system

Abstract

The system according to this embodiment aims to automatically advance the digital transformation of business processes. [Solution] The system according to the embodiment comprises a diagnostic unit, a proposal unit, an implementation support unit, and an effectiveness measurement unit. The diagnostic unit diagnoses business processes. The proposal unit proposes digital tools and automation solutions based on the business processes diagnosed by the diagnostic unit. The implementation support unit automatically implements the digital tools proposed by the proposal unit. The effectiveness measurement unit measures the effectiveness of the digital tools implemented by the implementation support unit.

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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult and time-consuming to efficiently promote the digital transformation of business processes.

[0005] The system according to the embodiment aims to automatically promote the digital transformation of business processes.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a diagnostic unit, a proposal unit, an implementation support unit, and an effectiveness measurement unit. The diagnostic unit diagnoses business processes. The proposal unit proposes digital tools and automation solutions based on the business processes diagnosed by the diagnostic unit. The implementation support unit automatically implements the digital tools proposed by the proposal unit. The effectiveness measurement unit measures the effectiveness of the digital tools implemented by the implementation support unit. [Effects of the Invention]

[0007] The system according to this embodiment can automatically advance the digital transformation of business processes. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The digital transformation (DX) support system according to an embodiment of the present invention is an AI-driven tool for companies to automatically advance digital transformation (DX). This DX support system is designed to provide consistent support from business process diagnosis to the execution of digitalization and effectiveness measurement, enabling companies to achieve DX without hassle. For example, the DX support system's business process diagnosis unit analyzes existing business processes and identifies priorities for digitalization and areas for improvement. Next, the DX support system's automation proposal unit proposes the most suitable digital tools and automation solutions for the business processes and supports their implementation. Furthermore, the DX support system's execution support unit automatically implements the proposed digital tools and performs necessary settings and customizations. Finally, the DX support system's effectiveness measurement unit monitors the progress of DX in real time, quantitatively measures the effects, and provides feedback on areas for improvement. In this way, it is designed to enable companies to achieve DX without hassle. In addition, the DX support system utilizes generative AI to provide the following functions. For example, the DX support system's data analysis unit automatically analyzes business data and provides insights indicating the need for digitalization. Next, the DX support system's process optimization unit optimizes business processes and automatically designs efficient flows. Furthermore, the DX support system's content generation unit automatically generates manuals and training content necessary for DX, supporting employee skill development. Finally, the DX support system's chatbot support unit responds to questions and concerns during the DX implementation process in real time, facilitating a smooth transition. In this way, the DX support system becomes a powerful tool for companies to automatically advance DX, achieving operational efficiency and improved competitiveness. As a result, the DX support system enables companies to achieve digital transformation without significant effort.

[0029] The DX support system according to this embodiment comprises a diagnostic unit, a proposal unit, an implementation support unit, and an effectiveness measurement unit. The diagnostic unit diagnoses business processes. For example, the diagnostic unit uses data analysis to understand the current state of business processes and identify areas for improvement. The diagnostic unit can also collect details of business processes through interviews and observations and derive diagnostic results. For example, the diagnostic unit analyzes each step of the business process in detail and identifies bottlenecks and inefficient parts. The proposal unit proposes digital tools and automation solutions based on the business processes diagnosed by the diagnostic unit. For example, the proposal unit uses AI to select the optimal digital tools and formulate an implementation plan. The proposal unit can also propose automation solutions necessary for improving business processes and support their implementation. For example, the proposal unit proposes robotic process automation (RPA) or AI-based automation tools suitable for automating business processes. The implementation support unit automatically implements the digital tools proposed by the proposal unit. For example, the implementation support unit uses AI to automatically configure and customize the digital tools. The implementation support unit can also provide post-implementation operational support and training. For example, the implementation support unit automatically performs the necessary settings for introducing digital tools and supports their operation after implementation. The effectiveness measurement unit measures the effectiveness of the digital tools introduced by the implementation support unit. The effectiveness measurement unit quantitatively evaluates the effectiveness of the digital tools, for example, by setting KPIs and calculating ROI. The effectiveness measurement unit can also collect user feedback and identify areas for improvement in the digital tools. For example, the effectiveness measurement unit evaluates the operational efficiency and cost reduction effects after the introduction of digital tools and provides feedback on areas for improvement. As a result, the DX support system according to this embodiment can consistently support everything from business process diagnosis to the execution of digitalization and effectiveness measurement.

[0030] The diagnostic department diagnoses business processes. For example, the diagnostic department uses data analysis to understand the current state of business processes and identify areas for improvement. Specifically, the diagnostic department collects data at each step of the business process and analyzes it using statistical methods and machine learning algorithms. This allows them to identify bottlenecks and inefficiencies in the business process. For example, they analyze processing time and error rates at each step of the business process to clarify the causes of delays and errors at specific steps. The diagnostic department can also gather detailed information about business processes through interviews and observations to derive diagnostic results. Interviews allow them to directly hear opinions from business personnel and understand the actual situation on the ground. Observations involve visiting the actual work site and observing the flow of work and work procedures in detail. This allows them to identify on-site issues and areas for improvement that cannot be seen through data analysis alone. Furthermore, the diagnostic department compiles the business process diagnostic results into a report and shares it with stakeholders. The report specifically describes the current problems and improvement proposals and is provided in a format that is easy for stakeholders to understand. This allows the diagnostic department to accurately understand the current state of business processes and make concrete improvement proposals.

[0031] The Proposal Department proposes digital tools and automation solutions based on the business processes diagnosed by the Diagnostic Department. For example, the Proposal Department uses AI to select the optimal digital tools and develop an implementation plan. Specifically, the Proposal Department performs AI-based data analysis to select the digital tools necessary for improving business processes. The AI ​​analyzes business process data and identifies the optimal digital tools and automation solutions. For example, it selects the optimal Robotic Process Automation (RPA) tool or AI-based automation tool based on data at each step of the business process. The Proposal Department also develops an implementation plan for the selected digital tools, determining the implementation schedule and resource allocation. Furthermore, the Proposal Department can propose and support the implementation of automation solutions necessary for improving business processes. For example, it proposes RPA tools or AI-based automation tools suitable for business process automation and provides specific procedures and configuration methods for implementation. This allows the Proposal Department to effectively propose and support the implementation of digital tools and automation solutions necessary for improving business processes.

[0032] The Implementation Support Department automatically implements the digital tools proposed by the Proposal Department. For example, the Implementation Support Department uses AI to automatically configure and customize digital tools. Specifically, the Implementation Support Department utilizes AI-based automation technology to configure and customize proposed digital tools. The AI ​​analyzes the information necessary for configuring and customizing digital tools and automatically performs optimal configurations and customizations. For example, to configure and customize an RPA tool, it analyzes business process data and automatically performs optimal configurations and customizations. Furthermore, the Implementation Support Department can also provide post-implementation operational support and training. For example, after the implementation of digital tools, it provides operational support, troubleshooting, and maintenance. It also provides training on how to use and configure digital tools to support users in effectively utilizing them. In this way, the Implementation Support Department can consistently handle everything from the implementation of digital tools to operational support, enabling users to effectively utilize digital tools.

[0033] The Effectiveness Measurement Department measures the effectiveness of digital tools introduced by the Implementation Support Department. For example, the Effectiveness Measurement Department quantitatively evaluates the effectiveness of digital tools through KPI setting and ROI calculation. Specifically, the Effectiveness Measurement Department collects data on business processes before and after the introduction of digital tools and evaluates their effectiveness based on KPIs and ROI. For example, it evaluates business process processing time, error rates, and cost reduction effects to quantitatively demonstrate the effectiveness of digital tools. The Effectiveness Measurement Department can also collect user feedback to identify areas for improvement in digital tools. User feedback is collected through surveys and interviews, providing specific details on the user experience and areas for improvement. This allows the Effectiveness Measurement Department to evaluate business efficiency and cost reduction effects after the introduction of digital tools and provide feedback on areas for improvement. Furthermore, the Effectiveness Measurement Department compiles the evaluation results into a report and shares it with stakeholders. The report specifically details the effectiveness of the digital tools and improvement suggestions, and is provided in a format that is easy for stakeholders to understand. This enables the Effectiveness Measurement Department to accurately evaluate the effectiveness of digital tools and make concrete improvement suggestions.

[0034] The Data Analysis Department automatically analyzes business data and provides insights that highlight the need for digitalization. For example, the Data Analysis Department collects business data such as sales data, customer data, and inventory data, and analyzes it using AI. For instance, it can analyze sales data to identify sales trends and seasonal fluctuations. It can also analyze customer data to understand customer purchasing behavior and preferences. Furthermore, it can analyze inventory data to optimize inventory and forecast demand. For example, it can forecast demand based on inventory data, providing insights to prevent inventory surpluses and shortages. This allows the Data Analysis Department to analyze business data and provide insights that highlight the need for digitalization. Some or all of the above processes in the Data Analysis Department may be performed using AI, or not. For example, the Data Analysis Department can input business data into AI, which then analyzes the data and generates insights.

[0035] The process optimization unit optimizes business processes and automatically designs efficient flows. For example, the process optimization unit uses process mining to understand the current state of business processes and identify areas for improvement for optimization. For example, the process optimization unit analyzes each step of the business process in detail to identify bottlenecks and inefficiencies. The process optimization unit can also use simulations to predict the effects of business process improvements and design optimal flows. For example, the process optimization unit simulates business processes and evaluates the effectiveness of improvement proposals. Furthermore, the process optimization unit can optimize business processes by applying best practices. For example, the process optimization unit improves business processes by referencing best practices from other companies and industries. This allows the process optimization unit to optimize business processes and design efficient flows. Some or all of the above processes in the process optimization unit may be performed using AI, or not. For example, the process optimization unit can input business process data into AI, which can then design the optimal flow.

[0036] The content generation unit automatically generates manuals and training content necessary for DX. For example, the content generation unit uses AI to automatically generate manuals such as operation manuals, FAQs, and troubleshooting guides. For instance, the content generation unit generates operation manuals based on detailed business processes, providing guidance to employees to smoothly carry out their work. The content generation unit can also automatically generate training content such as video tutorials, online courses, and workshop materials. For example, the content generation unit generates video tutorials based on training content for business processes, supporting employee skill improvement. This enables the content generation unit to automatically generate manuals and training content necessary for DX. Some or all of the above-described processes in the content generation unit may be performed using AI, or not. For example, the content generation unit can input business process data into AI, which can then generate manuals and training content.

[0037] The chatbot support unit responds in real time to questions and concerns during the DX implementation process. The chatbot support unit uses AI, for example, to respond quickly and accurately to user questions. For instance, it provides pre-prepared answers to common questions that arise during the DX implementation process. Furthermore, the chatbot support unit can use AI to generate appropriate answers to specific user questions. For example, it analyzes the user's question and generates an answer based on relevant information. This allows the chatbot support unit to respond in real time to questions and concerns during the DX implementation process. Some or all of the above-described processes in the chatbot support unit may be performed using AI, or not. For example, the chatbot support unit can input a user's question into AI, which can then generate an appropriate answer.

[0038] The diagnostic unit can improve the accuracy of its diagnosis by referring to historical business process data during the diagnosis process. For example, the diagnostic unit can analyze past business process data to identify frequently occurring problems and reflect them in the diagnosis. For example, the diagnostic unit can identify bottlenecks in business processes based on past data and suggest areas for improvement. The diagnostic unit can also predict future problems based on patterns obtained from historical data and include them in the diagnosis results. For example, the diagnostic unit can use past data to predict future business process problems and propose preventive measures. Furthermore, the diagnostic unit can use past data to provide detailed suggestions for improving specific business processes. For example, the diagnostic unit can provide specific suggestions for improving business processes based on past data. This allows the diagnostic unit to improve the accuracy of its diagnosis by referring to historical business process data. Some or all of the above processes in the diagnostic unit may be performed using AI, for example, or not. For example, the diagnostic unit can input past business process data into AI, which can analyze the data and generate the diagnosis results.

[0039] The diagnostic unit can apply different diagnostic algorithms to each category of business process during the diagnostic process. For example, the diagnostic unit can apply a diagnostic algorithm specialized in quality control to manufacturing processes. For instance, the diagnostic unit can perform a diagnosis based on manufacturing quality control data and suggest areas for improvement to enhance quality. The diagnostic unit can also apply a diagnostic algorithm that emphasizes customer satisfaction to service industry processes. For example, the diagnostic unit can perform a diagnosis based on service industry customer satisfaction data and suggest areas for improvement to enhance customer satisfaction. Furthermore, the diagnostic unit can apply a diagnostic algorithm that evaluates system performance to IT industry processes. For example, the diagnostic unit can perform a diagnosis based on IT industry system performance data and suggest system optimization proposals. This allows the diagnostic unit to apply different diagnostic algorithms to each category of business process. Some or all of the above processes in the diagnostic unit may be performed using AI, for example, or not. For example, the diagnostic unit can input business process category data into AI, which can then apply the most appropriate diagnostic algorithm for each category.

[0040] The diagnostics unit can perform diagnostics while considering the geographical distribution of business processes. For example, the diagnostics unit can compare business processes at geographically different locations and perform diagnostics that take into account the characteristics of each region. For example, the diagnostics unit can collect business process data from each region and analyze the characteristics of each region. The diagnostics unit can also identify problems that are likely to occur in specific regions based on geographical distribution and reflect them in the diagnostics. For example, the diagnostics unit can identify problems that frequently occur in specific regions and propose improvement plans tailored to those regions. Furthermore, the diagnostics unit can propose optimal improvement measures considering geographical factors. For example, the diagnostics unit can propose business process optimization plans considering geographical factors such as logistics routes and customer distribution. In this way, the diagnostics unit can perform diagnostics while considering the geographical distribution of business processes. Some or all of the above processes in the diagnostics unit may be performed using AI, for example, or not. For example, the diagnostics unit can input geographical distribution data into AI, and the AI ​​can analyze the data and generate diagnostic results.

[0041] The diagnostic unit can improve the accuracy of its diagnosis by referring to relevant literature on business processes during the diagnostic process. For example, the diagnostic unit can refer to the latest research papers and reflect areas for improvement in business processes in its diagnosis. For example, the diagnostic unit can propose improvements to business processes based on relevant academic papers. The diagnostic unit can also refer to industry best practices and include them in the diagnostic results. For example, the diagnostic unit can propose optimizations for business processes based on industry best practices. Furthermore, the diagnostic unit can optimize its diagnostic algorithm based on insights gained from relevant literature. For example, the diagnostic unit can improve the accuracy of its diagnosis by refining its diagnostic algorithm based on relevant literature. In this way, the diagnostic unit can improve the accuracy of its diagnosis by referring to relevant literature on business processes. Some or all of the above processes in the diagnostic unit may be performed using AI, for example, or not using AI. For example, the diagnostic unit can input relevant literature data into AI, and the AI ​​can analyze the data and generate diagnostic results.

[0042] The proposal department can adjust the level of detail in its proposals based on the importance of the digital tools. For example, it can provide detailed explanations and implementation procedures for high-importance digital tools. For instance, it can provide detailed explanations of the functions and benefits of high-importance digital tools and step-by-step implementation procedures. Alternatively, it can provide concise explanations and basic implementation procedures for less important digital tools. For example, it can provide a concise overview of less important digital tools and basic implementation procedures. Furthermore, the proposal department can adjust the priority of proposals according to their importance. For example, it can prioritize proposals for high-importance digital tools and postpone proposals for less important tools. This allows the proposal department to adjust the level of detail in its proposals based on the importance of the digital tools. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input digital tool importance data into AI, which can then adjust the level of detail in its proposals based on importance.

[0043] The proposal department can apply different proposal algorithms depending on the category of the digital tool during the proposal process. For example, for productivity tools, the proposal department can apply a proposal algorithm specifically focused on efficiency. For instance, the proposal department can highlight the functions and benefits of the productivity tools and provide specific implementation procedures for efficiency. Furthermore, for communication tools, the proposal department can apply a proposal algorithm that emphasizes team collaboration. For instance, the proposal department can highlight the functions and benefits of the communication tools and provide implementation procedures that help improve team collaboration. Additionally, for security tools, the proposal department can apply a proposal algorithm that emphasizes risk management. For instance, the proposal department can highlight the functions and benefits of the security tools and provide specific implementation procedures for risk management. This allows the proposal department to apply different proposal algorithms depending on the category of the digital tool. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input digital tool category data into the AI, which can then apply the most suitable proposal algorithm for each category.

[0044] The proposal department can prioritize proposals based on the timing of digital tool implementation. For example, it will prioritize proposals for tools that require immediate implementation. For instance, it will highlight the functions and benefits of digital tools that require immediate implementation and provide quick implementation procedures. Conversely, it can postpone proposals for tools that can be implemented later. For example, it will provide a brief overview of digital tools that can be implemented later and provide basic implementation procedures. Furthermore, the proposal department can adjust the level of detail in proposals depending on the implementation timing. For example, it will use detailed and concise explanations depending on the implementation timing. This allows the proposal department to prioritize proposals based on the timing of digital tool implementation. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input digital tool implementation timing data into AI, which can then prioritize proposals based on the implementation timing.

[0045] The proposal department can adjust the order of proposals based on the relevance of the digital tools. For example, the proposal department may prioritize proposing the tools most relevant to the business process. For example, the proposal department may prioritize proposing the digital tools most relevant to improving the business process. The proposal department may also postpone proposing less relevant tools. For example, the proposal department may provide a brief overview of less relevant digital tools and basic implementation procedures. Furthermore, the proposal department may adjust the level of detail of proposals according to their relevance. For example, the proposal department may provide detailed explanations for highly relevant tools and brief explanations for less relevant tools. This allows the proposal department to adjust the order of proposals based on the relevance of the digital tools. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department may input relevance data of the digital tools into AI, which can then adjust the order of proposals based on their relevance.

[0046] The Execution Support Department can select the optimal execution support method by referring to past implementation data during the execution support process. For example, the Execution Support Department can analyze past implementation data and select the most effective execution support method. For example, the Execution Support Department can identify successful cases based on past data and use those methods as a reference for execution support. The Execution Support Department can also propose execution support methods based on successful cases obtained from implementation data. For example, the Execution Support Department can propose the optimal execution support method for a specific business process based on past successful cases. Furthermore, the Execution Support Department can select the optimal execution support method for a specific business process using past data. For example, the Execution Support Department can present specific improvement proposals for business processes based on past data. This allows the Execution Support Department to select the optimal execution support method by referring to past implementation data. Some or all of the above processes in the Execution Support Department may be performed using AI, for example, or not. For example, the Execution Support Department can input past implementation data into AI, and the AI ​​can analyze the data to select the optimal execution support method.

[0047] The Execution Support Unit can customize the means of execution support based on the current status of the business process during execution support. For example, the Execution Support Unit can analyze the current status of the business process and provide the optimal means of execution support. For example, the Execution Support Unit can select the optimal means of execution support based on the current business process data. The Execution Support Unit can also adjust the means of execution support according to the progress of the business process. For example, the Execution Support Unit can provide the necessary support based on the progress data of the business process. Furthermore, the Execution Support Unit can customize the means of execution support based on the current situation. For example, the Execution Support Unit can grasp the current status of the business process and provide the optimal support means. This allows the Execution Support Unit to customize the means of execution support based on the current status of the business process. Some or all of the above processes in the Execution Support Unit may be performed using AI, for example, or without using AI. For example, the Execution Support Unit can input current status data of the business process into AI, and the AI ​​can analyze the data and select the optimal means of execution support.

[0048] The Execution Support Department can select the optimal execution support method when providing execution support, taking into account the geographical distribution of business processes. For example, the Execution Support Department can compare business processes at geographically different locations and provide execution support that takes into account the characteristics of each region. For example, the Execution Support Department can collect business process data from each region and analyze the characteristics of each region. The Execution Support Department can also identify problems that are likely to occur in specific regions based on geographical distribution and reflect them in the execution support. For example, the Execution Support Department can identify problems that frequently occur in specific regions and provide support tailored to those regions. Furthermore, the Execution Support Department can select the optimal execution support method by taking geographical factors into account. For example, the Execution Support Department can propose business process optimization plans by taking into account geographical factors such as logistics routes and customer distribution. This allows the Execution Support Department to select the optimal execution support method by taking into account the geographical distribution of business processes. Some or all of the above processes in the Execution Support Department may be performed using AI, for example, or not. For example, the Execution Support Department can input geographical distribution data into AI, and the AI ​​can analyze the data to select the optimal execution support method.

[0049] The Execution Support Department can improve the accuracy of its execution support by referring to relevant literature on business processes during the execution support process. For example, the Execution Support Department can refer to the latest research papers and reflect improvements to business processes in its execution support. For example, the Execution Support Department can present business process improvement proposals based on relevant academic papers. The Execution Support Department can also refer to industry best practices and include them in its execution support. For example, the Execution Support Department can present business process optimization proposals based on industry best practices. Furthermore, the Execution Support Department can optimize its execution support methods based on insights gained from relevant literature. For example, the Execution Support Department can improve its execution support methods based on relevant literature and enhance the accuracy of its execution support. This allows the Execution Support Department to improve the accuracy of its execution support by referring to relevant literature on business processes. Some or all of the above processes in the Execution Support Department may be performed using AI, for example, or without AI. For example, the Execution Support Department can input relevant literature data into AI, and the AI ​​can analyze the data to optimize the execution support methods.

[0050] The effectiveness measurement unit can predict current effects by referring to past effectiveness data during effectiveness measurement. For example, the effectiveness measurement unit can analyze past effectiveness data to predict current effects. For example, the effectiveness measurement unit can identify effectiveness trends based on past data and predict current effects. The effectiveness measurement unit can also predict future effects based on patterns obtained from effectiveness data. For example, the effectiveness measurement unit can use past data to predict future effects and suggest areas for improvement. Furthermore, the effectiveness measurement unit can use past data to predict the effectiveness of specific business processes in detail. For example, the effectiveness measurement unit can specifically predict the effectiveness of business processes based on past data. This allows the effectiveness measurement unit to predict current effects by referring to past effectiveness data. Some or all of the above processes in the effectiveness measurement unit may be performed using AI, for example, or without AI. For example, the effectiveness measurement unit can input past effectiveness data into AI, and the AI ​​can analyze the data to predict current effects.

[0051] The effectiveness measurement unit can apply different effectiveness measurement methods to each category of business process during effectiveness measurement. For example, the effectiveness measurement unit can apply an effectiveness measurement method specialized in quality control to manufacturing processes. For instance, the effectiveness measurement unit can perform effectiveness measurement based on manufacturing quality control data and suggest areas for improvement to enhance quality. The effectiveness measurement unit can also apply an effectiveness measurement method that emphasizes customer satisfaction to service industry processes. For example, the effectiveness measurement unit can perform effectiveness measurement based on service industry customer satisfaction data and suggest areas for improvement to enhance customer satisfaction. Furthermore, the effectiveness measurement unit can apply an effectiveness measurement method that evaluates system performance to IT industry processes. For example, the effectiveness measurement unit can perform effectiveness measurement based on IT industry system performance data and suggest system optimization proposals. This allows the effectiveness measurement unit to apply different effectiveness measurement methods to each category of business process. Some or all of the above processing in the effectiveness measurement unit may be performed using AI, for example, or without AI. For example, the effectiveness measurement unit can input business process category data into AI, and the AI ​​can apply the most appropriate effectiveness measurement method for each category.

[0052] The effectiveness measurement unit can analyze changes in effectiveness based on the implementation timing of business processes during effectiveness measurement. For example, the effectiveness measurement unit can analyze changes in effectiveness based on the implementation timing and identify areas for improvement. For example, the effectiveness measurement unit can compare effectiveness data for each implementation timing and analyze changes in effectiveness. The effectiveness measurement unit can also identify the optimal implementation timing based on effectiveness data for each implementation timing. For example, the effectiveness measurement unit can analyze data for each implementation timing and identify the optimal implementation timing. Furthermore, the effectiveness measurement unit can adjust the effectiveness measurement method according to the implementation timing. For example, the effectiveness measurement unit optimizes the effectiveness measurement method according to the implementation timing. This allows the effectiveness measurement unit to analyze changes in effectiveness based on the implementation timing of business processes. Some or all of the above processes in the effectiveness measurement unit may be performed using AI, for example, or without AI. For example, the effectiveness measurement unit can input implementation timing data into AI, and the AI ​​can analyze the data and predict changes in effectiveness.

[0053] The effectiveness measurement unit can analyze the effectiveness of business processes by referring to relevant market data during effectiveness measurement. For example, the effectiveness measurement unit can analyze the effectiveness of business processes by referring to relevant market data. For example, the effectiveness measurement unit can analyze effectiveness based on market research reports and competitive analysis data. The effectiveness measurement unit can also optimize the effectiveness measurement results based on insights gained from market data. For example, the effectiveness measurement unit can improve the effectiveness measurement method based on market data to improve the accuracy of effectiveness measurement. Furthermore, the effectiveness measurement unit can also analyze the effectiveness of business processes in detail using relevant market data. For example, the effectiveness measurement unit can specifically analyze the effectiveness of business processes based on market data. This allows the effectiveness measurement unit to analyze effectiveness by referring to relevant market data of business processes. Some or all of the above processes in the effectiveness measurement unit may be performed using AI, for example, or not using AI. For example, the effectiveness measurement unit can input relevant market data into AI, and the AI ​​can analyze the data and predict effectiveness.

[0054] The data analysis department can optimize its analysis algorithms by referring to historical data during data analysis. For example, the data analysis department can analyze historical data and select the optimal analysis algorithm. For example, the data analysis department can select an analysis algorithm based on historical data to improve the accuracy of data analysis. The data analysis department can also optimize its analysis algorithms based on patterns obtained from the data. For example, the data analysis department can improve the accuracy of data analysis by refining the analysis algorithm based on historical data. Furthermore, the data analysis department can use historical data to select the optimal analysis algorithm for a specific business process. For example, the data analysis department selects the optimal analysis algorithm for a business process based on historical data. This allows the data analysis department to optimize its analysis algorithms by referring to historical data. Some or all of the above processes in the data analysis department may be performed using AI, for example, or without AI. For example, the data analysis department can input historical data into AI, and the AI ​​can analyze the data and select the optimal analysis algorithm.

[0055] The Data Analysis Department can apply different analytical methods to each category of business process during data analysis. For example, the Data Analysis Department can apply analytical methods specialized in quality control to manufacturing processes. For instance, it can analyze manufacturing quality control data and suggest areas for improvement to enhance quality. Furthermore, the Data Analysis Department can apply analytical methods that emphasize customer satisfaction to service industry processes. For example, it can analyze service industry customer satisfaction data and suggest areas for improvement to enhance customer satisfaction. Additionally, the Data Analysis Department can apply analytical methods that evaluate system performance to IT industry processes. For example, it can analyze IT industry system performance data and suggest system optimization proposals. This allows the Data Analysis Department to apply different analytical methods to each category of business process. Some or all of the above-described processes in the Data Analysis Department may be performed using AI, or not. For example, the Data Analysis Department can input business process category data into an AI, which can then apply the most appropriate analytical method for each category.

[0056] The data analysis department can perform data analysis while considering the geographical distribution of business processes. For example, the data analysis department can compare business processes at geographically different locations and perform data analysis that takes into account the characteristics of each region. For example, the data analysis department can collect business process data from each region and analyze the characteristics of each region. The data analysis department can also identify problems that are likely to occur in specific regions based on geographical distribution and reflect this in the data analysis. For example, the data analysis department can identify problems that occur frequently in specific regions and perform analysis specific to those regions. Furthermore, the data analysis department can select the optimal data analysis method by considering geographical factors. For example, the data analysis department can propose optimization plans for business processes by considering geographical factors such as logistics routes and customer distribution. This allows the data analysis department to perform analysis while considering the geographical distribution of business processes. Some or all of the above processes in the data analysis department may be performed using AI, for example, or not. For example, the data analysis department can input geographical distribution data into AI, and the AI ​​can analyze the data and select the optimal analysis method.

[0057] The data analysis department can improve the accuracy of its analysis by referring to relevant literature on business processes during data analysis. For example, the data analysis department can refer to the latest research papers and reflect improvements to business processes in its data analysis. For example, the data analysis department can propose improvements to business processes based on relevant academic papers. The data analysis department can also refer to industry best practices and include them in its data analysis. For example, the data analysis department can propose optimizations for business processes based on industry best practices. Furthermore, the data analysis department can optimize its data analysis methods based on insights gained from relevant literature. For example, the data analysis department can improve its data analysis methods based on relevant literature and increase the accuracy of its data analysis. In this way, the data analysis department can improve the accuracy of its analysis by referring to relevant literature on business processes. Some or all of the above processes in the data analysis department may be performed using AI, for example, or not using AI. For example, the data analysis department can input relevant literature data into AI, and the AI ​​can analyze the data and select the optimal analysis method.

[0058] The process optimization unit can optimize the optimization algorithm by referring to past process data during process optimization. For example, the process optimization unit can analyze past process data and select the optimal optimization algorithm. For example, the process optimization unit can select an optimization algorithm based on past data to improve the accuracy of process optimization. The process optimization unit can also optimize the optimization algorithm based on patterns obtained from the data. For example, the process optimization unit can improve the optimization algorithm based on past data to improve the accuracy of process optimization. Furthermore, the process optimization unit can use past data to select the optimal optimization algorithm for a specific business process. For example, the process optimization unit selects the optimal optimization algorithm for a business process based on past data. This allows the process optimization unit to optimize the optimization algorithm by referring to past process data. Some or all of the above-described processes in the process optimization unit may be performed using AI, for example, or without using AI. For example, the process optimization unit can input past process data into AI, and the AI ​​can analyze the data to select the optimal optimization algorithm.

[0059] The process optimization unit can apply different optimization methods to each category of business process during process optimization. For example, the process optimization unit can apply an optimization method specialized in quality control to manufacturing processes. For instance, the process optimization unit can perform optimization based on manufacturing quality control data and suggest improvements for quality enhancement. The process optimization unit can also apply an optimization method that emphasizes customer satisfaction to service industry processes. For example, the process optimization unit can perform optimization based on service industry customer satisfaction data and suggest improvements for customer satisfaction enhancement. Furthermore, the process optimization unit can apply an optimization method that evaluates system performance to IT industry processes. For example, the process optimization unit can perform optimization based on IT industry system performance data and suggest system optimization proposals. This allows the process optimization unit to apply different optimization methods to each category of business process. Some or all of the above processing in the process optimization unit may be performed using AI, for example, or without AI. For example, the process optimization unit can input business process category data into AI, and the AI ​​can apply the most suitable optimization method for each category.

[0060] The process optimization unit can perform process optimization while considering the geographical distribution of business processes. For example, the process optimization unit can compare business processes at geographically different locations and perform process optimization considering the characteristics of each region. For example, the process optimization unit can collect business process data from each region and analyze the characteristics of each region. The process optimization unit can also identify problems that are likely to occur in a particular region based on geographical distribution and reflect them in process optimization. For example, the process optimization unit can identify problems that frequently occur in a particular region and perform optimization specific to that region. Furthermore, the process optimization unit can select the optimal process optimization method by considering geographical factors. For example, the process optimization unit can propose business process optimization plans considering geographical factors such as logistics routes and customer distribution. This allows the process optimization unit to perform optimization while considering the geographical distribution of business processes. Some or all of the above processing in the process optimization unit may be performed using AI, for example, or without AI. For example, the process optimization unit can input geographical distribution data into AI, and the AI ​​can analyze the data and select the optimal process optimization method.

[0061] The process optimization unit can improve the accuracy of process optimization by referring to relevant literature on business processes during the optimization process. For example, the process optimization unit can refer to the latest research papers and reflect improvements to business processes in the process optimization. For example, the process optimization unit can present business process improvement proposals based on relevant academic papers. The process optimization unit can also refer to industry best practices and include them in the process optimization. For example, the process optimization unit can present business process optimization proposals based on industry best practices. Furthermore, the process optimization unit can optimize process optimization methods based on insights gained from relevant literature. For example, the process optimization unit can improve process optimization methods based on relevant literature and improve the accuracy of process optimization. In this way, the process optimization unit can improve the accuracy of optimization by referring to relevant literature on business processes. Some or all of the above processing in the process optimization unit may be performed using AI, for example, or without AI. For example, the process optimization unit can input relevant literature data into AI, and the AI ​​can analyze the data and select the optimal process optimization method.

[0062] The content generation unit can optimize its generation algorithm by referring to past content data during content generation. For example, the content generation unit can analyze past content data and select the optimal generation algorithm. For example, the content generation unit can select a generation algorithm based on past data to improve the accuracy of content generation. The content generation unit can also optimize its generation algorithm based on patterns obtained from data. For example, the content generation unit can improve the generation algorithm based on past data to improve the accuracy of content generation. Furthermore, the content generation unit can use past data to select the optimal generation algorithm for a specific business process. For example, the content generation unit can select the optimal generation algorithm for a business process based on past data. This allows the content generation unit to optimize its generation algorithm by referring to past content data. Some or all of the above processes in the content generation unit may be performed using AI, for example, or without AI. For example, the content generation unit can input past content data into AI, which can analyze the data and select the optimal generation algorithm.

[0063] The content generation unit can apply different generation methods to each category of business process when generating content. For example, the content generation unit can apply a content generation method specialized in quality control to manufacturing processes. For instance, it can generate content based on manufacturing quality control data and provide guidance for quality improvement. The content generation unit can also apply a content generation method that emphasizes customer satisfaction to service industry processes. For example, it can generate content based on service industry customer satisfaction data and provide guidance for improving customer satisfaction. Furthermore, the content generation unit can apply a content generation method that evaluates system performance to IT industry processes. For example, it can generate content based on IT industry system performance data and provide system optimization suggestions. This allows the content generation unit to apply different generation methods to each category of business process. Some or all of the above-described processes in the content generation unit may be performed using AI, or not. For example, the content generation unit can input business process category data into AI, which can then apply the most suitable generation method for each category.

[0064] The content generation unit can generate content while considering the geographical distribution of business processes. For example, the content generation unit can compare business processes at geographically different locations and generate content that takes into account the characteristics of each region. For example, the content generation unit can collect business process data from each region and analyze the characteristics of each region. The content generation unit can also identify problems that are likely to occur in a particular region based on geographical distribution and reflect them in the content. For example, the content generation unit can identify problems that frequently occur in a particular region and generate content that is specific to that region. Furthermore, the content generation unit can select the optimal content generation method by considering geographical factors. For example, the content generation unit can propose optimizations for business processes by considering geographical factors such as logistics routes and customer distribution. This allows the content generation unit to generate content while considering the geographical distribution of business processes. Some or all of the above processing in the content generation unit may be performed using AI, for example, or without AI. For example, the content generation unit can input geographical distribution data into AI, and the AI ​​can analyze the data and select the optimal content generation method.

[0065] The content generation unit can improve the accuracy of content generation by referring to relevant literature on business processes during content generation. For example, the content generation unit can refer to the latest research papers and reflect improvements to business processes in the content. For example, the content generation unit can present business process improvement proposals based on relevant academic papers. The content generation unit can also refer to industry best practices and include them in the content. For example, the content generation unit can present business process optimization proposals based on industry best practices. Furthermore, the content generation unit can optimize the content generation method based on insights gained from relevant literature. For example, the content generation unit can improve the accuracy of content generation by refining the content generation method based on relevant literature. This allows the content generation unit to improve the accuracy of content generation by referring to relevant literature on business processes. Some or all of the above processes in the content generation unit may be performed using AI, for example, or without AI. For example, the content generation unit can input relevant literature data into AI, which can analyze the data and select the optimal content generation method.

[0066] The chatbot support unit can optimize its response algorithm by referring to past response data when the chatbot responds. For example, the chatbot support unit can analyze past response data and select the optimal response algorithm. For example, the chatbot support unit can select a response algorithm based on past data to improve the accuracy of responses. The chatbot support unit can also optimize its response algorithm based on patterns obtained from the data. For example, the chatbot support unit can improve the response algorithm based on past data to improve the accuracy of responses. Furthermore, the chatbot support unit can use past data to select the optimal response algorithm for a specific business process. For example, the chatbot support unit can select the optimal response algorithm for a business process based on past data. This allows the chatbot support unit to optimize its response algorithm by referring to past response data. Some or all of the above processes in the chatbot support unit may be performed using AI, for example, or without AI. For example, the chatbot support unit can input past response data into AI, which can analyze the data and select the optimal response algorithm.

[0067] The chatbot support unit can apply different response methods depending on the category of business process when responding to chatbot inquiries. For example, the chatbot support unit can apply a response method specialized in quality control to manufacturing processes. For instance, it can respond based on manufacturing quality control data and provide guidance for quality improvement. The chatbot support unit can also apply a response method that emphasizes customer satisfaction to service industry processes. For example, it can respond based on service industry customer satisfaction data and provide guidance for improving customer satisfaction. Furthermore, the chatbot support unit can apply a response method that evaluates system performance to IT industry processes. For example, it can respond based on IT industry system performance data and provide system optimization suggestions. This allows the chatbot support unit to apply different response methods depending on the category of business process. Some or all of the above processing in the chatbot support unit may be performed using AI, or not. For example, the chatbot support unit can input business process category data into AI, which can then apply the most appropriate response method for each category.

[0068] The chatbot support unit can provide responses that take into account the geographical distribution of business processes. For example, the chatbot support unit can compare business processes at geographically different locations and provide responses that take into account the characteristics of each region. For example, the chatbot support unit can collect business process data for each region and analyze the characteristics of each region. The chatbot support unit can also identify problems that are likely to occur in a particular region based on geographical distribution and reflect them in its responses. For example, the chatbot support unit can identify problems that frequently occur in a particular region and provide responses specific to that region. Furthermore, the chatbot support unit can select the optimal response method by taking geographical factors into consideration. For example, the chatbot support unit can propose business process optimization plans by taking geographical factors such as logistics routes and customer distribution into consideration. This allows the chatbot support unit to provide responses that take into account the geographical distribution of business processes. Some or all of the above processing in the chatbot support unit may be performed using AI, for example, or not. For example, the chatbot support unit can input geographical distribution data into AI, and the AI ​​can analyze the data and select the optimal response method.

[0069] The chatbot support unit can improve the accuracy of its responses by referring to relevant literature on business processes when responding to chatbot inquiries. For example, the chatbot support unit can refer to the latest research papers and reflect improvements to business processes in its responses. For example, the chatbot support unit can propose improvements to business processes based on relevant academic papers. The chatbot support unit can also refer to industry best practices and include them in its responses. For example, the chatbot support unit can propose optimizations for business processes based on industry best practices. Furthermore, the chatbot support unit can optimize its response methods based on insights gained from relevant literature. For example, the chatbot support unit can improve its response methods based on relevant literature and increase the accuracy of its responses. This allows the chatbot support unit to improve the accuracy of its responses by referring to relevant literature on business processes. Some or all of the above processes in the chatbot support unit may be performed using AI, for example, or not. For example, the chatbot support unit can input relevant literature data into AI, which can analyze the data and select the optimal response method.

[0070] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0071] The proposal department can adjust the level of detail in its proposals based on the importance of the digital tools. For example, it can provide detailed explanations and implementation procedures for high-importance digital tools. For instance, it can provide detailed explanations of the functions and benefits of high-importance digital tools and step-by-step implementation procedures. Alternatively, it can provide concise explanations and basic implementation procedures for less important digital tools. For example, it can provide a concise overview of less important digital tools and basic implementation procedures. Furthermore, the proposal department can adjust the priority of proposals according to their importance. For example, it can prioritize proposals for high-importance digital tools and postpone proposals for less important tools. This allows the proposal department to adjust the level of detail in its proposals based on the importance of the digital tools. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input digital tool importance data into AI, which can then adjust the level of detail in its proposals based on importance.

[0072] The Execution Support Department can select the optimal execution support method by referring to past implementation data during the execution support process. For example, the Execution Support Department can analyze past implementation data and select the most effective execution support method. For example, the Execution Support Department can identify successful cases based on past data and use those methods as a reference for execution support. The Execution Support Department can also propose execution support methods based on successful cases obtained from implementation data. For example, the Execution Support Department can propose the optimal execution support method for a specific business process based on past successful cases. Furthermore, the Execution Support Department can select the optimal execution support method for a specific business process using past data. For example, the Execution Support Department can present specific improvement proposals for business processes based on past data. This allows the Execution Support Department to select the optimal execution support method by referring to past implementation data. Some or all of the above processes in the Execution Support Department may be performed using AI, for example, or not. For example, the Execution Support Department can input past implementation data into AI, and the AI ​​can analyze the data to select the optimal execution support method.

[0073] The effectiveness measurement unit can predict current effects by referring to past effectiveness data during effectiveness measurement. For example, the effectiveness measurement unit can analyze past effectiveness data to predict current effects. For example, the effectiveness measurement unit can identify effectiveness trends based on past data and predict current effects. The effectiveness measurement unit can also predict future effects based on patterns obtained from effectiveness data. For example, the effectiveness measurement unit can use past data to predict future effects and suggest areas for improvement. Furthermore, the effectiveness measurement unit can use past data to predict the effectiveness of specific business processes in detail. For example, the effectiveness measurement unit can specifically predict the effectiveness of business processes based on past data. This allows the effectiveness measurement unit to predict current effects by referring to past effectiveness data. Some or all of the above processes in the effectiveness measurement unit may be performed using AI, for example, or without AI. For example, the effectiveness measurement unit can input past effectiveness data into AI, and the AI ​​can analyze the data to predict current effects.

[0074] The data analysis department can optimize its analysis algorithms by referring to historical data during data analysis. For example, the data analysis department can analyze historical data and select the optimal analysis algorithm. For example, the data analysis department can select an analysis algorithm based on historical data to improve the accuracy of data analysis. The data analysis department can also optimize its analysis algorithms based on patterns obtained from the data. For example, the data analysis department can improve the accuracy of data analysis by refining the analysis algorithm based on historical data. Furthermore, the data analysis department can use historical data to select the optimal analysis algorithm for a specific business process. For example, the data analysis department selects the optimal analysis algorithm for a business process based on historical data. This allows the data analysis department to optimize its analysis algorithms by referring to historical data. Some or all of the above processes in the data analysis department may be performed using AI, for example, or without AI. For example, the data analysis department can input historical data into AI, and the AI ​​can analyze the data and select the optimal analysis algorithm.

[0075] The process optimization unit can perform process optimization while considering the geographical distribution of business processes. For example, the process optimization unit can compare business processes at geographically different locations and perform process optimization considering the characteristics of each region. For example, the process optimization unit can collect business process data from each region and analyze the characteristics of each region. The process optimization unit can also identify problems that are likely to occur in a particular region based on geographical distribution and reflect them in process optimization. For example, the process optimization unit can identify problems that frequently occur in a particular region and perform optimization specific to that region. Furthermore, the process optimization unit can select the optimal process optimization method by considering geographical factors. For example, the process optimization unit can propose business process optimization plans considering geographical factors such as logistics routes and customer distribution. This allows the process optimization unit to perform optimization while considering the geographical distribution of business processes. Some or all of the above processing in the process optimization unit may be performed using AI, for example, or without AI. For example, the process optimization unit can input geographical distribution data into AI, and the AI ​​can analyze the data and select the optimal process optimization method.

[0076] The following briefly describes the processing flow for example form 1.

[0077] Step 1: The diagnostic department diagnoses the business process. The diagnostic department uses data analysis to understand the current state of the business process and identify areas for improvement. They also collect detailed information about the business process through interviews and observations to derive diagnostic results. For example, they analyze each step of the business process in detail to identify bottlenecks and inefficient areas. Step 2: The Proposal Department proposes digital tools and automation solutions based on the business processes diagnosed by the Diagnostic Department. The Proposal Department uses AI to select the optimal digital tools and develops an implementation plan. It also proposes automation solutions necessary for improving business processes and supports their implementation. For example, it may propose Robotic Process Automation (RPA) or AI-based automation tools. Step 3: The Implementation Support Department automatically implements the digital tools proposed by the Proposal Department. The Implementation Support Department uses AI to automatically configure and customize the digital tools. It also provides post-implementation operational support and training. For example, it automatically configures the settings necessary for the implementation of digital tools and provides support for their operation after implementation. Step 4: The Effectiveness Measurement Department measures the effectiveness of the digital tools introduced by the Implementation Support Department. The Effectiveness Measurement Department quantitatively evaluates the effectiveness of the digital tools through setting KPIs and calculating ROI. It also collects user feedback and identifies areas for improvement in the digital tools. For example, it evaluates the operational efficiency and cost reduction effects after the introduction of the digital tools and provides feedback on areas for improvement.

[0078] (Example of form 2) The digital transformation (DX) support system according to an embodiment of the present invention is an AI-driven tool for companies to automatically advance digital transformation (DX). This DX support system is designed to provide consistent support from business process diagnosis to the execution of digitalization and effectiveness measurement, enabling companies to achieve DX without hassle. For example, the DX support system's business process diagnosis unit analyzes existing business processes and identifies priorities for digitalization and areas for improvement. Next, the DX support system's automation proposal unit proposes the most suitable digital tools and automation solutions for the business processes and supports their implementation. Furthermore, the DX support system's execution support unit automatically implements the proposed digital tools and performs necessary settings and customizations. Finally, the DX support system's effectiveness measurement unit monitors the progress of DX in real time, quantitatively measures the effects, and provides feedback on areas for improvement. In this way, it is designed to enable companies to achieve DX without hassle. In addition, the DX support system utilizes generative AI to provide the following functions. For example, the DX support system's data analysis unit automatically analyzes business data and provides insights indicating the need for digitalization. Next, the DX support system's process optimization unit optimizes business processes and automatically designs efficient flows. Furthermore, the DX support system's content generation unit automatically generates manuals and training content necessary for DX, supporting employee skill development. Finally, the DX support system's chatbot support unit responds to questions and concerns during the DX implementation process in real time, facilitating a smooth transition. In this way, the DX support system becomes a powerful tool for companies to automatically advance DX, achieving operational efficiency and improved competitiveness. As a result, the DX support system enables companies to achieve digital transformation without significant effort.

[0079] The DX support system according to this embodiment comprises a diagnostic unit, a proposal unit, an implementation support unit, and an effectiveness measurement unit. The diagnostic unit diagnoses business processes. For example, the diagnostic unit uses data analysis to understand the current state of business processes and identify areas for improvement. The diagnostic unit can also collect details of business processes through interviews and observations and derive diagnostic results. For example, the diagnostic unit analyzes each step of the business process in detail and identifies bottlenecks and inefficient parts. The proposal unit proposes digital tools and automation solutions based on the business processes diagnosed by the diagnostic unit. For example, the proposal unit uses AI to select the optimal digital tools and formulate an implementation plan. The proposal unit can also propose automation solutions necessary for improving business processes and support their implementation. For example, the proposal unit proposes robotic process automation (RPA) or AI-based automation tools suitable for automating business processes. The implementation support unit automatically implements the digital tools proposed by the proposal unit. For example, the implementation support unit uses AI to automatically configure and customize the digital tools. The implementation support unit can also provide post-implementation operational support and training. For example, the implementation support unit automatically performs the necessary settings for introducing digital tools and supports their operation after implementation. The effectiveness measurement unit measures the effectiveness of the digital tools introduced by the implementation support unit. The effectiveness measurement unit quantitatively evaluates the effectiveness of the digital tools, for example, by setting KPIs and calculating ROI. The effectiveness measurement unit can also collect user feedback and identify areas for improvement in the digital tools. For example, the effectiveness measurement unit evaluates the operational efficiency and cost reduction effects after the introduction of digital tools and provides feedback on areas for improvement. As a result, the DX support system according to this embodiment can consistently support everything from business process diagnosis to the execution of digitalization and effectiveness measurement.

[0080] The diagnostic department diagnoses business processes. For example, the diagnostic department uses data analysis to understand the current state of business processes and identify areas for improvement. Specifically, the diagnostic department collects data at each step of the business process and analyzes it using statistical methods and machine learning algorithms. This allows them to identify bottlenecks and inefficiencies in the business process. For example, they analyze processing time and error rates at each step of the business process to clarify the causes of delays and errors at specific steps. The diagnostic department can also gather detailed information about business processes through interviews and observations to derive diagnostic results. Interviews allow them to directly hear opinions from business personnel and understand the actual situation on the ground. Observations involve visiting the actual work site and observing the flow of work and work procedures in detail. This allows them to identify on-site issues and areas for improvement that cannot be seen through data analysis alone. Furthermore, the diagnostic department compiles the business process diagnostic results into a report and shares it with stakeholders. The report specifically describes the current problems and improvement proposals and is provided in a format that is easy for stakeholders to understand. This allows the diagnostic department to accurately understand the current state of business processes and make concrete improvement proposals.

[0081] The Proposal Department proposes digital tools and automation solutions based on the business processes diagnosed by the Diagnostic Department. For example, the Proposal Department uses AI to select the optimal digital tools and develop an implementation plan. Specifically, the Proposal Department performs AI-based data analysis to select the digital tools necessary for improving business processes. The AI ​​analyzes business process data and identifies the optimal digital tools and automation solutions. For example, it selects the optimal Robotic Process Automation (RPA) tool or AI-based automation tool based on data at each step of the business process. The Proposal Department also develops an implementation plan for the selected digital tools, determining the implementation schedule and resource allocation. Furthermore, the Proposal Department can propose and support the implementation of automation solutions necessary for improving business processes. For example, it proposes RPA tools or AI-based automation tools suitable for business process automation and provides specific procedures and configuration methods for implementation. This allows the Proposal Department to effectively propose and support the implementation of digital tools and automation solutions necessary for improving business processes.

[0082] The Implementation Support Department automatically implements the digital tools proposed by the Proposal Department. For example, the Implementation Support Department uses AI to automatically configure and customize digital tools. Specifically, the Implementation Support Department utilizes AI-based automation technology to configure and customize proposed digital tools. The AI ​​analyzes the information necessary for configuring and customizing digital tools and automatically performs optimal configurations and customizations. For example, to configure and customize an RPA tool, it analyzes business process data and automatically performs optimal configurations and customizations. Furthermore, the Implementation Support Department can also provide post-implementation operational support and training. For example, after the implementation of digital tools, it provides operational support, troubleshooting, and maintenance. It also provides training on how to use and configure digital tools to support users in effectively utilizing them. In this way, the Implementation Support Department can consistently handle everything from the implementation of digital tools to operational support, enabling users to effectively utilize digital tools.

[0083] The Effectiveness Measurement Department measures the effectiveness of digital tools introduced by the Implementation Support Department. For example, the Effectiveness Measurement Department quantitatively evaluates the effectiveness of digital tools through KPI setting and ROI calculation. Specifically, the Effectiveness Measurement Department collects data on business processes before and after the introduction of digital tools and evaluates their effectiveness based on KPIs and ROI. For example, it evaluates business process processing time, error rates, and cost reduction effects to quantitatively demonstrate the effectiveness of digital tools. The Effectiveness Measurement Department can also collect user feedback to identify areas for improvement in digital tools. User feedback is collected through surveys and interviews, providing specific details on the user experience and areas for improvement. This allows the Effectiveness Measurement Department to evaluate business efficiency and cost reduction effects after the introduction of digital tools and provide feedback on areas for improvement. Furthermore, the Effectiveness Measurement Department compiles the evaluation results into a report and shares it with stakeholders. The report specifically details the effectiveness of the digital tools and improvement suggestions, and is provided in a format that is easy for stakeholders to understand. This enables the Effectiveness Measurement Department to accurately evaluate the effectiveness of digital tools and make concrete improvement suggestions.

[0084] The Data Analysis Department automatically analyzes business data and provides insights that highlight the need for digitalization. For example, the Data Analysis Department collects business data such as sales data, customer data, and inventory data, and analyzes it using AI. For instance, it can analyze sales data to identify sales trends and seasonal fluctuations. It can also analyze customer data to understand customer purchasing behavior and preferences. Furthermore, it can analyze inventory data to optimize inventory and forecast demand. For example, it can forecast demand based on inventory data, providing insights to prevent inventory surpluses and shortages. This allows the Data Analysis Department to analyze business data and provide insights that highlight the need for digitalization. Some or all of the above processes in the Data Analysis Department may be performed using AI, or not. For example, the Data Analysis Department can input business data into AI, which then analyzes the data and generates insights.

[0085] The process optimization unit optimizes business processes and automatically designs efficient flows. For example, the process optimization unit uses process mining to understand the current state of business processes and identify areas for improvement for optimization. For example, the process optimization unit analyzes each step of the business process in detail to identify bottlenecks and inefficiencies. The process optimization unit can also use simulations to predict the effects of business process improvements and design optimal flows. For example, the process optimization unit simulates business processes and evaluates the effectiveness of improvement proposals. Furthermore, the process optimization unit can optimize business processes by applying best practices. For example, the process optimization unit improves business processes by referencing best practices from other companies and industries. This allows the process optimization unit to optimize business processes and design efficient flows. Some or all of the above processes in the process optimization unit may be performed using AI, or not. For example, the process optimization unit can input business process data into AI, which can then design the optimal flow.

[0086] The content generation unit automatically generates manuals and training content necessary for DX. For example, the content generation unit uses AI to automatically generate manuals such as operation manuals, FAQs, and troubleshooting guides. For instance, the content generation unit generates operation manuals based on detailed business processes, providing guidance to employees to smoothly carry out their work. The content generation unit can also automatically generate training content such as video tutorials, online courses, and workshop materials. For example, the content generation unit generates video tutorials based on training content for business processes, supporting employee skill improvement. This enables the content generation unit to automatically generate manuals and training content necessary for DX. Some or all of the above-described processes in the content generation unit may be performed using AI, or not. For example, the content generation unit can input business process data into AI, which can then generate manuals and training content.

[0087] The chatbot support unit responds in real time to questions and concerns during the DX implementation process. The chatbot support unit uses AI, for example, to respond quickly and accurately to user questions. For instance, it provides pre-prepared answers to common questions that arise during the DX implementation process. Furthermore, the chatbot support unit can use AI to generate appropriate answers to specific user questions. For example, it analyzes the user's question and generates an answer based on relevant information. This allows the chatbot support unit to respond in real time to questions and concerns during the DX implementation process. Some or all of the above-described processes in the chatbot support unit may be performed using AI, or not. For example, the chatbot support unit can input a user's question into AI, which can then generate an appropriate answer.

[0088] The diagnostic unit can estimate the user's emotions and adjust the presentation method of the diagnostic results based on the estimated emotions. For example, if the user is stressed, the diagnostic unit can summarize the results concisely and present them in a visually easy-to-understand format. For example, the diagnostic unit can visually display the results using graphs or charts. If the user is relaxed, the diagnostic unit can also provide detailed results and add explanations for each item. For example, the diagnostic unit can provide detailed results in a text report format. Furthermore, if the user is in a hurry, the diagnostic unit can highlight only the important points and present them in a format that can be quickly understood. For example, the diagnostic unit can present the key points in bullet points. In this way, the diagnostic unit can adjust the presentation method of the diagnostic results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit can input the user's facial expression data into the AI, which can then estimate the emotions and adjust how the diagnostic results are presented.

[0089] The diagnostic unit can improve the accuracy of its diagnosis by referring to historical business process data during the diagnosis process. For example, the diagnostic unit can analyze past business process data to identify frequently occurring problems and reflect them in the diagnosis. For example, the diagnostic unit can identify bottlenecks in business processes based on past data and suggest areas for improvement. The diagnostic unit can also predict future problems based on patterns obtained from historical data and include them in the diagnosis results. For example, the diagnostic unit can use past data to predict future business process problems and propose preventive measures. Furthermore, the diagnostic unit can use past data to provide detailed suggestions for improving specific business processes. For example, the diagnostic unit can provide specific suggestions for improving business processes based on past data. This allows the diagnostic unit to improve the accuracy of its diagnosis by referring to historical business process data. Some or all of the above processes in the diagnostic unit may be performed using AI, for example, or not. For example, the diagnostic unit can input past business process data into AI, which can analyze the data and generate the diagnosis results.

[0090] The diagnostic unit can apply different diagnostic algorithms to each category of business process during the diagnostic process. For example, the diagnostic unit can apply a diagnostic algorithm specialized in quality control to manufacturing processes. For instance, the diagnostic unit can perform a diagnosis based on manufacturing quality control data and suggest areas for improvement to enhance quality. The diagnostic unit can also apply a diagnostic algorithm that emphasizes customer satisfaction to service industry processes. For example, the diagnostic unit can perform a diagnosis based on service industry customer satisfaction data and suggest areas for improvement to enhance customer satisfaction. Furthermore, the diagnostic unit can apply a diagnostic algorithm that evaluates system performance to IT industry processes. For example, the diagnostic unit can perform a diagnosis based on IT industry system performance data and suggest system optimization proposals. This allows the diagnostic unit to apply different diagnostic algorithms to each category of business process. Some or all of the above processes in the diagnostic unit may be performed using AI, for example, or not. For example, the diagnostic unit can input business process category data into AI, which can then apply the most appropriate diagnostic algorithm for each category.

[0091] The diagnostic unit can estimate the user's emotions and determine the priority of the diagnosis based on the estimated emotions. For example, if the user is stressed, the diagnostic unit will prioritize presenting the most important diagnostic items. For example, the diagnostic unit will prioritize presenting major problems in the business process to a stressed user. Also, if the user is relaxed, the diagnostic unit can present all diagnostic items in detail. For example, the diagnostic unit will explain the overall picture of the business process in detail to a relaxed user. Furthermore, if the user is in a hurry, the diagnostic unit can prioritize presenting items that require immediate attention. For example, the diagnostic unit will highlight business process problems that require immediate attention to a hurried user. In this way, the diagnostic unit can determine the priority of the diagnosis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit can input the user's facial expression data into the AI, which can then estimate emotions and determine the priority of the diagnosis.

[0092] The diagnostics unit can perform diagnostics while considering the geographical distribution of business processes. For example, the diagnostics unit can compare business processes at geographically different locations and perform diagnostics that take into account the characteristics of each region. For example, the diagnostics unit can collect business process data from each region and analyze the characteristics of each region. The diagnostics unit can also identify problems that are likely to occur in specific regions based on geographical distribution and reflect them in the diagnostics. For example, the diagnostics unit can identify problems that frequently occur in specific regions and propose improvement plans tailored to those regions. Furthermore, the diagnostics unit can propose optimal improvement measures considering geographical factors. For example, the diagnostics unit can propose business process optimization plans considering geographical factors such as logistics routes and customer distribution. In this way, the diagnostics unit can perform diagnostics while considering the geographical distribution of business processes. Some or all of the above processes in the diagnostics unit may be performed using AI, for example, or not. For example, the diagnostics unit can input geographical distribution data into AI, and the AI ​​can analyze the data and generate diagnostic results.

[0093] The diagnostic unit can improve the accuracy of its diagnosis by referring to relevant literature on business processes during the diagnostic process. For example, the diagnostic unit can refer to the latest research papers and reflect areas for improvement in business processes in its diagnosis. For example, the diagnostic unit can propose improvements to business processes based on relevant academic papers. The diagnostic unit can also refer to industry best practices and include them in the diagnostic results. For example, the diagnostic unit can propose optimizations for business processes based on industry best practices. Furthermore, the diagnostic unit can optimize its diagnostic algorithm based on insights gained from relevant literature. For example, the diagnostic unit can improve the accuracy of its diagnosis by refining its diagnostic algorithm based on relevant literature. In this way, the diagnostic unit can improve the accuracy of its diagnosis by referring to relevant literature on business processes. Some or all of the above processes in the diagnostic unit may be performed using AI, for example, or not using AI. For example, the diagnostic unit can input relevant literature data into AI, and the AI ​​can analyze the data and generate diagnostic results.

[0094] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the user is stressed, the suggestion unit can provide concise and easy-to-understand suggestions. For instance, it might present suggestions to a stressed user in a bulleted list format. Furthermore, if the user is relaxed, the suggestion unit can provide suggestions with detailed explanations. For example, it might explain the suggestions in detail to a relaxed user. Additionally, if the user is in a hurry, the suggestion unit can highlight only the most important points. For example, it might present suggestions to a user in a format that can be quickly understood. This allows the suggestion unit to adjust the way it presents its suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the suggestion unit may be performed using AI, or not. For example, the proposal function can input user facial expression data into an AI, which can then estimate the emotion and adjust the way the proposal is presented.

[0095] The proposal department can adjust the level of detail in its proposals based on the importance of the digital tools. For example, it can provide detailed explanations and implementation procedures for high-importance digital tools. For instance, it can provide detailed explanations of the functions and benefits of high-importance digital tools and step-by-step implementation procedures. Alternatively, it can provide concise explanations and basic implementation procedures for less important digital tools. For example, it can provide a concise overview of less important digital tools and basic implementation procedures. Furthermore, the proposal department can adjust the priority of proposals according to their importance. For example, it can prioritize proposals for high-importance digital tools and postpone proposals for less important tools. This allows the proposal department to adjust the level of detail in its proposals based on the importance of the digital tools. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input digital tool importance data into AI, which can then adjust the level of detail in its proposals based on importance.

[0096] The proposal department can apply different proposal algorithms depending on the category of the digital tool during the proposal process. For example, for productivity tools, the proposal department can apply a proposal algorithm specifically focused on efficiency. For instance, the proposal department can highlight the functions and benefits of the productivity tools and provide specific implementation procedures for efficiency. Furthermore, for communication tools, the proposal department can apply a proposal algorithm that emphasizes team collaboration. For instance, the proposal department can highlight the functions and benefits of the communication tools and provide implementation procedures that help improve team collaboration. Additionally, for security tools, the proposal department can apply a proposal algorithm that emphasizes risk management. For instance, the proposal department can highlight the functions and benefits of the security tools and provide specific implementation procedures for risk management. This allows the proposal department to apply different proposal algorithms depending on the category of the digital tool. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input digital tool category data into the AI, which can then apply the most suitable proposal algorithm for each category.

[0097] 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 stressed, the suggestion unit can provide a short, concise suggestion. For example, it can provide a short, bulleted suggestion to a stressed user. The suggestion unit can also provide a longer suggestion with more detailed explanations if the user is relaxed. For example, it can provide a detailed explanation of the suggestion to a relaxed user. Furthermore, if the user is in a hurry, the suggestion unit can provide a short, easily understandable suggestion. For example, it can provide a short, concise suggestion emphasizing the key points to a user in a hurry. In this way, the suggestion unit can adjust the length of the suggestion according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, 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, for example, or without AI. For example, the suggestion function can input user facial expression data into an AI, which can then estimate the emotion and adjust the length of the suggestion accordingly.

[0098] The proposal department can prioritize proposals based on the timing of digital tool implementation. For example, it will prioritize proposals for tools that require immediate implementation. For instance, it will highlight the functions and benefits of digital tools that require immediate implementation and provide quick implementation procedures. Conversely, it can postpone proposals for tools that can be implemented later. For example, it will provide a brief overview of digital tools that can be implemented later and provide basic implementation procedures. Furthermore, the proposal department can adjust the level of detail in proposals depending on the implementation timing. For example, it will use detailed and concise explanations depending on the implementation timing. This allows the proposal department to prioritize proposals based on the timing of digital tool implementation. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input digital tool implementation timing data into AI, which can then prioritize proposals based on the implementation timing.

[0099] The proposal department can adjust the order of proposals based on the relevance of the digital tools. For example, the proposal department may prioritize proposing the tools most relevant to the business process. For example, the proposal department may prioritize proposing the digital tools most relevant to improving the business process. The proposal department may also postpone proposing less relevant tools. For example, the proposal department may provide a brief overview of less relevant digital tools and basic implementation procedures. Furthermore, the proposal department may adjust the level of detail of proposals according to their relevance. For example, the proposal department may provide detailed explanations for highly relevant tools and brief explanations for less relevant tools. This allows the proposal department to adjust the order of proposals based on the relevance of the digital tools. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department may input relevance data of the digital tools into AI, which can then adjust the order of proposals based on their relevance.

[0100] The execution support unit can estimate the user's emotions and adjust its execution support methods based on the estimated emotions. For example, if the user is stressed, the execution support unit can provide simple and easy-to-understand execution support. For example, it can provide a concise manual or guide to a stressed user. Also, if the user is relaxed, the execution support unit can provide execution support that includes detailed explanations. For example, it can provide a detailed manual or training material to a relaxed user. Furthermore, if the user is in a hurry, the execution support unit can provide concise execution support that can be executed quickly. For example, it can provide a concise manual that highlights the key points to a user in a hurry. In this way, the execution support unit can adjust its execution support methods according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the execution support unit may be performed using AI, for example, or without AI. For example, the execution support unit can input user facial expression data into the AI, which can then estimate emotions and adjust the execution support method accordingly.

[0101] The Execution Support Department can select the optimal execution support method by referring to past implementation data during the execution support process. For example, the Execution Support Department can analyze past implementation data and select the most effective execution support method. For example, the Execution Support Department can identify successful cases based on past data and use those methods as a reference for execution support. The Execution Support Department can also propose execution support methods based on successful cases obtained from implementation data. For example, the Execution Support Department can propose the optimal execution support method for a specific business process based on past successful cases. Furthermore, the Execution Support Department can select the optimal execution support method for a specific business process using past data. For example, the Execution Support Department can present specific improvement proposals for business processes based on past data. This allows the Execution Support Department to select the optimal execution support method by referring to past implementation data. Some or all of the above processes in the Execution Support Department may be performed using AI, for example, or not. For example, the Execution Support Department can input past implementation data into AI, and the AI ​​can analyze the data to select the optimal execution support method.

[0102] The Execution Support Unit can customize the means of execution support based on the current status of the business process during execution support. For example, the Execution Support Unit can analyze the current status of the business process and provide the optimal means of execution support. For example, the Execution Support Unit can select the optimal means of execution support based on the current business process data. The Execution Support Unit can also adjust the means of execution support according to the progress of the business process. For example, the Execution Support Unit can provide the necessary support based on the progress data of the business process. Furthermore, the Execution Support Unit can customize the means of execution support based on the current situation. For example, the Execution Support Unit can grasp the current status of the business process and provide the optimal support means. This allows the Execution Support Unit to customize the means of execution support based on the current status of the business process. Some or all of the above processes in the Execution Support Unit may be performed using AI, for example, or without using AI. For example, the Execution Support Unit can input current status data of the business process into AI, and the AI ​​can analyze the data and select the optimal means of execution support.

[0103] The execution support unit can estimate the user's emotions and determine the priority of execution support based on the estimated emotions. For example, if the user is stressed, the execution support unit will prioritize providing the most important execution support. For example, the execution support unit will prioritize providing important support to a stressed user. Also, if the user is relaxed, the execution support unit can provide all execution support in detail. For example, the execution support unit will provide detailed support to a relaxed user. Furthermore, if the user is in a hurry, the execution support unit can prioritize providing execution support that requires a quick response. For example, the execution support unit will provide quick support to a user in a hurry. In this way, the execution support unit can determine the priority of execution support according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the execution support unit may be performed using AI, for example, or without AI. For example, the execution support unit can input user facial expression data into the AI, which can then estimate emotions and determine the priority of execution support.

[0104] The Execution Support Department can select the optimal execution support method when providing execution support, taking into account the geographical distribution of business processes. For example, the Execution Support Department can compare business processes at geographically different locations and provide execution support that takes into account the characteristics of each region. For example, the Execution Support Department can collect business process data from each region and analyze the characteristics of each region. The Execution Support Department can also identify problems that are likely to occur in specific regions based on geographical distribution and reflect them in the execution support. For example, the Execution Support Department can identify problems that frequently occur in specific regions and provide support tailored to those regions. Furthermore, the Execution Support Department can select the optimal execution support method by taking geographical factors into account. For example, the Execution Support Department can propose business process optimization plans by taking into account geographical factors such as logistics routes and customer distribution. This allows the Execution Support Department to select the optimal execution support method by taking into account the geographical distribution of business processes. Some or all of the above processes in the Execution Support Department may be performed using AI, for example, or not. For example, the Execution Support Department can input geographical distribution data into AI, and the AI ​​can analyze the data to select the optimal execution support method.

[0105] The Execution Support Department can improve the accuracy of its execution support by referring to relevant literature on business processes during the execution support process. For example, the Execution Support Department can refer to the latest research papers and reflect improvements to business processes in its execution support. For example, the Execution Support Department can present business process improvement proposals based on relevant academic papers. The Execution Support Department can also refer to industry best practices and include them in its execution support. For example, the Execution Support Department can present business process optimization proposals based on industry best practices. Furthermore, the Execution Support Department can optimize its execution support methods based on insights gained from relevant literature. For example, the Execution Support Department can improve its execution support methods based on relevant literature and enhance the accuracy of its execution support. This allows the Execution Support Department to improve the accuracy of its execution support by referring to relevant literature on business processes. Some or all of the above processes in the Execution Support Department may be performed using AI, for example, or without AI. For example, the Execution Support Department can input relevant literature data into AI, and the AI ​​can analyze the data to optimize the execution support methods.

[0106] The effectiveness measurement unit can estimate the user's emotions and adjust the display method of the effectiveness measurement based on the estimated user emotions. For example, if the user is stressed, the effectiveness measurement unit can display concise and easy-to-understand effectiveness measurement results. For example, the effectiveness measurement unit can visually display the effectiveness measurement results to stressed users using graphs and charts. The effectiveness measurement unit can also display detailed effectiveness measurement results if the user is relaxed. For example, the effectiveness measurement unit can provide detailed effectiveness measurement results in text report format to relaxed users. Furthermore, if the user is in a hurry, the effectiveness measurement unit can display effectiveness measurement results that highlight only the important points. For example, the effectiveness measurement unit can display effectiveness measurement results in bullet points to users in a hurry. In this way, the effectiveness measurement unit can adjust the display method of the effectiveness measurement according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the effectiveness measurement unit may be performed using AI, for example, or without AI. For example, the effectiveness measurement unit can input user facial expression data into AI, which can then estimate emotions and adjust the display method of the effectiveness measurement.

[0107] The effectiveness measurement unit can predict current effects by referring to past effectiveness data during effectiveness measurement. For example, the effectiveness measurement unit can analyze past effectiveness data to predict current effects. For example, the effectiveness measurement unit can identify effectiveness trends based on past data and predict current effects. The effectiveness measurement unit can also predict future effects based on patterns obtained from effectiveness data. For example, the effectiveness measurement unit can use past data to predict future effects and suggest areas for improvement. Furthermore, the effectiveness measurement unit can use past data to predict the effectiveness of specific business processes in detail. For example, the effectiveness measurement unit can specifically predict the effectiveness of business processes based on past data. This allows the effectiveness measurement unit to predict current effects by referring to past effectiveness data. Some or all of the above processes in the effectiveness measurement unit may be performed using AI, for example, or without AI. For example, the effectiveness measurement unit can input past effectiveness data into AI, and the AI ​​can analyze the data to predict current effects.

[0108] The effectiveness measurement unit can apply different effectiveness measurement methods to each category of business process during effectiveness measurement. For example, the effectiveness measurement unit can apply an effectiveness measurement method specialized in quality control to manufacturing processes. For instance, the effectiveness measurement unit can perform effectiveness measurement based on manufacturing quality control data and suggest areas for improvement to enhance quality. The effectiveness measurement unit can also apply an effectiveness measurement method that emphasizes customer satisfaction to service industry processes. For example, the effectiveness measurement unit can perform effectiveness measurement based on service industry customer satisfaction data and suggest areas for improvement to enhance customer satisfaction. Furthermore, the effectiveness measurement unit can apply an effectiveness measurement method that evaluates system performance to IT industry processes. For example, the effectiveness measurement unit can perform effectiveness measurement based on IT industry system performance data and suggest system optimization proposals. This allows the effectiveness measurement unit to apply different effectiveness measurement methods to each category of business process. Some or all of the above processing in the effectiveness measurement unit may be performed using AI, for example, or without AI. For example, the effectiveness measurement unit can input business process category data into AI, and the AI ​​can apply the most appropriate effectiveness measurement method for each category.

[0109] The effectiveness measurement unit can estimate the user's emotions and adjust the importance of effectiveness measurements based on the estimated emotions. For example, if the user is stressed, the effectiveness measurement unit will prioritize displaying the most important effectiveness measurement items. For example, the effectiveness measurement unit will highlight key effectiveness measurement items for stressed users. The effectiveness measurement unit can also display all effectiveness measurement items in detail if the user is relaxed. For example, the effectiveness measurement unit will provide detailed effectiveness measurement results to relaxed users. Furthermore, if the user is in a hurry, the effectiveness measurement unit can prioritize displaying effectiveness measurement items that require immediate attention. For example, the effectiveness measurement unit will highlight effectiveness measurement items that require immediate attention for users in a hurry. In this way, the effectiveness measurement unit can adjust the importance of effectiveness measurements according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the effectiveness measurement unit may be performed using AI, for example, or without AI. For example, the effectiveness measurement unit can input user facial expression data into AI, which can then estimate emotions and adjust the importance of effectiveness measurement.

[0110] The effectiveness measurement unit can analyze changes in effectiveness based on the implementation timing of business processes during effectiveness measurement. For example, the effectiveness measurement unit can analyze changes in effectiveness based on the implementation timing and identify areas for improvement. For example, the effectiveness measurement unit can compare effectiveness data for each implementation timing and analyze changes in effectiveness. The effectiveness measurement unit can also identify the optimal implementation timing based on effectiveness data for each implementation timing. For example, the effectiveness measurement unit can analyze data for each implementation timing and identify the optimal implementation timing. Furthermore, the effectiveness measurement unit can adjust the effectiveness measurement method according to the implementation timing. For example, the effectiveness measurement unit optimizes the effectiveness measurement method according to the implementation timing. This allows the effectiveness measurement unit to analyze changes in effectiveness based on the implementation timing of business processes. Some or all of the above processes in the effectiveness measurement unit may be performed using AI, for example, or without AI. For example, the effectiveness measurement unit can input implementation timing data into AI, and the AI ​​can analyze the data and predict changes in effectiveness.

[0111] The effectiveness measurement unit can analyze the effectiveness of business processes by referring to relevant market data during effectiveness measurement. For example, the effectiveness measurement unit can analyze the effectiveness of business processes by referring to relevant market data. For example, the effectiveness measurement unit can analyze effectiveness based on market research reports and competitive analysis data. The effectiveness measurement unit can also optimize the effectiveness measurement results based on insights gained from market data. For example, the effectiveness measurement unit can improve the effectiveness measurement method based on market data to improve the accuracy of effectiveness measurement. Furthermore, the effectiveness measurement unit can also analyze the effectiveness of business processes in detail using relevant market data. For example, the effectiveness measurement unit can specifically analyze the effectiveness of business processes based on market data. This allows the effectiveness measurement unit to analyze effectiveness by referring to relevant market data of business processes. Some or all of the above processes in the effectiveness measurement unit may be performed using AI, for example, or not using AI. For example, the effectiveness measurement unit can input relevant market data into AI, and the AI ​​can analyze the data and predict effectiveness.

[0112] The data analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated emotions. For example, if the user is stressed, the data analysis unit can provide concise and easy-to-understand data analysis results. For example, the data analysis unit can provide users who are stressed with data analysis results in the form of a bulleted list of key points. The data analysis unit can also provide detailed data analysis results if the user is relaxed. For example, the data analysis unit can provide detailed data analysis results for relaxed users. Furthermore, if the user is in a hurry, the data analysis unit can provide data analysis results that highlight only the important points. For example, the data analysis unit can provide users who are in a hurry with data analysis results that highlight the key points. In this way, the data analysis unit can adjust the data analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data analysis unit may be performed using AI, for example, or without AI. For example, the data analysis department can input user facial expression data into an AI, which can then estimate emotions and adjust the data analysis method accordingly.

[0113] The data analysis department can optimize its analysis algorithms by referring to historical data during data analysis. For example, the data analysis department can analyze historical data and select the optimal analysis algorithm. For example, the data analysis department can select an analysis algorithm based on historical data to improve the accuracy of data analysis. The data analysis department can also optimize its analysis algorithms based on patterns obtained from the data. For example, the data analysis department can improve the accuracy of data analysis by refining the analysis algorithm based on historical data. Furthermore, the data analysis department can use historical data to select the optimal analysis algorithm for a specific business process. For example, the data analysis department selects the optimal analysis algorithm for a business process based on historical data. This allows the data analysis department to optimize its analysis algorithms by referring to historical data. Some or all of the above processes in the data analysis department may be performed using AI, for example, or without AI. For example, the data analysis department can input historical data into AI, and the AI ​​can analyze the data and select the optimal analysis algorithm.

[0114] The Data Analysis Department can apply different analytical methods to each category of business process during data analysis. For example, the Data Analysis Department can apply analytical methods specialized in quality control to manufacturing processes. For instance, it can analyze manufacturing quality control data and suggest areas for improvement to enhance quality. Furthermore, the Data Analysis Department can apply analytical methods that emphasize customer satisfaction to service industry processes. For example, it can analyze service industry customer satisfaction data and suggest areas for improvement to enhance customer satisfaction. Additionally, the Data Analysis Department can apply analytical methods that evaluate system performance to IT industry processes. For example, it can analyze IT industry system performance data and suggest system optimization proposals. This allows the Data Analysis Department to apply different analytical methods to each category of business process. Some or all of the above-described processes in the Data Analysis Department may be performed using AI, or not. For example, the Data Analysis Department can input business process category data into an AI, which can then apply the most appropriate analytical method for each category.

[0115] The data analysis unit can estimate the user's emotions and prioritize data analysis based on the estimated emotions. For example, if the user is stressed, the data analysis unit will prioritize providing the most important data analysis items. For example, the data analysis unit will highlight key data analysis items to stressed users. Also, if the user is relaxed, the data analysis unit can provide all data analysis items in detail. For example, the data analysis unit will provide detailed data analysis results to relaxed users. Furthermore, if the user is in a hurry, the data analysis unit can prioritize providing data analysis items that require immediate attention. For example, the data analysis unit will highlight data analysis items that require immediate attention to users in a hurry. In this way, the data analysis unit can prioritize data analysis according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data analysis unit may be performed using AI, for example, or without AI. For example, the data analysis department can input user facial expression data into an AI, which can then estimate emotions and determine the priority of data analysis.

[0116] The data analysis department can perform data analysis while considering the geographical distribution of business processes. For example, the data analysis department can compare business processes at geographically different locations and perform data analysis that takes into account the characteristics of each region. For example, the data analysis department can collect business process data from each region and analyze the characteristics of each region. The data analysis department can also identify problems that are likely to occur in specific regions based on geographical distribution and reflect this in the data analysis. For example, the data analysis department can identify problems that occur frequently in specific regions and perform analysis specific to those regions. Furthermore, the data analysis department can select the optimal data analysis method by considering geographical factors. For example, the data analysis department can propose optimization plans for business processes by considering geographical factors such as logistics routes and customer distribution. This allows the data analysis department to perform analysis while considering the geographical distribution of business processes. Some or all of the above processes in the data analysis department may be performed using AI, for example, or not. For example, the data analysis department can input geographical distribution data into AI, and the AI ​​can analyze the data and select the optimal analysis method.

[0117] The data analysis department can improve the accuracy of its analysis by referring to relevant literature on business processes during data analysis. For example, the data analysis department can refer to the latest research papers and reflect improvements to business processes in its data analysis. For example, the data analysis department can propose improvements to business processes based on relevant academic papers. The data analysis department can also refer to industry best practices and include them in its data analysis. For example, the data analysis department can propose optimizations for business processes based on industry best practices. Furthermore, the data analysis department can optimize its data analysis methods based on insights gained from relevant literature. For example, the data analysis department can improve its data analysis methods based on relevant literature and increase the accuracy of its data analysis. In this way, the data analysis department can improve the accuracy of its analysis by referring to relevant literature on business processes. Some or all of the above processes in the data analysis department may be performed using AI, for example, or not using AI. For example, the data analysis department can input relevant literature data into AI, and the AI ​​can analyze the data and select the optimal analysis method.

[0118] The process optimization unit can estimate the user's emotions and adjust the process optimization method based on the estimated emotions. For example, if the user is stressed, the process optimization unit can provide a concise and easy-to-understand process optimization method. For example, the process optimization unit can provide a concise process optimization method in bullet points to a stressed user. The process optimization unit can also provide a detailed process optimization method if the user is relaxed. For example, the process optimization unit can provide a detailed process optimization procedure to a relaxed user. Furthermore, if the user is in a hurry, the process optimization unit can provide a process optimization method that emphasizes only the important points. For example, the process optimization unit can provide a concise process optimization method that emphasizes the key points to a user in a hurry. In this way, the process optimization unit can adjust the process optimization method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the process optimization unit may be performed using AI, for example, or without AI. For example, the process optimization unit can input user facial expression data into AI, which can then estimate emotions and adjust the process optimization method.

[0119] The process optimization unit can optimize the optimization algorithm by referring to past process data during process optimization. For example, the process optimization unit can analyze past process data and select the optimal optimization algorithm. For example, the process optimization unit can select an optimization algorithm based on past data to improve the accuracy of process optimization. The process optimization unit can also optimize the optimization algorithm based on patterns obtained from the data. For example, the process optimization unit can improve the optimization algorithm based on past data to improve the accuracy of process optimization. Furthermore, the process optimization unit can use past data to select the optimal optimization algorithm for a specific business process. For example, the process optimization unit selects the optimal optimization algorithm for a business process based on past data. This allows the process optimization unit to optimize the optimization algorithm by referring to past process data. Some or all of the above-described processes in the process optimization unit may be performed using AI, for example, or without using AI. For example, the process optimization unit can input past process data into AI, and the AI ​​can analyze the data to select the optimal optimization algorithm.

[0120] The process optimization unit can apply different optimization methods to each category of business process during process optimization. For example, the process optimization unit can apply an optimization method specialized in quality control to manufacturing processes. For instance, the process optimization unit can perform optimization based on manufacturing quality control data and suggest improvements for quality enhancement. The process optimization unit can also apply an optimization method that emphasizes customer satisfaction to service industry processes. For example, the process optimization unit can perform optimization based on service industry customer satisfaction data and suggest improvements for customer satisfaction enhancement. Furthermore, the process optimization unit can apply an optimization method that evaluates system performance to IT industry processes. For example, the process optimization unit can perform optimization based on IT industry system performance data and suggest system optimization proposals. This allows the process optimization unit to apply different optimization methods to each category of business process. Some or all of the above processing in the process optimization unit may be performed using AI, for example, or without AI. For example, the process optimization unit can input business process category data into AI, and the AI ​​can apply the most suitable optimization method for each category.

[0121] The process optimization unit can estimate the user's emotions and determine the priority of process optimization based on the estimated emotions. For example, if the user is stressed, the process optimization unit will prioritize providing the most important process optimization items. For instance, the process optimization unit will highlight key process optimization items to a stressed user. Furthermore, if the user is relaxed, the process optimization unit can provide all process optimization items in detail. For instance, the process optimization unit will provide detailed process optimization procedures to a relaxed user. Additionally, if the user is in a hurry, the process optimization unit can prioritize providing process optimization items that require immediate attention. For instance, the process optimization unit will highlight process optimization items requiring immediate attention to a user in a hurry. This allows the process optimization unit to determine the priority of process optimization according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the process optimization unit may be performed using AI, for example, or without AI. For example, the process optimization unit can input user facial expression data into AI, which can then estimate emotions and determine the priority of process optimization.

[0122] The process optimization unit can perform process optimization while considering the geographical distribution of business processes. For example, the process optimization unit can compare business processes at geographically different locations and perform process optimization considering the characteristics of each region. For example, the process optimization unit can collect business process data from each region and analyze the characteristics of each region. The process optimization unit can also identify problems that are likely to occur in a particular region based on geographical distribution and reflect them in process optimization. For example, the process optimization unit can identify problems that frequently occur in a particular region and perform optimization specific to that region. Furthermore, the process optimization unit can select the optimal process optimization method by considering geographical factors. For example, the process optimization unit can propose business process optimization plans considering geographical factors such as logistics routes and customer distribution. This allows the process optimization unit to perform optimization while considering the geographical distribution of business processes. Some or all of the above processing in the process optimization unit may be performed using AI, for example, or without AI. For example, the process optimization unit can input geographical distribution data into AI, and the AI ​​can analyze the data and select the optimal process optimization method.

[0123] The process optimization unit can improve the accuracy of process optimization by referring to relevant literature on business processes during the optimization process. For example, the process optimization unit can refer to the latest research papers and reflect improvements to business processes in the process optimization. For example, the process optimization unit can present business process improvement proposals based on relevant academic papers. The process optimization unit can also refer to industry best practices and include them in the process optimization. For example, the process optimization unit can present business process optimization proposals based on industry best practices. Furthermore, the process optimization unit can optimize process optimization methods based on insights gained from relevant literature. For example, the process optimization unit can improve process optimization methods based on relevant literature and improve the accuracy of process optimization. In this way, the process optimization unit can improve the accuracy of optimization by referring to relevant literature on business processes. Some or all of the above processing in the process optimization unit may be performed using AI, for example, or without AI. For example, the process optimization unit can input relevant literature data into AI, and the AI ​​can analyze the data and select the optimal process optimization method.

[0124] The content generation unit can estimate the user's emotions and adjust the content generation method based on the estimated emotions. For example, if the user is stressed, the content generation unit can generate concise and easy-to-understand content. For example, the content generation unit can provide a stressed user with concise content in the form of bullet points. The content generation unit can also generate content that includes detailed explanations if the user is relaxed. For example, the content generation unit can provide a relaxed user with detailed content. Furthermore, if the user is in a hurry, the content generation unit can generate content that emphasizes only the important points. For example, the content generation unit can provide a user in a hurry with concise content that emphasizes the key points. In this way, the content generation unit can adjust the content generation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the content generation unit may be performed using AI, for example, or without AI. For example, the content generation unit can input user facial expression data into the AI, which can then estimate emotions and adjust the content generation method accordingly.

[0125] The content generation unit can optimize its generation algorithm by referring to past content data during content generation. For example, the content generation unit can analyze past content data and select the optimal generation algorithm. For example, the content generation unit can select a generation algorithm based on past data to improve the accuracy of content generation. The content generation unit can also optimize its generation algorithm based on patterns obtained from data. For example, the content generation unit can improve the generation algorithm based on past data to improve the accuracy of content generation. Furthermore, the content generation unit can use past data to select the optimal generation algorithm for a specific business process. For example, the content generation unit can select the optimal generation algorithm for a business process based on past data. This allows the content generation unit to optimize its generation algorithm by referring to past content data. Some or all of the above processes in the content generation unit may be performed using AI, for example, or without AI. For example, the content generation unit can input past content data into AI, which can analyze the data and select the optimal generation algorithm.

[0126] The content generation unit can apply different generation methods to each category of business process when generating content. For example, the content generation unit can apply a content generation method specialized in quality control to manufacturing processes. For instance, it can generate content based on manufacturing quality control data and provide guidance for quality improvement. The content generation unit can also apply a content generation method that emphasizes customer satisfaction to service industry processes. For example, it can generate content based on service industry customer satisfaction data and provide guidance for improving customer satisfaction. Furthermore, the content generation unit can apply a content generation method that evaluates system performance to IT industry processes. For example, it can generate content based on IT industry system performance data and provide system optimization suggestions. This allows the content generation unit to apply different generation methods to each category of business process. Some or all of the above-described processes in the content generation unit may be performed using AI, or not. For example, the content generation unit can input business process category data into AI, which can then apply the most suitable generation method for each category.

[0127] The content generation unit can estimate the user's emotions and determine the priority of the content to generate based on the estimated emotions. For example, if the user is stressed, the content generation unit will prioritize generating the most important content. For example, the content generation unit will highlight and provide key content to a stressed user. The content generation unit can also generate all content in detail if the user is relaxed. For example, the content generation unit will provide detailed content to a relaxed user. Furthermore, if the user is in a hurry, the content generation unit can prioritize generating content that requires immediate attention. For example, the content generation unit will highlight and provide content that requires immediate attention to a user in a hurry. In this way, the content generation unit can determine the priority of the content to generate according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI 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 content generation unit may be performed using AI, for example, or without AI. For example, the content generation unit can input user facial expression data into the AI, which can then estimate emotions and determine the priority of content to generate.

[0128] The content generation unit can generate content while considering the geographical distribution of business processes. For example, the content generation unit can compare business processes at geographically different locations and generate content that takes into account the characteristics of each region. For example, the content generation unit can collect business process data from each region and analyze the characteristics of each region. The content generation unit can also identify problems that are likely to occur in a particular region based on geographical distribution and reflect them in the content. For example, the content generation unit can identify problems that frequently occur in a particular region and generate content that is specific to that region. Furthermore, the content generation unit can select the optimal content generation method by considering geographical factors. For example, the content generation unit can propose optimizations for business processes by considering geographical factors such as logistics routes and customer distribution. This allows the content generation unit to generate content while considering the geographical distribution of business processes. Some or all of the above processing in the content generation unit may be performed using AI, for example, or without AI. For example, the content generation unit can input geographical distribution data into AI, and the AI ​​can analyze the data and select the optimal content generation method.

[0129] The content generation unit can improve the accuracy of content generation by referring to relevant literature on business processes during content generation. For example, the content generation unit can refer to the latest research papers and reflect improvements to business processes in the content. For example, the content generation unit can present business process improvement proposals based on relevant academic papers. The content generation unit can also refer to industry best practices and include them in the content. For example, the content generation unit can present business process optimization proposals based on industry best practices. Furthermore, the content generation unit can optimize the content generation method based on insights gained from relevant literature. For example, the content generation unit can improve the accuracy of content generation by refining the content generation method based on relevant literature. This allows the content generation unit to improve the accuracy of content generation by referring to relevant literature on business processes. Some or all of the above processes in the content generation unit may be performed using AI, for example, or without AI. For example, the content generation unit can input relevant literature data into AI, which can analyze the data and select the optimal content generation method.

[0130] The chatbot support unit can estimate the user's emotions and adjust the chatbot's response method based on the estimated emotions. For example, if the user is stressed, the chatbot support unit will respond in a calm tone. For example, the chatbot support unit will provide answers in a calm tone to users who are stressed. The chatbot support unit can also respond in a friendly tone if the user is relaxed. For example, the chatbot support unit will provide answers in a friendly tone to users who are relaxed. Furthermore, if the user is in a hurry, the chatbot support unit can provide quick and concise responses. For example, the chatbot support unit will provide quick and concise answers to users who are in a hurry. In this way, the chatbot support unit can adjust the chatbot's response method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the chatbot support unit may be performed using AI, for example, or without AI. For example, the chatbot support unit can input user facial expression data into the AI, which can then estimate the user's emotions and adjust its response accordingly.

[0131] The chatbot support unit can optimize its response algorithm by referring to past response data when the chatbot responds. For example, the chatbot support unit can analyze past response data and select the optimal response algorithm. For example, the chatbot support unit can select a response algorithm based on past data to improve the accuracy of responses. The chatbot support unit can also optimize its response algorithm based on patterns obtained from the data. For example, the chatbot support unit can improve the response algorithm based on past data to improve the accuracy of responses. Furthermore, the chatbot support unit can use past data to select the optimal response algorithm for a specific business process. For example, the chatbot support unit can select the optimal response algorithm for a business process based on past data. This allows the chatbot support unit to optimize its response algorithm by referring to past response data. Some or all of the above processes in the chatbot support unit may be performed using AI, for example, or without AI. For example, the chatbot support unit can input past response data into AI, which can analyze the data and select the optimal response algorithm.

[0132] The chatbot support unit can apply different response methods depending on the category of business process when responding to chatbot inquiries. For example, the chatbot support unit can apply a response method specialized in quality control to manufacturing processes. For instance, it can respond based on manufacturing quality control data and provide guidance for quality improvement. The chatbot support unit can also apply a response method that emphasizes customer satisfaction to service industry processes. For example, it can respond based on service industry customer satisfaction data and provide guidance for improving customer satisfaction. Furthermore, the chatbot support unit can apply a response method that evaluates system performance to IT industry processes. For example, it can respond based on IT industry system performance data and provide system optimization suggestions. This allows the chatbot support unit to apply different response methods depending on the category of business process. Some or all of the above processing in the chatbot support unit may be performed using AI, or not. For example, the chatbot support unit can input business process category data into AI, which can then apply the most appropriate response method for each category.

[0133] The chatbot support unit can estimate the user's emotions and determine the chatbot's response priority based on the estimated emotions. For example, if the user is stressed, the chatbot support unit will prioritize providing the most important responses. For example, the chatbot support unit will highlight key responses to a stressed user. The chatbot support unit can also provide all responses in detail if the user is relaxed. For example, the chatbot support unit will provide detailed responses to a relaxed user. Furthermore, if the user is in a hurry, the chatbot support unit can prioritize providing responses that require a quick response. For example, the chatbot support unit will highlight responses that require an immediate response to a user in a hurry. In this way, the chatbot support unit can determine the chatbot's response priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the chatbot support unit may be performed using AI, for example, or without AI. For example, the chatbot support unit can input user facial expression data into the AI, which can then estimate emotions and determine response priorities.

[0134] The chatbot support unit can provide responses that take into account the geographical distribution of business processes. For example, the chatbot support unit can compare business processes at geographically different locations and provide responses that take into account the characteristics of each region. For example, the chatbot support unit can collect business process data for each region and analyze the characteristics of each region. The chatbot support unit can also identify problems that are likely to occur in a particular region based on geographical distribution and reflect them in its responses. For example, the chatbot support unit can identify problems that frequently occur in a particular region and provide responses specific to that region. Furthermore, the chatbot support unit can select the optimal response method by taking geographical factors into consideration. For example, the chatbot support unit can propose business process optimization plans by taking geographical factors such as logistics routes and customer distribution into consideration. This allows the chatbot support unit to provide responses that take into account the geographical distribution of business processes. Some or all of the above processing in the chatbot support unit may be performed using AI, for example, or not. For example, the chatbot support unit can input geographical distribution data into AI, and the AI ​​can analyze the data and select the optimal response method.

[0135] The chatbot support unit can improve the accuracy of its responses by referring to relevant literature on business processes when responding to chatbot inquiries. For example, the chatbot support unit can refer to the latest research papers and reflect improvements to business processes in its responses. For example, the chatbot support unit can propose improvements to business processes based on relevant academic papers. The chatbot support unit can also refer to industry best practices and include them in its responses. For example, the chatbot support unit can propose optimizations for business processes based on industry best practices. Furthermore, the chatbot support unit can optimize its response methods based on insights gained from relevant literature. For example, the chatbot support unit can improve its response methods based on relevant literature and increase the accuracy of its responses. This allows the chatbot support unit to improve the accuracy of its responses by referring to relevant literature on business processes. Some or all of the above processes in the chatbot support unit may be performed using AI, for example, or not. For example, the chatbot support unit can input relevant literature data into AI, which can analyze the data and select the optimal response method.

[0136] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0137] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the user is stressed, the suggestion unit can provide concise and easy-to-understand suggestions. For instance, it might present suggestions to a stressed user in a bulleted list format. Furthermore, if the user is relaxed, the suggestion unit can provide suggestions with detailed explanations. For example, it might explain the suggestions in detail to a relaxed user. Additionally, if the user is in a hurry, the suggestion unit can highlight only the most important points. For example, it might present suggestions to a user in a format that can be quickly understood. This allows the suggestion unit to adjust the way it presents its suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the suggestion unit may be performed using AI, or not. For example, the proposal function can input user facial expression data into an AI, which can then estimate the emotion and adjust the way the proposal is presented.

[0138] The execution support unit can estimate the user's emotions and adjust its execution support methods based on the estimated emotions. For example, if the user is stressed, the execution support unit can provide simple and easy-to-understand execution support. For example, it can provide a concise manual or guide to a stressed user. Also, if the user is relaxed, the execution support unit can provide execution support that includes detailed explanations. For example, it can provide a detailed manual or training material to a relaxed user. Furthermore, if the user is in a hurry, the execution support unit can provide concise execution support that can be executed quickly. For example, it can provide a concise manual that highlights the key points to a user in a hurry. In this way, the execution support unit can adjust its execution support methods according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the execution support unit may be performed using AI, for example, or without AI. For example, the execution support unit can input user facial expression data into the AI, which can then estimate emotions and adjust the execution support method accordingly.

[0139] The effectiveness measurement unit can estimate the user's emotions and adjust the display method of the effectiveness measurement based on the estimated user emotions. For example, if the user is stressed, the effectiveness measurement unit can display concise and easy-to-understand effectiveness measurement results. For example, the effectiveness measurement unit can visually display the effectiveness measurement results to stressed users using graphs and charts. The effectiveness measurement unit can also display detailed effectiveness measurement results if the user is relaxed. For example, the effectiveness measurement unit can provide detailed effectiveness measurement results in text report format to relaxed users. Furthermore, if the user is in a hurry, the effectiveness measurement unit can display effectiveness measurement results that highlight only the important points. For example, the effectiveness measurement unit can display effectiveness measurement results in bullet points to users in a hurry. In this way, the effectiveness measurement unit can adjust the display method of the effectiveness measurement according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the effectiveness measurement unit may be performed using AI, for example, or without AI. For example, the effectiveness measurement unit can input user facial expression data into AI, which can then estimate emotions and adjust the display method of the effectiveness measurement.

[0140] The data analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated emotions. For example, if the user is stressed, the data analysis unit can provide concise and easy-to-understand data analysis results. For example, the data analysis unit can provide users who are stressed with data analysis results in the form of a bulleted list of key points. The data analysis unit can also provide detailed data analysis results if the user is relaxed. For example, the data analysis unit can provide detailed data analysis results for relaxed users. Furthermore, if the user is in a hurry, the data analysis unit can provide data analysis results that highlight only the important points. For example, the data analysis unit can provide users who are in a hurry with data analysis results that highlight the key points. In this way, the data analysis unit can adjust the data analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data analysis unit may be performed using AI, for example, or without AI. For example, the data analysis department can input user facial expression data into an AI, which can then estimate emotions and adjust the data analysis method accordingly.

[0141] The process optimization unit can estimate the user's emotions and adjust the process optimization method based on the estimated emotions. For example, if the user is stressed, the process optimization unit can provide a concise and easy-to-understand process optimization method. For example, the process optimization unit can provide a concise process optimization method in bullet points to a stressed user. The process optimization unit can also provide a detailed process optimization method if the user is relaxed. For example, the process optimization unit can provide a detailed process optimization procedure to a relaxed user. Furthermore, if the user is in a hurry, the process optimization unit can provide a process optimization method that emphasizes only the important points. For example, the process optimization unit can provide a concise process optimization method that emphasizes the key points to a user in a hurry. In this way, the process optimization unit can adjust the process optimization method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the process optimization unit may be performed using AI, for example, or without AI. For example, the process optimization unit can input user facial expression data into AI, which can then estimate emotions and adjust the process optimization method.

[0142] The proposal department can adjust the level of detail in its proposals based on the importance of the digital tools. For example, it can provide detailed explanations and implementation procedures for high-importance digital tools. For instance, it can provide detailed explanations of the functions and benefits of high-importance digital tools and step-by-step implementation procedures. Alternatively, it can provide concise explanations and basic implementation procedures for less important digital tools. For example, it can provide a concise overview of less important digital tools and basic implementation procedures. Furthermore, the proposal department can adjust the priority of proposals according to their importance. For example, it can prioritize proposals for high-importance digital tools and postpone proposals for less important tools. This allows the proposal department to adjust the level of detail in its proposals based on the importance of the digital tools. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input digital tool importance data into AI, which can then adjust the level of detail in its proposals based on importance.

[0143] The Execution Support Department can select the optimal execution support method by referring to past implementation data during the execution support process. For example, the Execution Support Department can analyze past implementation data and select the most effective execution support method. For example, the Execution Support Department can identify successful cases based on past data and use those methods as a reference for execution support. The Execution Support Department can also propose execution support methods based on successful cases obtained from implementation data. For example, the Execution Support Department can propose the optimal execution support method for a specific business process based on past successful cases. Furthermore, the Execution Support Department can select the optimal execution support method for a specific business process using past data. For example, the Execution Support Department can present specific improvement proposals for business processes based on past data. This allows the Execution Support Department to select the optimal execution support method by referring to past implementation data. Some or all of the above processes in the Execution Support Department may be performed using AI, for example, or not. For example, the Execution Support Department can input past implementation data into AI, and the AI ​​can analyze the data to select the optimal execution support method.

[0144] The effectiveness measurement unit can predict current effects by referring to past effectiveness data during effectiveness measurement. For example, the effectiveness measurement unit can analyze past effectiveness data to predict current effects. For example, the effectiveness measurement unit can identify effectiveness trends based on past data and predict current effects. The effectiveness measurement unit can also predict future effects based on patterns obtained from effectiveness data. For example, the effectiveness measurement unit can use past data to predict future effects and suggest areas for improvement. Furthermore, the effectiveness measurement unit can use past data to predict the effectiveness of specific business processes in detail. For example, the effectiveness measurement unit can specifically predict the effectiveness of business processes based on past data. This allows the effectiveness measurement unit to predict current effects by referring to past effectiveness data. Some or all of the above processes in the effectiveness measurement unit may be performed using AI, for example, or without AI. For example, the effectiveness measurement unit can input past effectiveness data into AI, and the AI ​​can analyze the data to predict current effects.

[0145] The data analysis department can optimize its analysis algorithms by referring to historical data during data analysis. For example, the data analysis department can analyze historical data and select the optimal analysis algorithm. For example, the data analysis department can select an analysis algorithm based on historical data to improve the accuracy of data analysis. The data analysis department can also optimize its analysis algorithms based on patterns obtained from the data. For example, the data analysis department can improve the accuracy of data analysis by refining the analysis algorithm based on historical data. Furthermore, the data analysis department can use historical data to select the optimal analysis algorithm for a specific business process. For example, the data analysis department selects the optimal analysis algorithm for a business process based on historical data. This allows the data analysis department to optimize its analysis algorithms by referring to historical data. Some or all of the above processes in the data analysis department may be performed using AI, for example, or without AI. For example, the data analysis department can input historical data into AI, and the AI ​​can analyze the data and select the optimal analysis algorithm.

[0146] The process optimization unit can perform process optimization while considering the geographical distribution of business processes. For example, the process optimization unit can compare business processes at geographically different locations and perform process optimization considering the characteristics of each region. For example, the process optimization unit can collect business process data from each region and analyze the characteristics of each region. The process optimization unit can also identify problems that are likely to occur in a particular region based on geographical distribution and reflect them in process optimization. For example, the process optimization unit can identify problems that frequently occur in a particular region and perform optimization specific to that region. Furthermore, the process optimization unit can select the optimal process optimization method by considering geographical factors. For example, the process optimization unit can propose business process optimization plans considering geographical factors such as logistics routes and customer distribution. This allows the process optimization unit to perform optimization while considering the geographical distribution of business processes. Some or all of the above processing in the process optimization unit may be performed using AI, for example, or without AI. For example, the process optimization unit can input geographical distribution data into AI, and the AI ​​can analyze the data and select the optimal process optimization method.

[0147] The following briefly describes the processing flow for example form 2.

[0148] Step 1: The diagnostic department diagnoses the business process. The diagnostic department uses data analysis to understand the current state of the business process and identify areas for improvement. They also collect detailed information about the business process through interviews and observations to derive diagnostic results. For example, they analyze each step of the business process in detail to identify bottlenecks and inefficient areas. Step 2: The Proposal Department proposes digital tools and automation solutions based on the business processes diagnosed by the Diagnostic Department. The Proposal Department uses AI to select the optimal digital tools and develops an implementation plan. It also proposes automation solutions necessary for improving business processes and supports their implementation. For example, it may propose Robotic Process Automation (RPA) or AI-based automation tools. Step 3: The Implementation Support Department automatically implements the digital tools proposed by the Proposal Department. The Implementation Support Department uses AI to automatically configure and customize the digital tools. It also provides post-implementation operational support and training. For example, it automatically configures the settings necessary for the implementation of digital tools and provides support for their operation after implementation. Step 4: The Effectiveness Measurement Department measures the effectiveness of the digital tools introduced by the Implementation Support Department. The Effectiveness Measurement Department quantitatively evaluates the effectiveness of the digital tools through setting KPIs and calculating ROI. It also collects user feedback and identifies areas for improvement in the digital tools. For example, it evaluates the operational efficiency and cost reduction effects after the introduction of the digital tools and provides feedback on areas for improvement.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements described above, including the business process diagnosis unit, automation proposal unit, execution support unit, effectiveness measurement unit, data analysis unit, process optimization unit, content generation unit, and chatbot support unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the business process diagnosis unit is implemented by the control unit 46A of the smart device 14 and performs business process analysis. The automation proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes optimal digital tools and automation solutions. The execution support unit is implemented by, for example, the control unit 46A of the smart device 14 and automatically implements digital tools. The effectiveness measurement unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and monitors the progress of DX in real time and quantitatively measures its effects. The data analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically analyzes business data. The process optimization unit is implemented by, for example, the control unit 46A of the smart device 14 and optimizes business processes. The content generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and automatically generates manuals and training content necessary for DX. The chatbot support unit is implemented, for example, by the control unit 46A of the smart device 14, and responds in real time to questions and inquiries during the DX implementation process. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0153] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] Each of the multiple elements described above, including the business process diagnosis unit, automation proposal unit, execution support unit, effectiveness measurement unit, data analysis unit, process optimization unit, content generation unit, and chatbot support unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the business process diagnosis unit is implemented by the control unit 46A of the smart glasses 214 and performs business process analysis. The automation proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes optimal digital tools and automation solutions. The execution support unit is implemented by, for example, the control unit 46A of the smart glasses 214 and automatically implements digital tools. The effectiveness measurement unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and monitors the progress of DX in real time and quantitatively measures its effects. The data analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically analyzes business data. The process optimization unit is implemented by, for example, the control unit 46A of the smart glasses 214 and optimizes business processes. The content generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and automatically generates manuals and training content necessary for DX. The chatbot support unit is implemented, for example, by the control unit 46A of the smart glasses 214, and responds in real time to questions and concerns during the DX implementation process. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0169] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.).

[0181] 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.

[0182] 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.

[0183] 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.

[0184] Each of the multiple elements described above, including the business process diagnosis unit, automation proposal unit, execution support unit, effectiveness measurement unit, data analysis unit, process optimization unit, content generation unit, and chatbot support unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the business process diagnosis unit is implemented by the control unit 46A of the headset terminal 314 and performs business process analysis. The automation proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes optimal digital tools and automation solutions. The execution support unit is implemented by, for example, the control unit 46A of the headset terminal 314 and automatically implements digital tools. The effectiveness measurement unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and monitors the progress of DX in real time and quantitatively measures its effects. The data analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically analyzes business data. The process optimization unit is implemented by, for example, the control unit 46A of the headset terminal 314 and optimizes business processes. The content generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and automatically generates manuals and training content necessary for DX. The chatbot support unit is implemented, for example, by the control unit 46A of the headset terminal 314, and responds in real time to questions and inquiries during the DX implementation process. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0185] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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).

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.).

[0198] 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.

[0199] 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.

[0200] 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.

[0201] Each of the multiple elements described above, including the business process diagnosis unit, automation proposal unit, execution support unit, effectiveness measurement unit, data analysis unit, process optimization unit, content generation unit, and chatbot support unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the business process diagnosis unit is implemented by the control unit 46A of the robot 414 and performs business process analysis. The automation proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes optimal digital tools and automation solutions. The execution support unit is implemented by, for example, the control unit 46A of the robot 414 and automatically implements digital tools. The effectiveness measurement unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and monitors the progress of DX in real time and quantitatively measures its effects. The data analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically analyzes business data. The process optimization unit is implemented by, for example, the control unit 46A of the robot 414 and optimizes business processes. The content generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and automatically generates manuals and training content necessary for DX. The chatbot support unit is implemented, for example, by the control unit 46A of the robot 414, and responds in real time to questions and inquiries during the DX implementation process. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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."

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] (Note 1) The diagnostics department diagnoses business processes, Based on the business processes diagnosed by the aforementioned diagnostic department, the proposal department proposes digital tools and automation solutions. An implementation support unit that automatically implements the digital tools proposed by the aforementioned proposal unit, The system includes an effectiveness measurement unit that measures the effectiveness of the digital tools introduced by the aforementioned execution support unit. A system characterized by the following features. (Note 2) The company has a data analytics department that automatically analyzes business data and provides insights that highlight the need for digitalization. The system described in Appendix 1, characterized by the features described herein. (Note 3) It features a process optimization unit that optimizes business processes and automatically designs efficient flows. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes a content generation unit that automatically generates manuals and training content necessary for digital transformation (DX). The system described in Appendix 1, characterized by the features described herein. (Note 5) It features a chatbot support unit that provides real-time answers to questions and concerns during the DX implementation process. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned diagnostic unit, It estimates the user's emotions and adjusts how the diagnostic results are presented based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned diagnostic unit, During the diagnostic process, we improve the accuracy of the diagnosis by referring to historical data of business processes. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned diagnostic unit, During the diagnostic process, different diagnostic algorithms are applied to each category of business process. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned diagnostic unit, It estimates the user's emotions and determines the priority of diagnoses based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned diagnostic unit, During the diagnostic process, the geographical distribution of business processes will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned diagnostic unit, During the diagnostic process, we improve the accuracy of the diagnosis by referring to relevant literature on business processes. The system described in Appendix 1, characterized by the features described herein. (Note 12) 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 13) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the digital tools. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of digital tool. The system described in Appendix 1, characterized by the features described herein. (Note 15) 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 16) The aforementioned proposal section is, When making proposals, prioritize them based on the timing of their implementation. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the digital tools used. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned execution support unit, It estimates the user's emotions and adjusts the method of providing support based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned execution support unit, During implementation support, the optimal implementation support method is selected by referring to past implementation data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned execution support unit, During implementation support, customize the means of implementation support based on the current status of the business process. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned execution support unit, It estimates the user's emotions and determines the priority of execution support based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned execution support unit, When providing implementation support, the optimal implementation support method will be selected considering the geographical distribution of business processes. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned execution support unit, When providing implementation support, we improve the accuracy of the support by referring to relevant literature on business processes. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned effect measurement unit is We estimate the user's emotions and adjust how the performance measurement is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned effect measurement unit is When measuring effectiveness, past effectiveness data is used to predict current effectiveness. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned effect measurement unit is When measuring effectiveness, different effectiveness measurement methods are applied to each category of business process. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned effect measurement unit is We estimate user emotions and adjust the importance of performance measurement based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned effect measurement unit is When measuring effectiveness, analyze changes in effectiveness based on the timing of the business process implementation. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned effect measurement unit is When measuring effectiveness, analyze the effects by referring to relevant market data for business processes. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned data analysis unit, We estimate user sentiment and adjust the data analysis method based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned data analysis unit, When analyzing data, refer to historical data to optimize the analysis algorithm. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned data analysis unit, When analyzing data, different analytical methods are applied to each category of business process. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned data analysis unit, We estimate user sentiment and prioritize data analysis based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned data analysis unit, When performing data analysis, consider the geographical distribution of business processes. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned data analysis unit, When analyzing data, referencing relevant literature on business processes improves the accuracy of the analysis. The system described in Appendix 2, characterized by the features described herein. (Note 36) The process optimization unit, It estimates the user's emotions and adjusts the process optimization method based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 37) The process optimization unit, During process optimization, the optimization algorithm is optimized by referring to past process data. The system described in Appendix 3, characterized by the features described herein. (Note 38) The process optimization unit, When optimizing processes, different optimization methods are applied to each category of business process. The system described in Appendix 3, characterized by the features described herein. (Note 39) The process optimization unit, It estimates user emotions and determines process optimization priorities based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 40) The process optimization unit, When optimizing processes, the geographical distribution of business processes should be taken into consideration. The system described in Appendix 3, characterized by the features described herein. (Note 41) The process optimization unit When optimizing the process, refer to relevant documents of the business process to improve the accuracy of optimization The system according to Appendix 3, characterized by the above. (Appendix 42) The content generation unit Estimate the user's emotion and adjust the content generation method based on the estimated user's emotion The system according to Appendix 4, characterized by the above. (Appendix 43) The content generation unit When generating content, refer to past content data to optimize the generation algorithm The system according to Appendix 4, characterized by the above. (Appendix 44) The content generation unit When generating content, apply different generation methods for each category of business processes The system according to Appendix 4, characterized by the above. (Appendix 45) The content generation unit Estimate the user's emotion and determine the priority of the content generated based on the estimated user's emotion The system according to Appendix 4, characterized by the above. (Appendix 46) The content generation unit [[ID=4I]] When generating content, perform generation considering the geographical distribution of the business process The system according to Appendix 4, characterized by the above. (Appendix 47) The content generation unit When generating content, refer to relevant documents of the business process to improve the accuracy of generation [[ID=5 / 2]]The system according to Appendix 4, characterized by the above. (Appendix 48) The chatbot support unit Estimate the user's emotion and adjust the response method of the chatbot based on the estimated user's emotion The system according to appended claim 5, characterized in that... (Appended claim 49) The chatbot support unit Optimizes the response algorithm by referring to past response data when the chatbot responds The system according to appended claim 5, characterized in that... (Appended claim 50) The chatbot support unit Applies different response methods for each category of business processes when the chatbot responds The system according to appended claim 5, characterized in that... (Appended claim 51) The chatbot support unit Estimates the user's emotion and determines the response priority of the chatbot based on the estimated user emotion The system according to appended claim 5, characterized in that... (Appended claim 52) The chatbot support unit Responds considering the geographical distribution of business processes when the chatbot responds [[ID=三十二]]The system according to appended claim 5, characterized in that... (Appended claim 53) The chatbot support unit Improves the accuracy of the response by referring to relevant documents of business processes when the chatbot responds The system according to appended claim 5, characterized in that...

Explanation of reference signs

[0221] 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. The diagnostics department diagnoses business processes, Based on the business processes diagnosed by the aforementioned diagnostic department, the proposal department proposes digital tools and automation solutions. An implementation support unit that automatically implements the digital tools proposed by the aforementioned proposal unit, The system includes an effectiveness measurement unit that measures the effectiveness of the digital tools introduced by the aforementioned execution support unit. A system characterized by the following features.

2. The company has a data analytics department that automatically analyzes business data and provides insights that highlight the need for digitalization. The system according to feature 1.

3. It features a process optimization unit that optimizes business processes and automatically designs efficient flows. The system according to feature 1.

4. It includes a content generation unit that automatically generates manuals and training content necessary for digital transformation (DX). The system according to feature 1.

5. It features a chatbot support unit that provides real-time answers to questions and concerns during the DX implementation process. The system according to feature 1.

6. The aforementioned diagnostic unit, It estimates the user's emotions and adjusts how the diagnostic results are presented based on the estimated emotions. The system according to feature 1.

7. The aforementioned diagnostic unit, During the diagnostic process, we improve the accuracy of the diagnosis by referring to historical data of business processes. The system according to feature 1.

8. The aforementioned diagnostic unit, During the diagnostic process, different diagnostic algorithms are applied to each category of business process. The system according to feature 1.

9. The aforementioned diagnostic unit, It estimates the user's emotions and determines the priority of diagnoses based on the estimated user emotions. The system according to feature 1.

10. The aforementioned diagnostic unit, During the diagnostic process, the geographical distribution of business processes will be taken into consideration. The system according to feature 1.

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