaching department provides personalized coaching based on each employee's skill level and work performance. For example, the coaching department can analyze each employee's work performance in detail and generate individual training plans and manuals. For example, the coaching department can provide a data entry training plan to an employee with low data entry skills. For example, the coaching department can provide individual coaching to improve work efficiency and accuracy. Step 5: The generation unit generates individual training plans and manuals based on the coaching provided by the coaching unit. For example, the generation unit can automatically generate individual training plans and manuals based on data provided by the coaching unit. For example, the generation unit can provide training plans tailored to each employee's skill level and work performance. For example, the generation unit can optimize the content of training plans and manuals to improve work efficiency and accuracy. Step 6: The Visualization Unit provides a business flow visualization tool to make business manuals visually easy to understand. For example, the Visualization Unit can provide a tool to visually display business flows. For example, the Visualization Unit can provide a visual display to make business procedures intuitively understandable. For example, the Visualization Unit can optimize the visual display to improve the efficiency and accuracy of business operations. Step 7: The Integration Department provides a platform for employees to share knowledge, and an AI agent analyzes the posted content to pick out useful information and integrate it into manuals. For example, employees can post information related to their work, and the AI ​​agent can analyze the posted content to pick out useful information. For example, the Integration Department can update manuals based on the posted content to improve the efficiency and accuracy of operations. For example, the Integration Department can promote information sharing among employees and improve overall work efficiency.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] Each of the multiple elements described above, including the observation unit, creation unit, proposal unit, coaching unit, generation unit, visualization unit, and integration unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the observation unit observes the user's work process using the camera 42 and microphone 38B of the smart device 14, and the control unit 46A analyzes the work flow in detail. The creation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and automatically generates a work manual based on the data provided by the observation unit. The proposal unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and makes suggestions for improving work efficiency and accuracy. The coaching unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and provides personalized coaching based on each employee's skill level and work performance. The generation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and generates individual training plans and manuals based on the data provided by the coaching unit. The visualization unit is implemented, for example, by the control unit 46A of the smart device 14, and provides a business flow visualization tool. The integration unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and provides a platform for employees to share knowledge with each other. An AI agent analyzes the posted content, picks out useful information, and integrates it into a manual. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0154] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] Each of the multiple elements described above, including the observation unit, creation unit, proposal unit, coaching unit, generation unit, visualization unit, and integration unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the observation unit observes the user's work process using the camera 42 and microphone 238 of the smart glasses 214, and the control unit 46A analyzes the work flow in detail. The creation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and automatically generates a work manual based on the data provided by the observation unit. The proposal unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and makes suggestions for improving work efficiency and accuracy. The coaching unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and provides personalized coaching based on each employee's skill level and work performance. The generation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and generates individual training plans and manuals based on the data provided by the coaching unit. The visualization unit is implemented, for example, by the control unit 46A of the smart glasses 214, and provides a business flow visualization tool. The integration unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and provides a platform for employees to share knowledge with each other, where an AI agent analyzes the posted content, picks out useful information, and integrates it into a manual. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0170] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.).

[0182] 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.

[0183] 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.

[0184] 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.

[0185] Each of the multiple elements described above, including the observation unit, creation unit, proposal unit, coaching unit, generation unit, visualization unit, and integration unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the observation unit observes the user's work process using the camera 42 and microphone 238 of the headset terminal 314, and the control unit 46A analyzes the work flow in detail. The creation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and automatically generates a work manual based on the data provided by the observation unit. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and makes suggestions for improving work efficiency and accuracy. The coaching unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and provides personalized coaching based on each employee's skill level and work performance. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and generates individual training plans and manuals based on the data provided by the coaching unit. The visualization unit is implemented, for example, by the control unit 46A of the headset terminal 314, and provides a business flow visualization tool. The integration unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and provides a platform for employees to share knowledge with each other. An AI agent analyzes the posted content, picks out useful information, and integrates it into a manual. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0186] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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).

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.).

[0199] 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.

[0200] 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.

[0201] 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.

[0202] Each of the multiple elements described above, including the observation unit, creation unit, proposal unit, coaching unit, generation unit, visualization unit, and integration unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the observation unit observes the user's work process using the camera 42 and microphone 238 of the robot 414, and the control unit 46A analyzes the work flow in detail. The creation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and automatically generates a work manual based on the data provided by the observation unit. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and makes suggestions for improving work efficiency and accuracy. The coaching unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and provides personalized coaching based on each employee's skill level and work performance. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and generates individual training plans and manuals based on the data provided by the coaching unit. The visualization unit is implemented, for example, by the control unit 46A of the robot 414, and provides a business flow visualization tool. The integration unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and provides a platform for employees to share knowledge with each other. An AI agent analyzes the posted content, picks out useful information, and integrates it into a manual. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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."

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] (Note 1) An observation unit that observes the user's business processes, A creation unit that automatically creates the necessary manuals based on the business processes observed by the observation unit, Based on the manual created by the aforementioned creation department, the proposal department makes suggestions for improving work efficiency and accuracy. The coaching department provides personalized coaching based on each employee's skill level and work performance, A generation unit that generates individual training plans and manuals based on the coaching provided by the aforementioned coaching unit, The Visualization Department provides a business process visualization tool to make business manuals visually easier to understand, It provides a platform for employees to share knowledge among themselves, and includes an integration department where an AI agent analyzes posted content, picks out useful information, and integrates it into manuals. A system characterized by the following features. (Note 2) The observation unit is, We will conduct a detailed analysis of the user's workflow and identify which parts require manualization. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, We analyze each step of the business workflow in detail, eliminate redundant processes, and propose the optimal workflow. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We will make specific suggestions to improve the accuracy of our work. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned coaching department, We analyze each employee's work performance in detail and generate individual training plans and manuals. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned visualization unit, We provide a business process visualization tool to make business manuals visually easier to understand. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned integration unit is We provide a platform for employees to share knowledge, and an AI agent analyzes the posted content to pick out useful information and integrate it into manuals. The system described in Appendix 1, characterized by the features described herein. (Note 8) The observation unit is, The system estimates the user's emotions and adjusts the timing of observations based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The observation unit is, Analyze the user's past work history and select the optimal observation method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The observation unit is, During observation, 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 observation unit is, Estimate user emotions and prioritize tasks to observe based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The observation unit is, During observation, the system prioritizes observing highly relevant tasks, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The observation unit is, During observation, we analyze users' social media activity and observe related tasks. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned creation unit, The system estimates the user's emotions and adjusts the wording of the manual based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned creation unit, When creating manuals, adjust the level of detail based on the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned creation unit, When creating manuals, different creation algorithms are applied depending on the category of the work. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned creation unit, The system estimates the user's emotions and adjusts the length of the manual based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned creation unit, When creating manuals, prioritize the manuals based on when the tasks will be performed. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned creation unit, When creating manuals, adjust the order of the manuals based on the relevance of the tasks. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of the work. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When submitting proposals, prioritize them based on the timeline for when the work will be implemented. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on their relevance to the work. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned coaching department, It estimates the user's emotions and adjusts the coaching method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned coaching department, During coaching sessions, we analyze the user's past work performance to select the most suitable coaching method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned coaching department, During coaching sessions, the coaching methods are customized based on the user's current skill level. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned coaching department, It estimates the user's emotions and determines the priority of coaching based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned coaching department, During coaching sessions, the optimal coaching method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned coaching department, During coaching sessions, we analyze the user's social media activity and propose coaching strategies. The system described in Appendix 1, characterized by the features described herein. (Note 32) The generating unit is The system estimates the user's emotions and adjusts the training plan based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The generating unit is When generating a training plan, the system analyzes the user's past training history to select the optimal plan. The system described in Appendix 1, characterized by the features described herein. (Note 34) The generating unit is When generating a training plan, customize the plan's methods based on the user's current work situation. The system described in Appendix 1, characterized by the features described herein. (Note 35) The generating unit is It estimates the user's emotions and prioritizes training plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The generating unit is When generating a training plan, the system selects the optimal plan by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 37) The generating unit is When generating a training plan, the system analyzes the user's social media activity and suggests methods for developing the plan. The system described in Appendix 1, characterized by the features described herein. (Note 38) The visualization unit estimates the user's emotion and adjusts the visualization method based on the estimated user emotion The system according to appended claim 1, characterized in that (Appended claim 39) The visualization unit analyzes the user's past business processes at the time of visualization to select an optimal visualization method The system according to appended claim 1, characterized in that (Appended claim 40) The visualization unit customizes the visualization means based on the user's current business situation at the time of visualization The system according to appended claim 1, characterized in that (Appended claim 41) The visualization unit estimates the user's emotion and determines the priority of visualization based on the estimated user emotion The system according to appended claim 1, characterized in that (Appended claim 42) The visualization unit [[ID=3 (35)]]selects an optimal visualization method considering the user's geographical location information at the time of visualization [[ID=3 (37)]]The system according to appended claim 1, characterized in that (Appended claim 43) The visualization unit analyzes the user's social media activities at the time of visualization and proposes visualization means The system according to appended claim 1, characterized in that (Appended claim 44) The integration unit estimates the user's emotion and determines the priority of the information to be integrated based on the estimated user emotion The system according to appended claim 1, characterized in that [[ID=5 (54)]] (Appended claim 45) The integration unit analyzes the user's past posting history at the time of integration to select optimal information The system according to Appendix 1, characterized in that (Appendix 46) The integration unit Customizes the integration means based on the user's current business situation during integration The system according to Appendix 1, characterized in that (Appendix 47) The integration unit Estimates the user's emotions and adjusts the display method of the information to be integrated based on the estimated user's emotions The system according to Appendix 1, characterized in that (Appendix 48) The integration unit Selects the optimal information considering the user's geographical location information during integration The system according to Appendix 1, characterized in that (Appendix 49) The integration unit Analyzes the user's social media activities and proposes integration means during integration The system according to Appendix 1, characterized in that

Explanation of Signs

[0222] 10, 210, 310, 410 Data processing system 12 Data processing device 14 Smart device 214 Smart glasses 314 Headset-type terminal 414 Robot

Claims

1. An observation unit that observes the user's business processes, A creation unit that automatically creates the necessary manuals based on the business processes observed by the observation unit, Based on the manual created by the aforementioned creation department, the proposal department makes suggestions for improving work efficiency and accuracy. The coaching department provides personalized coaching based on each employee's skill level and work performance, A generation unit that generates individual training plans and manuals based on the coaching provided by the aforementioned coaching unit, The Visualization Department provides a business process visualization tool to make business manuals visually easier to understand, It provides a platform for employees to share knowledge among themselves, and includes an integration department where an AI agent analyzes posted content, picks out useful information, and integrates it into manuals. A system characterized by the following features.

2. The observation unit is, We will conduct a detailed analysis of the user's workflow and identify which parts require manualization. The system according to feature 1.

3. The aforementioned proposal section is, We analyze each step of the business workflow in detail, eliminate redundant processes, and propose the optimal workflow. The system according to feature 1.

4. The aforementioned proposal section is, We will make specific suggestions to improve the accuracy of our work. The system according to feature 1.

5. The aforementioned coaching department, We analyze each employee's work performance in detail and generate individual training plans and manuals. The system according to feature 1.

6. The aforementioned visualization unit, We provide a business process visualization tool to make business manuals visually easier to understand. The system according to feature 1.

7. The aforementioned integration unit is We provide a platform for employees to share knowledge, and an AI agent analyzes the posted content to pick out useful information and integrate it into manuals. The system according to feature 1.

8. The observation unit is, The system estimates the user's emotions and adjusts the timing of observations based on those emotions. The system according to feature 1.

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